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Universidade do MinhoEscola de Engenharia
Cláudio Araújo Monteiro dos Santos
Development of an Integrated Frameworkfor the definition of Technology Strategies
janeiro de 2014
Tese de DoutoramentoLíderes para as Indústrias Tecnológicas
Trabalho efectuado sob a orientação doProfessora Doutora Maria Madalena Teixeira de Araújo
e co-orientação deProfessor Doutor Nuno André Curado Mateus Correia
Cláudio Araújo Monteiro dos Santos
Development of an Integrated Frameworkfor the definition of Technology Strategies
Universidade do MinhoEscola de Engenharia
iii
Acknowledgments
Over the last four years, I had the opportunity to meet and work with people who will
undoubtedly leave marks for the rest of my life. They made this challenging doctoral
program an enriching experience for me…
I would like to thank my supervisor, Professor Madalena Araújo, for the guidance,
support in the scientific writing and her kind personality that made this long journey
more enjoyable.
To my co-supervisor, Professor Nuno Correia, for his valuable orientation and that little
“extra push” needed to make something that looked impossible, into something
perfectly feasible to pursue.
To Professors Joel Clark and Jeremy Gregory for their availability to receive me as
visiting student and for the suggestions made at certain points of this thesis.
My gratitude to the coordination of the MIT Portugal EDAM-LTI Program at the
University of Minho, namely the people with whom I dealt directly: Mário Meira and
Professors Olga Carneiro and Alexandre Ferreira, for their availability and care in
ensuring the normal functioning of the program.
To the colleagues that made this a pleasant “roller-coaster ride”: Anton Sabaleuski,
Eduarda Silva, Georgios Koronis, Pedro Duarte, Ricardo Sá and Rui Rocha.
I’m extremely grateful to the industrial partner, particularly to the CEO and CTO, for
supporting the application of the studies described in this thesis, and also to the
collaborators that participated in the audit. Likewise, I would like to thank all the
experts who were available for the interviews and participated in the survey.
A special word of thanks to Fábio Moreira for his support in the development of the
software. His optimistic and hardworking attitude decisively contributed to make this
possible.
Last, but definitely not least, to my family. Without their unconditional love and
support, this would not have been possible.
iv
v
This research was supported by the Portuguese Foundation for Science and Technology
(scholarship reference (SFRH/BD/33727/2009), within the framework of the EDAM
MIT Portugal Program.
vi
vii
Development of an Integrated Framework for the definition of
Technology Strategies
Abstract
In business environments, technology is playing a strategic role in the competitiveness
of organizations. The path-dependence of technological trajectories creates competitive
advantages that are unique to the organization since they are hard for competitors to
copy, thus becoming a differentiating factor between organizations in a given
environment. On the other hand, the irreversible nature of technology development
investments requires considerable care in assessing technological options. The
complexity involved in assessing technologies on a strategic perspective requires the
continuous development of new tools and methodologies for the analysis, evaluation
and selection of technologies. In this direction, technology strategy frameworks have
been proposed to support the incorporation of technology in corporate planning.
Technology strategy frameworks, intended to be applied in organizations are constituted
of two basic elements: activities and tools. Activities are processes, routines and
managerial tasks aimed at managing technologies. Tools are techniques and methods
needed to carry out such activities. An analysis on existing frameworks reveals a
consolidation into four core activities: internal analysis, external analysis, generation
and selection.
The combination of tools has been frequently suggested in the literature as a potential
approach to address possible deficiencies in existing stand-alone methods and the needs
of organizations. However, existing technology strategy frameworks are mostly focused
in the characterization of activities and recommendations of applicable tools. This thesis
presents an alternative path, which is based on the development of a novel technology
strategy framework whose main contributions arise from synergies and interactions
between various tools.
Methodologies resulting from the combination of tools are proposed for three core
activities. The internal analysis activity deals with the assessment of internal
technological competences and capabilities. A novel methodology is developed
combining audits with the Real Time Delphi method in a Group Support System, which
viii
takes into consideration the internal dynamics of organizations and social concerns, that
may inhibit a greater engagement of participants and the collection of more realistic
assessments. For the external analysis activity, which is aimed at identifying likely
future technological paths, a methodology that combines the Delphi method with
Quality Function Deployment matrix is proposed to provide a holistic perspective of
technology, influenced by external drivers and determinants. In the selection activity,
aimed at the selection of the most promising projects from the generation activity, a
methodology that integrates risk management practices with a Multi Criteria model is
proposed for the selection of different types of R&D projects. A prototype software was
developed to support the application of this methodology. The developed methodologies
were tested in the industrial partner of the thesis.
The integration of these methodologies results in a technology strategy framework that
may serve as a background platform for organizations to justify their technology
development projects. The framework includes mechanisms to facilitate the
communication of strategic guidelines that influence the generation of new project
ideas, the homogenization of organizations’ risk policies and enables a faster
implementation of corrective and improvement actions in the technology innovation
process. The proposed technology strategy framework may also contribute towards a
more traceable, transparent and structured strategic process.
This research has implications to both academia, in deepening the understanding
regarding technology strategy frameworks and underlying analytical and decision
making tools, and practitioners, for proposing a structured process that addresses
relevant issues about the strategic management of technology inside organizations.
Future work should focus on the refinement of methodologies, which can be performed
within the context of larger frameworks or individually.
Keywords: technology, strategy, framework, integrated, methodologies, tools
ix
Desenvolvimento de um Framework Integrado para a definição de
Estratégias Tecnológicas
Resumo
No mundo dos negócios, a tecnologia tem desempenhado um papel estratégico na
competitividade das organizações. As trajetórias tecnológicas favorecem a criação de
vantagens competitivas que são únicas para as organizações uma vez que são difíceis de
copiar pelos competidores, logo tornando-se um fator de diferenciação entre as
organizações em um determinado ambiente. Por outro lado, a natureza irreversível dos
investimentos em desenvolvimento tecnológico exige considerável atenção na avaliação
de opções tecnológicas. A complexidade envolvida na avaliação de tecnologias sob uma
perspetiva estratégica requer o contínuo desenvolvimento de novas ferramentas e
metodologias para análise, avaliação e seleção de tecnologias. Neste sentido,
frameworks de apoio à estratégia tecnológica tem sido propostas para auxiliar a
incorporação da tecnologia no planeamento corporativo.
Frameworks de apoio à estratégia tecnológica aplicáveis em organizações são
constituídas de dois elementos básicos: atividades e ferramentas. Atividades são
processos, rotinas e tarefas de gestão que visam a gestão da tecnologia. Ferramentas são
técnicas e métodos necessários para realizar tais atividades. Uma análise sobre
frameworks existentes na literatura revela uma consolidação em quatro atividades
chave: análise interna, análise externa, geração e seleção.
A combinação de ferramentas tem sido frequentemente sugerida na literatura como uma
abordagem apropriada para tratar possíveis deficiências em métodos individuais e
considerar as necessidades das organizações. No entanto, frameworks de apoio à
estratégia tecnológica existentes estão focadas principalmente na caracterização de
atividades e recomendações de ferramentas aplicáveis. Esta tese apresenta um caminho
alternativo, que se baseia no desenvolvimento de um novo framework de apoio à
estratégia tecnológica cujos principais contributos advém das sinergias e interações
entre várias ferramentas.
Metodologias resultantes da combinação de ferramentas são propostas para três
atividades chave. A atividade de análise interna aborda a avaliação de competências e
x
capacidades tecnológicas internas. Uma nova metodologia é desenvolvida que combina
auditorias com o método Real Time Delphi em um Sistema de Apoio a Grupos, que leva
em consideração a dinâmica interna da organizações e questões sociais, que podem
inibir um maior compromisso dos participantes e a recolha de avaliações mais realistas.
Para a atividade da análise externa, que visa a identificação de prováveis trajetórias
tecnológicas, uma metodologia que combina o método Delphi com uma matriz Quality
Function Deployment é proposta para o desenvolvimento de uma perspetiva holística
sobre tecnologia, influenciada por drivers e determinantes externos. Na atividade de
seleção, que visa a seleção dos projetos mais promissores vindos da atividade de
geração, uma metodologia que integra práticas de gestão de risco com um modelo Multi
Critério é proposta para a seleção de diferentes tipos de I&D. Um software protótipo foi
desenvolvido para facilitar a aplicação desta metodologia. As metodologias
desenvolvidas são testadas no parceiro industrial da tese.
A integração destas metodologias resulta em um framework de apoio à estratégia
tecnológica que pode servir como uma plataforma para as organizações justificarem
seus projetos de desenvolvimento tecnológico. O framework inclui mecanismos que
visam facilitar a comunicação das linhas orientadoras estratégicas que influenciam a
geração de novas ideias de projetos, a homogeneização das políticas de risco
organizacionais e permitir uma implementação mais rápida de ações de correção e
melhoria no processo de inovação tecnológica. O framework de apoio à estratégia
tecnológica pode também contribuir para um processo estratégico mais rastreável,
transparente e estruturado.
Esta investigação tem implicações tanto para o meio académico, no aprofundamento do
conhecimento sobre frameworks de apoio à estratégia tecnológica e ferramentas
analíticas e de apoio à decisão subjacentes, como para profissionais da indústria, por
propor um processo estruturado que aborda questões relevantes sobre a gestão
estratégica da tecnologia nas organizações. Trabalho futuro deve focar-se no
refinamento das metodologias, que pode ser realizado no contexto de um framework
alargado ou individualmente.
Palavras-chave: tecnologia, estratégia, framework, integrado, metodologias,
ferraementas
xi
Table of contents
CHAPTER 1 ..................................................................................................... 1
Introduction ................................................................................................................... 1
1.1 Motivation and background .............................................................................. 2
1.2 Scope ................................................................................................................ 5
1.3 Objective of the thesis ...................................................................................... 8
1.4 Organization of the thesis ............................................................................... 11
CHAPTER 2 ................................................................................................... 15
Frameworks and tools for technology strategy formulation: an overview of the
literature ...................................................................................................................... 15
2.1 Introduction .................................................................................................... 16
2.2 Key concepts and definitions .......................................................................... 17
2.2.1 Technology ................................................................................................. 17
2.2.2 Strategy ....................................................................................................... 18
2.2.3 Innovation ................................................................................................... 19
2.3 Technology strategy ....................................................................................... 23
2.3.1 Driving forces, content and decisions......................................................... 24 2.3.2 Dichotomies ................................................................................................ 29
2.4 Technology strategy frameworks ................................................................... 30
2.4.1 Contributions from the literature ................................................................ 32 2.4.2 Core activities and applicable tools ............................................................ 40
2.4.2.1 Internal analysis................................................................................. 41
2.4.2.2 External analysis ............................................................................... 43
2.4.2.3 Generation ......................................................................................... 45
2.4.2.4 Selection ............................................................................................ 46
2.5 Critical analysis and research gaps ................................................................. 47
2.6 Conclusions .................................................................................................... 51
CHAPTER 3 ................................................................................................... 53
Research methods ....................................................................................................... 53
3.1 Introduction .................................................................................................... 54
3.2 Choices in the research design ........................................................................ 56
3.3 Research design .............................................................................................. 62
CHAPTER 4 ................................................................................................... 65
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A methodology for technology innovation auditing considering social dynamics..... 65
4.1 Introduction .................................................................................................... 66
4.2 Literature review ............................................................................................. 67
4.2.1 Definitions of capability and competences of organizations ...................... 68 4.2.2 Auditing instruments .................................................................................. 72 4.2.3 Group support systems ............................................................................... 75
4.3 Methodology development ............................................................................. 79
4.3.1 The innovation process in the industrial partner ........................................ 80
4.3.2 Audit modules ............................................................................................ 82
4.3.3 Method of application ................................................................................. 92
4.4 Methodology application ................................................................................ 93
4.5 Conclusions .................................................................................................... 99
CHAPTER 5 ................................................................................................. 103
A methodology for identification of strategic technological competences through
analysis of relationships between future events ........................................................ 103
5.1 Introduction .................................................................................................. 104
5.2 Literature review ........................................................................................... 105
5.2.1 Combined foresight methodologies .......................................................... 110 5.2.2 External drivers that influence technology change in the machine tool
industry .................................................................................................................. 115
5.3 Delphi survey ................................................................................................ 123
5.4 Methodology development ........................................................................... 132
5.5 Methodology application .............................................................................. 139
5.6 Conclusions .................................................................................................. 142
CHAPTER 6 ................................................................................................. 145
R&D project selection incorporating risk ................................................................. 145
6.1 Introduction .................................................................................................. 146
6.2 Literature review ........................................................................................... 147
6.2.1 R&D project selection .............................................................................. 147 6.2.2 Risk management processes ..................................................................... 156
6.3 Methodology development ........................................................................... 164
6.3.1 Criteria and information requirements ..................................................... 165
6.3.1.1 Project selection criteria .................................................................. 166 6.3.1.2 Execution mode criteria .................................................................. 173
6.3.1.3 Multi criteria method ....................................................................... 179 6.3.2 Risk assessment and management ............................................................ 181
xiii
6.3.2.1 Schedule and cost risk ..................................................................... 184
6.3.2.2 Performance risk in basic research, applied research and advanced
technology development projects ..................................................................... 188 6.3.2.3 Performance risk in product development projects ......................... 193
6.4 Methodology for R&D projects selection incorporating risk management . 200
6.4.1 Risk management and control .................................................................. 214
6.4.2 Resource competition ............................................................................... 216
6.5 Methodology application .............................................................................. 217
6.6 Conclusions .................................................................................................. 224
CHAPTER 7 ................................................................................................. 227
Integrated technology strategy framework ............................................................... 227
7.1 Introduction .................................................................................................. 228
7.2 Outputs from proposed methodologies......................................................... 229
7.2.1 Internal analysis ........................................................................................ 229 7.2.2 External analysis ....................................................................................... 231
7.2.3 Selection ................................................................................................... 233
7.3 Intelligence systems and information requirements for the generation of
projects ...................................................................................................................... 236
7.4 Integrated technology strategy framework ................................................... 244
7.5 Conclusions .................................................................................................. 249
CHAPTER 8 ................................................................................................. 253
Conclusions and future work .................................................................................... 253
8.1 Conclusions .................................................................................................. 254
8.2 Future work................................................................................................... 262
References ..................................................................................................... 265
Appendix 1 .................................................................................................... 289
Appendix 2 .................................................................................................... 293
Appendix 3 .................................................................................................... 295
Appendix 4 .................................................................................................... 297
Appendix 5 .................................................................................................... 317
Appendix 6 .................................................................................................... 329
xiv
Appendix 7 .................................................................................................... 335
xv
List of figures
Figure 1.1 - Estimated global GDP per capita and major technological advancements.
Source: (McKinsey&Global, 2013) .................................................................................. 2
Figure 1.2 – Core activities............................................................................................... 6
Figure 1.3 - Organization of the dissertation .................................................................. 11
Figure 2.1 - Determinants of technology strategy. Adapted from: (Burgelman et al.,
2004) ............................................................................................................................... 26
Figure 2.2 - Progression of technological programs. Adapted from: (Mitchell, 1990) .. 27
Figure 2.3 - The elements of technology strategy. Source: (Ford, 1988) ....................... 34
Figure 2.4 - A framework for the development of technology strategy. Adapted from
(Hax and No, 1992) ........................................................................................................ 35
Figure 2.5 - The dimensions of technology strategy (a) and the context foresight process
(b) types of technology strategy actions. Source: (Chiesa, 2001) .................................. 36
Figure 2.6 - Structured framework for assessing technological threats and opportunities.
Source: (du Preez and Pistorius, 1999) ........................................................................... 37
Figure 2.7 - Technology strategy framework. Source: (Davenport et al., 2003) ........... 38
Figure 2.8 - Technology learning process. Source: (Burgelman et al., 2004)................ 39
Figure 3.1 - Research methods ....................................................................................... 55
Figure 3.2 - The three dimensions of explanatory programs. Source: (Abbott, 2004) ... 61
Figure 3.3 - The Wheel of Science. Adapted from (Wallace, 1971) .............................. 62
Figure 4.1 – Internal analysis activity in the technology strategy process ..................... 67
Figure 4.2 - The competences hierarchy. Adapted from (Javidan, 1998) ...................... 70
Figure 4.3 - The competence pyramid: a visual representation. Source: (Walsh and
Linton, 2001) .................................................................................................................. 71
Figure 4.4 - Audit modules ............................................................................................. 83
Figure 4.5 - Five-point Likert scale legend used ............................................................ 93
Figure 4.6 - Example of a web interface ........................................................................ 94
Figure 4.7 – Distribution of invited participants among the departments ...................... 95
Figure 4.8 - IQR and median of each audit statement .................................................... 97
Figure 5.1 - External analysis activity in the technology strategy process................... 104
Figure 5.2 - General classification of foresight activities. Source: (Vecchiato and
Roveda, 2010) ............................................................................................................... 108
Figure 5.3 - Foresight methods and orientations. Source: (Rohrbeck and Arnold, 2007)
...................................................................................................................................... 111
Figure 5.4 - Determinants of technical change in the machine tool industry. Source:
(Kathuria, 1999) ........................................................................................................... 121
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Figure 5.5 - Actors and drivers that influence technological change in the machine tool
industry. ........................................................................................................................ 123
Figure 5.6 – Technological map ................................................................................... 125
Figure 5.7 - Adapted QFD matrix for complex events relationship analysis ............... 137
Figure 5.8 - Relationships between technology-related events and competences ........ 138
Figure 5.9 - Events relationship analysis ...................................................................... 140
Figure 6.1 – The Selection activity in the technology strategy process ....................... 146
Figure 6.2 - A classification of project portfolio selection methods. Source:
(Iamratanakul et al., 2008) ........................................................................................... 151
Figure 6.3 - Overview of the technology readiness level scale. Source: (Mankins, 2009)
...................................................................................................................................... 155
Figure 6.4 - Relationships between risk categories. Source: (INCOSE, 2006) ............ 158
Figure 6.5 - Risk Management process. Source: (Standardization, 2009a) ................. 160
Figure 6.6 - The structure of an AHP hierarchy ........................................................... 180
Figure 6.7 - Shapes of triangular (a) and beta (b) distributions .................................... 185
Figure 6.8 - Project duration (a) and cost (b) distributions from a Monte Carlo
simulation ..................................................................................................................... 185
Figure 6.9 - Utility curves for performance measures: large is better (a), small is better
(b) and nominal is best (c) ............................................................................................ 190
Figure 6.10 – Sensitivity analysis on NPV (a) and ANPV (b) ..................................... 199
Figure 6.11 - Projects clustering into duration, cost and performance (in product
development projects) ranges and utility based loss functions..................................... 201
Figure 6.12 - Methodology for R&D project selection incorporating risk. .................. 203
Figure 6.13 - Resources introduction - Form A............................................................ 204
Figure 6.14 - Project ranges definition – Form B ......................................................... 205
Figure 6.15 - Utility based loss function definition - Form C ...................................... 205
Figure 6.16 - Set up new project - Form 1 ................................................................... 206
Figure 6.17 - Scope and goals - Form 2 ....................................................................... 206
Figure 6.18 - Strategic justification - Form 3 ............................................................... 207
Figure 6.19 - Project relevance – Form 4.1 .................................................................. 208
Figure 6.20 - Execution mode criteria - Form 6 ........................................................... 209
Figure 6.21 - Schedule data - Form 9.1 ........................................................................ 210
Figure 6.22 - Cost data - Form 10.1 ............................................................................. 210
Figure 6.23 - Performance data – Form 11 ................................................................... 211
Figure 6.24 - Market data – Form 14............................................................................ 212
Figure 6.25 - Financial data - Form 15 ......................................................................... 212
xvii
Figure 6.26 - Project selection - Form 16 ..................................................................... 213
Figure 6.27 - Chart for schedule risk management and tracking ................................. 215
Figure 6.28 - Chart for cost risk management and tracking ......................................... 215
Figure 6.29 - Chart for performance risk management and tracking ........................... 216
Figure 6.30 – Schedule (a), cost (b) and performance (c) utility based loss functions for
Project B ....................................................................................................................... 219
Figure 6.31 – Distributions of duration, cost and performance for project B .............. 221
Figure 6.32 - Criteria and sub criteria hierarchy model used in the project selection .. 223
Figure 7.1 - A generic technology intelligence process. Source: (Norling et al., 2000)
...................................................................................................................................... 237
Figure 7.2 - Three types of organizing technology intelligence process: (a) hierarchical,
(b) participatory and (c) hybrid. Source: (Lichtenthaler, 2007) ................................... 238
Figure 7.3 - Integrated technology strategy framework. .............................................. 246
Figure 8.1 – Proposed generic technology strategy framework ................................... 258
xviii
xix
List of tables
Table 2.1 - Innovation process theories evolution. Adapted from: (Rothwell, 1994) .... 22
Table 2.2 – A review of technology strategy decisions .................................................. 28
Table 2.3 – A summary of proposed dimensions for innovation audits ......................... 42
Table 2.4 - Types of technology foresight tools. Source: (Mishra et al., 2002) ............. 44
Table 3.1 - Relationship between forms of research questions and research strategies.
Source: (Yin, 2002) and (Saunders et al., 2009) ............................................................ 57
Table 3.2 - Research plan ............................................................................................... 64
Table 4.1 – Dimensions of reviewed audits ................................................................... 74
Table 4.2 - Review on technological innovation capabilities of innovative firms ......... 87
Table 4.3 – Capability assessment module of the audit ................................................. 89
Table 4.4 – Ranking of most consensual dimensions ..................................................... 98
Table 5.1- List of scientific publications analyzed ....................................................... 126
Table 5.2 - List of identified future events and their references................................... 129
Table 5.3 - Calculation for time of realization ............................................................. 132
Table 5.4 - Delphi survey results analysis .................................................................... 133
Table 5.5 - Rank of strategic technological competences ............................................ 142
Table 6.1 - Risk management tools in PMBOK and ISO 31000. Sources:
(Standardization, 2009b) (Institute, 2008) .................................................................... 161
Table 6.2 - Risk management processes and selected examples from the literature. ... 162
Table 6.3 - Review on basic research project selection criteria ................................... 167
Table 6.4 - Review on applied research project selection criteria ................................ 168
Table 6.5 - Review on advanced technology development project selection criteria .. 169
Table 6.6 - Review on product development project selection criteria ........................ 170
Table 6.7 - Technology acquisition mode decision criteria. Source: (Lee et al., 2009,
Cho and Yu, 2000, Chiesa, 2001) ................................................................................. 174
Table 6.8 - The fundamental scale of absolute numbers. Source: (Saaty, 2008) ......... 181
Table 6.9 - Summary of models, tools and metrics used in the methodology.............. 214
Table 6.10 - Ranges, indifference values and utility based loss functions for each project
...................................................................................................................................... 218
Table 6.11 - Risk analysis and economic attractiveness indicators for each project.... 221
Table 7.1 - The outputs from the internal analysis activity .......................................... 231
Table 7.2 - The outputs from the external analysis activity ......................................... 233
Table 7.3 - The outputs from the selection activity ...................................................... 236
xx
Table 7.4 – Proposed organization of information needs and sources. ........................ 242
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List of abbreviations
AHP - Analytic Hierarchy Process
AI - Artificial Intelligence
ANPV - Annualized Present Value
AR - Applied Research
ATD - Advanced Technology Development
BCV - Best Case Value
BDA - Behavioral Decision Aids
BR - Basic Research
CAD - Computer Aided Design
CAM - Computer Aided Manufacturing
CECIMO - Comité Européen De Coopération Des Industries De La Machine-Outil
CEO - Chief Executive Officer
CKO - Chief Knowledge Officer
CMO - Chief Marketing Officer
CTO - Chief Technology Officer
CTO - Chief Technology Officer
DEA - Data Envelopment Analysis
DHM - Decentralized Hierarchical Modeling
DP - Dynamic Programming
DSW - Delphi-Scenario Writing
EU - European Union
FMS - Flexible Manufacturing System
GDP - Gross Domestic Product
GP - Global Performance
GP - Goal Programming
GSS - Group Support System
HACCP - Hazard Analysis And Critical Control Points
HAZOP - Hazard And Operability Studies
ICA - Innovative Comparison Audit
ICT - Information And Communication Technology
IP - Integer Programming
IQR - Interval Quartile Range
IR - Identify Risks
IRR - Internal Rate Of Return
KET - Key Enabling Technology
KPI - Key Performance Indicator
LIB - Large Is Better
LOPA - Layer Protection Analysis
xxii
LP - Linear Programming
MAUT - Multi Attribute Utility Theory
MCDA - Multi-Criteria Decision Analysis
MCR - Monitor And Control Risks
MICMAC - Matrix Cross-Reference Multiplication Applied to a Classification
MILP - Mixed Integer Linear Programming
MLV - Most Likely Value
NASA - National Aeronautics and Space Administration
NGT - Nominal Group Technique
NIB - Nominal Is Best
NLP - Nonlinear Programming
NPV - Net Present Value
OECD - Organization For Economic Co-Operation And Development
OR - Operations Research
PD - Product Development
PEEST - Politics Economy Environment Society And Technology
PERT - Program Evaluation Research Technique
PIM - Product Innovation Management
PM - Performance Measure
PMBOK - Project Management Body Of Knowledge
PMI - Project Management Institute
PQlR - Perform Qualitative Risk Analysis
PQnR - Perform Quantitative Risk Analysis
PRM - Plan Risk Management
PROMETHEE - Preference Ranking Organization Method For Enrichment Evaluation
PRR - Plan Risk Responses
PSI - Parameter Space Investigation
QFD - Quality Function Deployment
R&D - Research & Development
RA - Risk Analysis
RBV - Resource Based View
RE - Risk Evaluation
RI - Risk Identification
ROI - Return On Investment
RQ - Research Question
SD - Standard Deviation
SIB - Small Is Better
STEEP - Society Technology Economics Environment and Politics
STU - Strategic Technology Unit
SWIFT - Structure “What If?”
SWOT - Strengths, Weaknesses, Opportunities and Threats
xxiii
TIC - Technological Innovation Capability
TIPA - Technological Innovation Process Audit
TPA - Technological Position Audit
TRL - Technology Readiness Level
US - United States
VBA - Visual BASIC for Applications
WCV - Worst Case Value
WIV - Worst Impact Value
CHAPTER 1
Introduction
Technology development has been frequently cited as a key driver for the
economic growth of nations and the competiveness of companies. The
strategic implications of technology lead to a continuous interest towards
the development of new frameworks, processes and tools to support
organizations in evaluating and deciding on which research and technology
development projects to pursue. Given these considerations, the objective of
this thesis is to propose novel methodologies that address critical issues
identified in core activities of the technology strategy formulation process.
More specifically, new methodologies are proposed for three activities:
internal analysis, external analysis and selection. The integration of the
proposed methodologies can contribute to conceptualization of a technology
strategy framework with improved characteristics comparatively to existing
proposals in the literature.
Chapter 1
2
1.1 Motivation and background
The transformative influence of technology in modern life is indisputable. By
introducing new ways for people to communicate to each other, finding new sources of
entertainment, enhancing people’s mobility, and in improving quality of life, technology
has a key role in extending human capabilities.
Despite the notable presence of technology in many aspects of human life, the ability to
assess its impact still remains limited. According to a report published by McKinsey &
Global consultancy group about disruptive technologies likely to change the global
economy in the coming decades, there are two reasons for this (McKinsey&Global,
2013): 1) its impact is felt in various spheres of human life (such as education,
entertainment, health and safety and many others) and 2) technological innovations are
adopted and diffused at unpredictable rates, which makes it difficult to understand the
true value of a technology at an early stage.
In a macro perspective, historical data on the global Gross Domestic Product (GDP) has
been used as a measure of the impact of technological advancements since the invention
of the printing press, as depicted in Figure 1.1. Industrial Revolutions have produced
steep growths in global GDP per capita, driven by technological breakthroughs that
produced major gains in productivity and in economic growth.
Figure 1.1 - Estimated global GDP per capita and major technological advancements. Source:
(McKinsey&Global, 2013)
Chapter 1
3
In the business world, the implications of technology are felt in two different ways:
through productivity gains when new technologies are introduced in operations and
processes of companies, and through increasing revenues when technologies are
embodied in existing or completely new products or services, giving them superior
performance and quality, in the customers’ perspective. The investment in new
technologies plays a strategic role in building competitive advantages (Porter, 1983)
(Clark, 1989), and has long been acknowledged in corporate planning (Fusfeld, 1978).
Tingling and Parent have listed a number of reasons why organizations should engage
in strategic management of technology (Tingling and Parent, 2004, p. 331):
Technologies account on average for more than one-third of all business capital
spending, it is vital to create/maintain a competitive advantage and is one of the
most important competitive decisions that managers must make;
Evaluation and selection enable organizations to perceive benefits and issues of
particular technologies prior to acquisition;
Many technologies provide increasing returns and benefit from network effects,
i.e., conditions under which utility increases with the number of adoptions.
Technology has many implications in the way businesses are managed. One of the most
important implication concerns the development of business models aimed at
commercial exploitation of technologies (Baden-Fuller and Haefliger, 2013). Other
implications are felt in the internalization efforts of companies (Hemmert, 2004), in the
way marketing research is conducted (Rust and Espinoza, 2006), in the development of
communication channels (Julsrud et al., 2012), in operational profitability, productivity
and accumulated assets (Pegels and Thirumurthy, 1996) and in improving customer
relations and overall customer satisfaction (Ryding, 2010). Disruptive technologies can
also make existing products obsolete (Whaley and Burrows, 1987), thus having the
capability of changing business models and the profile of entire industrial sectors.
Technology is not important per se but only when related to innovation objectives
(Chiesa, 2001). As discussed by Cordero, products lifecycle are shortening in many
industrial sectors because customers are willing to pay for innovative products, and
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those firms that are not capable of organizing their technology, product development
and manufacturing processes to supply this demand faster, will lose their competitive
edge (Cordero, 1991). This suggests the need for establishing closer relationships
between the technological capabilities of firms and the needs of their customers.
In a management perspective, companies are increasingly forced to rethink their
management processes, namely concerning the development and launching of new
technologies, products and services. This capability to constantly innovate has become
an imperative in times when investments in new technology development bring market
opportunities but, at the same time, also shorten product lifecycles. It is therefore
consensual to postulate that companies that position themselves in the forefront of
technology and innovation management practices will be in a better position to
formulate and implement winning strategies.
The process involved in the analysis and selection of technologies for future
developments, which is at the core of the technology strategy formulation process
(Chiesa, 2001), requires extreme consideration of technical and market factors
(Burgelman et al., 2004). The relative irreversibility of technology investments is
another factor that contributes to the need for a careful analysis of technological options
(Pindyck, 1988). At the same time, the path-dependency in technological trajectories
also creates competitive advantages that are unique to the organization (Teece et al.,
1997), since they are hard for competitors to copy (Barney, 1991), thus becoming a
differentiating factor between organizations in a given environment.
The complexity involved in assessing technologies on a strategic perspective requires
the continuous development of new tools and methodologies for the analysis, evaluation
and selection of technologies. This view is supported by Phaal and colleagues, who
suggest it is of great interest to work towards the development of robust, economic and
practical to implement, integrated and flexible tools to support the management of
technological innovations (Phaal et al., 2006). In other words, in the development of a
technology strategy framework which characterizes a process that supports
organizations to systematically evaluate the most promising technologies for the future?
This theme proves to be of considerable relevance to industry and business, for the
reasons presented above. For academia, it contributes to the conceptualization and
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development of new methodologies intended to boost organizations’ analytical and
decision-making capabilities towards the formulation of scientific and technological
programs.
1.2 Scope
This thesis focuses on technology strategy formulation process in organizations. This is
the process through which organizations make a series of decisions, such as which
technologies to develop, which competences and capabilities to invest in, when to
introduce such technologies, etc., with the objective of gaining and sustaining
competitive advantages (Chiesa, 2001, Burgelman et al., 2004). In order to understand
this process numerous technology strategy frameworks have been proposed in the
literature: frameworks have been consistently used in management theory and practice
to facilitate the understanding of a topic or area of study, define a structure and support
the decision making process (Shehabuddeen, 2000). As previously mentioned, the role
of technology in the competitiveness of companies takes on different forms, depending
on the type of application (products, processes, services, etc.), which makes the
development of a technology strategy framework capable of encompassing all the
different considerations involved in different types of applications a task of enormous
complexity. These contribute with enormous complexity to the technology strategy
formulation process. In order to address this issue and given its considerable relevance
to the business aspect of technology, the thesis’ scope is centered on the strategic
process involved in the analysis and selection of technologies for product applications.
Technology management frameworks – in which strategy formulation is an integral part
- have two basic elements: activities and tools (Centidamar et al., 2010). According to
Centidamar and colleagues, activities are processes, routines and managerial tasks
aimed at managing technologies. Tools are techniques and methods needed to carry out
activities. Such perspective on technology strategy frameworks is adopted in this thesis.
The different technology strategy frameworks, which are thoroughly reviewed in the
next chapter, point to a small set of core activities: internal analysis, external analysis,
generation and selection, as depicted in Figure 1.2.
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6
Internal
Analysis
External
Analysis
Generation
Selection
Figure 1.2 – Core activities.
Technology strategy formulation begins with analyses conducted on the internal and
external environments of organizations (Chiesa and Mazini, 1998, Burgelman et al.,
2004), much like any other form of strategy (Ford, 1988). Internal analysis generically
refers to an assessment of internal strengths and weaknesses with respect to the
technological innovation process, and an analysis on the current skills and technological
competences of the organization. The external analysis is an investigation of likely
technological developments, emerging customers’ needs, strategic competences and
other issues of importance in technological developments that might have an impact in
the future. There is no recommendation or evidence that suggests any improvement to
the process dependent on these two analyses being performed sequentially or in parallel.
These activities are the ones that define the overall strategic guidelines for the following
activities.
Under these strategic guidelines, the next activity – generation - concerns the transition
from strategy to projects, i.e., the generation of projects that begin as ideas for research,
new technologies or products. They are aimed at different objectives, such as leveraging
existing and/or building new competences and skills, position the organization in a new
market, increase revenues and others.
The generated projects are then submitted to an assessment procedure in order to
evaluate their overall merit. This is the objective of the selection activity. This
procedure also supports the selection and prioritization of projects when limited
resources restrict the organization from executing all the project ideas generated.
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The identified set of core activities may not cover all possibilities - and organizations
may introduce some variations in their approach, according to their needs - nevertheless,
this abstraction is in line with the different frameworks proposed in the literature, and
provides transparency in communication with other management activities.
Each of the core activities encompasses a tool, or a combination of tools, in order to
support managers in their analysis and decision-making. The choice of methods for
implementation in an organization depends on many factors, such as availability of data,
the dynamics of cooperation networks for innovation, the organization’s own
experience in the business, and others. There is also no widely accepted method or
technique for each activity, but methods intended to facilitate analytical thinking,
communication of ideas and decision making throughout the organization are favored.
As previously described, the development of tools is one of the most prominent research
streams in this strategic management of technology. An approach commonly followed
in this research stream is related to the integration of tools for addressing possible
deficiencies and gaps in existing stand-alone tools, as suggested by some authors (Liao,
2005, Phaal et al., 2006).
While the technology strategy formulation process seems to have consolidated into the
four aforementioned core activities, the same cannot be said with respect to tools.
Although many conceptual technology strategy frameworks are proposed in the
literature, none have, to the researchers’ knowledge, proposed an integrated technology
strategy framework based on the judicious selection and combination of tools. It is
argued that following this approach could contribute towards improved methodologies
capable of dealing with the relevant issues in each core activity. In order to address this
gap, this thesis proposes a novel approach towards the development of a technology
strategy framework, through the integration of the proposed methodologies for each
identified core activity that, in the end, result in an improved technology strategy
framework that brings together the individual contributions of each methodology.
The development of the tools presented in this thesis has the support of an industrial
partner, a mid-sized manufacturer of sheet metal processing equipment. Support was
given in many ways: in providing 1) access and insight into understanding the dynamics
of the innovation process inside the organization; 2) contact with experts for exploratory
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interviews and in 3) the feedback from the application of the tools. The tools developed
as part of this thesis are applied in a number of cases from the industrial partner, for
illustrative purposes and for an early assessment of their applicability in real
environments. This partnership contributes with new knowledge that is relevant to
academia and practitioners, by scientifically researching identified gaps and addressing
industrial needs.
In fact, the strategic process of small and medium enterprises (SMEs) has been
somewhat ignored by literature. Thus, the partnership with a mid-sized manufacturer is
believed to be one of the most relevant aspects of this thesis. Furthermore, it can be said
that the size of the industrial partner contributes in the search of issues, needs and gaps
that maybe similar to a considerable number of companies, as opposed to large
companies with more established processes.
As a summary, this thesis outlines research gaps in existing tools aimed at specific core
activities of the technology strategy formulation process, proposes new methods to
address these gaps, and presents their application in a number of real cases. These tools
are then integrated, according to their respective activity, resulting in a new technology
strategy framework.
1.3 Objective of the thesis
The objective of this thesis is to propose a technology strategy framework whose
contributions stems from the integration of methodologies developed for its constituting
core activities. These methodologies are then developed to address critical issues
identified in each core activity of the technology strategy formulation process, which
were not properly addressed in the current literature. It is argued that improvements
made in these methodologies contribute to an improved technology strategy formulation
process when integrated in a single framework. The main research question is: “How
can different tools and methods be combined and integrated to improve the process
through which organizations develop their technology strategy?” This question requires
an understanding of the core activities that constitute the technology strategy
formulation process, thus forcing a critical analysis of the literature in order to answer
the underlying subsequent question: “Which core activities constitute the technology
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strategy formulation process?”. An analysis of existing frameworks in the literature,
briefly outlined in the previous section, suggests the organization around four core
activities. Following this, the methods and tools developed as part of this thesis are
applicable within the context of each of these activities.
An exception was made for the generation activity, regarded as the fuzziest and the
most dependent on creative capability and management structure of the organization.
Since it covers areas of knowledge that are beyond the scope of this thesis, namely
knowledge management and intelligence systems, no tool was proposed for this activity.
Notwithstanding this, this activity must necessarily be considered to enable the
integration of the tools in a new technology strategy framework. Nonetheless, for the
reasons presented above, its analysis in the context of this thesis, will have a lesser
degree of depth when compared to the three other targeted activities.
The main research question was further subdivided into three research questions that are
directly related with the identified research gaps in the tools commonly used in each of
the targeted activity. These research gaps are analyzed in the literature review of this
thesis. These three sub research questions are:
Internal analysis: “How can the internal dynamics and social issues be addressed in
organizations in the internal analysis activity?”
External analysis: “How can the influence of external drivers in technological
development be assessed in the external analysis activity?”
Selection: “How can risk management practices be incorporated in the project
selection activity?”
The strategy used in each targeted activity - internal analysis, external analysis and
selection - was to combine different tools in order to address possible deficiencies in
existing propositions. The proposal of a new technology strategy framework is built
upon partial objectives, listed below:
1) Internal analysis: to develop an innovation audit that identifies the internal
processes with best and worst performance, as well as intrinsic characteristics to the
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organization that may hamper the diffusion of a culture more focused on innovation.
This new audit should take into consideration the internal dynamics of organizations
and social concerns that may inhibit a greater engagement of participants during its
implementation, with the objective of collecting a more realistic assessment of the
innovation capability of the organization;
2) External analysis: to develop a methodology that supports organizations in
identifying promising technologies and competences through an examination of
relationships between identified future events. The methodology should promote,
among organizations internal analysts and/or hired consultants, an open debate around
the dynamics and interactions between factors and drivers that influence technology
diffusion;
3) Selection: to propose a methodology to support decision makers in research and
development (R&D) project selection. This methodology should integrate risk
assessment early on project selection, which is observed as an improved application of
project risk management since it allows managers to identify risks while they still have
time in the project lifecycle to overcome them. Thereby, the objective is to develop a
method that enables managers to quantify risk in the beginning of project
conceptualization and planning that among other tangible and intangible factors related
to different types of R&D and technology maturity rates, will serve as criteria for
subsequent R&D project selection. The development of a software prototype to enable
the application of the methodology was an additional objective for this part of the thesis;
4) Integrated technology strategy framework: the methods and tools for each of
the targeted activities are integrated into a single technology strategy framework. The
relationships between each activity are shaped by the information flows occurring from
the application of each proposed tool, thus resulting in tailored or customized
methodologies for each targeted activity. They aim at improving the technology strategy
formulation process in a considerable number of ways that will be described throughout
this thesis.
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11
1.4 Organization of the thesis
The work presented in this thesis is organized according to Figure 1.3. Detailed
explanation is provided below.
Sel
ecti
on
Chapter 7
Integrated Technology
Strategy Framework
Exte
rnal
An
aly
sis
Inte
rnal
An
aly
sis
Chapter 5
A Method for Identification of
Strategic Technological
Competencies through
Analysis of Relationships
between Future Events
Chapter 3
Research Methodology
Chapter 2
Frameworks and Tools for
Technology Strategy
Formulation: An Overview of
the Literature
Chap. 1
Introduction: Motivation and
background, Scope,
Objectives of the thesis and
Organization of the thesis
Chapter 8
Conclusions and Future
Work
Chapter 4
A Methodology for
Technology Innovation
Auditing Considering Social
Dynamics
Chapter 6
R&D Project Selection
Incorporating Risk
Figure 1.3 - Organization of the dissertation
In Chapter 2, an overview of the literature is presented, with emphasis on themes related
to technology strategy frameworks and underlying activities and tools. The objective is
to review the numerous frameworks that support the development of a technology
strategy in organizations, as well as the tools and methods related to targeted activities.
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12
The goal is to present an overview of the most commonly used tools in each targeted
activity and examine existing research gaps. Moreover, a deeper analysis of the tools is
provided in the literature review sections of the following chapters dedicated to each
core activity.
Chapter 3 presents and describes the research methodology and the design of the
research process applied in this thesis, in order to answer the research questions
identified. Moreover, this chapter presents the research plan used with the different
conceptualization and development approaches of the proposed new methodologies.
The subsequent three chapters present the developed methodologies for each targeted
activity. Additionally, each chapter presents a description of the application of the
methodology in the industrial partner of the thesis.
Chapter 4 focuses on the internal analysis activity and presents a methodology that
combines Innovation Audits and the Real Time Delphi method, aimed at the self-
assessment of inner strengths and weaknesses in innovation capability of organizations
and the description of the set of competences of the organization. The proposed
methodology shows a number of advantages when compared to existing solutions,
particularly in what considers the dynamics of organizations with regard to the
evolution of internal capabilities and competences, and the minimization of the social
downside risks involved in self-assessments of organizations.
Chapter 5 is centered in the external analysis activity. In this chapter, semi-structured
interviews with experts from industry and academia resulted in the identification of a
number of events likely to have a major impact in the future of an industry. The results
of a Delphi survey conducted with a panel of experts serve as inputs to a modified
Quality Function Deployment matrix to enable cross-relationship analyses between
technology and non-technology related events, in order to analyze the impact of events
in market, regulations, and other areas in the diffusion of technologies. The end result is
a set of strategic guidelines that inform the most promising technologies and
competences for the future.
Chapter 6 refers to the selection activity. The goal was to develop a methodology to
support the selection of strategic R&D projects that integrates risk management
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13
practices. Given that uncertainty and risk of many kinds (technical, market, schedule
and others) is inherent in the development of new technologies and products, the idea
was to integrate risk-analysis early on the project’s life cycle so that the organizations
have more time available to prepare and implement risk mitigation plans. The same
methodology considered different criteria for R&D project selection, in line with the
different technology maturity rates or R&D types. A description of the software
prototype version was written in Visual BASIC for applications (VBA) programming
language for Microsoft Excel®, used to facilitate the application of the tool in a real
case from the industrial partner of the thesis, is also provided in this chapter.
Chapter 7 presents the proposed technology strategy framework, the ultimate objective
of this thesis. This framework is the result of the integration of the tools presented in the
previous chapters. A number of considerations on the generation activity are also
described in order to allow an effective integration of the tools in the framework. These
considerations correspond to the role of information gathering and analysis tools
(intelligence systems) in stimulating the generation of new project ideas and the
characterization of project proposals for the next activity - selection.
Chapter 8 presents the final conclusions, limitations, recommendations and outlines
suggestions for future work.
Finally, the references are included at the end of the thesis, along with a number of
appendices that present: 1) the competences assessment module of the audit; 2) the
guidelines used in the experts’ interviews, 3) a technological map, 4) the software
forms, 5) the project proposal templates and 6) the matrices containing the pairwise
comparisons performed in the project selection analysis. A CD-ROM containing the
experts’ interviews transcripts and the software installation file is also attached to this
thesis.
CHAPTER 2
Frameworks and tools for technology strategy
formulation: an overview of the literature
Technology has a strategic role in the competitiveness of organizations. In
order to address the technological dimension in business, authors have
proposed a number of technology strategy frameworks, in order to provide
structure to the communication of ideas and concepts, thus facilitating
decision-making and action. In this sense, this chapter presents an overview
of the literature with respect to technology strategy frameworks. It begins by
describing the definitions used for three concepts intimately related to this
theme: technology, strategy and innovation. Then, the role of technology
strategy in the competitiveness is explained, along with the driving forces,
content, typical decisions involved and underpinning dichotomies. Existing
technology strategy frameworks are characterized using attributes related
to two schools of strategy (Resource Based View versus Positioning), and a
meta-framework. Emphasis is also put in understanding underlying
activities and applicable tools of this process. An observation of such
frameworks reveals that the process has been consolidated into four core
activities: internal analysis, external analysis, generation and selection,
each one of them encompassing a number of applicable tools. While existing
frameworks convey important ideas, the development of a new technology
strategy framework based on improved tools is believed to contribute with
new knowledge to this area, in addition to enhancing the applicability of
frameworks to organizations. As such, research gaps in the most commonly
used tools in each activity are investigated and described, leading to the
formulation of the research questions of this thesis.
Chapter 2
16
2.1 Introduction
The growing “technification of society” (Van Wyk, 2010, p. 203) has put technology at
the center of all attentions. Through the incorporation into products, services and
management operations, the impact of a new technology can be measured in increased
revenues, for the added value of innovative products and services, and in costs
reduction, through efficiency gains in operations. Depending on the magnitude of these
gains, the introduction of a technology can have serious implications for the
competitiveness of organizations. In order to get the most out of technology,
organizations should consider internal issues, such as their own technological
capabilities, as well as external, such as likely technological trajectories for the future
(Chiesa, 2001). One is thus justified to postulate, as many have, that technology should
be managed under a strategic perspective (e.g. (Burgelman et al., 2004)).
Strategic management of technology and innovation is part of the broader strategic
management field. It is also intrinsically connected to other knowledge areas, in a
complex system that can be seen to involve people, organizations, technologies,
processes, products and services, and aims at improving organizational performance
towards bringing refined or completely new solutions for the society.
Therefore, the greatest challenge in researching strategic technology management is to
frame the vast areas of knowledge related to this subject: strategic management,
organizational management, knowledge management, innovation management, R&D
management (Sahlman and Haapasalo, 2009), new product development, competences
and capabilities (Gregory, 1995), marketing and customers, behavior, culture and
human resources (Phaal et al., 2006). Thus, the theoretical background of strategic
technology management can be challenging to describe.
This chapter acknowledges this complexity and limits the areas of relevance to this
thesis, and focuses on the process of strategy formulation technology in organizations,
with particular emphasis in understanding the activities and tools that constitute this
process.
Chapter 2
17
This chapter presents a literature review on the topics mentioned above. Section 2.2
presents definitions of the theme’s underlying key concepts. Section 2.3 introduces the
historical survey about the topic of technology strategy, its context, proposed
frameworks and dichotomies. Section 2.4 reviews a number of technology strategy
frameworks proposed in literature. Section 2.5 presents an analysis on activities and
tools of these frameworks and section 2.6 presents the conclusions of this chapter.
2.2 Key concepts and definitions
Before introducing the concept of technology strategy and their main contributions in
the field, this section presents the definitions used in this thesis for three key concepts
intimately related to this theme, namely technology, strategy and innovation.
2.2.1 Technology
Technology can be defined in Webster’s dictionary as “the use of science in industry,
engineering, etc., to invent useful things or to solve problems” . The definition provided
by Gendron, stresses possible applications of technology: “A technology is any
systematized practical knowledge, based on experimentation and/or scientific theory,
which is embodied in productive skills, organization, or machinery” (Gendron, 1977,
p.23). Such definitions also stress what is not technology, i.e., any type of knowledge
that does not have any practical application. Thus, technology should be the result of
activities that turn inventions and discoveries into applications. Technology is
distinguished from Science, which is more related to general knowledge and a greater
understanding of nature. Science can also be applied in the search for practical
solutions, i.e., in technological developments. However, technological developments
may also be the result of knowledge derived from experience.
In business environments, technology is a fundamental cornerstone for the
competitiveness of companies. However, and as Chiesa suggested, technology has no
business value if not linked to innovative objectives (Chiesa, 2001). In other words,
technology will only bring value to a company if it is able to increase sales through its
incorporation into products or services, or by increasing efficiency when used in
processes. Therefore, technology has also strategic implications for organizations.
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18
Technology is able to provide strategic advantages to a company through three
mechanisms (Zahra, 1996): 1) through the creation of barriers that deter the entry of
rivals; 2) the introduction of novel products or technologies that attract new customers
and 3) through changes in the rules of competition in the industry.
2.2.2 Strategy
The concept of strategy has been highly discussed in literature. A classical definition
relates the development of long-term goals, adoption of courses of action and allocation
of resources for this purpose (Chandler, 1962). A more comprehensive and
contextualizing definition about the role of strategy in the relationship between business
and its environment is provided by Mintzberg: "Strategy may be viewed as a mediating
force between the organization and its environment. Strategy formulation therefore
involves the interpretation of the environment and the development of consistent
patterns in streams of organizational decisions ("strategies") to deal with it.”
(Mintzberg, 1979, p. 25). The definition of Mintzberg is followed in this thesis.
There are several implications of strategy to an organization (Porter, 1996): strategy
supports the creation of an unique and valuable position, the requirements to make
trade-offs in strategic competitive moves and the enhancement of the interactions
between companies’ activities. Managing strategy (“Strategic Management”) relates to
those actions and decisions taken by managers to improve the company’s performance
in the external environment in which it operates according to established objectives and
goals (Ansoff, 1979, Nag et al., 2007) .
Technology has been long acknowledged as a critical element in corporate strategy
planning (Fusfeld, 1978); it is regarded as one of the most important factors in the
competitiveness of companies (Porter, 1983, Tingling and Parent, 2004), and therefore,
should be managed on a strategic manner. As suggested by Bettis and Hitt, there are
four major technological trends in the new competitive landscape across many
industries, with serious implications to the way companies define and manage their
strategy(ies) (Bettis and Hitt, 1995):
Increasing rate of technological change and diffusion: the accelerated pace at
which new technologies are developed and introduced in the market has
Chapter 2
19
dramatically decreased products lifecycle. This factor, coupled with the
increasing ease with which technologies are imitated by competitors, are forcing
companies to continually be focused on innovation, which is the new basis for
competition;
The information age: the vast proliferation of information technologies in the
daily lives of organizations helps create environments rich in information
exchange, computational power and communications, with capabilities far
greater than those observed a two decades ago;
Increasing knowledge intensity: the cumulativeness and path dependency traits
of technological knowledge (i.e., the technological capability of a company
depends on the trajectory taken from what was capable in the past)implies that
organizational learning is a critical factor in gaining and sustaining competitive
advantage in the new competitive landscape;
The emergence of positive feedback industry: technology based industries
experience positive feedback, because once products and technologies are
developed, they face decreasing costs as production and sales increases, and
thus, increasing returns occur.
Such trends impose a new competitive landscape to organizations, characterized by
increasing uncertainty, ambiguous and converging industries, decreasing transactions
costs and competition based on knowledge accumulation and deployment. This new
competitive landscape requires a new managerial mindset, much more flexible,
cooperative and innovation oriented. Given these circumstances, the concept of
innovation is explored in the following sub section.
2.2.3 Innovation
Despite being a widely known concept, the definition of innovation is not consensual.
An early and commonly accepted definition of innovation is provided by Freeman:
“industrial innovation includes the technical, design, manufacturing, management and
commercial activities involved in the marketing of a new (or improved) product or the
first commercial use of a new (or improved) process or equipment” (Freeman, 1982).
Porter follows the same line of thought, but stresses that to be considered as
innovations, products and technologies should be marketable (Porter, 1990). On the
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20
other hand, Cobbenhangen does not support this association and distinguishes
innovation as being the mere “renewal of services products, processes and organization”
from successful innovation, “the economic exploitation of innovation” (Cobbenhangen,
2000). The definitions from Freeman and Porter are used in this thesis for being
extensively accepted and for excluding other types of innovations that are not object of
analysis in this thesis (in services, business models, marketing, processes, etc.),
therefore focusing only on innovations in technologies and products.
These definitions imply a well-known distinction between invention and innovation
(Roberts, 1988) - inventions are ideas put into work, while innovation is converting
such inventions into businesses or into other practical applications. Innovations and
inventions have different success criteria: while invention is rather technical than
commercial, in innovation it is the opposite. In technological innovations, more than
solely putting technical knowledge into practice, the applications should be exploited
commercially. The activities that bring technological inventions into innovations require
cross-functional and cross-disciplinary organizations’ capabilities (Pavitt, 1998).
One of the first authors to study the role of innovation and entrepreneurship in economic
growth was the Austrian economist Joseph Schumpeter. In his words, innovation creates
imperfect competition, thus opening doors of opportunities for technologies to be
exploited in markets. Innovation is based on technological change, and is a result of a
process that he calls “creative destruction”, which is a continuous process of search for
developing something new that destroys old rules and paradigms, driven by
entrepreneurs’ search for increasing profits (Schumpeter, 1950).
The impact of innovations in markets is felt in various ways. According to Christensen,
there are two major categories of innovations (Christensen, 1995). The first one is
sustaining innovations, which does not create new markets and value networks.
Sustaining innovation is subdivided in evolutionary, when incremental improvements in
products in existing markets is expected by customers, and
revolutionary/discontinuous/radical, which are breakthrough improvements that
displace previous generations of products and technologies, aimed at high end markets
where performance is important, but not affecting existing markets. The other major
category is disruptive innovations, which are innovations that displace existing business
Chapter 2
21
by aiming at the low end of the market, then gradually conquering new markets and
discontinuing established products and technologies. An example of a disruptive
innovation was the Ford Model T automobile in 1908, which was mass produced and
more affordable to a large portion of the population, and thus helped create a new
market for automobiles, which up to that time was considered a luxury product.
Innovations can be sourced in many ways too. They can arise from the ability of
individuals/firms to see connections, to identify opportunities and explore them (Tidd et
al., 2005), from the obsolescence of technologies and products when reaching the end of
their life-cycle (Cobbenhangen, 2000) and by the reconfiguration of existing product
technologies, named architectural innovations (Henderson and Clark, 1990). Economist
Eric von Hippel argued that end-user innovation – early participation of users in the
innovation process leading to higher adoption rate and quality of innovations - is the
most important source for innovations (Hippel, 1988). More recently, a new paradigm
named Open Innovation, proposed by Chesbrough, argues that companies should seek
external and internal ideas and paths to the markets, in order to advance their
technology (Chesbrough, 2003).
Throughout the development of innovation process theory, a number of proposals have
emerged to support a better understanding of this process. Rothwell has identified five
generations of innovation process theories during the last five decades (Rothwell, 1994).
Earlier approaches to innovation theory emphasized the moderating role of
technological evolution and market dynamics. As the theory evolved, it was found that
innovation belonged to a much more complex kind of phenomenon. Table 2.1 provides
a summary description of each generation.
As stated by Wonglimpiyarat (Wonglimpiyarat, 2004), the technology innovation
process is shaped by forces of technology push (Schumpeter, 1939), demand pull
(Schmookler, 1962) or interactions between the two (Freeman, 1982). Technology
innovation projects are usually multi-staged investments, which mean that they are
constituted of sequential investments made during its execution, through specific stages,
such as research, development, prototyping, testing, industrialization and
commercialization. But, as the evolutionary theory of the innovation process suggests,
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this linear approach is giving place to more complex process architectures, involving
vertical integration and cooperation networks with other entities.
Table 2.1 - Innovation process theories evolution. Adapted from: (Rothwell, 1994)
Period Theory and Description
1950 – Mid 1960s
Technology push - Marketplace importance for driving innovation
was relegated to second place, as products and services were merely
a consequence of science and technology progress.
Mid 1960s – Early 1970s Market pull – Market forces pull new product developments into
the marketplace.
Early 1970s – Mid 1980s
The “Coupling” model - Innovation process is sequential but not
necessarily continuous, being composed of complex connections
between internal organizational functions, external networks, the
science and technology community and the marketplace.
Early 1980s – Early 1990s
The Functional Integration model- Activities in new product
development are made concurrently and integrated among the
different organizational functions in the company, instead of being a
sequential process approach. The goal is to reduce project
completion time and need to rework in the final stages of the
process, such as in manufacturing and marketing.
Mid 1990 to present
The Systems Integration and Networking model - The increasing
use of information and communication technology (ICT) has placed
speed, flexibility and responsiveness as one of innovation’s most
important factors. Networking imposes continuous improvements in
the innovation process, focused on quality control and customers’
needs.
In fact, evidence suggests that in earlier stages of a company’s life, activities are more
centered in R&D, so it is reasonable to say they are technology-driven. As companies
grow and technology matures, marketing activities become more important for the
commercial success of the innovation, so they become market driven, with strategic
procedures becoming more formalized and technology strategy being formulated within
the corporate strategy (Berry and Taggart, 1998). In fact, organizations in technological
markets seeking to use innovation as one of its key differentiators are highly encouraged
to develop a technology strategy in order to guide the investments’ priorities in the field
of technology development and application (Chiesa, 2001, Burgelman et al., 2004).
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2.3 Technology strategy
The strategic role of technological development is a topic discussed since the 1970's,
but the concept of technology strategy was born in the next decade. According to Friar
and Horwitch, technology strategy emerged as a new dimension of strategic dimension
(Friar and Horwitch, 1985). Also according to Friar and Horwitch, technological
innovation is observed as a part of technology strategy (Friar and Horwitch, 1985).
Technology strategy has close links with other functional areas of a company, such as
marketing, finance, manufacturing and human resources and has a profound impact on
the businesses of a company, in creating synergies, extending product life cycles and
creating opportunities for vertical integration. Technology strategy is also different from
R&D (research and development) strategy, which is related to solely acquiring
technology through in-house activities (Ford, 1988).
As an emergent theme, Itami and Numagami went deeper and investigated three types
of dynamic interactions between technology and strategy (Itami and Numagami, 1992):
the effect of current technology on current strategy of the company, the effect of current
strategy on future technology and the effect of current technology on future strategy.
They argue that the first has been the dominant paradigm, i.e., on strategy capitalizing
on technology. Until then, top managers with technology backgrounds were a rarity, and
strategy and technology were, for the most part, regarded as independent themes inside
companies. Acknowledging the increasing importance of technology though, the
authors claim that a shift into the direction of the other two perspectives is necessary,
meaning that strategy should be designed to make the best use of technological
knowledge in the future, and technology should work as driver for the strategy
formulation process.
In the mid-1990s, Drejer took a historical perspective and describes four schools of
technology management (Drejer, 1997): R&D management, Innovation management,
Technology planning (the dominant school at that time) and the Strategic management
of technology. The Strategic management of technology school emerged as a response
to an increasing need to understand the implications of technology in business. In turn,
this evolved from a purely technical viewpoint to a broader perception of technology,
encompassing organizational, business and societal implications, and consequently,
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sourcing ideas from different disciplines. This view has evolved as time passed by and
current research streams are related to the role of knowledge management (Nieto, 2003,
Park and Kim, 2006), networks and open innovation (Chesbrough, 2003) and research
in tools and techniques for technology management (Phaal et al., 2006). In this last
research stream, technology strategy is observed as an integral part of technology
management (Gregory, 1995, Centidamar et al., 2010).
The idea of technology as a fundamental source for competitive advantage is now
universally assumed by scholars and practitioners alike but research indicates that
difficulties arise from developing proper methods for managing technology. Research in
this area is often criticized for being ambiguous and lacking consensus (Pilkington and
Teichert, 2006), with “a vast number of contributions emerging in divergent manner
rather than a convergent one” (Drejer, 2002, p. 364), partially because of their
multidisciplinary approach and even cultural differences (Gales, 2008, Cetindamar et
al., 2009b). Tools and techniques employed in frameworks play an important role to
support the definition, evaluation and selection of strategic directions. According to
Phaal and colleagues, the multidisciplinary nature requires frameworks combining
different tools and techniques in order to guide thinking and action throughout the
process (Phaal et al., 2006).
According to Centidamar and colleagues, the judicious choice of tools and techniques to
be used in such frameworks is as important as framing forces, activities and processes
(Centidamar et al., 2010). As such, frameworks to support the formulation of
technology strategy in organizations and applicable tools and techniques are reviewed
and analyzed later, in section 2.4.2.
2.3.1 Driving forces, content and decisions
The multidisciplinary characteristic of technology strategy contributes to the complexity
of the theme and consequent difficulty in determining the key variables and forces that
shape the management of technology strategy. For example, Drejer identified five
“driving” disciplines that constitute the core of managing technology (Drejer, 1997):
innovation, philosophy of technology, business strategy, Resource Based View (RBV)
of the firm and core competence.
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A simplified approach was adopted by Antoniou and Ansoff in identifying the variables
that influence the choice of technology strategies (Antoniou and Ansoff, 2004).
According to these authors, there are two groups of variables: internal and external. The
internal variables are leadership role and power center. The external ones are: 1)
technological progress; 2) technology life cycle; 3) product life cycle and 4) competitive
dynamics. This separation related to one of the dichotomies in the technology strategy
formulation process, which will be discussed next in section 2.3.2.
Burgelman and colleagues took an evolutionary perspective to help explain how
technology strategy is formulated and changed over time (Burgelman et al., 2004).
According to this perspective, the evolutionary forces that shape technology strategy
comprise internal and external generative and integrative forces, as illustrated in Figure
2.1. Generative mechanisms include technology evolution and strategic action. A
company’s technology strategy is based on its technological capabilities, but
technologies can also evolve independently of the company’s efforts (external
environment) and thus affect the company’s directions for technological developments.
Strategic action, on the other hand, captures organizational learning processes, which is
inevitably originated from inside the company (internal environment).
Integrative mechanisms are selective forces through which companies define their
technology strategy. According to Burgelman et al, they comprise both the
organizational and industry contexts (Burgelman et al., 2004). The organizational
context (internal environment) influences the company’s ability to manage strategic
technological developments. The industry context (external environment) is concerened
with how the industry structure (competition, technology standards, social aspects of
industry development and others) influence the technology strategy formulation process
within a company.
This recurring division into internal and external variables observed in these two studies
constitutes one of the dichotomies underlying the process of technology strategy
formulation.
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Technology
Strategy
Internal
Environment
External
Environment
Gen
erat
ive
mec
han
ism
s
Inte
gra
tive
mec
han
ism
s
Strategic
Action
Organizational
Context
Technology
Evolution
Industry
Context
Figure 2.1 - Determinants of technology strategy. Adapted from: (Burgelman et al., 2004)
Technology strategy implementation is made difficult due to a number of reasons (Ford,
1988). Among these reasons is the relative technological illiteracy of most top
managers. The role of technology on organizations is shaped by the relative obscurity of
technological concepts to their managers, the unpredictability of technological
developments results and the frequency in which periods of continuity alternate with
periods of discontinuity. Therefore, technology “adds a significant measure of
uncertainty into the organizational calculus” (Goodman and Lawless, 1994, p. 5).
Technology development investments require careful attention from decision makers
due to irreversibility of these investments: once a technology has been selected for
development and investments are made, they cannot be recovered unless successfully
developed and commercialized. Uncertainty in many fronts – technical and commercial
risk of failure, funding availability, etc. - is pervasive throughout the whole technology
strategy process, and is a critical element to be considered. As suggested by Mitchell,
strategic positioning lies in a period where financial commitments have been made, but
uncertainty is still considerable (Mitchell, 1990), as depicted in Figure 2.2. Strategic
positioning refers to defining the objectives of the program and the steps taken into
developing projects that best aligns to these objectives.
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UN
CE
RT
AIN
TY
COMMITMENTS $
KNOWLEDGE
BUILDING
STRATEGIC
POSITIONING
BUSINESS
INVESTMENT
Figure 2.2 - Progression of technological programs. Adapted from: (Mitchell, 1990)
According to Karagozoglu, strategic planning in companies should be capable of coping
with environmental uncertainty (Karagozoglu, 1993). Strategic planning can also be
understood under the framework of dynamic capabilities (Teece et al., 1997). Dynamic
capabilities consist of internal and external firm-specific competences capable of
addressing rapidly changing environments. They are related to management capabilities
and are hard to imitate since they are formed by complex combinations of
organizational, functional and technological skills.
The content of technology strategy concerns the required elements that constitute a
technology strategy program, which is aimed at guiding technology diversification and
to promote technology integration inside companies (Christensen, 2002). According to
Burgelman and colleagues, four stances constitute a technology strategy program
(Burgelman et al., 2004):
Competitive strategy stance: should include the technologies to be developed
to obtain competitive advantage, whether to be a leader in the technologies or
not, when to introduce the technology in the market and whether to license-out
the technology or not;
Value chain stance: should include the scope of the technology strategy in
relation to the value chain of the company, or, in other words, which
technological capabilities to develop internally;
Resource commitment stance: should include the investment level in
technology development;
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Management stance: should include the management approach and
organization design of technology and innovation.
Arasti et al. argued that the basic elements of technology strategy programs are closely
related to the decisions to be made (Arasti et al., 2010). As a matter of fact, the output
of the technology strategy formulation is a set of decisions. The stances and decisions
proposed by Burgelman et al. corroborate this perspective (Burgelman et al., 2004). A
review of the most prominent decisions is summarized in Table 2.2.
Table 2.2 – A review of technology strategy decisions
Authors Technology strategy decisions
(Porter, 1985)
1.Selection of technologies
2.Decide to be a leader or a follower
3.Decide whether to sell technology or to keep it
(Hax and Majluf, 1991)
1.Organization of technology intelligence efforts
2. Selection of technologies
3.Timing of introduction of the technologies in the market
4.Technology acquisition mode (examples: internally, externally or in
cooperation)
5.Identification and exploitation of technological interrelationships that
exist across distinct but related business
6.Selection, evaluation, resource allocation and control of projects
7. Organization and management approach of technology and innovation
(Chiesa, 2001)
1.Selection of technologies
2.Timing of development and introduction of technologies in the market
3.Technology acquisition mode (examples: internally, externally or in
cooperation)
(Lindsay, 2001)
1.Selection of technologies
2.Technology availability and feasibility
3.Technology acquisition mode (examples: internally, externally or in
cooperation)
4.Process to ensure best return of investment
(Burgelman et al., 2004)
1.Selection of technologies
2.Required technological competences and capabilities
3.Investment level in technological developments
4.Technology acquisition mode (examples: internally, externally or in
cooperation)
5.Timing of introduction of technologies in the market
6.Organization and management approach of technology and innovation
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In order to assist companies answering these questions, numerous structured
frameworks and tools to support analysis and decision-making are proposed in the
literature. Such frameworks and tools are reviewed in section 2.4.
2.3.2 Dichotomies
An analytical overview on the approaches developed to support the formulation of
technology strategies in organizations reveals the existence of two dichotomies. These
dichotomies represent opposing schools of thought in strategy related literature,
particularly on the role that different strategy paradigms have in the generation of value
for the organizations.
The first dichotomy concerns the debate of the “rationalist” and “incrementalist”
views on strategy making. The rational point of view advocates the use of tools,
techniques, methods, processes and structures to support the formulation of a strategy.
The incremental perspective, on the other hand, acknowledges the complexity and the
changing nature of competitive environments. Therefore, organizations are only able to
develop an incomplete knowledge of their inner strengths and weaknesses and of the
dynamics of environment. Organizational capability to understand and predict the future
is limited, if not impossible. Organizations should then make small steps into their
objectives, measure their effectiveness and be flexible when faced with new information
and unexpected events.
There is no consensus on which approach brings better results to organizations. The
incremental perspective is favored by Tidd and colleagues, who argued that the
inevitable uncertainties and complexities force companies to constantly adapt their
strategies (Tidd et al., 2005), thus avoiding rigidities (Leonard-Barton, 1992). In this
sense, Tidd et al. also argue that the dynamic capabilities framework (Teece et al.,
1997), described previously, offers the most appropriate perspective on strategy making.
A more diplomatic stance is followed by Chiesa, who stated that both perspectives can
be useful, depending on the circumstances (Chiesa, 2001). Chiesa uses the metaphor of
the large and small corporations. The first, which traditionally benefits from the use of
tools and techniques to support the structure of a problem, is often challenged to design
organizations to become more agile and flexible to changes. The small corporation,
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commonly more flexible and adaptable, are often faced with increasingly complex
problems that require tools and techniques to help them get a better understanding of the
problem and enable decision and action making. This suggests that, while the definitive
solution or procedure to the formulation of a technology strategy is quite likely
impossible to achieve, structures, methods and tools are able to provide a shared
platform of communication and understanding throughout organizations, supporting
them in facing the challenges ahead.
The other dichotomy in strategy formulation relates to the external and internal
perspectives, or the Positioning and Resourced Based View schools (Chiesa, 2001). The
Positioning school (an “outside-in” perspective) advocates that the most successful
companies are the ones capable of positioning themselves in environments where they
can enjoy sustainable competitive advantages (Porter, 1985, Hax and Majluf, 1991).
Therefore, this school of thought is centered on the analysis of business environments,
in which technology is a clear important element to guide the development of internal
plans. The Resource Based View’s ‘inside-out’ perspective, suggests that it is the
specification of a resource profile, which includes managerial capabilities and
technological competences (Marino, 1996a, Walsh and Linton, 2001), that enables
optimal product-market activities (Wernerfelt, 1984, Prahalad and Hamel, 1990, Bone
and Saxon, 2000).
2.4 Technology strategy frameworks
Frameworks have been extensively used as means for structuring complex ideas and
concepts, in order to support “the understanding and communication of structure and
relationship within a system” (Shehabuddeen et al., 2006, p. 325). In the management
field, frameworks are particularly useful to support decision making and action.
Criticisms have been raised about the lack of rigor and consistency in the definition,
development and implementation of frameworks. In an attempt to overcome this issue,
Phaal et al present a meta-framework to describe the nature and constituents of
management frameworks (Phaal et al., 2004).
According to the referred meta-framework, there are two key dimensions that
characterize frameworks: applied-conceptual and static-dynamic, described as follows:
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Conceptual: related to the abstraction or understanding of a situation;
Applied: concerned with practical implementation issues in real environments;
Static: the structure and position of elements (maps, models, processes,
procedures, techniques an tools) within a system;
Dynamic: related to the interactions between the elements of a system.
The underlying idea indicates that while conceptual frameworks represent forces,
drivers, concepts and ideas, applied frameworks represent approaches to support action
and decision making. Applied frameworks thus require methods, processes, techniques
and tools to “interface” with the real world.
The definitions proposed for static and dynamic frameworks resemble the “internal
versus external” dichotomy. It can be said that static frameworks are more related to the
rationalist approach to strategy, since its purpose is to provide structure to a procedure,
process, model, with its underlying tools and techniques. The dynamic frameworks, in
turn, relate to the incremental view, since they emphasize the fast changing nature of
competitive environments and the need of organizations to remain flexible, agile and
constantly reflect on the effectiveness of their implemented strategies. In other words,
they claim that the interactions between organizations’ capabilities and the environment
are of critical importance.
A large number of frameworks have been proposed to support the formulation of a
technology strategy in business environments. The most prominent frameworks are
reviewed in the following sub section using, as supporting basis for analysis, the key
dimensions proposed by Phaal et al. to characterize frameworks (Phaal et al., 2004).
Additionally, classifications proposed by Arasti and Packniat on a number of
technology strategy frameworks are also considered in the next sub section, with respect
to the schools and perspectives of strategy (positioning versus resource based and
incremental versus rational) in order to deepen the knowledge about the nature of the
frameworks (Arasti and Packniat, 2010). After this analysis, the applicable methods and
tools are introduced in the following sub section.
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2.4.1 Contributions from the literature
Frameworks to support the formulation of a technology strategy found in literature are
often contributions from industry practitioners, who based on their experience and
expertise, propose structured methodologies to conduct the process. Consequently, a
large number of the reviewed frameworks are applied in nature, i.e., they deal with real
implementation issues and how the process should be conducted in organizations.
Additionally a number of conceptual frameworks, which take into account driving
forces, key activities and information needs for the development of a technology
strategy, are also reviewed in this sub section.
An early attempt of framing technology strategy is proposed by Porter (Porter, 1985). In
this work, it is argued that the essence of strategy is to position companies in favorable
competitive environments, meaning, where competitive advantage is likely to be
obtained. In this sense, technology is seen as a determinant of industry structure and a
critical factor in generating sustained competitive advantages for companies. The
influence of technology is analyzed in two levels: the industry-level and the company-
level. The first is analyzed through the lens of the five forces: technology affects the
rivalry among competitors, by modifying cost structures, substitution costs and exit
barriers, the potential new entrants, through economies of scale, access to distribution
channels and others, the threat of substitute products or services, through perceived
product or service differentiation, lower price, etc. and the bargaining power of
customers and suppliers, where technology can bring lower switching costs, change
price sensitivity and even impact the whole industry structure, by reducing or increasing
the number of customers and suppliers. The influence of technology at company-level is
observed in the value chain, in terms of costs and/or differentiation advantages. As such,
companies can adopt four generic strategies: cost leadership, cost focus, product
differentiation or differentiation focus, which can be realized through technological
innovations in products, services or processes, or even through vertical innovations.
Porter proposed a step-by-step approach to technology strategy development: 1)
identification of technologies in the company’s value chain; 2) identification of relevant
technologies available in other industries; 3) definition of likely patterns of
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technological change; 4) definition of key technologies for the company to obtain and
sustain competitive advantage; 5) valuation of the company’s capabilities and required
investments in technology development and 6) selection of a strategy to support the
company’s competitive position.
While Porter’s framework aligns with the Positioning school, given the emphasis put on
technological change rather than on internal capabilities and competences of the
company. It is also a rationalist and applied framework, such is the multi-stage process
for technology strategy formulation.
In the framework proposed by Ford, it is argued that technology strategy development is
supported by three core activities (Ford, 1988): acquisition (for example, license-in, in-
house development, subcontract R&D activities and others), management of
technologies and exploitation (technologies can be exploited internally, through their
incorporation into the company’s own products of manufacturing systems or externally,
via licensing-out the technology, contract third parties to produce and/or market the
technologies or through joint ventures ), as represented in Figure 2.3. An internal audit
is also proposed to support the development of a technology strategy. The audit consists
of a number of questions aimed at helping companies to reflect on their potential for the
development and exploitation of technologies, among other relevant issues. The author
argued that a technology audit is a good starting point for developing a technology
strategy.
Although somewhat ambiguous, it can be said that Ford’s framework follows the
positioning school as well as Porter’s, since the unit of analysis is the technology, rather
than competences or capabilities. The framework only describes generic activities
(acquisition, management and exploitation) but the relationships between them are not
clear. Furthermore, such activities are more related to decisions rather than specific
processes. As such, it is a conceptual framework, and also incremental, given the
iteratively role of technology management in the process.
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Figure 2.3 - The elements of technology strategy. Source: (Ford, 1988)
A linear approach was proposed by Hax and No, and is outlined in Figure 2.4 (Hax and
No, 1992). The link between technology and business strategy is emphasized in this
framework, which characterizes the primary tasks that are relevant in the development
of a technology strategy. The process begins with the identification of technology
requirements that align with the strategy of the company, first at a corporate and then at
a business level. An interpretation of these requirements leads to the definition of
strategic technology units (STUs), which essentially identifies the skills, disciplines and
technologies (current or new to the company) necessary to gain competitive advantage.
Once these are defined, the next tasks concern the identification of technology trends
(“technology environmental scan”) and of internal technological strengths and
weaknesses of the company against its competitors (“technology internal scrutiny”).
These stages resemble the “internal versus external” dichotomy in technology strategy,
as mentioned previously. The attractiveness and strength of each STU is then assessed
through a technology portfolio matrix tool, and opportunities and weaknesses are
identified. Finally, broad and specific action programs, budgets and reevaluation
policies are established in the last stages of the process.
Hax and No’s framework considers both the internal and external environments, so it is
not clear the strategy school to which it belongs. It can be said though, that since the
unit of analysis is technology rather than competences, it is closer to the positioning
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school. The explicit step by step process, depicting specific activities, clearly
characterizes an applied and rational framework.
Corporate Strategy
Technology requirements
Mission of the firm
Strategic thrusts and planning
challenges
Business strategy
Technology requirements
Mission of the firm
Broad and specific action
programs
Identification of STUs
Definition of strategic technology
units
Formulation of the
Technology Strategy
Technology policies
A set of multilayer broad action
programs
Strategic
Programming
Definition and evaluation of specific
action programs
Strategic
Programming
Definition and evaluation of specific
action programs
Technology Internal
Scrutiny
Technology strenghts and
weaknesses
Distinctive technology
competencies for all strategic
categories of decisions
Technology
Environmental Scan
Technology intelligence
Technology opportunities and
threats
Technology attractiveness
Figure 2.4 - A framework for the development of technology strategy. Adapted from (Hax and No, 1992)
The need to consider the dynamics of competitive environments with the development
of a technology strategy is highlighted by Chiesa (Chiesa, 2001). According to the
framework proposed by Chiesa, information must be gathered to answer three key
decisions: selection, timing and acquisition, as described in Table 2.2. For this purpose,
the author proposes what he calls the context foresight process. This process consists of
two types of analyses: the external context driven analysis, which aims at identifying
the market shape and customers’ needs, related applications and the technologies
required for such applications; and the internal context driven analysis, which is
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concerned with the identification of the set of technological competences and
capabilities within the company and the likely applications that could be generated from
them. In the end of this process, a technology-application matrix is built, which serves
as basis for defining broad action programs that should include decisions regarding
which technologies to develop (selection), the acquisition mode of such technologies
and when to develop and introduce the technology in the market.
The output of this process is a match between the internal and external analysis, which
means the identification of a technological skill base that is necessary for the company
to obtain competitive advantage. As a result, there are five types of technology strategy
actions: competence deepening, competence fertilizing, competence complementing,
competence destroying and competence refreshing. Each of these strategies is
appropriate according to the novelty of technologies and applications to the
organization, positioned in a technology versus application chart, as portrayed in Figure
2.5b. Different trajectories between these strategies are possible, allowing companies to
adapt to the dynamics of their competitive environments.
Figure 2.5 - The dimensions of technology strategy (a) and the context foresight process (b) types of
technology strategy actions. Source: (Chiesa, 2001)
Chiesa’s framework, centered on internal competences, suggests a resource based
approach. The context foresight process, consists in two types of analysis (internal and
external) which feed a technology versus application matrix and implies a structured
process to support the decisions of the technology strategy formulation (selection,
acquisition and timing), which are related to each other. Therefore, it can be classified
as an applied framework. Finally, because of the dynamic nature of the possible
strategic actions, it can be said that this is an incremental framework.
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The need to explore other driving forces derived from the political, economic and social
domains, beyond the typical technology-market interaction, is an issue that should be
addressed, according to du Preez and Pistorius (du Preez and Pistorius, 1999). They
proposed a structured framework for assessing technological threats and opportunities,
as seen on Figure 2.6. This framework highlights the information requirements in the
earlier stages of the process, namely on scanning and monitoring the dynamics of the
environment, organizing and classifying this information Then, this information should
feed a series of analyses using, for this purpose, established techniques and tools. The
authors suggested a number of these methods, without going into much detail. Next,
opportunities and threats are assessed, along with audits conducted on organizational
capabilities and analyses on the interaction between technologies and market
applications. The result of this process is a number of scenarios, which is followed by
the development of possible response actions and selection and implementation of
appropriate strategies.
Figure 2.6 - Structured framework for assessing technological threats and opportunities. Source: (du Preez
and Pistorius, 1999)
Hence, du Preez and Pistorius’s proposition, like Hax and No’s, considers both internal
and external environments in the process, but the unit of analysis is still the technology
and the environment, such is the emphasis put on “scanning” and “monitoring”
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activities. Therefore, the framework leans towards the positioning school. It is definitely
an applied framework, such is the suggested structured process. Finally, the framework
implies a more rational approach, even though some reflection of the strategy
implemented is considered, as demonstrated by the returning arrows in Figure 2.6.
In line with Ford’s framework, Davenport also argued that the formulation of a
technology strategy revolves around three activities: acquisition, management and
exploitation (Davenport et al., 2003). However, they extend the framework to include a
number of other contributing components beyond just technology, namely the
technological knowledge and the learning capability of organizations, or the absorptive
capacity as coined by Cohen and Levinthal (Cohen and Levinthal, 1990). This
framework highlights the role of external networks and the acquisition modes in
nurturing internal technological competences and capabilities of organizations.
Therefore, it may be said that it is centered in the RBV perspective on strategy.
Additionally, and as with Ford’s framework, it is a conceptual framework, but
dynamic/incremental, as it emphasizes the iterative role of continuous learning in the
strategy process.
Figure 2.7 - Technology strategy framework. Source: (Davenport et al., 2003)
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Burgelman and colleagues observed the technology strategy development as an
organizational learning process (Burgelman et al., 2004), as depicted in Figure 2.8.
Experience and learning play a critical role in defining a technology strategy, and a
structured process is not proposed. It is more related to the Resource Based View school
of strategy, given the relevance of organizational competences in developing a strategy.
It is also a conceptual framework, as no sequence or structure process is described.
Burgelman et al. also emphasized the driving forces of this process, as seen in Figure
2.1, and therefore this framework can be understood as an incremental framework, since
it considers the influence of internal and external environments in shaping a strategy
and, like Davenport et al.’s framework, emphasized the strategy learning process.
Technological
capabilities
Technology
strategyExperience
Figure 2.8 - Technology learning process. Source: (Burgelman et al., 2004)
The frameworks analyzed in this sub section show different but complementary views
on the process of technology strategy formulation. The observed dichotomies represent
extreme perspectives and efforts in bringing some logic to this complex theme. It is
understood that the positioning school is only appropriate in industrial contexts with
well-defined boundaries and where the products’ required levels of performance and
cost are known, and for this reason there is nowadays a greater tendency to consider the
internal perspective, or the Resource Based view in technology strategy (Bone and
Saxon, 2000). In more dynamic environments, a reality that is increasingly present in a
large number of industries, technological paradigms and standards have shorter lifetime,
and therefore technology itself no longer plays a primary role, leaving room for analysis
based on core competences (Prahalad and Hamel, 1990).
Because of this, developments outside the sphere of influence of organizations should
clearly not be ignored. Surely, trends observed on several fronts, such as in markets,
economy, regulations and in society in general can represent both opportunities and
threats to the development and diffusion of new technological solutions. According to
Chiesa, it is the combination of both internal and external analysis that “supports
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understanding of the dynamic interaction between the firm's strategy and the context
evolution”, and, as such, “the starting point of the strategy process should be, on the
one hand, the customer needs and the related functions to be solved, and, on the other,
the firm’s base of technological competence” (Chiesa, 2001, p. 48).
The same is true for the incremental versus rationalist dichotomy. The aforementioned
metaphor of small but agile company seeking processes and tools to support its decision
making process and the large but rigid searching for greater flexibility in the face of
uncertainty suggests that a greater balance between structure and flexibility can be
positive in a wide range of situations.
The scope of this thesis is centered in applied frameworks and the development of novel
tools and methodologies. Nonetheless, the review and analysis previously carried out on
conceptual frameworks are of extreme relevance in the conceptualization of drivers and
activities.
According to Centidamar et al., applied frameworks consist of two elements: activities
and tools (Centidamar et al., 2010). As a matter of fact, an observation of the proposed
applied frameworks clearly shows that the process of technology strategy formulation is
consolidated into four core activities: internal analysis, external analysis, generation
and selection. This is particularly evident in the frameworks proposed by Hax and No,
du Preez and Pistorius and Chiesa (Hax and No, 1992, du Preez and Pistorius, 1999,
Chiesa, 2001). Each of these core activities is supported by a wide range of tools and
techniques, which although being still unable to capture the whole complexity inherent
in the strategic process, support structuring a problem and decision making. As the
research streams in strategic management of technology indicate higher emphasis on
tools and techniques, it is thus understood that research conducted in refining and
integrating tools into the technology strategy formulation process can positively
contribute to an improved process in organizations. These issues are further explored in
the following subsection.
2.4.2 Core activities and applicable tools
The four core activities identified in applied technology strategy frameworks imply a
structured and linear process. Clearly, generation should precede selection. Internal and
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external analyses, which are aimed at identifying the strategic guidelines that the
organization should pursue, should precede generation. Internal and external analyses
are activities that can be done simultaneously though. The role of each activity in the
overall technology strategy formulation is described with more detail in sub sections
2.4.2.1, 2.4.2.2, 2.4.2.3 and 2.4.2.4 respectively.
The dynamics of the environment requires greater flexibility in this process, that is, the
possibility to change directions when evidence indicates that assumed strategic
guidelines proved not to be advantageous for the organization. Still, at a certain point in
time, a decision needs to be made. In this line of thought, it is understood that a
structured process that supports communication, understanding and decision-making in
the organizations, can contribute to the management of the complexity involved in the
technology strategy formulation process.
A fifth activity would normally be included, namely concerning the reflection on the
results achieved, thus becoming a cyclical process. This reflection activity is, in essence,
an analysis of the results achieved in the implementation of a strategy vis-à-vis the
initial goals set. The four core activities identified in this review are related to the
necessary steps until the moment of decision-making, and therefore the reflection
activity is not the object of analysis in this thesis. Notwithstanding, the risks involved in
technological developments should not be ignored, and thus any technology strategy
formulation framework should consider uncertainty.
A plethora of tools have been developed for each core activity of the technology
strategy formulation process. An introductory review on these tools is presented in the
following subsections. More comprehensive reviews are presented in Chapter 4,
Chapter 5 and Chapter 6, which are dedicated to each activity.
2.4.2.1 Internal analysis
Internal analysis is related to the identification of the technological competences
available to the company, an assessment on the strengths and weaknesses in managing
the innovation process inside the organization, and in finding opportunities for
improvements.
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For these purposes, audits have been extensively used as a tool for organizations to self-
assess their technological capabilities. The objectives include the self-assessment of the
conditions necessary for innovation, determination of the degree to which best practice
is being used, and identification of deficiencies (Cooper and Kleinschmidt, 1986) in the
innovation process.
A review found seven audits proposed in literature. All of which consist of a number of
statements where participants are asked to provide their judgments, using a Likert scale,
concerning the organizational performance in certain key dimensions. Another common
feature to all audits was the clustering of audit statements into dimensions. Despite
minor differences between the reviewed audits, a considerable number of dimensions
are shared across them, as it can be seen in Table 2.3.
Table 2.3 – A summary of proposed dimensions for innovation audits
Reference Dimensions
(Goodman and Lawless, 1994) Technology; Market; Organization; Environment; Industry
Structure; Firm analysis
(Chiesa et al., 1996)
Core Processes: Concept Generation, Product Development, Process
Innovation, Technology Acquisition; Enabling Processes:
Leadership, Resourcing, Systems and Tools
(Burgelman et al., 2004)
Resource availability and allocation; Understanding of competitors’
innovative strategies and industry evolution; Understanding of the
firm’s technological environment; The firm structural and cultural
context; Strategic management capacity to deal with entrepreneurial
behavior
(Cormican and O’Sullivan, 2004) Strategy and leadership; Culture and climate; Planning and selection;
Structure and performance; Communication and collaboration
(Tidd et al., 2005) Strategy; Learning; Linkages; Processes; Innovative organization
(Radnor and Noke, 2006) Structures; Leadership; Outputs; Teams
(COTEC and IAPMEI, 2008)
Culture, Leadership, Strategy, Human capital, Competences,
Networking, Structures, R&D management process, Learning and
continuous improvement, Protection and valuation of results
The implementation of these audits in the context of organizations is usually done
during group meetings. As can be seen in Table 2.3, some of the dimensions considered
in audits address sensitive issues, such as leadership, management of the process of
technological innovation and the very organizational structure. The discussion around
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these dimensions may lead to internal divisions, compromising the effectiveness of the
audits and resulting in biased assessments. This suggests that audits should take into
account social issues in their design, in order to encourage participants and thus provide
more realistic assessments of organizations’ capabilities. To the researcher's knowledge,
and given the reviewed audits, no author has attempted at addressing these issues.
2.4.2.2 External analysis
External analysis deals with the set of activities aimed at identifying the future state of
markets, customer needs and events that may shape the patterns of technological
development. With these goals in mind, numerous tools are proposed in the literature,
and have been grouped into what is known as Technology foresight.
Technology foresight is formally defined as the “systematic process to identify future
technology developments and their interactions with society and the environment for the
purpose of guiding actions designed to produce a more desirable future” (Group,
2004). Technology foresight is perceived as an evolution of what is known as
technology forecasting. While technology forecasting is more concerned with accuracy
and predictability, technology foresight focuses on socio-economic contextual factors
interacting with emerging technical capabilities that affect commercial products and
services (Porter, 2010). Its focus is centered in creating shared visions of the future that
stakeholders are willing to endorse by the actions they choose to take today (UNIDO,
2005). Vecchiato and Roveda argued that the main contribution of technology foresight
lies not in predicting the future, but rather in preparing managers to handle the future
(Vecchiato and Roveda, 2010). Technology foresight is seen as being directly useful
and necessary for strategy formulation (Reger, 2001).
Technology foresight tools are generally applied to the identification, organization and
extrapolation of patterns of technical development and the determination of emerging
technologies (Council, 2002). A significant number of tools can be found in literature
and authors have attempted to cluster and classify them according to some shared
attributes. In an attempt to establish a methodology that matches a technology
forecasting technique to a technology, Mishra, based on earlier works by Ayers,
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Wheelwright and Makridakis, proposed a classification of three types (Mishra et al.,
2002), as described in Table 2.4.
Table 2.4 - Types of technology foresight tools. Source: (Mishra et al., 2002)
Type Description Examples
Subjective assessment
tools
Structured interaction group
techniques based on subjective
opinion of experts.
Jury of executive opinion; sales force
composite methods; formal surveys,
market research-based assessments,
individual subjective probability
assessments, scenario development,
Delphi method, nominal group
technique, brainstorming
Quantitative tools
Quantitative tools that address
what is possible to achieve by
extrapolating current hard data
from current technological
capabilities.
Cross-impact matrices; analogy
methods; morphological research; trend
extrapolation; growth models;
regression and substitution analysis.
Normative tools
Tools that address desirable
outcomes based on future
technological needs.
Operations Research (OR) models and
simulations; network techniques;
relevance trees, system dynamics,
dynamic modeling and
phenomenological modeling.
Despite the numerous proposed tools, evidence points to a greater preference nowadays
for tools that foster participation, creativity and communication, that is, greater
emphasis is being put into “soft factors” as critical success factors in the
conceptualization of a foresight activity. This is the conclusion of a survey with large
European multinationals that incorporate foresight into their operations (Daheim and
Uerz, 2008). In this same study, an emergent paradigm is identified in foresight studies,
more focused in an open dialogue about the dynamic interactions between social,
technological and economic forces, in what is known as Context-based (open) foresight.
Moreover, there is a greater tendency towards the development of easily
comprehensible, timely and cheap sources of technology foresight (Coates et al., 2001).
This new direction is also supported by an earlier study (Makridakis, 1996). By citing
the book “The Rise and Fall of Strategic Planning”, from Henry Mintzberg, Makridakis
described the history of how many Japanese companies abandoned formal and rigid
strategic planning methods, that were too focused on accuracy of predictions, in favor of
creating creative-friendly atmospheres within the companies.
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In sum, it can be concluded that the current trend in technology foresight is related to
development of new tools for external analyses should adopt a holistic perspective,
focusing on the possible relationships between various factors beyond the technological
domain, and in structures that promote communication and creativity inside
organizations.
2.4.2.3 Generation
The generation activity is related to the activities involved in generating project ideas,
based upon the strategic guidelines provided in previous analyses (internal and external)
and on information needs required to the project generation process. In short, the
generation activity concerns the translation of technology strategy guidelines into R&D
strategy, which is “the definition of the set of R&D projects required to achieve the
fixed objectives in terms of technology acquisition defined within the overall strategic
framework of the firm” (Chiesa, 2001, p. 19).
Inevitably, the transition from technology strategy to R&D projects requires more
information than just mere technologies defined in a broad level, which is the output of
the internal and external analyses. It requires information about patents, products and
technologies from competitors, applicable standards and regulations, and surely,
creativity from individuals and teams involved in the process (Yoon, 2008). Technology
strategy formulation is both an analytical and creative process, and the generation
activity is arguably the most dependent on people’s creativity (Bone and Saxon, 2000).
Therefore, the generation activity requires a technology intelligence system aimed at
keeping an extensive database that gathers, analyzes and disseminates relevant
information (Savioz et al., 2001, Lichtenthaler, 2004) to actors in the organization. This
information not only serves the purpose of generating projects with better supporting
information, but also enables a more transparent comparison between projects, which is
the essential objective of the next activity, selection.
Finally, the generation activity comprises tools within areas of knowledge that are out of
the scope of this thesis. However, the information needs required to characterize and
compare R&D projects are mapped and presented in Chapter 7.
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2.4.2.4 Selection
This activity concerns the selection of strategic projects that best ensure competitive
advantages to the organization. As mentioned by Meyer: “The operational expression of
a technology strategy is the set of projects that an organization wants to implement.
Determining a strategy is selecting the projects and the portfolio of projects.” (Meyer,
2008, p. 151). Also according to Shakhsi-Niaei et al. and making a connection with the
decisions involved in the formulation of a technology strategy, research on project
selection can be applied to technology selection problem as well (Shakhsi-Niaei et al.,
2011). In this sense, the problem of selection should consider acquisition mode
selection, in the case of technologies, as well as the mode of execution of projects.
In an extensive review on project selection literature, Archer and Ghasemzadeh
highlight eleven propositions that need to be met for a successful practical
implementation (Archer and Ghasemzadeh, 1999). These propositions describe a
number of project selection methodological requirements: incorporation of both internal
and external factors before selection is performed, flexibility in decisions, organization
in activities that allow decision makers to move logically towards an integrated
consideration of projects, appropriate use of methods and criteria to ensure equitable
comparison of projects, re-evaluations and adjustments, consideration of dependencies
with other projects, incorporation of interactive mechanisms and consideration of
resource competition with current projects under execution. This last proposition is
arguably one of the major reasons why a number of projects are selected but not
completed on time (Ghasemzadeh and Archer, 2000).
Other works stress the importance of incorporating non-financial or intangible aspects
in project selection (Lopes and Flavell, 1998, Meade and Presley, 2002a, Liao, 2005,
Steffens et al., 2007). In this problem domain, decisions are mostly made based on the
“gut feeling”, knowledge, intuition and experience of decision makers, which often find
support in multi-criteria decision-making tools (Tan et al., 2011).
The maturation rate of a technology or technology readiness levels (Anderson and
Nolte, 2005) is related with different types of R&D projects: basic research, applied
research and advanced technology development. Each of this R&D type concerns
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different objectives and requirements, and, therefore, should be evaluated according to
different criteria (Tidd et al., 2005). With respect to project selection criteria, they
should inevitably consider both positive and negative aspects of projects, or their
benefits and risks (Chiesa, 2001). Uncertainty is widely recognized as one of the
greatest challenges in developing new technologies and products, due to market
dynamics and technical risks involved in R&D projects, resulting in considerable failure
rates. Thus, risk management should be conducted in all R&D projects’ phases in order
to increase probabilities of success (Wang et al., 2010).
In line with the above ideas, and according to Gabriel et. al, any robust and realistic
project selection methodology should consider two key modeling aspects (Gabriel et al.,
2006): multiple criteria, in order to facilitate the inclusion of both objective and
subjective (intangible) characteristics of the projects, and probabilistic components, in
order to deal with uncertainty.
2.5 Critical analysis and research gaps
The proponents of the previously described technology strategy frameworks based their
conceptualizations on the authors' own analysis of current literature (at the time of the
research), perspectives, philosophical stances (related to the strategy schools of thought)
and experience with organizations. While the frameworks proposed in literature and
reviewed in this chapter are valid proposals and contribute with valuable insights, a
novel approach, based on the development of improved tools and methodologies
towards the conceptualization of technology strategy frameworks, could contribute with
new knowledge to this field, which addresses the needs of organizations in terms of
applicability of frameworks and tools. In this sense, this thesis presents a new approach
towards the development of a technology strategy framework.
Given the strategic implications that technology has in the competitiveness of
organizations, as explained before, there is a renewed interest in the development of
new applied technology strategy frameworks. The state of the art in technology strategy
frameworks supports the proposition that the technology strategy formulation process is
consolidated in four core activities. Each of these core activities encompasses a number
of applicable tools. Thus, and in line with research streams related to the development
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of novel tools and methodologies addressing particular research gaps, it is argued that
the development of a technology strategy framework that builds on the integration of
improvements made in tools and methodologies for each core activity, can contribute to
an improved technology strategy formulation process. Thus, the main research question
of this thesis can be expressed:
How can different tools and methods be combined and integrated to improve the
process through which organizations develop their technology strategy?
The analysis conducted on each of the four identified core activities of the technology
strategy formulation process revealed a number of research gaps related to the
development of analytical and decision-support tools.
The internal analysis step has been largely accomplished through audits. While internal
audits reveal important traits of organizational performance in managing technological
innovations as well as in identifying critical technological competences, their
effectiveness can be compromised since it usually considers very sensitive issues prone
to generate social fracture within the organization, such as the role of leadership or the
whole organizational structure in the technological management process.
In addition, existing audits proposed in literature tend to overlook the internal dynamics
of organizations, i.e., the evolution of internal technology management capabilities.
Over time, organizational capabilities may undergo profound changes as a result of the
adoption of new practices and improvement actions for example. Therefore, one time
audits performed in the long past may be outdated, especially if done well before
strategy formulation, and thus can be of little use to organizations. This calls for a real
time or near real time audit system for assessing organizational capabilities.
These considerations for the internal analysis activity indicate two research gaps: the
consideration of social issues in the audits and the existence of a platform which allows
a dynamic assessment of the organizational capabilities in the technological innovation
process. Since the two research gaps are related to the same activity, they can be
considered in the same research question, with the goal of developing an improved
methodology capable of addressing these research gaps:
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How can the internal dynamics and social issues be addressed in organizations in the
internal analysis activity?
A platform incorporating tools that foster an open dialogue, creativity, participation of
various agents and that includes sources of knowledge, internal and external to the
organization, was identified as a major requirement in the external analysis activity.
This requirement is aligned with the emerging paradigm in future studies known as
context based (open) foresight, as mentioned earlier.
Despite the growing recognition about the importance of open foresight in future studies
and the existence of numerous tools, somehow an approach that ensures an open
discussion about the influence of events on other domains, such as the economy,
politics, social and others in technological development has been ignored. That is, a tool
that more than just promoting open debates, also considers the dynamic interactions
between technology and other forces of the environment is required.
Thus, the research gap in the external analysis activity is related to the development of a
tool that promotes not only an open debate about the future relevant events but also
discussions on how the interactions between various forces influence the patterns of
technological change. This leads to the following research question:
How can the influence of external drivers in technological development be assessed in
the external analysis activity?
In the selection activity, numerous issues are identified, and described below. They can
be understood as requirements for a project selection methodology (Archer and
Ghasemzadeh, 1999) and, despite a number these issues having been addressed in a
number of methodological developments proposed in literature, they were not
considered into a broader operational context, namely in technology strategy
formulation process.
As previously mentioned, the selection activity is closely related to the technologies
selection decision. Therefore, this phase requires not only a mechanism that facilitates
the selection of R&D projects and technologies, as well as a method to support the
selection of the projects mode of execution and of the technologies acquisition mode.
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Another requirement of this phase is related to a broader perspective in R&D project
selection, i.e., the inclusion of intangibles and non-financial variables to enable the
comparison of alternatives. This requirement aligns with a multidimensional perspective
on projects, focused on strategic factors related to the sustainability of the organization's
competitive advantage in the long term, rather than only on short-term financial returns.
Clearly, financial variables should always be incorporated in these analyses, but must be
balanced with other variables, that although being difficult or even impossible to
measure objectively, are of utmost importance for a more comprehensive overview on
projects.
The comparison between projects should also consider the different goals set for each
type of R&D project (Tidd et al., 2005). For example, basic and applied research
projects should take into account aspects related to knowledge and competence building
in strategic areas, while most advanced developments in technologies and products must
take into greater account aspects related to the business side itself, performance of the
markets and expected financial returns. Therefore, this suggests that different criteria
must be applied according to different types of R&D and technology maturity rate
levels.
Uncertainty is a recurrent topic discussed in project management. And although
uncertainty is addressed in some project selection methodologies proposed in literature,
it has not become an integrative part of a control mechanism that, after projects have
been selected, provides feedback information to managers. The consideration of
uncertainty on early stages of the project life cycle – such as the project selection
activity – allows more time for managers to act upon the project risks (Institute, 2008),
the downside of uncertainty. Although risk assessments carried out at an early activity
such as project selection may be based on incomplete information, the assessment
should be updated as more information is obtained throughout the execution of selected
projects, so that adjustments can be made in re-evaluation points of the project
(milestones, gates, etc.), thus making clear the need for an integrated risk assessment
and control mechanism during project selection. As previously stated as well, risk
assessments should also consider the impacts in schedule and cost derived from
resources competition with projects already underway.
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The integration of a risk assessment and control mechanism on an early phase of the
projects’ lifecycle, such is the project selection phase, constitutes the research gap in the
selection activity. Thus, the following research question can be formulated:
How can risk management practices be incorporated in the project selection activity?
The development of new tools that address the research gaps identified in this section
and their integration into a framework to support the formulation of technology strategy,
hopefully contributes to a more robust strategic process. As such, the identified research
gaps in each of the core activities can also be understood as framework system
requirements because, in addition to responding to the gaps in existing tools, the
proposed solutions are integrated into a wider framework or process.
The research stream on technology management tools suggests the integration or
combination of tools as a viable strategy for the development of more robust
methodologies (Liao, 2005, Phaal et al., 2006). The proper integration of tools, aimed at
accommodating possible deficiencies, gaps or inadequate theoretical considerations
found in existing stand-alone tools, thus support managers in organizations to perform
realistic analyses and make more solid decisions. This strategy is adopted in this thesis
as a way to address the identified research gaps. The research methodology used is
detailed in Chapter 3.
2.6 Conclusions
Technology strategy is a multidisciplinary theme, which requires the consideration of
converging areas of knowledge. Furthermore, different perspectives and research
streams followed by academics bring more complexity to this discipline.
The dichotomies related to technology strategy, when analyzed in greater depth as done
in this chapter, present more complementary than opposing viewpoints. This suggests
an appropriate balance in the conceptualization of frameworks to support technology
strategy, that incorporates structure (rationalist view) and flexibility (incremental view),
as well as the internal (resource based view) and external (positioning school) dynamics
of organizations.
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The uncertainties and constant changes in competitive environments turn the
formulation of optimal strategies for companies into a difficult task. It is concluded that
supporting management structures capable of engaging different functional areas of an
organization, thus providing a common platform of communication throughout the
organization, bring benefits for organization in the formulation of their technology
strategies. But such structures should be coupled with flexibility and dynamism in the
face of changes in the external environment. That is, structured frameworks should also
encourage incrementalism, allowing modifications and adjustments in light of new
information.
The development of new analytical and decision making tools has become one of the
main research streams in technology management. The identification of the four core
activities in the formulation of technology strategy, namely internal analysis, external
analysis, generation and selection, allowed a delimitation of the tools most applicable to
each activity. In an overview of these tools, themes related to dynamism and social
aspects in organizations’ self-assessments, a more holistic perspective on technology,
consideration of different rates of technologies maturation and integration of risk
management practices in project selection were identified as research gaps in the
development of a technology strategy framework that integrates various tools. In this
context, the following chapters of this thesis present methodological advancements in
each of the activities, which are later to be integrated into a framework.
CHAPTER 3
Research methods
This chapter presents the themes related to the research process, which a
number of authors treat as research methodology. The research questions
posed in the previous chapter are answered in this thesis following a
deductive approach, departing from the theory which sustains that the
technology strategy formulation process is conceptualized in terms of four
core activities, and researching towards the generalization or
conceptualization of an improved technology strategy framework. The
research hypotheses addressed were twofold: that an improved technology
strategy framework can be conceptualized through research conducted in
tools underpinning the four core activities, and the tools can be selected and
combined in order to develop improved methodologies. The proposed
methodologies are tested in the industrial partner of the thesis. This
research can also be categorized as exploratory, since it is aimed at refining
and testing methods and procedures. The research plan for the following
chapters of this thesis is presented in the end.
Chapter 3
54
3.1 Introduction
This chapter of the thesis describes the research methods used to answer the research
questions posed in Chapter 2. It begins by presenting the steps of the research methods
applied in this thesis, then, the choices made in the research design are justified and,
finally, the description of the research plan is discussed in the following sections.
Figure 3.1 portrays the research methods followed in this thesis. The research interest is
to improve the process through which organizations formulate a technology strategy.
Such frameworks are constituted of activities and tools. The novel technology strategy
framework developed in this thesis results from the research conducted in one of the
most prominent research streams in technology management: research on tools and
methods. The idea is for research to be conducted on tools underpinning the activities of
the technology strategy formulation process, in order to address research gaps
contributing for the development of improved methodologies. Along with this, needs
from practice are also considered, resulting from an analysis conducted with the
industrial partner of the thesis1. The integration of these methodologies will serve as a
supporting basis for the conceptualization of a new technology strategy framework.
The strategy followed for the development of the new methodologies is to combine
different tools in order to address possible deficiencies in stand-alone tools.
Methodologies are developed for three core activities of the technology strategy
formulation process, and implemented in the industrial partner of the thesis. The
proposed methodologies and their implementation are described in three chapters
devoted to each activity. The final two chapters present the conceptualization of the
novel technology strategy framework, resulting from the integration of the three
proposed methodologies, and the final conclusions and recommendations for future
work.
1 The work presented in this thesis is aligned with the guidelines of the Leaders for Technical Industries doctoral program. These
guidelines suggest that academic work within this program is to be conducted in cooperation with an industrial partner, thus
requiring explicit societal relevance.
Chapter 3
55
The terms “tool” and “methodology” are used extensively hereafter. An important
distinction needs to be made at this point to avoid confusion, concerning the
terminology used in this thesis. Tool is defined as a procedure or technique defined in
itself. Examples are the Delphi survey, Analytic Hierarchy Process, and others.
Methodology is defined as a combination of tools in a systematic manner so to be
applied to a specific case.
Research interest
Topic: Technology strategy framework
Improve the process through which
organizations develop their
technology strategy
Research idea
Research on tools and
methods and their integration
into a novel technology
strategy framework
Modelling and
combination of tools
Types of technology strategy
frameworks
Identify core activities
Commonly used tools in each
core activity
Literature analysis
Gaps in literature and
needs from practice
Methodology
development
Issues of concern in
practice
Industrial partner anaysis
Methodology
application
Integration of the
methodologies
Final conclusions and
future work
Strategy
Figure 3.1 - Research methods
Chapter 3
56
3.2 Choices in the research design
Research designs “are plans and procedures for research that span the decisions from
broad assumptions to detailed methods of data collection and analysis” (Creswell,
2008, p. 3). This definition suggests that research designs are constituted of different
decisions or choices made at different levels of understanding about the research to be
conducted.
Saunders and colleagues presented a model consisting of seven decisions or “layers”, in
what they name the “research onion” (Saunders et al., 2009). The fundamental idea
behind it is that research methodologies can be characterized in multiple layers,
spanning from philosophies (the outermost layer) to techniques and procedures (the
innermost layer), all the way through approaches, strategies, choices and time horizons
(the intermediate layers, from the outermost to the innermost). It is beyond the scope of
this chapter to present a detailed description of each layer. Only the most relevant layers
to this thesis will be described instead.
Kumar presented a classification of different types of research (Kumar, 2005), which is
complementary to the perspective offered by Saunders and colleagues with their
“research onion”. According to Kumar, research can be categorized in relation to three
non-mutually exclusive viewpoints: application, objectives and inquiry mode.
In the application viewpoint, research is subdivided in two types: pure and applied
research. Pure research is related to the development and testing theories and
hypotheses, not necessarily linked to any practical applications. On the other extreme,
applied research is concerned with the application of research techniques, procedures
and methods for the purpose of enhancing understanding about a phenomenon or
phenomena.
The objective viewpoint is subdivided in four categories. The objective of descriptive
research is the portrayal of an accurate profile of persons, events, situations
and/organizations. In correlational research, the goal is to study the possible existence
of a relationship between two or more variables that characterize a situation (for
example, the relationship between expertise level of employees and innovation
Chapter 3
57
capability of an organization). Explanatory research is a deeper investigation into the
causal relationships between two or more variables that characterize a situation, aimed
at explaining why and how these relationships are manifested. Finally, exploratory
research is aimed at clarifying the understanding of a problem about which there is still
little knowledge, or at the refinement and/or test measurement tools and procedures.
The inquiry perspective is related to the research process used to find answers to the
research questions. It is subdivided in two groups: qualitative and quantitative research.
These two groups are similar to the ones described in the choice layer from Saunders
and colleagues’ “research onion”. In quantitative research the idea is essentially to
quantify change or variation in a determined situation, phenomenon or problem and thus
tend to be more of the correlational and explanatory types of research. On the other
hand, qualitative research is aimed at describing a determined situation, phenomenon or
problem, and therefore tend to be more of the descriptive and exploratory types.
The design of a research methodology, which includes a series of choices to be made,
depends very much on the type of research question to be answered. Yin and Saunders
and colleagues suggested which research method is more adequate to different forms of
research questions (Yin, 2002, Saunders et al., 2009). This relationship is portrayed in
Table 3.1.
Table 3.1 - Relationship between forms of research questions and research strategies. Source: (Yin, 2002)
and (Saunders et al., 2009)
Research strategy Form of research question
Experiment, history and case
study how, why?
Action research how?
Grounded theory, ethnography,
survey and archival analysis
who, what, where, how many, how
much?
The research questions (RQ) for the research here reported are outlined below:
RQ1: How can different tools and methods be combined and integrated to improve the
process through which organizations develop their technology strategy?
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RQ2: How can the internal dynamics and social issues be addressed in organizations in
the internal analysis activity?
RQ3: How can the influence of external drivers in technological development be
assessed in the external analysis activity?
RQ4: How can risk management practices be incorporated in the project selection
activity?
Following Yin and Saunders and colleagues propositions, the research questions of this
thesis would suggest the use of experiment, history, case study and/or action research. A
comparison between these research strategies reveals some distinguishable traits.
History is used when the researcher has no access or control over behavioral events and
is not focused on contemporary events. Experiment, on the other hand, is commonly
used in the study of causal links, in which the researcher has direct control over
behavioral elements (for example, in laboratory settings or controlled social
experiments). Case study and action research lies between these two extremes. Focused
on contemporary events, in case studies the researcher is involved in an empirical
investigation of a particular contemporary phenomenon within its real life context using
multiple sources of evidence (Robson, 2002), and where the boundaries between
phenomenon and the context within which is being studied are not clearly evident (Yin,
2002). However, in case studies the researcher does not have control of behavioral
events, which means he/she is limited to study, analyze and describe situations.
Emphasis on contemporary events is a characteristic of action research as well, but in
this research strategy the researcher is involved with the members of an organization
over a matter of genuine concern to them (Eden and Huxham, 1996). It differs from
other research strategies since it is focused on action and the promotion of change
within an organization, through a cyclical process of diagnosis, planning, action and
evaluation (Saunders et al., 2009). Even though the researcher is seen as a facilitator of
change, it can be said that he/she has only partial control over behavioral events, since
he/she is immersed in the wider context of an organization.
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Research in management science, in which the topic of technology strategy is included,
is experiencing an increasing trend towards employment of field based research,
motivated by the high speed of changes in managerial methods (Marianne W., 1998).
This suggests that research in this area should be conducted within the contexts of
organizations, for the purpose of improving both the relevance and workability of
theoretical foundations. The greatest difficulty in its implementation concerns the
generalization of findings (Bryman, 1989), however, “field studies in one setting raise
questions about the external validity of the findings”, also contributing to “generate
new insights that are useful for building theory” (Burgelman, 1985, p. 42). A wider
access to organizations enables greater understanding about the context in which they
operate. On the other hand, careful attention should be paid to avoid biased analyses,
and issues outside the sphere of the organizations should be explored to enrich the
research.
As such, the new methodologies proposed in this thesis are tested in the context of the
industrial partner of the thesis - a medium sized manufacturer of sheet metal processing
equipment. The development of these methodologies is based on needs identified with
the industrial partner and a detailed literature review on the methods currently proposed,
in order to search for research gaps. Careful attention was paid to the identification of
generalizable research gaps, which can be of concern to other organizations as well,
since in the future the intention is to expand this research to other organizations, to
support the assessment of the new methodologies’ applicability in a wider range of
settings. Additionally, and in this regard, the researcher assumes the role of a facilitator,
explaining concepts to the members of the industrial partner, supporting the
implementation of the methodologies and collecting relevant information and feedback
for the improvement and refinement of methods for future applications. This position
aligns with the role of the practitioner-researcher described by Saunders and colleagues
as a mechanism for field based research (Saunders et al., 2009).
The characterization described above, about the research strategy used in this thesis,
does not point to a clear and distinguishable research strategy. Although the researcher
is somehow involved in the implementation of the methodologies in the industrial
partner, there is only one iteration in each application and not a cyclic process of
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diagnosis, planning, action and evaluation, thus not featuring an action research method.
The researcher is still limited to describing the steps that led to the development and
implementation of the methodologies, analysis of results and feedback of the industrial
partner, and not necessarily assuming the role of a facilitator of change as in action
research. The insights derived from the application of the methodologies in the
industrial partner support both methodological refinements for application in other
organizations, and the conceptualization of a technology strategy framework - the
ultimate goal of this thesis. In sum, this is a “methodologies” type of thesis, and the idea
is for the methodologies proposed here to raise the interest of both academicians and
practitioners.
Abbott’s three dimensional portrayal of explanatory programs provides additional clues
to the characterization of the research strategy used in this thesis (Abbott, 2004).
According to Abbott, explanatory programs are defined as general styles of thinking
about questions of explanation. Three types of programs are identified: the syntactic
program, which explains the social world by abstract models, the semantic program,
which explains the world of social particulars by assimilating it to general patterns, i.e.,
searching for regularities over time or across social space. The pragmatic program
separates more clearly the effects of different potential interventions or causes from one
another.
Although more directed to the social sciences, Abbott’s explanatory programs find
resonance in the work conducted in this thesis. Observing Figure 3.2, the research
methodology applied in this thesis can be situated in the quadrant formed by the
pragmatic and syntactic program axes.
The methodologies presented in this thesis can be characterized as representations of
systems that mimic the World or particular actions (ex.: organizational self-assessments,
selection of strategic projects, and other management actions). Such methodologies aim
at simulating reality in order to address real problems and needs. Thus, they can be
classified as “modeling formalizations”, from the syntactic program axis. Furthermore,
the application of the methodologies in cases from the industrial partner aligns with the
method of “experimentation”, since the objective is to assess their applicability in real
Chapter 3
61
environments, comparing the performance of the framework developed with the results
of previous industrial projects, on a postmortem perspective.
Commonsense
Understanding
SYNTACTIC
PROGRAM
SEMANTIC PROGRAMPRAGMATIC
PROGRAM
Pattern SearchEthnography
Modelling
Formalization
Historical
Narration
Standard Causal
Analysis
Experimentation
Figure 3.2 - The three dimensions of explanatory programs. Source: (Abbott, 2004)
Additionally, and following Kumar’s propositions, the work conducted in this thesis can
be categorize as applied research, given the cooperation with an industrial partner and
research conducted into procedures and tools. It is also exploratory research since it is
aimed at testing new methodologies within the context of an organization.
The Wheel of Science (Wallace, 1971) provides a simple but valid way to describe the
approach used in this thesis. As depicted in Figure 3.3, two approaches can be used in
research: deductive and inductive. The research approach is dependent on the extent to
which the research is clear about the theory at the beginning of the research. A
deductive approach is followed when departing from a theory or theories previously
developed (existing knowledge) hypothesis (or hypotheses) are stated by the researcher
which expectedly will be applicable to the reality under study, subsequently tested
through observations. Analysis on the results of such tests leads to empirical
generalizations. The inductive approach, on the other hand, starts from empirical
generalizations derived by the researcher from observations he/she collected in
particular contexts, moving towards the development of theories that, hopefully, can
later be tested.
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62
Empirical
generalizations
IND
UC
TIO
N
Observations
Theories
HypothesesD
ED
UC
TIO
N
Figure 3.3 - The Wheel of Science. Adapted from (Wallace, 1971)
The research conducted in this thesis follows a deductive approach, departing from a
theory supporting that the technology strategy process formulation can be
conceptualized in terms of activities and tools (Centidamar et al., 2010), consolidated
into four core activities, as analyzed in Chapter 2: internal analysis, external analysis,
generation and selection. A hypothesis is put forward, stating that improvements in
frameworks can be made through research conducted in tools underpinning the core
activities. The combination of tools is the approach followed, resulting in novel
methodologies. The methodologies proposed in this thesis are models that aim at
addressing issues and gaps not entirely or properly explored in literature, and are tested
in cases from the industrial partner of the thesis. This leads to the conceptualization of a
new technology strategy framework, made possible by integrating the methodological
developments proposed for each core activity of the process.
3.3 Research design
The research design or research plan presented in Table 3.2 provides an overview of the
objectives of this thesis and related research questions, as well as the research methods
used for each development and the respective chapter where such developments are
described.
In Chapter 2, literature on different types of technology strategy frameworks was
reviewed. From this analysis, core activities are identified, as well as commonly used
tools in each activity. Gaps in literature and needs from practice are identified, resulting
from a number of contacts with the industrial partner of the thesis during an internship
period, to support the development of new methodologies.
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The next three chapters present the new methodologies proposed for each core activity
under consideration – Chapter 4(internal analysis), Chapter 5 (external analysis) and
Chapter 6 (selection). Each of these chapters also presents a detailed literature review,
with greater emphasis on the tools used in the activity to which the chapter refers to, and
from which the conceptualization of the new methodology is originated. The strategy
followed is to combine different tools in order to address deficiencies in existing stand-
alone tools (Liao, 2005, Phaal et al., 2006). In the specific case of Chapter 5, semi-
structured interviews, analysis of industry reports and scientific papers were conducted
to feed a Delphi survey on relevant events about the future of the sheet metal processing
equipment industry. Each chapter includes a section where the testing of the proposed
methodologies in cases from the industrial partner of the thesis are described.
Chapter 7 presents the new conceptual technology strategy framework (the main
objective of this thesis), which results from the integration of the proposed
methodologies from the previous three chapters. Chapter 8 presents the final
conclusions of this thesis and outlines suggestions for future work to be conducted.
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64
Table 3.2 - Research plan
Objectives Research questions Research method Chapters
Main objective: propose a new technology strategy
framework, which is based on the development and/or
combination of methods and tools for its constituting core
activities
RQ1: How can different tools be
combined and integrated into the
activities underpinning the
technology strategy formulation
process?
- Chapter 7
Development of an internal audit that addresses the problems
involved in organizational self-assessments. RQ2: How can the internal
dynamics and social issues be
addressed in organizations in the
internal analysis activity?
Conceptualization: literature
review and modelling
formalization
Empirical evidence:
implementation in the industrial
partner
Chapter 4
Development of a methodology to support the identification
of strategic technologies RQ3: How can the influence of
external drivers in technological
development be assessed in the
external analysis activity?
Conceptualization: literature
review and modelling
formalization
Empirical evidence:
implementation in the industrial
partner
Chapter 5
Propose a methodology to support R&D project selection that
incorporates risk management practices. RQ4: How can risk management
practices be incorporated in the
project selection activity?
Conceptualization: literature
review and modelling
formalization
Empirical evidence:
implementation in the industrial
partner
Chapter 6
Publication in conference:
SANTOS, C., ARAUJO, M. & CORREIA, N. Year. Convergence of judgments in
technological innovation audit: A case study application in a sheet metal processing
equipment manufacturer. In: Technology Management for Emerging Technologies
(PICMET), 2012 Proceedings of PICMET '12:, July 29 2012-Aug. 2 2012 2012. 1892-
1903.
CHAPTER 4
A methodology for technology innovation auditing
considering social dynamics
The identification of available technological competences and the self-
assessment of inner strengths and weaknesses in technological innovation
process are framed in the internal analysis activity of the technology
strategy formulation process. The most commonly used tool for this type of
assessments are audits, which are an organization’s self-assessments of
internal capabilities and competences, typically implemented in group
meetings. Reported issues such as the presence of dominant personalities,
time pressures and bias imposed through organizational hierarchy may
compromise the effectiveness of such meetings. To overcome these problems
and borrowing ideas from Group Support Systems and consensus building
techniques, namely the Real Time Delphi Method, a novel technological
innovation audit that encourages participation of the staff involved in
technological innovations is proposed. This new form of audit has been
tested in the industrial partner of the thesis with very positive results, which
may indicate that it can be a useful approach in organizations with no
formal innovation department or team, such as the one tested. It provides a
solid basis for the identification of internal strengths and weaknesses in the
technological innovation process, and also offers a bottom up view free
from social pressures.
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66
4.1 Introduction
Competition at a global scale imposes greater speed in introducing new technologies
and products, and thus a need for greater efficiency in the innovation process. This
capability to continuously introduce innovations has become a critical factor for a
sustainable competitive advantage.
Innovation is a complex phenomenon that encompasses multiple facets. In the context
of businesses and organizations, innovation is the result of the joint efforts from
multiple departments and diverse competences.
Innovations can also take multiple forms, such as marketing, services, products or
processes, etc.. In the scope of the formulation of a technology strategy, the central
theme of this thesis, the form that is the focus of this chapter is technological
innovation. Technological innovation is formally defined as “all of the scientific,
technological, organizational, financial and commercial steps, including investments in
new knowledge, which actually, or are intended to, lead to the implementation of
technological new or improved products and processes.” (OECD, 2002, p. 18).
The assessment of internal technological capabilities and competences emerges as an
important step in formulating a technology strategy, in the scope of the internal analysis
activity (Chiesa, 2001, Burgelman et al., 2004), as illustrated in Figure 4.1. For that
purpose, both academics and practitioners in innovation management have developed
numerous methods and tools to support firms in the assessment of internal strengths and
weaknesses.
Although much attention has been devoted to the development of these tools, mainly
through technological innovation process auditing, the social implications of their
application have been ignored. The effectiveness of face-to-face group meetings in
innovation audits may be compromised by the presence of dominant personalities, the
likely bias imposed by hierarchical dependency relationships between members, time
pressures, geographical dispersion of participants and other sensitive issues which may
inhibit an open discussion of ideas and engagement. In this chapter, a novel innovation
audit that encourages participation of the staff involved in technological innovations is
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proposed and tested, as part of the internal analysis of a technology strategy
development.
Internal
Analysis
External
Analysis
Generation
Selection
Figure 4.1 – Internal analysis activity in the technology strategy process
This chapter is structured as follows: section 4.2 presents the literature review on the
relevant themes for this research; in section 4.3, the steps taken into the development of
the audit are described; in section 4.4, the application of the audit in the industrial
partner is described, along with the analysis of results and feedback from participants,
and, finally, section 4.5, presents the final discussions and conclusions from this study.
4.2 Literature review
The assessment of internal capabilities and competences in the technological innovation
process of organizations implies the knowledge and understanding of a number of
concepts that has not yet found consensus among scholars yet. This section presents the
main definitions found in the literature and introduces the conceptual base that led to the
development of the technological innovation audit. Then, a review on innovation audits
proposed in the literature is presented, followed by an analysis on their implementation
shortcomings in the context of organizations, namely with respect to managing group
meetings, and how Group Support Systems (GSS) aim at addressing these issues.
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4.2.1 Definitions of capability and competences of organizations
The self-assessment of internal strengths and weaknesses in this organizational process,
aimed at a strategic analysis, borrows a number of concepts from the Resource Based
View (RBV) School of strategy, which was introduced in Chapter 2.
The development of this branch of strategic management contributed to the formulation
of four widely referred concepts, which, together, constitute the fundamental pillars of
RBV: resources, capabilities, competences and core competences. These concepts
give support to the framing of activities and help firms identify their most distinctive
strengths, and also find the gaps that should be taken care of as part of their strategic
plans. In other words they are relevant for the assessment of firms’ internal strengths
and weaknesses.
These key concepts have been widely referred in the literature, yet their inherent generic
meanings may be an obstacle for the development of this scientific field and,
consequently, to a more widespread use by practitioners. Moreover, similar terms such
as strengths, skills, competences, capabilities, organizational knowledge, intangible
assets, and others, are interchangeably used by different authors (Campbell and Luchs,
1997). The importance of consensus seeking in RBV key concepts was emphasized by
Marino (Marino, 1996b), which called for a management process that ultimately results
in a common platform of understanding and commitment, concerning the core
competences and capabilities of the firm. The following text provides some of the
foremost definitions drawn from related literature contributions.
Resources are all tangible and intangible assets of organizations (Wernerfelt, 1984).
Barney provided additional details to Wernerfelt’s broad definition: resources are “all
assets, capabilities, organizational processes, firm attributes, information, knowledge,
etc. controlled by a firm” (Barney, 1991, p. 101). Examples of resources include
machinery, employees’ skills, brand image, organizational culture etc..
The term capabilities is defined as “a set of business processes strategically
understood” (Stalk et al., 1992, p. 62), or the “firm’s capacity to deploy resources,
usually in combination, using organizational processes, to effect a desired end” (Amit
and Schoemaker, 1993, p. 35). Also according to the later authors they are information-
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based, tangible or intangible processes that are firm-specific and developed over a
period of time by complex interactions of the firm’s resources. Examples of
organizations’ capabilities are the marketing and production management processes.
The notion of capabilities has been extended by other authors: through the exploration
of the non-static nature of some capabilities, the term dynamic capabilities was coined
by Teece et al. (Teece et al., 1997) to fill two gaps in previous strategy perspectives:
one referring to the renewal of competences to reach congruence with changing
business settings (shorter time-to-market, rapid technological change, increased
competition, etc.), the other emphasizing the reconfiguration, adaptation and integration
of internal and external organizational skills, resources and competences to match the
requirements of changing environments. Dynamic capabilities is formally defined as
“the firm’s ability to integrate, build, and reconfigure internal and external
competences to address rapidly changing environments” (Teece et al., 1997, p. 516).
Specific strategic and organizational processes such as product development, alliancing
and strategic decision making are dynamic capabilities according to Eisenhardt and
Martin (Eisenhardt and Martin, 2000), due to their ability to integrate, reconfigure, gain
and release resources to match and even create market change. According to
Centidamar et al., technology management activities are analyzed though the lens of the
dynamic capabilities theory (Cetindamar et al., 2009a), which has become the most
popular research stream in strategic management (Huang, 2011).
With regard to the technological innovation process, the technological innovation
capability of firms plays a critical role. The technological innovation capability, or TIC
as coined by Yam et al. (Yam et al., 2004), is a comprehensive set of characteristics of
an organization that facilitates and supports innovation strategies (Burgelman et al.,
2004). These characteristics are based on activities aimed at executing and coordinating
the tasks necessary to manage technology (Centidamar et al., 2010), which, according to
Drejer, should be a substantial part of managing a firm (Drejer, 2002). Examples of
such TIC, as suggested by Yam et al., include the systematic monitoring of technology
development, efficiency of R&D personnel communication, relationship management
with major customers, among others.
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Competences are the “cross-functional integration and co-ordination of capabilities”
(Javidan, 1998, p. 62), and incorporates a technology or knowledge-based component as
opposed to capabilities, which are based on processes and business routines (Marino,
1996b).
The concept of core competence was introduced in the early nineties and proposed as
the most important way to be successful in the global competition. In the seminal work
of Prahalad and Hamel, this concept was introduced and defined as the “collective
learning in the organization, especially how to coordinate diverse skills and integrate
multiple streams of technologies” (Prahalad and Hamel, 1990, p. 81). According to the
same proponents of this concept, a competence to be core, should satisfy a set of
specific requirements: provide potential access to a wide variety of markets, be a
significant contribution to customer’s perception of the benefits of end products and be
difficult to imitate by competitors.
A simple hierarchical model was provided by Javidan (Javidan, 1998) to analyze the
relationships between these concepts. In this model, depicted in Figure 4.2, resources,
per se, add little value and are relatively easy to acquire. By creating functional
capabilities, more value is added along with more difficulty as well. Synergies among
capabilities create competences, the next level in the hierarchy, which in turn adds even
more value and more difficulty. The highest level relies on core competences, which are
valuable both to customers and to the firm and also difficult to imitate. Therefore, they
are also the most difficult to reach.
Core competences
Competences
Capabilities
Resources
Increasing
Value Difficulty
Figure 4.2 - The competences hierarchy. Adapted from (Javidan, 1998)
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71
Despite the numerous definitions, some studies focused on the distinction of certain
concepts, notably between the capabilities and competences, as emphasized by Walsh
and Linton (Walsh and Linton, 2001). In this work, the terms “managerial capabilities”
and “technical competences” are presented to indicate that capabilities are more related
to specific business practices, processes and culture, while competences are firm
specific technologies and production skills, a vision that is also shared by Prahalad,
Hamel and Marino (Prahalad and Hamel, 1990, Marino, 1996b). A competence pyramid
framework was also proposed to support the identification of capabilities and
competences in firms and industries. In this framework, competences are divided into
two categories: physical-product production competences and service-product
production competences. The first is subdivided into materials competences
(technological skills that transforms raw material into products) and fabrication and
assembly competences (manufacture and assembly of components, subsystems and
systems). Service competences are separated into knowledge-based competences (value
is directly dependent on the skills and expertise of the individual providing a service,
such as a physician) and knowledge-embedded competences (value is embedded in the
system or process delivering the product). Managerial capabilities are placed on the top
of the pyramid, being supported by the bundle of technical competences (skills and
knowledge) and representing the management processes aimed at getting business value
from technology. A visual representation of this framework is depicted in Figure 4.3.
Figure 4.3 - The competence pyramid: a visual representation. Source: (Walsh and Linton, 2001)
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72
Contrary to other authors’ proposals, the definitions of competences and capabilities
described by Walsh and Linton provide clarity and easy understanding, namely in the
organizational ability to manage technology. Therefore, these definitions are adopted as
the conceptual base that leads to the development of the audit, as is described in sub
section 4.3.2.
4.2.2 Auditing instruments
An innovation audit composed by three protocols are proposed by Goodman and
Lawless (Goodman and Lawless, 1994): the technological innovation process audit
(TIPA) aimed at reducing the downside risks of technological innovation investments,
the innovative comparison audit (ICA), intended at comparing the innovative
capabilities of the firm with competitors in the industry and the technological position
audit (TPA) aimed at assessing the positioning of the firm in relation to the broad
technological developments that are relevant to the firm’s business. For its application
in the context of an organization, the company’s top managers are the suggested
participants to fill the audit.
A significant contribution for this field is provided by Chiesa et al. (Chiesa et al., 1996),
who proposed an audit focused on industrial firms comprised of two parts: the process
audit and the performance audit. The first is an assessment on the processes necessary
to conduct innovations. These are core (concept generation, product development,
process innovation and technology acquisition) and enabling (leadership, resourcing,
systems and tools and increased competitiveness) innovation processes. Each of the
identified processes and sub processes, totalizing a number of twenty three drawn from
extensive literature review, is assessed using a four-point Likert scale, in what the
authors call the Innovation Scorecard. The second measures the effectiveness of the
innovation process, and includes a set of metrics that assess their impact on the
competitiveness of the firm. The functionality, usability and usefulness of the audit were
tested in eight companies. In terms of implementation, half of the audits were conducted
by a single individual (in one case the managing director), and the others were
conducted by a team. The time to perform the audit ranged from four to twenty days.
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73
Another innovation scorecard is proposed by Cormican and O’Sullivan (Cormican and
O’Sullivan, 2004). This innovation score identifies whether best practices are in place in
a firm with respect to product innovations. Through a number of interviews with
members of the senior management team in industrial firms, the authors have identified
important factors that facilitate product innovations. Based on these findings, a Product
Innovation Management (PIM) scorecard is proposed, consisting of fifty statements that
should be evaluated on a five-point Likert Scale. The PIM scorecard was implemented
in eight companies and was validated by senior managers.
In Burgelman et al. (Burgelman et al., 2004), an innovation audit framework is
designed incorporating five dimensions of innovation strategies: resource availability
and allocation, understanding of competitors’ innovative strategies and industry
evolution, understanding the firm’s technological environment, the firm structural and
cultural context, and the strategic management capacity to deal with entrepreneurial
behavior. This publication suggested the firm’s strategic planning department, including
an ad hoc team with employees from strategic planning, R&D, new product managers
and key functional managers to complete the audit.
An audit instrument called the Innovation Compass was proposed by Radnor and Noke
(Radnor and Noke, 2006), also directed towards product innovations. The responses of
firms are recorded in a database, allowing an individual firm to benchmark itself against
other firms (“gap analysis”). Qualitative data from semi-structured interviews with
cross-functional employees were used to enrich the quantitative data collected from the
audit, which was composed by forty two statements reflecting necessary traits and
capabilities for innovation, to be assessed on a five point ascending Likert scale. The
same interviewed employees were requested to complete the audit. There is no explicit
mention concerning the use of staff meetings in the process.
An audit instrument commonly used by some Portuguese firms is the Innovation
Scoring (COTEC and IAPMEI, 2008)2. The Innovation Scoring is a final score derived
from the weighted sum of the scores from a series of statements that reflect a number of
2 COTEC is a not for profit business association that counts on the support of its associated companies and all agents of the
Portuguese National Innovation System (NIS) to accomplish its goals through the implementation of initiatives in a variety of areas.
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innovation dimensions. A five-point Likert scale is used and respondents are required to
score each question on two perspectives: approach and application. According to the
publication, cross-departmental teams should complete the audit.
A simple innovation audit framework was proposed by Tidd et al. (Tidd et al., 2005).
This audit comprised of forty statements to be filled with a seven-point Likert scale that
reflects the management of innovation in five “dimensions”. It is a more generic audit,
aimed at several types of innovations (product, processes and services). There is no
explicit mention about who should complete the audit, in an organization.
Despite the differences between the audits described above, a common feature to all of
them is concerned with the clustering of statements into thrusts. This clustering is based
on relevant dimensions of the innovation process, and also corroborates the multi-
faceted characteristic of the innovation process itself. Table 4.1 presents the dimensions
depicted in the reviewed audits.
Table 4.1 – Dimensions of reviewed audits
Reference Dimensions or thrusts
(Goodman and Lawless,
1994)
Technology; Market; Organization; Environment; Industry
Structure; Firm analysis
(Chiesa et al., 1996)
Core Processes: Concept Generation, Product Development,
Process Innovation, Technology Acquisition; Enabling
Processes: Leadership, Resourcing, Systems and Tools
(Burgelman et al., 2004)
Resource availability and allocation; Understanding of
competitors’ innovative strategies and industry evolution;
Understanding of the firm’s technological environment; The firm
structural and cultural context; Strategic management capacity to
deal with entrepreneurial behavior
(Cormican and O’Sullivan,
2004)
Strategy and leadership; Culture and climate; Planning and
selection; Structure and performance; Communication and
collaboration
(Tidd et al., 2005) Strategy; Learning; Linkages; Processes; Innovative organization
(Radnor and Noke, 2006) Structures; Leadership; Outputs; Teams
(COTEC and IAPMEI, 2008)
Culture, Leadership, Strategy, Human capital, Competences,
Networking, Structures, R&D management process, Learning
and continuous improvement, Protection and valuation of results
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The publications described above mention who should complete the audits but little
attention has been given to greater effectiveness of group meetings, particularly in the
management of divergent perspectives from functional areas within an organization. In
some of these studies, a preference for cross-departmental teams over senior managers
to complete the audits is revealed. This is a logical preference , since top and senior
managers may offer a top-down view, against the multi-faceted characteristic of the
innovation process that, quite often, involves people from different departments
(sometimes with opposing views), who are in the field and may have better
comprehension of inner strengths and weaknesses. Such is the case of many small and
medium enterprises (SMEs), especially the ones with no formal innovation
team/department.
With respect to handling divergent opinions from cross-departmental employees, no
recommendation has been found in these studies. The discussion of sensitive issues
inherent to the innovation process, such as leadership and organizational culture, can
inhibit an open discussion and suggestions of improvement actions, thus compromising
the effectiveness of the audit. The issues found in group meeting dynamics are further
analyzed in the next section.
4.2.3 Group support systems
Group meetings are commonly held to support the exchange of ideas, collaborative
decision making, problem solving and communication in organizations. Formally,
meetings are “a focused interaction of cognitive attention, planned or chance, where
people agree to come together for a common purpose, whether at the same time and
same place, or at different times in different places” (Romano and Nunamaker, 2001,
p.1). The underlying idea is that the collective knowledge and skills enable analysis and
decision making of higher quality than the individual knowledge and skills (Martz et al.,
1992, Yukl, 1998).
Although its importance is undeniable, evidence has shown that its overuse has led to
unproductive group meetings (Green and Lazarus, 1991), job dissatisfaction (Rogelberg
et al., 2010) and high costs, either due to the time directly devoted to the meetings, or to
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opportunity costs, i.e. time wasted that could be used in more productive activities
(Rogelberg et al., 2011).
Surely, a dramatic reduction in the number of group meetings is definitely not a
solution. This could lead to limiting employees access to needed information and
insight, and discouraging desirable employees’ attitude such as job and communication
satisfaction, organizational identification and turnover intentions (Rogelberg et al.,
2011). Thus, meetings are needed for building successful teamwork (Kauffeld and
Lehmann-Willenbrock, 2011).
Normally, group meetings are conducted in face-to-face style in organizations.
Although such type of meetings have their benefits, namely, in non-verbal
communication, such meeting structures have numerous problems too, as listed by
Dowling and St. Louis (Dowling and St. Louis, 2000):
Obtaining meaningful responses: the opportunity of an individual participating
in a face-to-face meeting to express an insightful opinion or suggestion may be
lost if not made immediately, before the conversation takes a new direction.
Additionally, time pressures may reduce and even inhibit individuals willingness
to contribute to the conversation;
Limitations with group size: the larger the group, the lower it is the
opportunity for each individual to make their contribution, especially when there
is a time limit for meetings;
Associated costs: time and resources spent in coordinating and scheduling a
large number of meetings can be substantial. Furthermore, while a person is
attending a meeting, he/she is essentially unavailable to others.
Nowadays, the impact that information and communication technologies (ICT) have in
the lives of people and organizations cannot be simply ignored. In that sense, the
development of group support systems (GSS) is improving communications between
and within individuals and organizations.
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GSSs is a type of electronic meeting system technology, consisting of networked
computers, which is designed to facilitate and provide increased structure to group
meetings and collaborative work (Dennis et al., 1988, Watson-Manheim et al., 2011). In
general, GSS include a set of tools and techniques to facilitate and manage group
discussions, issue exploration, problem definition and analysis, consensus seeking,
group writing, activity coordination, knowledge sharing and accumulation, data and
decision analysis (Ngwenyama et al., 1996). Such systems also free individuals from
group conformity and scrutiny (Shirani et al., 1998), a situation that often happens in
face-to-face group meetings.
The typical technological infrastructure of a GSS facilitates communication among
participants through asynchronous communication, anonymity and collective memory –
access to previous participants’ inputs - (Nunamaker et al., 1991). When designed to
support decision-making, GSS may also use a divergent information gathering software
tool in order to collect a large number of ideas quickly (Adkins et al., 2003).
The asynchronous communication feature of GSS offsets the limitations derived from
meetings’ duration restrictions. This means that participants can log in the same
meeting, but at different times. In a review of various studies that compares
asynchronous GSSs and face-to-face group meetings, Tung and Turban (Tung and
Turban, 1998) highlights a number of advantages in GSS:
Choice shift: studies demonstrate that greater choice shift (from initial
individual preference towards group choice) was achieved in distributed and
asynchronous meetings with GSS support that in face-to-face meetings with
computerized support, thus facilitating consensus building;
Conflict management: studies suggest that asynchronous group meetings were
able to overcome disagreements and manage conflicts at a faster rate than in
face-to-face meetings;
Focus of participants: studies concluded that in asynchronous and distributed
group meetings participants were more task oriented and more productive than
in face-to-face groups;
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Performance: a study conducted in a group idea generation setting shows that
the quality of ideas were superior in distributed groups than in proximate groups.
However, other studies also demonstrate that in some topics GSS has lower
performance than face-to-face group meetings, such as in decision speed (Gallupe and
McKeen, 1990) and effectiveness of leadership and coordination competence over time
(Burke et al., 1995). Contradictory findings in terms of group cohesion were found by
Chidambram et al. (Chidambaram et al., 1990): in initial meetings, less group cohesion
was found in face-to-face meeting than in GSS supported meetings, but the opposite
happened in subsequent meetings.
Anonymity is one of the fundamental pillars of GSS since it restricts the influence of
hierarchical organizational structures on the views and opinions of individuals with
lower positions in that structure, thus enabling equal participation and less biased
evaluations. Other benefits include the discussion of more sensitive issues that would
otherwise be put apart, more tolerance with minority groups and avoidance of fear of
punishment. Negative aspects pointed by Er and Ng included waste of time with
unworkable ideas, use of strong language and lack of emotional support from the
deprivation of social interaction with peers (Er and Ng, 1995).
Earlier research has shown that the combination of GSS with structured group
management techniques such as the Delphi method and Nominal Group Technique
(NGT) support consensus building and decision quality (Huber, 1982, Beruvides, 1995).
Such structured techniques are used to coordinate communication, and are particularly
useful in asynchronous group meetings (Dowling and St. Louis, 2000).
The influence of GSS in group interaction and, in turn, group productivity has been
shaping the socio-technical designs of organizations. Although face-to-face meetings
may be necessary in a new group, once it is formed and participants know each other,
alternative means of communication arise (Lantz, 2001), especially in larger groups,
where GSS has proved to be most effective (Nunamaker Jr et al., 1996). Recent trends
point to the use of the structure of social networking in GSS (Antunes et al., 2012,
Chang and Lo, 2012).
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Given the inability of existing audits in handling issues related to social pressures
during their implementation and the benefits of GSS described above, there is an
opportunity for embedding the audit into a GSS. This combination may lead to a more
effective communication within the organization, thus leading to a more realistic self-
assessment. The development of this new methodology is described below.
4.3 Methodology development
The process that led to the development of the methodology that combines auditing and
GSS was conducted in a number of steps. First, in order to deepen the understanding of
the technological innovation process in organizations, semi-structured interviews were
conducted with key personnel of the industrial partner, which covered aspects such as
the structure of the technological innovation process, innovation goals and perceived
improvements needed.
In the next section, the proposed audit is presented. The conceptual basis follows the
definitions of managerial capabilities and technological competences, as suggested by
Walsh and Linton (Walsh and Linton, 2001). In accordance with these concepts, the
audit is divided into two modules: competences assessment module and capabilities
assessment module. The first contains a template aimed at compiling the technical
expertise inside organizations. The second is composed by a series of statements
resulting from an extensive literature review on empirical studies on traits and
characteristics of innovative organizations, and deriving from the analysis on the
technological innovation process of the industrial partner of the thesis.
Finally, the method of application of capability assessment module of the audit is
described. Given the benefits pointed out before, namely at addressing the issues and
problems derived from the dynamics of group meetings - the traditional method of
application of the audits - the audit is embedded in a GSS. The method of application
followed the principles of Real Time Delphi method, in which participants can view and
vote anonymously, hopefully converging to a more realistic assessment of technological
innovation capability of the organization. A web platform was used to incorporate the
capabilities of the module audit.
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4.3.1 The innovation process in the industrial partner
The industrial partner of the thesis is a medium sized manufacturer of capital intensive
equipment for metal processing. The industrial partner has an offer of different
machines in shears, press brakes and metal laser cutting machines for a large number of
intermediate and end user production firms, typically firms in the renewable energies,
aerospace, locksmiths, shipyard industries, metal construction, automotive, furniture
and household and electronic appliances industries.
Although many of its competitors are smaller companies, the biggest are large
companies. These large competing companies are of two types: the ones offering high-
end state-of-the-art equipment (companies from Germany, Switzerland, Italy and Japan)
while others offer standard equipment at very low cost (China and Turkey) but both
offer standard equipment. The industrial partner could be seen as being in the middle of
these two segments. The industrial partner’s top management has acknowledged that, in
order to remain competitive and deepen its differentiation from the competitors, it needs
to define a technological innovation strategy.
This section presents a characterization of the technological innovation process in the
industrial partner of the thesis. During a period of approximately four months, meetings
and informal contacts with the Chief Technology Officer (CTO), the Commercial
Director and the Head of Production were conducted in order to provide a perspective
on the business environment of the industrial partner and the organization of the
technological innovation process. Based on notes taken during this period, the following
text provides a brief characterization of the innovation process of the industrial partner
of the thesis.
The industrial partner does not possess an internal dedicated technological innovation or
R&D department, or a formal innovation team. The organization of the innovation
process inside the industrial partner involves collaboration among staff members from
different departments. While new developments emerge primarily from the Technical
Department, input from other departments are welcome. Relevant contributions to the
technological innovation process inside the industrial partner are made by the sales and
marketing, production, purchasing and logistics, and post sales services.
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A number of R&D activities are pursued by research partners (suppliers, research
institutions, universities and business partners). R&D activities range from incremental
improvements on existing products to developments of new products in order to comply
with specific requests from customers.
Recently, for the first time, the industrial partner followed a planned method for the
development of a new product. The company identified an opportunity from the
Ecodesign European Directive3 to develop an eco-friendly machine with lower energy
consumption. The development of this machine involved visits and interviews with
selected customers to identify their needs, regular group meetings to discuss alternative
approaches, clear definition of target market segments, and the establishment of
collaborations with research institutions.
The industrial partner has set the following goal as part of their technology strategy:
“launch at least one technological innovation per year”. This goal has been surpassed
in recent years, according to an interview with the CTO. The industrial partner
understands that customizations requested by customers quite often change the
product’s main attributes, allowing them to be categorized as product innovations.
Technical and technological knowledge is often held by few people across the company.
Although much of the technical and technological knowledge is properly documented in
paper or digital format, a considerable portion of knowledge is tacit, existing only in the
minds of few people.
Another concern expressed during the interviews is related to the lack of interest and
participation of employees during group meetings. Reasons given for this included the
presence of dominant personalities and/or hierarchical superiors and even shyness on
the part of some participants.
The development and application of a novel audit aimed at counteracting these issues is
subsequently described.
3 For more information on the Ecodesign European Directive, visit http://ec.europa.eu/enterprise/policies/sustainable-
business/ecodesign/index_en.htm
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4.3.2 Audit modules
The development of the audit is based on key concepts from RBV literature, as
described earlier: competences and capabilities. Because of its clarity and easy
understanding, the model proposed by Walsh and Linton (Walsh and Linton, 2001),
which is based on the definitions of capabilities being related to management practices
and competences to organizations’ technologies and production skills, is hereby adopted
as the conceptual basis of the audit. These definitions are also aligned with the
objectives of the internal analysis activity of the technology strategy formulation
process, namely the identification of available technological competences to the
organization and the assessment of inner strengths and weaknesses in the technological
innovation process (Chiesa, 2001).
In line with these definitions, the audit is divided into two modules, each dedicated to
these concepts, as illustrated in Figure 4.4. Each module of the proposed audit includes
different mechanisms, which are described next.
According to the definitions being followed, competences are an intangible concept.
Related with the technical expertise embedded in technologies and products, and
involved in production processes, a quantitative assessment of organizational
performance in these areas becomes a complex task, essentially because much of this
knowledge is tacit, which makes an accurate assessment extremely difficult. Despite the
need to measure competences pointed by Walsh and Linton (Walsh and Linton, 2002),
an evaluation of this type is beyond the scope of this study.
Instead, this competences assessment module contains a compilation of the technical
expertise in the organization. A template, included in Appendix 1, serves this purpose.
Based on the aforementioned definition of competences, the template has four parts:
human resources, manufacturing processes, intellectual property and products and
technologies.
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Audit
Modules
Competences Capabilities
Collective assessment of
the firm’s technological
innovation capability,
which is embedded in
managerial methods and
business processes.
Compiled list of knowledge
and skills embedded in
technologies, products and
production processes
Figure 4.4 - Audit modules
The human resources part details the technical expertise of the organization’s
employees. Since the idea is to describe the set of technical skills and knowledge
pertaining to technological development, the focus should rely on employees assigned
to the engineering and technical department, logistics, production and others, i.e.,
basically excluding employees dedicated to administrative tasks. The manufacturing
processes section contains information about the machines and equipment used in
production processes, while the intellectual property part, contains information on
patents from technologies, systems and products develop by the organization. Finally, in
products and technologies, a list of the organization products and underlying
technologies is provided. Because technologies may be developed internally or acquired
externally and integrated in the products, a distinction between technologies developed
internally and outsourced is also requested.
The information requested in the template may be already present in the company, in
other forms, such as reports from human resources department, machinery inventory
lists, etc.. Still, the template is of good help for compiling such information, in order to
make it easier to get to know the set of technical competences that the organization
possesses.
The capabilities assessment module is a series of statements reflecting characteristics of
innovative firms, where participants from an organization are invited to evaluate the
organizational performance. The statements are based on an extensive review on traits
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and characteristics of innovative firms and insights from the case analysis of the
industrial partner of the thesis.
The task of identifying traits and characteristics of innovative firms involved an
extensive literature review on empirical studies aimed at validating intrinsic
characteristics of organizations that contribute to their innovation capability. Empirical
studies about innovation in organizations are characterized by the inclusion of a high
number of variables, which resulted in a high diversity of measurements and
methodologies, contributing to the difficulty in establishing generalizations (Becheikh et
al., 2006).
The list presented in Table 4.2 resulted from a comprehensive literature review which
attempts, as much as possible, be representative of all factors and characteristics that
influence the technological innovation capability of organizations. No distinction has
been made between the manufacturing and service sectors, as evidence has suggested
that innovation characteristics do not differ much between these sectors (de Jong and
Marsili, 2006, Helena, 2011). Furthermore, the characteristics of small and medium
enterprises (SMEs) were not ignored, in order to cover the case of the industrial partner
of the thesis.
Additionally, some of the reviewed empirical studies are focused on a limited set of
variables. For this reason, a number of literature reviews on innovation characteristics in
organizations were included in order to fill possible gap. (Hoffman et al., 1998, Adams
et al., 2006, Becheikh et al., 2006).
After an extensive review, the list was divided into nine dimensions or thrusts grouped
by similarity. Table 4.2 provides a summary of shared characteristics of innovative
firms.
The characteristics identified in Table 4.2 form the basis of the technological innovation
capability audit. Seven characteristics added. These characteristics were included in the
audit to cover any possible characteristics not identified in the literature review of
empirical studies. Moreover, characteristics that may be generalized to other
organizations were sought, although it is recognized that there are differences in the
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characteristics that influence the technological innovation capability of organizations
from different industrial sectors.
The Processes dimension includes one more statement: “The firm complies with
regulations throughout the technological innovation process”. The case analysis of the
industrial partner revealed that not complying with safety and environmental regulations
in this industry may hamper the commercialization of innovations.
In the Technology Strategy Management dimension, the characteristic “firm’s
competence in technology strategy” has a very broad meaning and requires further
division. Technology strategy decisions are made based on the “information gathered
on the future shape of competition and industries, the forecast of technological progress
and the evolution of the external and internal context of the firm”(Chiesa, 2001). It
implicitly states that innovative firms need to know their core competences (Prahalad
and Hamel, 1990) and foresee which technological opportunities and pitfalls lie ahead.
Consequently these two statements are included in the audit: “The firm knows its core
competences for competitive advantage.” and “The firm engages in activities such as
technology forecasting, roadmapping and/or foresight to identify future opportunities
and threats.”
For obvious reasons, two statements were included that relate to the implementation and
reflection of the formulated technology strategy – “The firm's technology strategy is
effectively implemented” and “The firm regularly reflects on the effectiveness of the
defined and implemented technology strategy.”
The Market Orientation dimension includes three more statements:
(1)“The firm is able to satisfy customers' specific needs, through tailor-made or
customized products.” – the sheet metal processing industry is very diversified and is
characterized by firms with many different requirements. Being capable of satisfying
those specific needs reveals the innovative trait of a firm.
(2) “The firm is able to project an image of superior technological sophistication in the
market.” – innovative firms in this industry should be able to maintain a strong
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reputation with respect to the technological sophistication of their products in order to
differentiate themselves from the competition.
(3)“The firm uses information from customers' technical support to generate ideas for
technological innovations.” – as observed in the case analysis, information from
customer support is often critical to improvements of features in existing products and
may also serve as basis for new developments.
The Learning dimension includes one more statement - “The firm is able to leverage
knowledge in other products and markets, unrelated to the current ones.” – to reflect
the capability of innovative firms to apply knowledge in new markets and differentiate
their product portfolio.
The final and complete audit is presented in Table 4.3. As with the reviewed audits, it
also comprises a number of dimensions. The first three dimensions (Culture, Leadership
and Learning) reflect intangible organizational characteristics. Key words such as
“common vision”, “empowered”, “motivated” and “teamwork” gather the necessary
attributes for an innovation driven culture. Traits such as risk management, product
exploitation and customer value management capabilities are put together under the
“Leadership” thrust. The approaches organizations adopt towards knowledge
management and skills development are reunited under the “Learning” thrust of the
audit.
The next two dimensions – “Technology Strategy Management” and “Processes” –
represent best practices and strategic issues related with innovation management.
Alignment with business goals, the existence of innovation performance measurement
systems, goals driven innovation process and adoption of protection mechanisms are
some of the key characteristics of these two dimensions.
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Table 4.2 - Review on technological innovation capabilities of innovative firms
Dimensions Characteristic References
Structure
Flexibility to adapt to environmental changes. (Hadjimanolis, 2000, Adams et al., 2006, Becheikh et al., 2006)
Autonomy level, decentralization and distribution of decision making
power in the organization.
(Adams et al., 2006, Martínez-Román et al., 2011)
Communication channels that speed up decision-making. (Hadjimanolis, 2000, Khan and Manopichetwattana, 1989, Adams et al., 2006, Martínez-Román
et al., 2011)
Processes
Use of control systems and documented plans describing targets, goals
and milestones.
(Adams et al., 2006, Becheikh et al., 2006)
Use of innovation protection mechanisms (patents, trade secrets, staff
retention and others).
(Becheikh et al., 2006, Allred and Park, 2007)
Structured approach to innovation management and use of management
practices.
(Galende and de la Fuente, 2003, Amara and Landry, 2005, Adams et al., 2006, Mol and
Birkinshaw, 2009)
Culture
Employees are empowered and individual behavior encouraging
innovation is rewarded.
(Vangelis, 2002, Becheikh et al., 2006, Laforet and Tann, 2006, Martínez-Román et al., 2011)
Organizational culture that supports teamwork. (Hadjimanolis, 2000, Adams et al., 2006, Becheikh et al., 2006)
Staff attitude and intrinsic motivation in learning. (Vangelis, 2002, Martínez-Román et al., 2011)
A shared vision about the innovation objectives inside the firm. (Adams et al., 2006, Keskin, 2006, Laforet and Tann, 2006);
Technology
Strategy
Management
Alignment with business overall goals. (Adams et al., 2006, Becheikh et al., 2006)
Explicit, clear and precise innovation goals and strategies. (Khan and Manopichetwattana, 1989, Adams et al., 2006, Becheikh et al., 2006, Vangelis, 2002,
Laforet and Tann, 2006)
Firm competence in the area of technology strategy. (Khan and Manopichetwattana, 1989, Hoffman et al., 1998, Hadjimanolis, 2000, Vangelis, 2002)
Consideration of strategic factors rather than purely financial reasons. (Adams et al., 2006, Becheikh et al., 2006)
Resourcing
Integration of high qualified scientists and engineers for widening the
knowledge base of the company.
(Acs and Audretsch, 1988, Khan and Manopichetwattana, 1989, Hoffman et al., 1998, Del Canto
and González, 1999, Caloghirou et al., 2004, Adams et al., 2006, Becheikh et al., 2006, Mol and
Birkinshaw, 2009, Radas and Božić, 2009, Martínez-Román et al., 2011)
Investment in appropriate manufacturing systems and technologies. (Del Canto and González, 1999, Hadjimanolis, 2000, Adams et al., 2006, Becheikh et al., 2006,
Laforet and Tann, 2006)
Access to subsidy schemes to fund innovation. (Keizer et al., 2002, Amara and Landry, 2005, Adams et al., 2006, Martínez-Román et al., 2011)
Avoid excessive by debt to equity ratio by employing, ensuring financial
balance of the company in funding innovations.
(Becheikh et al., 2006, Martínez-Román et al., 2011)
Resources for commercialization of innovations (sales force,
distributional and promotional support, etc.).
(Adams et al., 2006, Becheikh et al., 2006)
Considerable budget dedicated to innovation (through either internal or
external R&D activities).
(Acs and Audretsch, 1988, Hadjimanolis, 2000, Keizer et al., 2002, Galende and de la Fuente,
2003, Bhattacharya and Bloch, 2004, Caloghirou et al., 2004, Amara and Landry, 2005, Adams
et al., 2006, Martínez-Román et al., 2011)
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Table 4.2 (continued)
Dimensions Characteristic References
Leadership
Entrepreneurial orientation and capabilities of top management. (Khan and Manopichetwattana, 1989, Hadjimanolis, 2000, Adams et al., 2006, Laforet and Tann,
2006)
Strong leadership provided by highly educated top management, with a
good background in sales/marketing/management accounting.
(Hoffman et al., 1998, Hadjimanolis, 2000, Keizer et al., 2002, Martínez-Román et al., 2011)
Managing risk level in the innovation process. (Khan and Manopichetwattana, 1989, Hadjimanolis, 2000, Adams et al., 2006, Martínez-Román
et al., 2011)
Top management perceptions about the importance of innovation for
customers’ satisfaction and competitive advantage.
(Hadjimanolis, 2000, Adams et al., 2006, Laforet and Tann, 2006)
Market
Orientation
Research and monitoring for increasing customers and market dynamics
knowledge.
(Khan and Manopichetwattana, 1989, Hoffman et al., 1998, Galende and de la Fuente, 2003,
Adams et al., 2006, Keskin, 2006, Laforet and Tann, 2006)
Internationalization level (exports, presence in foreign markets, etc.). (Del Canto and González, 1999, Vangelis, 2002, Galende and de la Fuente, 2003, Bhattacharya
and Bloch, 2004, Becheikh et al., 2006, Mol and Birkinshaw, 2009, Radas and Božić, 2009)
Early integration of marketing in product planning and interaction
between various company units.
(Hoffman et al., 1998, Becheikh et al., 2006)
Networking
Use of external sources of knowledge and information for monitoring
competitors and understanding evolution of customers’ needs
(Khan and Manopichetwattana, 1989, Hadjimanolis, 2000, Vangelis, 2002, Caloghirou et al.,
2004, Amara and Landry, 2005, Becheikh et al., 2006, Mol and Birkinshaw, 2009)
Links with universities, research institutes and knowledge centers. (Hoffman et al., 1998, Keizer et al., 2002, Vangelis, 2002, Caloghirou et al., 2004, Becheikh et
al., 2006, Radas and Božić, 2009)
Collaborations with other firms (suppliers, customers, competitors, etc.)
in strategic alliances.
(Caloghirou et al., 2004, Amara and Landry, 2005, Radas and Božić, 2009)
Learning
Utilization of knowledge (recognize relevant external knowledge,
internalize new external knowledge, exploit new knowledge for
innovations), known as absorptive capacity.
(Khan and Manopichetwattana, 1989, Keizer et al., 2002, Caloghirou et al., 2004, Adams et al.,
2006, Becheikh et al., 2006, Keskin, 2006, Mol and Birkinshaw, 2009, Martínez-Román et al.,
2011)
Knowledge repository available to staff related to the innovation process
inside the firm.
(Adams et al., 2006, Keskin, 2006)
Training programs for employees and managers.
(Khan and Manopichetwattana, 1989, Hoffman et al., 1998, Hadjimanolis, 2000, Vangelis, 2002,
Caloghirou et al., 2004, Adams et al., 2006, Becheikh et al., 2006, Laforet and Tann, 2006,
Martínez-Román et al., 2011)
Reverse engineering to learn about competitors’ developments. (Caloghirou et al., 2004)
Allow time for people involved in innovation process to investigate
novel technological developments.
(Adams et al., 2006)
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“Marketing” and “Networking” are related with the role that external entities –
customers, competitors, suppliers, distributors, funding agencies and research
institutions - play in the innovation management process inside an organization. It also
includes a statement concerning the integration of design and marketing early on the
product development process, which was identified as a key characteristic of innovative
and market oriented companies.
Finally, the “Structure” and “Resourcing” dimensions are focused on the organizational
“infrastructure” that supports the innovation process, such as effective and efficient
communication channels, responsiveness and autonomy, budget and accessibility of
external grants to fund innovation projects, appropriateness of design resources,
complementary assets to support the commercialization of innovations and workforce
background and others.
Table 4.3 presents the statements and dimensions included in the capability assessment
module of the audit.
Table 4.3 – Capability assessment module of the audit
1 Code Culture
1.1 Cult1 The firm encourages teamwork among staff involved in the technological innovation
process.
1.2 Cult2 The firm's staff involved in the technological innovation process share a common
vision about the innovation goals.
1.3 Cult3 The personnel involved in technological innovations have the ability to self-motivate.
1.4 Cult4 The firm encourages and rewards individual behaviors directed at the technological
innovation process.
2 Code Leadership
2.1 Lead1 The top management of the firm has experience in sales and marketing.
2.2 Lead2 The top management of the firm has the ability to manage risk in technological
innovation projects.
2.3 Lead3 The firm's top management recognizes the importance of technological innovation to
achieve competitive advantage.
2.4 Lead4 The top management of the firm's entrepreneurial orientation.
3 Code Learning
3.1 Lear1 The firm is able to use its accumulated knowledge into products and markets
unrelated to the existing ones.
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Table 4.3 (continued)
3.2 Lear2 The firm allows engineers and technicians to investigate new technologies during
their working time.
3.3 Lear3 The firm employs reverse engineering and benchmarking to understand the
developments of new products by competitors.
3.4 Lear4 The firm provides adequate training programs for employees involved in the process
of technological innovation.
3.5 Lear5 The firm has a repository or other practice document management of knowledge (e.g.
latest technologies, new products, etc.).
3.6 Lear6 The firm has the capacity to recognize and internalize new knowledge for
technological developments.
3.7 Lear7 The technical knowledge generated and collected through technological innovations
is available to everyone in the firm.
4 Code Technology strategy management
4.1 Tecma1 The firm's technology strategy is aligned with the business strategy.
4.2 Tecma2 The firm regularly reflects on the effectiveness of technology strategy that was
defined and implemented.
4.3 Tecma3 The firm uses methodologies such as technological forecasting and / or roadmapping
to identify future opportunities and threats.
4.4 Tecma4 The firm knows what its core competences to achieve competitive advantage.
4.5 Tecma5 The firm takes into account not only purely financial factors, but also strategic issues
during the technological innovation process.
4.6 Tecma6 The firm has clear and well defined objectives with regard to technological
innovation.
4.7 Stra7 The firm's technology strategy is effectively implemented.
5 Code Processes
5.1 Proc1 The firm seeks to adopt good management practices in the process of technological
innovation (workshops, brainstorming sessions, ideas management, etc.).
5.2 Proc2 The firm meets the standards and regulations in the process of technological
innovation.
5.3 Proc3 The firm uses innovation protection mechanisms such as patents, trade secrets and
staff retention.
5.4 Proc4 The firm disseminates its plans, objectives and milestones.
5.5 Proc5 The firm makes use of systems and mechanisms for performance monitoring of the
process of technological innovation.
5.6 Proc6 The firm employs a structured approach in the process of technological innovation.
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Table 4.3 (continued)
6 Code Market orientation
6.1 Mark1 The firm uses information from technical support to customers to generate ideas for
innovations.
6.2 Mark2 The firm is able to project an image of considerable technological sophistication of
their products in the market.
6.3 Mark3 The firm is able to meet specific needs of its customers through customization of its
products.
6.4 Mark4 The firm is considerably internationalized.
6.5 Mark5 The firm integrates the marketing function at the beginning of technological
developments.
6.6 Mark6 The firm monitors the market dynamics, movements of competitors and emerging
customer needs.
7 Code Networking
7.1 Netw1 The firm has links with research centers (universities, laboratories, etc.) that allow the
collection of information of the latest developments and technological trends.
7.2 Netw2 The firm is able to promote partnerships and strategic alliances with other companies
to technological innovations.
7.3 Netw3 The firm has a network that allows the collection of information about the
movements of competitors and changing customer needs.
8 Code Structure
8.1 Stru1 The firm has a decentralized decision-making process.
8.2 Stru2 The firm has communication channels that speed up decision making.
8.3 Stru3 The firm has considerable autonomy level in the decision-making process.
8.4 Stru4 The firm has a flexible structure allowing it to adapt to environmental changes
(economy and the markets).
9 Code Resourcing
9.1 Reso1 The firm has a considerable number of skilled human resources specialized in
different functions related to the technological innovation process.
9.2 Reso2 The firm has adequate facilities and equipment that enables competitive advantages
in terms of cost and quality in the manufacturing of its products.
9.3 Reso3 The firm has the resources needed to support the commercialization of technological
innovations (e.g., sales team, distribution channels, etc.).
9.4 Reso4 The firm seeks to maintain sustainable financial balance in support of new
technological developments.
9.5 Reso5 The firm has access to subsidies and incentives for new developments.
9.6 Reso6 The firm applies considerable resources in the process of technological innovation
(financial, human and other).
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4.3.3 Method of application
The method of application of the audit is focused on the capabilities assessment module.
For the reasons mentioned earlier, a quantitative assessment of competences is not
practiced. The applications of the audits reviewed in this chapter and described in the
publications suggested that organizations are more comfortable with the quantitative
assessment of organizations’ capabilities. In addition, and in order to make evaluation as
close as possible to the reality of the company practices, it was sought to include the
largest possible number of views from people directly related with the innovation
process inside an organization. The method used for this purpose is described below.
The problems identified in the literature review section concerning group meetings have
not yet been addressed in the application of innovation audits, to best of the researcher’s
knowledge. In an attempt to overcome them, the method of application of the audit took
into account the need to become more inclusive, as well as being capable of managing
divergent opinions of collaborators from different departments and functional areas but
with a direct role in the innovation process. In consonance with the line of research that
points to a combination of structured group management techniques and asynchronous
GSS for facilitating consensus building, as mentioned in section 4.2.3, the capability
assessment module of the audit is applied using a commonly used tool for this purpose -
the Delphi Method – in a web platform environment to enable the communication,
which is described as follows.
The Delphi method has four basic principles (Rowe et al., 1991): 1) anonymity of
participants; 2) iteration through a number of rounds; 3) controlled feedback, where
participants are able to comment and critique on the judgments of others; and 4)
statistical group response, where descriptive statistics of the quantitative judgments are
provided to participants after each round. The Delphi method is “characterized as a
method for structuring a group communication process so that the process is effective in
allowing a group of individuals, as a whole, to deal with a complex problem” (Linstone
and Turoff, 2002).
A web-based and round-less approach named Real Time Delphi (Gordon and Pease,
2006) was proposed to increase the speed of the application of the method, which is
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commonly considered to be time-consuming. The problem of engaging stakeholders in
evaluating organizations has been studied before by Monica (Monica R, 2010), who
compares two methods of implementing a Delphi survey (a paper-pencil and Real Time
Delphi) for the purpose of framing an evaluation and finds that both approaches
constitute a powerful tool for engaging stakeholders. Another study by Gnatzy et al.
supports Real Time Delphi method, suggesting that this method works as effectively as
conventional round-based Delphi (Gnatzy et al., 2011).
This study proposes a Real Time Delphi method for the application of the capability
assessment module presented earlier. This approach has many advantages: 1) it offers a
bottom-up view over the technological innovation process inside the organization; 2) it
is more inclusive, i.e., all participants are freely able to express their opinions, having
empowerment effect on people; 3) it allows an exchange of ideas from a wider pool of
knowledge; 4) there are no geographic and time constraints (as when a number of
participants are on a business travel or a pre-defined time length for meetings); 5) the
real time feature enables the visualization of anonymous scores and comments, working
as a discussion forum and 6) makes the audit widespread throughout the organization.
4.4 Methodology application
Each statement contained in the capability assessment module of the audit is assessed
using a Likert scale. A five-point ascending Likert scale was used, as shown in Figure
4.5, in line with the approach used in other studies (Goodman and Lawless, 1994)4
(Cormican and O’Sullivan, 2004, COTEC and IAPMEI, 2008).
Capability is not
practised or is inexistent
in the company
Capability is poorly
practised or almost
inexistent in the company
Capability is somewhat
practised or present in the
company
Capability is practised or
present in the company
Capability is strongly
practised or present in the
company
1 2 3 4 5
Figure 4.5 - Five-point Likert scale legend used
4 In Goodman and Lawless audit, the TIPA module uses a four point Likert scale, while in the ICA module, a five point Likert scale
is used.
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The audit was run in a web platform for participants to score each statement and place
their comments in an anonymous way. The instrument used was the Surveylet provided
by the Calibrum Corporation (http://calibrum.com/). Participants can visualize, in real
time, the anonymous comments and score distributions from the other participants. This
feature is only enabled after participants fill the audit for the first time, in order to avoid
initial biased judgments. Participants can change their scores whenever they want and
save their partially filled evaluations to complete at a later time.
The application of the capability assessment module of the audit is preceded by a
kickoff meeting to bring awareness to participants about the importance of technology
innovation and explain the objective of the audit. During the presentation of the
methodology the participants inquired on the possible problem of lack of sufficient
knowledge to score a particular statement. In these cases, it was suggested that
participants explicitly state their lack of knowledge in the comments box and choose a
score of “three” in the respective audit statement. Instructions for filling the audit were
provided, and access details were sent via email to each participant. Figure 4.6 presents
an example of the screen visible to participants.
Figure 4.6 - Example of a web interface
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Twenty participants were invited to fill the audit questionnaire. The selection of
participants had to meet one criterion: they all should have an important and direct role
in the technological innovation process inside the industrial partner, i.e., they should be
the “experts” on the process in the firm. Figure 4.7 presents the participants’ distribution
among the company’s departments. As expected, due to the highly technical nature of
technological innovations, there is a dominance of the technical department.
Figure 4.7 – Distribution of invited participants among the departments
The capability assessment module of the audit was open for a period of one week.
Notifications were sent during this period encouraging participation and reminding
participants to visualize the score distributions and comments of others, and to review
their original answers. Scoring each statement was set as mandatory, while comments
were not. Consensus is measured using the IQR (interval quartile range), the difference
between third/upper (Q3) and first/lower (Q1) quartiles. It has been suggested that an
IQR equal to or less than 1.00 indicates convergence of opinions (Linstone and Turoff,
2002)5 (Ravens and Hahn, 2000). This criterion is applied in this study for assessing
consensus building.
After the period destined to complete capability assessment module of the audit, an
analysis on convergence of judgments was made. Even though participants could
5 The authors suggest that. a criterion for reaching consensus would be an IQR of less than two units on a ten-unit scale. In this
study, since a five point Likert scale is used, the criterion for reaching consensus is an IQR of one or less.
70%
15%
10%
5% Technical department
Sales and marketing
department
Purchasing and
Logistics department
Production department
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visualize in real time the anonymous judgments made by the other participants, the
analysis that follows is not part of the built-in features of the web platform. Such
analysis was performed by the researcher who had access to the results at the end of the
process.
Seventeen participants filled in the audit. One respondent did not complete the audit, so
his/her answers were considered invalid and eliminated from the analysis. Whenever a
respondent posted a comment stating he/she did not have sufficient knowledge to
answer, its respective rate was not taken into consideration. There were twenty seven of
such cases, and no single statement had more than three occurrences.
Participants provided two hundred and fifty one valid comments (excluding the ones
stating lack of knowledge on the subject). The content of the comments range from pure
criticism to positive opinions, but also includes ideas and suggestions. For
confidentiality reasons, they are not be reproduced in this thesis.
Figure 4.8 presents the charts containing the descriptive statistics from each statement.
The length of each bar is the IQR, with Q1 its left-hand limit and Q3 its right-hand
limit, the Q3. The line separating the darker part of the lightest part of each bar is the
median. In some cases, the median is equal to one of the quartiles, in other cases all
three statistics are equal, which visually is equal to no bar appearing in Figure 4.8.
Consensus was not achieved in seven of the forty seven statements from the audit –
Proc5, Proc6, Tecma3, Reso2, Mark1, Lear2 and Lear7, as seen on Figure 4.8.
A high concentration of participants from the technical department made a comparison
of the collective perceptions between different departments impractical, since the
opinion of other departments would be of little statistical significance. This analysis
would be more appropriate in firms showing a more homogeneous distribution of
responsibilities through the different departments.
Using the IQR to calculate the average dispersion of each dimension from the
technological innovation audit it is possible to rank the capabilities, from the most
consensual to the least consensual, as seen on Table 4.4.
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Figure 4.8 - IQR and median of each audit statement
1 1.5 2 2.5 3 3.5 4 4.5 5
Cult1
Cult2
Cult3
Cult4
Lead1
Lead2
Lead3
Lead4
Lear1
Lear2
Lear3
Lear4
Lear5
Lear6
Lear7
Tecma1
Tecma2
Tecma3
Tecma4
Tecma5
Tecma6
Tecma7
Proc1
Proc2
Proc3
Proc4
Proc5
Proc6
Mark1
Mark2
Mark3
Mark4
Mark5
Mark6
Netw1
Netw2
Netw3
Stru1
Stru2
Stru3
Stru4
Reso1
Reso2
Reso3
Reso4
Reso5
Reso6
Q1
Q3
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Table 4.4 – Ranking of most consensual dimensions
Rank no. Dimension Average IQR
1 Culture 0.81
2 Networking 0.83
3 Market orientation 0.88
4 Resourcing 0.96
5 Structure 1.00
6 Technology strategy management 1.00
7 Leadership 1.00
8 Learning 1.07
9 Processes 1.21
Four dimensions showed consensus on every single statement – Structure, Culture,
Leadership and Networking. Observing the medians of each dimension in Figure 4.8, it
is possible to see that Structure was the only dimension failing to receive a score of four
or more, while in the Leadership dimension all statements received a score of four or
more.
The two dimensions where the greatest dissensus was observed were “Processes” and
“Learning”. Although the reasons for this dissensus have not been explored, one
possible explanation could be that a set of practices that are followed in one functional
area of the firm may not be practiced with the same intensity in another area, indicating
a need for greater homogenization in the firm. A final report containing the results of
the audit was delivered to the top management of the industrial partner, and it was
recommended that the firm’s management make the final interpretation of the results,
taking into account the comments made by the respondents as well.
At the end of the auditing period, feedback was then requested from participants by e-
mail in order to assess the overall usability and appropriateness of the whole process.
Respondents felt that, as a whole, the content of the statements was clear and captured
the full spectrum of technological innovation capability of a firm. A number of
participants suggested that, although not mandatory, the comments were even more
important than the scores. The period of one week was seen as sufficient for completing
the questionnaire, reviewing answers, and enabling deep thinking. Some participants
suggested the inclusion of more statements related to the Market Orientation dimension,
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such as the excessive dominance of the technical department in the decision-making
process.
Respondents stated that this type of tool is useful for bringing up problematic issues in a
particular operational side of a firm that, for many reasons, may not be evident to other
areas of the firm and top management. They felt that the anonymity of the audit
encourages open discussion, free from social and professional hierarchic pressures.
4.5 Conclusions
The internal analysis activity has been predominantly performed through self-
assessments in the form of audits. The combination of a structured group management
technique – the Delphi method – in an online GSS platform proposed in this chapter
resulted in a novel application of an innovation audit. Working as a dynamic discussion,
where participants can contrast their own observations with those of other participants,
the proposed approach also brings openness to the process. Employees from lower
hierarchical positions and from different departments can have their voice heard and
contribute towards a more realistic evaluation of the innovation capability of the
organization.
It becomes important to note that the purpose is not to replace the need for face-to-face
group meetings completely, but to take advantage of information technologies in order
to make this procedure more efficient. Posterior face-to-face group meetings may be
focused solely on the problems identified in the audit.
Despite all the benefits highlighted for this approach, some implications related with the
practical application of the method cannot be ignored. For example, the issue of
anonymity may not be completely true in some settings. People do talk before and after
group meetings, and these extra channels of communication are not visible in GSS and
may change the evaluations initially made. Besides this, in some cases it is not the
anonymity that reduces the dominance of people from high hierarchical positions, but
the experience, expertise, knowledge and power of argumentation that win discussions.
These constitute limitations of the suggested approach.
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In terms of process design, some issues were found. Even though there were only a few
situations where a participant demonstrated insufficient knowledge to provide answers it
is still our recommendation that an additional option be included, such as "I do not have
sufficient knowledge to answer" in the Likert scale. The initial hypothesis that all
participants would have sufficient knowledge to answer all questions was proven false.
Another limitation related with the design of the whole process concerned a slightly
high concentration of evaluation in the intermediate points of the Likert scale. Possibly,
an audit using a larger Likert scale can overcome this problem. In this case, an IQR
proportionally greater should be applied to assess consensus building.
More research should be conducted concerning the traits and characteristics included in
the innovation audit, and its assumption that those do not differ much between
manufacturing and service sectors. It is understood that despite future possible
differences in innovation models and in audit statements for manufacturing and service
firms, the same principles of the Real Time Delphi for auditing organizations can be
applied to both cases.
Future developments could include a facilitator to coordinate the discussion of ideas, in
parallel with the assessments made by the participants. The role of the facilitator in the
Delphi method has been limited to sending instructions, questionnaires, surveys and
reports. But studies suggest that the role of the facilitator should be strengthened in
GSS, intervening in order to set the rules and guidelines of the process, formulate the
problems and expected outcomes and facilitate consensus building among participants.
In order to ensure fairness and impartiality in the process, facilitators should preferably
be someone from outside the company.
Another interesting development would be to integrate a performance management
system in the audit, such as a form of Key Performance Indicators (KPIs). Using some
metrics related with the innovation process and linking them to their respective
statements contained in audits could facilitate the assessments made by the participants
by providing more information about the performance of the organization, and thus,
supporting greater consensus building. Such system could turn into a real time
measurement of the “health” of the innovation process in the organization.
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More cases should be analyzed in order to generalize the findings from this study.
Nonetheless, the present study could become an important contribution, by focusing
more attention on the social implications involved in evaluating organizations. With
regard to organizations without a formal innovation department or team, the bottom-up
anonymous approach can be observed as highly important in identifying inner strengths
and weaknesses, making the staff conscious about them, enabling reflection and
thinking on how to circumvent problems.
Publication in conference:
SANTOS, C., ARAÚJO, M. & CORREIA, N. Year. Exploring Inter-Relationships between
Events to Identify Strategic Technological Competencies: A Combined Approach. In:
WORLD ACADEMY OF SCIENCE, E. A. T., ed. International Conference on Innovation,
Management and Technology, 2013 Istanbul. World Academy of Science, Engineering and
Technology, 796-804.
Manuscript submitted for publication in journal:
SANTOS, C., ARAÚJO, M. & CORREIA, N. An Integrated Methodology to Identify
Strategic Technological Competencies through Analysis of Complex Events Relationships:
A Case Study. Technological Forecasting and Social Change.
CHAPTER 5
A methodology for identification of strategic
technological competences through analysis of
relationships between future events
The uncertainties related with the trajectories that technology will follow in
an industry are forcing companies to develop new tools to assist them in the
development of their medium and long term strategic plans. Technology
foresight is a field that has been contributing with an extensive array of
tools to support companies in the analysis of the most likely technological
developments of the future. Among these tools, the Delphi method, based on
the opinion of experts, stands out as one of the most used. Although the
method is capable of indicating possible times of occurrence and evaluates
the impact of future events individually, it does not consider possible
relationships between events. In order to improve the analytical capability
of the Delphi method, this chapter presents a new methodology that
integrates the Delphi method with an adapted Quality Function Deployment
matrix. This methodology aims at facilitating an analysis about the
influence of environmental factors (e.g., market-related, regulations-related,
etc.) in the diffusion of certain technologies, for the purpose of providing the
strategic guidelines for the subsequent activity of the technology strategy
formulation process, i.e., the generation of project ideas. This methodology
was applied in the industrial partner of the thesis. A group of specialists in
sheet metal forming technologies, from industry and academia, was invited
to fill out a Delphi survey. The survey results fed the events’ cross
relationship analysis. In the end, the most promising technologies for the
future and underlying technological competences are identified.
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5.1 Introduction
The increasing technological progress observed in a wide range of industries brings
greater challenges for companies, particularly in the definition of their innovation
strategy and subsequent R&D process planning. This process requires that considerable
care is taken with a number of decisions, with special emphasis on the selection of
technological development projects, which in turn guides investments in leveraging
internal or acquiring new competences. Thus, technological progress plays a critical role
in the competiveness of companies. Companies capable of anticipating the technologies
that will have the greatest market potential and adopt strategies to adequately take
advantage of these opportunities will be in a better position to ensure a sustained
growth. The identification of promising technologies is one of the main objectives of the
external analysis activity (see Figure 5.1) in the scope of the technology strategy
process.
Internal
Analysis
External
Analysis
Generation
Selection
Figure 5.1 - External analysis activity in the technology strategy process
The analysis of technological progress should not consider technology in isolation,
instead it should consider externalities related, for example, with market dynamics,
economics and other factors, which can positively or negatively influence the diffusion
process. A holistic perspective and an understanding of the possible influence and
relationships between these factors are required to produce more reliable scenarios, i.e.,
scenarios that characterize likely events on different fronts.
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This chapter presents a study conducted in conjunction with the industrial partner of the
thesis about the future of the sheet metal processing machinery industry. Analyses
conducted in selected publications and on transcripts from interviews conducted with
industry and academia experts contributed to the identification of future events in the
sheet metal processing equipment industry. These events were included in a Delphi
survey, and invitations were sent to experts to fill the survey. After the period
designated to fill the survey, the results were then used as inputs to a methodology
aimed at analyzing the impact of external factors (market, economy, etc.) on technology
diffusion. The end result of the methodology is a set of strategic guidelines that
communicate the direction of the organization in terms of technology development for
the future and, supporting the generation of project ideas (the next activity the in the
technology strategy formulation process).
This chapter is structured as follows: section 5.2 presents the literature review on
themes related with technology foresight and drivers in the machine tool industry.
Section 5.3 introduces the preparation of the Delphi survey, its application and analysis
of results. Section 5.4 describes the development of the methodology for complex cross
relationship analysis between future events. Section 5.5 presents its application in the
industrial partner of the thesis. Finally, section 5.6 presents the conclusions.
5.2 Literature review
In times when continuous innovation is a critical requirement for sustaining the
competitiveness of companies, it becomes increasingly important to understand the
drivers of change in business, in order to develop strategic plans that are best suited to
deal with the future. According to Rohrbeck and Gemünden, the ability to produce
forecasts is also related with the innovation capability of companies (Rohrbeck and
Gemünden, 2011) and, for this purpose, various methods and tools have been
developed.
These methods and tools have been grouped under the name of foresight. Variant names
include technology foresight, corporate foresight and strategic foresight, depending on
the emphasis that authors desire to give, with respect to the objectives of the foresight
exercises. For example, Vecchiato and Roveda argue that the term strategic foresight is
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used to establish the relationship between foresight and strategy formulation explicit
(Vecchiato and Roveda, 2010), while Ruff (Ruff, 2006) used the term corporate
foresight to characterize the studies conducted on long-term predictions in business
environments, markets and new technologies and their implications for corporate
strategies and innovation. Despite conceptual differences, it has also been proposed that
these terms are, in effect, synonymous (Liebl and Schwarz, 2010).
The practice of foresight followed distinct trajectories paths in different countries
(Martin, 2010, Miles, 2010). The is the case of two of the most important development
centers of this field: in the United States, the development of methods is focused on
very advanced and quantitative methods, coming primarily from the military area, while
the French school focused in the development of what is known as la prospective,
which was based on critical thinking in decision-making, and more centered in the
human factor, values, freedom and reflection on the endpoint of action (Coates et al.,
2010).
Although the practice of foresight in the definition of public policies for science and
technology is well documented, the same cannot be said about the practice at the
corporate level. For obvious strategic reasons, few studies are disclosed. However,
based on a study conducted with European companies that perform foresight exercises,
von der Gracht states that the historical development of this field followed four
dominant paradigms, in chronological order (von der Gracht et al., 2010):
1. Expert-based foresight;
2. Model-based foresight;
3. Trend-based foresight;
4. (Context-based) open foresight.
The first paradigm (expert-based foresight) emerged in the 1970s and is supported by
the idea that the future can be predicted by means of experts’ opinions (Brown, 1968).
Back then, most companies that followed this approach outsourced most of their
foresighting activities to experts, such as research institutes and universities. However,
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the perspective of third parties offered opportunities for the consideration of possible
interdependencies between future developments in technology and the capabilities and
competences of organizations. The main methods used in that period included Delphi
studies, roadmapping and scenario building techniques.
The following paradigm – Model-based foresight – is based on the application of more
quantitative approaches, such as computerized models using large amounts of data. As
with the previous paradigm, studies were quite often delegated to third parties. These
also proved to have low impact and relevance for the organization. Examples of
methods from this paradigm include Matrix Cross-Reference Multiplication Applied to
a Classification (MICMAC) (Godet and Research, 1979), Cross Impact Method
(Gordon, 1994b) and the Technological substitution models (Fisher and Pry, 1971)
The third and current dominant paradigm, Trend-based foresight, supports the notion
that the future can be grasped by scanning and monitoring trends and projecting them.
Contrary to earlier paradigms, this brings higher communicability of results, but at the
same time comes with the risk of turning organizations merely reactive or driven by
trends in the environment. Typical methods from this paradigm include patent and data
mining techniques (Bonino et al., 2010).
The emergent paradigm, (Context-based) open foresight (Miemis et al., 2012),
acknowledges the simple idea that, in fact, the future cannot be predicted, calculated or
projected. Time spent on calculations, methodology, data collection, discussions and
analyses seem not to be paying off. Therefore, foresight should give higher emphasis to
open communication, discussion of contradictory information, divergent opinions,
subjectivity, uncertainty and provide basis for action-making. This paradigm embraces
the dynamic interaction of social, technological and economic forces, which arise from
the network society where the boundaries of technology, economy, politics and culture
are merging. As such, this paradigm is related to the concept of open innovation
(Chesbrough, 2003) Characteristics of this paradigm include transparency,
methodological hybridity, context orientation and open participation of relevant
stakeholders, from inside and outside the organization. Experts based methods still play
an important role, but are integrated to encourage open discussion and fused with
decision making, instead of just preparing for it.
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The broader and open perception from this emerging paradigm is supported by recent
studies. And, although emphasis is given to technology in this thesis, i.e. to technology
foresight, a number of publications have argued that technology evolution should not be
observed in isolation, but consider relevant information on users’ needs and the
environment (Cooper, 1979, Gupta et al., 1986, Moenaert et al., 1994, Song et al., 1996,
Reger, 2001, Becker and Lillemark, 2006). This suggests that an integration of inputs
and coordination of activities between the R&D department (technological perspective)
and the Marketing department is highly desirable in corporate settings.
The coverage of foresight activities in companies is studied in detail by Vecchiato and
Roveda (Vecchiato and Roveda, 2010). Based on a field research with many large
multinational companies and analysis of literature on foresight, the authors propose a
generic classification of foresight activities, divided into three criteria: the major focus
(field of investigation), the scope (level of analysis) and the time horizon. Figure 5.2
presents a schematic representation of this classification.
Figure 5.2 - General classification of foresight activities. Source: (Vecchiato and Roveda, 2010)
The “field” axis refers to the area of research. This relates to the driving forces in the
business micro and macro environments. The micro level environment is related to
drivers in the industries or industries in which the organization operates. Examples of
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drivers in the micro level environment include emerging customers’ needs and
competitors’ and suppliers’ moves. This type of drivers is named Market Drivers of
Change. Foresight activities should also address the macro level environment, which is
outside the influence of the company. The macro level environment addresses driving
forces in politics, economy, environment, society and technology (PEEST), constituting
all together the Non-Market Drivers of Change.
The “scope” axis represents the different business levels: the macro level or broad
definition of an industry (example: machine tool industry), the meso level or specific
business or segment (example: sheet metal machine industry) and the micro level or
specific operational unit or projects within organizations. Finally, the “time horizon”
axis refers to time interval of the foresight study. Normally, micro level tends to be
short or medium oriented, while meso and macro level, long term oriented.
According to the authors, these three criteria (field, scope and time horizon) define the
content of foresight in organizations. Still, there are two remaining issues relevant for
setting up the foresight activities in organizations: the organizational approach, which
concern the organization of foresight activities in a company (example: autonomous and
permanent unit, heterogeneity of group of experts, etc.) and methodological issues, such
as which foresight tools or methods to use, taking into consideration the company’s
information needs.
In companies, technology foresight can be a one-time activity or an ongoing process,
which can be performed by a single business, group or even a whole industry (Carlson,
2004). Depending on the size of the business, it can also have different types of value
(Coates et al., 2001). In large organizations, innovation is increasingly dependent on
networks of cooperation; which demand more external information, so technology
foresight should be carefully used to inform technology strategy. In small companies
though, often characterized with limited time and resources, there may be strong
restrictions to investing in technology foresight. However, in the early decades of the
21st century and the strategic role of technological innovation, many small companies
are now forced to become more technologically informed. According to Coates et al.,
there is a great need nowadays for the development of easily comprehensible, timely
and cheap sources of technology foresight for smaller business (Coates et al., 2001).
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The view is also supported by Phaal et al., who argues that technology management
tools should aim to be robust (have theoretical support and be reliable), economic and
practical to implement (not too complex or resource intensive), integrated (be combined
or linked with other frameworks, processes and tools used in the business) and flexible
(adapted to suit different contexts) (Phaal et al., 2006).
Acknowledging that the benefits of the integration of methodologies and perspectives
constitute an emerging paradigm for foresight activities and that there is a need for
robust, practical and flexible technology management tools, the next sub section
reviews a number of methodological proposals found in the literature.
5.2.1 Combined foresight methodologies
The combination of tools is a noticeable trend in technology management research
(Phaal et al., 2006). The current hypothesis among technology foresight scholars and
practitioners is that the development of hybrid methodologies and integrated
frameworks can increase the effectiveness of the forecasts(Wang and Lan, 2007, Reger,
2001, Porter, 2010). As stated by Anderson et al., “one should combine the results from
different methods, which would help in reducing errors arising from faulty assumptions,
biases, or mistakes in the data” (Anderson et al., 2008, p. 602). Also according to
(Heger and Rohrbeck, 2012), integration of methods is beneficial for: tailoring the
methodology to the task, integrating perspectives and creating a holistic view that takes
into account interdependencies between the different aspects of the analysis.
The discussion on this combination of tools dates back to mid ninety eighties, where a
combination of forecasts were found to be particularly useful if they come from
different data sources (Armstrong, 1986). Flores and White (Flores and White, 1988)
followed the same idea, and proposed a framework to support this combination of
methods, with two dimensions: 1) selection of the base forecasts, i.e. which forecasts to
include (quantitative, qualitative or both) and 2) selection of the method of combination,
concerned with how methods are combined, which can be either systematically, when
methods have a mathematical basis and can thus be replicated, or intuitively, which
applies to individual or group expertise and judgment.
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Through a philosophical discussion, Maio Mackay and Metcalfe reinforce the idea
about the inclusion of multiple perspectives in methods selection and combination
(Maio Mackay and Metcalfe, 2002). According to these authors, the consideration of
different opinions, perspectives and backgrounds ensures that objective, interpretative
and personal types of knowledge are included in the forecast, thus increasing accuracy
and understanding about the possibilities of the future.
In line with the emerging paradigm that points to the inclusion of multiple perspectives
in strategy making, Rohrbeck and Arnold examine the most suitable methods for
dealing with the market and technology perspectives, which are of particular interest in
the context of corporations (Rohrbeck and Arnold, 2007). Rohrbeck and Arnold found
that among the many foresight methods, Roadmapping, Scenario Technique, Quality
Function Deployment and Delphi studies are methods capable of coping with multiple
perspectives, as represented in Figure 5.3.
Customers diaries
Ethnographic study
Socio-cultural Currents
Customer Scenarios
Focus Topics
Qualitative Survey
Quantitative Survey
Competitor Analysis
Trend Report
Lead-User Analysis
Lead-Market Analysis
Market-Oriented
methodsIntegrating methods
Technology-Oriented
methods
Roadmapping
Scenario Technique
Quality Function
Deployment
Delphi Studies
Technological
Competitor Analysis
Technology Scouting
Publication Analysis
Patent Analysis
Conference Analysis
S-Curve Analysis
Delphi Studies
Learning Curve
Option Pricing Models
Simulations
Benchmarking
Figure 5.3 - Foresight methods and orientations. Source: (Rohrbeck and Arnold, 2007)
The Delphi method has been often integrated with other foresight methods, for the
purposes of either enhancing their analytical capability or contributing as input to others
(Rowe and Wright, 2011). In a trans-European study about the most used methods and
combinations, which involved both the private and public sectors (Popper et al., 2007),
the Delphi method was found to be more often combined with Brainstorming, Scenarios
and Future Workshops; Roadmapping with Expert Panels, Key Technologies and Future
Workshops, and Scenario Technique with Expert Panels and Future Workshops.
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Two integrating methods from Figure 5.3, Quality Function Deployment and
Roadmapping, are combined by Lee et al. (Lee et al., 2013) to study relationships
between the demands of future Smart Cities (Giffinger and Gudrun, 2010) and specific
services, devices and technologies. After the completion of the QFD matrix by experts,
the results are conceptualized in a roadmapping format.
The other two integrating methods from Figure 5.3 – Delphi method and Scenario
analysis - have often been combined. A review of the combinations of these two
methods was found by Nowack et al. (Nowack et al., 2011). The authors argued that
integrating the Delphi method with Scenario analysis brings benefits in terms of
creativity, objectivity and credibility of foresight studies. A number of studies
combining these two methods are described in the following text.
An attempt at combining Delphi method and Scenario analysis is found in Kameoka et
al. (Kameoka et al., 2004), into a methodology named Delphi-Scenario writing (DSW).
Following an eight step process, the methodology begins with a Delphi survey
conducted with a panel of experts on a number of future technologies, products and
services. The results of the survey are put on a need versus time of realization chart,
which is then converted into a scenario flow chart that represents the sequence of key
factors (needs and technologies) over time. This scenario then provides insights into
strategies necessary to pursue, although the transformation from scenarios to strategy is
not made explicit in the article.
A purely quantitative forecasting method, the Technological Substitution Model, which
is commonly used to forecast generations of technologies, is combined with the Delphi
method and Scenario analysis by Tseng et al. (Tseng et al., 2009) to forecast the global
market shares of television displays technologies. Past data about market shares of each
television display technology is used to estimate the parameters of the Technological
Substitution Model. Despite being relevant information for managers, one can raise
issues with respect to accuracy of predictions based on past data, namely because events
in the macro environment may severely change the penetration rates of technologies.
In order to offset a limitation of the Delphi method concerning its inability to consider
interrelationships between events, a novel methodology which combines the Delphi
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method with Cross Impact Analysis was proposed by Bañuls and Turoff (Bañuls and
Turoff, 2011). This methodology begins with a group of specialists compiling a list of
relevant future events. In a second step, experts are invited to make their estimates of
the cross-impact relationships, particularly on the probability of occurrence of events,
conditional probabilities and impact on other events. Using the computational
simulation algorithm proposed by the authors, scenarios are constructed, representing
sequences of events’ sets and subsets. Although the logic behind the combination of
these methods seems appropriate, the use of complex computational models to predict
the future ignores the benefits that can be obtained from ideas exchange between people
and groups with different perspectives, as mentioned previously.
Still on the limitations of the Delphi method, a number of other issues concerning the
design of the method were identified. It is argued that such issues constitute limitations
to the analytical capability of the Delphi method. They are described below.
One important limitation of the Delphi method concerns the overwhelming amount of
information collected. Typically, a Delphi survey would ask a panel of experts their best
guesses about the impact, the time and likelihood of occurrence of a series of events.
Though regarded as relevant information for strategic purposes, still provides little
clarity about the priorities of the organization in relation to future events. In other
words, what is the weight of each vector (impact, probability and time of occurrence) in
the decision-making process inside the organization? This indicates a need for
information synthesis in the Delphi method, in order to assist decision-makers to focus
on the most relevant future events to the organization.
The Delphi method, destined to provide a glimpse of the most likely future
technological developments and the events that may increase their diffusion, gives little
information about the relationships between them. In other words, how future events in
the market, economy, regulations or others may influence technology diffusion. This
issue has been identified by Bañuls and Turoff as a potential limitation of the method
(Bañuls and Turoff, 2011). Thus, this is the second issue, concerning potential
drawbacks, about the Delphi method.
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The third issue identified is related to an absence of strategic guidance or the link with
technology strategy. The emerging paradigm related with foresight exercises in
organizations points to greater integration of divergent perspectives, which together
should be directed towards the definition of a strategy. Although many methods
incorporate multiple perspectives and consider the definition of a strategy as its ultimate
goal, a more explicit link between foresight and strategy is still required. This means
that the results of foresight studies should provide the information necessary to feed a
technology strategy program; a requirement that has not been fully observed in current
foresight methods. This factor turns out to be of fundamental relevance in the emergent
paradigm that points to fusing foresight with decision making and priority setting (von
der Gracht et al., 2010) along with the inclusion of multiple perspectives (Maio Mackay
and Metcalfe, 2002) and combination of tools.
According to Burgelman et al., the content of a technology strategy should include
(Burgelman et al., 2004, p. 142):
technologies to be developed;
required technological competences and capabilities;
investment level in technological developments;
technology acquisition mode (examples: internally, externally or in cooperation);
timing of introduction of the technologies in the market;
organization and management approach of technology and innovation.
In line with the propositions supporting robust, economic and practical to implement,
integrated, flexible and easily comprehensible technology management tools (Coates et
al., 2001, Phaal et al., 2006), there is a need for novel approaches for information
synthesis and events relationship analysis in Delphi surveys. Although it has become
consensual how important future studies have become to strategic analysis, few studies
have gone this far.
In consonance with Burgelman’s proposal of what should be included in a technology
strategy program and recognizing the importance of addressing multiple perspectives in
foresight activities, this chapter presents a study conducted with the industrial partner of
the thesis. This study includes a Delphi survey conducted with a panel of experts, which
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is later served as input for a methodology that aims at analyzing the influence of
external factors and drivers on technological change in the industry, in order to provide
the elements needed for the technology strategy of a company. A new methodology is
proposed in this chapter aimed at facilitating the interpretation of the results from a
Delphi survey, which serves as inputs to an adapted Quality Function Deployment tool
(QFD) to analyze complex events relationships.
Prior to introducing the proposed methodology, and in order to increase the
understanding about the dynamics of technological innovation in the machine tool
industry, the following sub section provides an analysis on the current key factors and
drivers that influence technological change in this industry. This analysis becomes an
important step into the development of a framework that characterizes the forces that
will influence the future of technological development in the machine tool industry,
which is of vital importance to the construction of the interviews guide with experts and
analysis of publications, as will be described later.
5.2.2 External drivers that influence technology change in the machine tool
industry
This section presents an overview of the main influencing factors in the adoption and
diffusion of technologies in the machine tool industry. The industrial partner is a
manufacturer of sheet metal processing equipment. The sheet metal machinery is one of
the key segments in the machine tool industry, since more than a half of the world
production of metal is destined to sheet metal parts (Streppel et al., 2008), which are
destined to a multitude of industries, from the automotive, aeronautics to consumer
goods production, food processing and packaging. Therefore, it is assumed that the
analysis on the machine tool industry that follows applies to the sheet metal machinery
segment.
The following text is also a result of an analysis on a number of industry related
publications (magazines, special reports, etc.) and interviews with industry experts, to
identify the main environmental factors which influence the direction of technology
development, in this industry. These insights support the categorization of actors and
drivers with a critical role for the technology diffusion in the machine tool industry. The
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analysis on the factors that influence technology adoption and diffusion has proved to
be necessary for a better understanding of the dynamics of technological evolution in
this industry, which is essential to support the preparation of the Delphi survey and the
application of the methodology of events relationships analysis, which will be described
later.
The importance of technology in the competitiveness of companies is well illustrated by
the changes brought about by the integration of digital technology controls and
computers into machine tools. A report prepared by the Institute for Innovation
Research and Technology Management from the University of Munich provides a
historical synopsis of the technological evolution in the machine tool industry
(University of Munich, 2001). A summary of the events is provided below.
The introduction of numerical controls during the seventies caused a major
discontinuity of the United States (US) traditional machine tools product line, which
until that decade held the biggest global market share among producing countries6.
Between 1972 and 1986, American companies lost their leading position to Germany,
which dominated 30% world’s export trade in 1977, and later by Japan, that in the
beginning of the eighties conquered 20% of the world market, parallel to the
introduction of computers into numerical controls.
A major event contributed to the widespread integration of numerical controls in
machine tools: the oil crisis in 1973 and subsequent energy crisis deeply affected the
largest industrialized nations of the world. Machine tool users searched then for ways to
increase the efficiency of their operations, and numerical control in machine tools
offered improved flexibility and reduced operating costs.
Through the numerical controls technology, many mechanical functions were replaced
by electronics, and operators became free from being close to the machine all the time,
since numerical controls enabled machines to be programmed and to perform a
6 In the post war years until the 1970s, machine tool manufacturers from the United States (US) reached a 30% global market share.
Source: UNIVERSITY OF MUNICH, I. F. I. R. A. T. M. 2001. The recent history of the machine tool industry and the effects of
technological change.
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sequence of operations automatically. This technology also allowed an increase of the
range of possible shapes to be produced by the machine tools and combination of
multiple functions in a single machine, such as milling, drilling, boring, etc.. Demand
for such machines in the US and Western Europe, which were experiencing higher labor
costs, rose dramatically. The concept of machining centers was born.
The development of this technology was greatly supported by national programs
promoted by most of the industrialized countries. Primarily Japan and Germany were
able to develop completely automated systems. On the other hand, US companies were
losing competitiveness, since they decided to reduce their investments in technology
development during the two oil crises, while competitors were doing exactly the
opposite. Only later, during the eighties, greater investments in research and
development programs were pursued by American companies.
The historical facts described above illustrate the influence of certain external factors in
the diffusion of a technology. This industry, characterized in the past as having long re-
investment cycles, i.e., machines have long lifetime span before being replaced by a
new one, was forced to seek more efficient solutions due to the turbulence caused by
events outside of their sphere of influence. The oil crisis, labor issues and national
programs supported by certain countries were identified as factors that contributed -
obviously with different degrees of importance - to increase the penetration rate of
machine tools with numerical controls in a number of markets. Despite this, one cannot
ignore the disruptive nature of technology, which brought much higher productivity
gains and efficiency for operations, constituting an important differentiator. But these
events undoubtedly helped to amplify the shock that this technology brought to market.
The machine tool industry is nowadays seen as having a strategic role in the
competitiveness of nations, being considered by the European Union (EU) commission
one of the Key Enabling Technologies7 (KETs) given its importance to the development
of new goods and services and to restructuring and modernization of industrial
7 Source: COMMISSION, E. 2012. Key Enabling Technologies – A bridge to growth and jobs [Online]. Brussels. Available:
http://europa.eu/rapid/press-release_MEMO-12-484_en.htm [Accessed 08-23-2013 2013].
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processes. Therefore, the EU commission launched a series of research programs
intended to boost innovation and competitiveness of European machine tool
manufacturers. It is expected that, in the coming years, new technological developments
will considerably change the landscape of this industry.
The machine tool industry is now faced with new challenges. The Comité Européen De
Coopération Des Industries De La Machine-Outil (CECIMO) is the European
Association of the Machine Tool Industries, which is constituted by national
associations of 15 European countries that together account to 99% of the total machine
production in Europe and 30% worldwide. This association regularly publishes special
reports which contain important insights about the current and future status of the
machine tool industry. The most recent one is from 2011, named “Study on the
Competitiveness of the European Machine Tool Industry” (CECIMO, 2011).
This report addresses the grand societal challenges of the 21st century: globalization of
the economy, climate change, scarcity of resources, ageing of society and sustainable
mobility. These are global drivers expected to influence the future technological
developments of the machine tool industry. The following text is based on this report.
The machine tool market is divided into two large segments: the low-cost & high-
volume and high-end & customized machine tools. The first segment is experiencing
fast growth, driven by the emerging economies of Asia and South America, while the
second has been more restricted to more developed economies of Europe and North
America, which are still recovering from the economic downturn resulting from the
financial crisis of 2008. This crisis has deeply affected the machine tool industry, since
the first reaction of customers is cut in budgets of capital expenditures.
The new century is characterized by the loss of significance of Europe to Asian
countries such as China, Korea, Japan and Taiwan, which emerged stronger from the
crisis. China, despite still having a considerable technological gap in relation to other
leading countries, overtook Japan as the second largest machine tool producer in the
world, after CECIMO countries.
Along with increasing concerns with the environment, European countries are setting
stringent safety and energy-efficiency standards as part of their regulatory framework,
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to raise the cost of entry of new entrants in the European market. Examples include the
Blue Competence Machine Tool initiative (CECIMO, 2013), aimed at increasing
awareness and knowledge about sustainability in the European machine tool companies
and the EcoDesign directive (Commission, 2009), aimed at promoting approaches that
consider, early on the design phase, the environmental impacts of products during their
entire lifecycle.
Another increasing concern of developed countries is related with the restriction of
imports of machines that do not satisfy minimum environmental, safety and health
requirements as set in nations’ regulatory framework. A number of European industry
associations, including CECIMO, have intensified market surveillance of non-compliant
capital goods (CECIMO, 2012), which are mostly produced in developing countries,
and are now facing restrictions in entering the European market.
The experience with the 2008 financial crisis, whose effects are still felt to this day,
turned machine tool customers more sensitive to demand volatility. To address this
issue, machine tool manufacturers are investing in the development of flexible
manufacturing systems (FMS), including the modularization of equipment and its
integration into production systems, to better respond to this emerging need of their
customers.
The fact that growth is concentrated in emerging markets that demand low cost
solutions could be a serious bottleneck for technology innovation in this industry,
however, what has been observed is that the challenges of economic globalization has
led to developments directed towards more efficient management of operations
overseas. A clear example of this is happening with the automotive industry, which
covers approximately one third of machine tool market. Many automotive companies
have been forced to relocate their operations in emerging economies. The challenge of
managing manufacturing facilities in the distance has led to the development of remote
access technologies for monitoring operations as well as equipment diagnosis. As
expected, after sales services have become extremely important, and many machine tool
manufacturers are implementing tele-service technologies to support customers based in
different parts of the world.
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Although the automotive industry continues to be an important market for machine
tools, other emerging markets such as med-tech industry, electronics and optics are
expected to increase the demand for micro and nano-machining technologies.
Recently, machine tool manufactures have also faced serious problems with raw
materials (specially steel) and components supply, due to lack of availability and
increase in purchasing costs. This issue, together with increasing interest of the
automotive industry for lightweight constructions, has posed new technical challenges
for machine tool manufacturers. Lightweight constructions are desirable for energy
saving reasons, as the concern for environmental sustainability is a major driver across
many industries. Two strategies are followed in lightweight constructions: downsizing
of car engines and use of alternative materials. The first requires smaller parts and low
tolerances, which is a good opportunity for machine tools incorporating high precision
technologies, such as net or near net shape processing technologies. Also, additive
manufacturing or 3D printing may replace traditional metal machining operations for
these smaller parts. The second includes the use of materials such as composites,
aluminum and high strength steel. Composites require molds that need to be machined
by machine tools, and current technologies for processing aluminum and high strength
steel have not yet achieved satisfactory levels of performance, so there is plenty of room
for improvements.
The sharp decrease in birth rates and the consequent ageing of society, more observed in
developed countries, comes with the problems of shortage of workforce and a gap
between skills available and needed. Along with other reasons such as cost and
usability, machine tool manufacturers are investing in automation technologies and in
improvements in machine-user interface, such as vision systems for “smart” machines,
i.e., machines with self-monitoring and repairing capabilities. As a matter of fact,
innovation in the machine tool industry occurs mainly in software nowadays.
Improvements are being made in software for machine tool simulation, tool-path
verification and rendering.
The successful diffusion of technologies requires a range of systemic factors (Carlsson
and Jacobsson, 1994), which are internal and external to companies. Studies that
attempt to characterize the influence of external drivers on technological change in the
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machine tool industry are not abundant. An example was proposed by Kathuria
(Kathuria, 1999) who proposed a framework. This framework examines the role of
externalities in inducing technological change in this industry. It portrays four actors,
whose interactions basically define the environment of machine tools producers:
suppliers, users/customers, state and competitors. Interactions between these actors
determine the rate of technological change in the industry. Such interactions occur in
three forms: pressures, incentives and information flow. A schematic representation of
these actors and interactions is illustrated in Figure 5.4.
Figure 5.4 - Determinants of technical change in the machine tool industry. Source: (Kathuria, 1999)
Pressures are exerted by users/customers demanding lower cost products and/or
products with superior technologies and differentiated (Porter et al., 1980, Porter and
Chandler, 1985), and by competitors responding to this demand as well.
Users/customers can also provide incentives through customization of products and co-
development (Hippel, 1986, Lee, 1996). Suppliers provide incentives through co-
developments likewise, and by supplying key components and systems with improved
technology.
The role of the state in promoting technological change can be seen in a number of
incentives (Kathuria, 1999): as a customer of machine tools, especially in defense
industries, as a protector, by protecting domestic industries against foreign competition
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– an example is the stricter market surveillance from European countries, as mentioned
before - and as a restructurer, through initiatives that aim at improving National
Innovation Systems (research institutions, universities, laboratories, etc.), such as new
technology development programs, and by providing direct funding through subsidies
or tax incentives, and promoting free trade agreements.
An additional actor, not mentioned in Kathuria’s study, was identified by Ariss (Ariss et
al., 2000) the regulatory bodies or agencies. These agencies, which can be national or
supranational, pass regulations and set standards that many times influence the
directions of technological developments. A clear example of these is the numerous new
regulations that favor the so-called "green technologies", derived from a growing
concern with the environment.
Drawing from the ideas presented above, a new framework is proposed in this chapter.
Unlike Kathuria’s study, which is dedicated to the actors, their influence in machine
tool producers and the resulting technical change, this framework attempts to describe
the role of external determinants in promoting the diffusion of technologies.
External determinants are defined as all the non-technology related determinants that
induce technological change in the machine tool industry. This perspective expand
Kathuria’s proposition, which is based on actors and information flows, by including
external drivers at a higher level, such as drivers in Politics, Economics, Environment
and Social contexts. This division resembles the STEEP analysis, without the
Technology element, since the objective is to relate these external drivers with
technological developments.
The interactions between actors and drivers take course through a process that begins
with the identification of future challenges, the analysis of these challenges, taking
initiatives, implementing them and monitoring their effect. These initiatives are nothing
more than a response through the development of new technologies and solutions,
aimed at mitigating the effect of current problems, anticipating a future problem or even
creating a new market. Figure 5.5 illustrates these ideas.
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Politics
User/customers
Government/State
Suppliers
Competitors
Systems of innovation
Regulatory agencies
developed countries market
surveillance
developed countries pressure to adopt
stricter environmental regulations
Economic
increasing volatility of demand
emergence of Asian countries
globalization of operations
scarcity of resources
climate change
sustainable mobility
Environment
Social
ageing of society
lack of skilled labour
Technological change
Non technological driversActors
Figure 5.5 - Actors and drivers that influence technological change in the machine tool industry.
In the figure above, the double-headed arrow indicates that the interactions between
actors and drivers is made in both directions, i.e., drivers triggers initiatives from actors,
and actors shape the environment through the implementation of these initiatives. These
interactions result in pressures and incentives for the development of technologies to
respond to emerging needs. Thus, in this framework, technological change is a function
of the interactions between drivers and actors.
These relationships have been mentioned throughout this sub section. This analysis and
development of the framework is of fundamental importance for the preparation of the
Delphi survey and the application of the proposed method for analysis of relationships
between future events, which will be demonstrated later in this chapter.
Next, the steps taken in the preparation and application of the Delphi survey about the
future of the sheet metal processing industry are described.
5.3 Delphi survey
The preparation and application of the Delphi survey involved several steps. First an
analysis was conducted on key technological areas in conjunction with the industrial
partner of the thesis; followed by a literature review in sheet metal technologies and
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semi structured interviews with industry and academia experts, in order to detect trends
and future expectations. After modeling the literature outcomes intertwined with the
interviewees’ opinions into technology industry trends, a Delphi survey was prepared
and filled by a number of experts. Finally, after a period designated for filling the
survey, the results were analyzed using appropriate convergence metrics for this
purpose. More detailed explanation about these steps is provided below.
1. Analysis and identification of the key technological areas: an important point to note
about this industry is its multi technological characteristic. This is because the
development of this type of machinery requires expertise in various engineering
disciplines: electronics, mechanics, software development, etc.. This means that the
technological evolution in this industry results from the convergence of many distinct
technology trajectories, with obvious consequences in the innovation process.
An analysis on systems and subsystems in sheet metal processing technologies was
carried out, with support of the Chief Technology Officer (CTO) and engineers from the
technical department of the industrial partner. This analysis was necessary for two
reasons 1) identify the key technological areas in the sheet metal processing industry in
order to observe their likely evolution and 2) support the identification of which
technological competences and skills the experts to be interviewed in this study should
have. Figure 5.6 presents a representation of these systems, sub systems and
technological areas.
The top of the pyramid depicts the process technologies, i.e., those technologies through
which products deliver their primary function. In the case of the industrial partner,
whose products are basically capital goods – goods destined to the production of other
products - these are the sheet metal forming technologies, more specifically sheet metal
laser cutting, shearing and bending. In the intermediate area of the pyramid the critical
technological systems are represented. These technologies, though not directly related to
the sheet metal forming process itself, serve as support or as auxiliary systems, and
contribute significantly to its performance. The following systems were identified:
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Metal forming
machinery
Optical
systemsMotion
systems
Structural
systemsSafety
systems
Tooling
systems
Process automation and
integration systems
ICT
Automation
RoboticsMaterials MechatronicsMachine
design
Optics and
photonicsIndustrial
electronicsSensing
Process technologies
Critical technological
systems
Broad technological areas
and competences
Figure 5.6 – Technological map
Safety systems: refer to the technologies aimed at protecting workers against
hazards during the operation of a machine. Examples in the sheet metal
machinery include presence sensors, safety light curtains and others;
Motion systems: technologies destined to convey momentum to moving parts
in machines. These include pneumatic, hydraulic and electro-magnetic
systems (such as servo motors);
Optical systems: technological systems for the generation, guidance and
positioning of high powered light beams for metal forming purposes;
Tooling systems: mechanical assemblies and parts destined to form metal into
different shapes. These include dies, punches and blades;
Structural systems: technologies used in the main structural bodies of the
machines. These are mostly material technologies;
Process automation and integration systems: concerns technologies destined
to process automation but also their integration in complex production
systems. These include several systems, such as: robots for material handling,
process planning and optimization (Computer Aided Design – CAD - and
Computer Aided Manufacturing - CAM) software, sensors, intelligent
compensation systems, materials storage systems, and many others.
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Finally, the bottom of the pyramid depicts the broad technological areas and
competences related to the systems mentioned above. Nine technological areas were
identified: Information and Communication Technologies (ICT), Materials, Robotics,
Automation, Optics and Photonics, Sensing, Mechatronics, Machine Design and
Industrial Electronics.
Although the technology map above may not be complete, it is still representative of a
number of the most technological competences in the sheet metal forming machinery
industry.
2. Literature review on selected publications: at this stage, a review on selected
publications was done to identify major technological trends. Special care was taken to
cover both academic and industry publications.
The scientific database Elsevier’s ScienceDirect® was used in this research. In order to
filter the most relevant publications, keywords such as “trends”, “new developments”,
“progress”, “forming” and “sheet metal” were used. In the end, five publications were
considered for analysis, which are described in Table 5.1.
Table 5.1- List of scientific publications analyzed
Reference Title of the publication
(Zhang et al., 2004) “Some new features in the development of metal forming
technology”
(Dubey and Yadava, 2008) “Laser beam machining - A review”
(Jeswiet et al., 2008) “Metal forming progress since 2000”
(Damoulis et al., 2010)
“New trends in sheet metal forming analysis and
optimization through the use of optical measurement
technology to control springback”
(Ingarao et al., 2011)
“Sustainability issues in sheet metal forming processes:
an overview”
Additionally, some insights were gathered from special industry publications, namely
the “Study on the competitiveness of the European machine tool industry” (CECIMO,
2011) and the “Factories of the Future Public Private Partnership – Strategic Multi-
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Annual Workshop”8 (Ad-hoc Industrial Advisory Group, 2010). These reports provide
important insights not only about the future of manufacturing technologies (machine
tools included), but also trends in the regulatory environment, market and economy,
both at the European and international levels.
3. Semi-structured interviews with experts: the identification of the key technology
areas in step 1 led to the identification of the technological competences that
subsequently guided the selection process of the experts to be interviewed. The
objective was to be able to cover the largest possible number of competences so as to
ensure a richer view of possible future scenarios.
The industrial partner supported the selection of the industry expert to participate in the
interviews. Invitations were sent via electronic mail to eighteen experts. Two declined
and two, having accepted, did not answer in useful time. Fourteen interviews were
scheduled. One expert invited another expert to participate in the interview, totaling a
number of fifteen experts. Eight of the experts came from the industry, the other seven
experts have academic background.
Experts from academia should have background in at least one of the nine technological
areas and have experience in technology development projects related to the metal
forming industry. Experts coming from academia were invited by researcher. Interviews
with experts were preferred to traditionally asking experts for their opinions in a first
round of a typical Delphi survey, since it is believed that deeper insights could be
collected from personally interviewing them.
The interviews guide is related with the three axes of classification of foresight studies
proposed by Vecchiato and Roveda (Vecchiato and Roveda, 2010) and introduced in
section 5.2. The guide is divided into three parts: the first part deals with the macro-
environment, or, in other words, the non-market drivers of change in the sheet metal
processing industry. The second part addresses the emerging needs of typical customers
from seven industrial sectors (automotive, aeronautics, shipyard industries, renewable
8 Factories of the Future is a Public-Private Partnership, consisting of a programmatic research effort to boost the innovative
potential of the manufacturing industry in Europe.
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energies, household appliances, metallic buildings and furniture) and, as such, is related
to micro level environment and the market drivers of change. The last part of the
interview guide is focused on technology, and addresses issues such as which
technologies will have the greatest impact in the sheet metal industry, their adoption
rates in different industrial sectors, drivers and barriers for diffusion, likely substitute
technologies and the state of the art in technological variables for the future. The
defined time horizon is ten years. The objective of this interview guide is to capture a
holistic perspective, i.e., not solely focused on technology but also on the external
drivers that influence diffusion rates. The interview guide can be found in Appendix 2.
Ten interviews were conducted face-to-face, while the remaining five via audio
conference. All experts authorized the recording of the interview, except one. The
interviews lasted an average of forty five minutes, the shortest of which lasted thirty
minutes and the longest one lasted one hundred minutes, approximately.
During the interviews, a map that depicts the main technologies and systems in metal
forming machinery, which was derived from the technology analysis from Figure 5.6
was shown to the experts in order to facilitate communication. This map is found in
Appendix 3. At the same time, they were requested to not restrict their insights to the
portrayed technologies, but to think of likely future developments resulting from the
convergence with other technologies.
4. Identification of future events: a careful analysis on the transcripts of the interviews
and the ideas derived from the selected publications conducted by the researcher
allowed the identification of twenty-seven events for the sheet metal processing
equipment industry. Although most of these events relate to technological
developments, some also relate to possible changes in market structure, regulation and
in the economy. Table 5.2 lists the identified future events and their references.
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129
Table 5.2 - List of identified future events and their references
No Events Expert Scientific publication Industry report
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 A B C D E F G
1 Countries with low labor costs introduce a growing number of
innovations in sheet metal processing technologies x x x x x x
2 South Korean companies introduce worldwide innovations in
sheet metal processing technologies x x
3 Machine orders from low-labor-cost countries involve greater
automation x x x x x
4 Large numbers of countries base their security rules on the
principles set out in the European law x x x
5 Europe implements stricter machine tool market surveillance
as a consequence of more stringent environmental regulations x x x x x
6 Imported and low-cost machinery faces difficulties entering
the European market x x x x x
7 The concept of modularity in machine design is extended to
adaptive production systems x x x x x x
8 Laser processing replaces traditional sheet metal cutting
processes (punching machines, shears, etc.) x x x x x x x
9 The market prefers laser applications for remote processing of
sheet metal over more traditional processes x x x x
10
The metal construction sector adopts sheet processing
machinery which incorporates the lean philosophy (lean
production)
x x x x
11 Massive adoption of virtual imaging technology for machine
tooling and process control x x x x x
12 Massive adoption of laser forming as complementary process
for corrections in hard-to-form materials x x x x x x x x x
13 Laser processing machines with CO2 sources loses significant
market share x x x x
14 Hybridization (multiple processes in a single machine) is
massively adopted in sheet metal processing equipment x x x x x x x x x x x x
15 Trend towards modular architecture in sheet metal processing
machinery x x x x x x x
16 Massive adoption of tablets in sheet metal processing
machines x x
17 Polymer concrete becomes the main structural material for
sheet metal machine tools x x x x
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130
Table 5.2 (continued)
No Events Expert Scientific publication Industry report
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 A B C D E F G
18 Machines for sheet metal processing incorporate lighter
materials in moving parts x x x x x x x
19 Massive adoption of sheet processed high-strength and ultra-
high-strength steel x x x x x x
20 The sheet processing of new materials gains 10% of the
market share of steel x x x x x x x x x
21
Interfaces with voice, gestures and language recognition
technologies are applied in the sheet metal processing
machines
x x x x x x
22 Reaction times of millisecond are achieved in the active
control of machines x x x x
23 Massive adoption of active monitoring technologies and
intelligent machines with self-learning capabilities x x x x x x x x x x x
24 Massive adoption of remote monitoring of sheet metal
machine tools x x x x x x x
25
Forming forces in hybrid engines (servo motors and hydraulic
systems) exceed the forces of large hydraulic machines of
today
x x x x x x
26 Unit cost per linear measurement (meters, centimeters, etc.) is
half of the present-day cost in sheet metal cutting processes x x x
27 Unit cost per linear measurement (meters, centimeters, etc.) is
half of the present-day cost in sheet bending processes x x x
Legend:
Scientific papers: A: (Zhang et al., 2004); B: (Dubey and Yadava, 2008); C: (Jeswiet et al., 2008); D: (Damoulis et al., 2010) and E: (Ingarao et al., 2011)
Industry reports: F: (CECIMO, 2011) and G: (Ad-hoc Industrial Advisory Group, 2010)
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131
5. Preparation of Delphi survey: the twenty-seven events fed a Real Time Delphi
survey (Gordon and Pease, 2006). This is a web-based and round-less approach to the
traditional version of the Delphi method, which was often regarded as very time-
consuming.
The platform used was the Surveylet provided by the Calibrum Corporation9. For each
event, experts were expected to answer four questions:
What is your knowledge level in this subject? (available answers: from 1(low) to
4 (high))
What is the expected impact of this event? (available answers: from 1(low) to 4
(high))
When will it happen? (available answers: < 5 years, 5-10 years, 10-20 years, >
20 years, Never)
How likely is it to occur? (available answers: from 1(low) to 4 (high))
Experts were also able to provide their textual comments at will. If the expert has no
knowledge about the specific topic, he/she was advised to leave it blank. After filling
the survey for the first time, the invited experts were able to visualize in real time the
anonymous comments and answer from other experts, and change their original answers
whenever they wanted.
6. Results analysis: the answers provided by the experts are analyzed using appropriate
statistical metrics to assess convergence in responses. Sixty four experts were invited,
and a period of three weeks was designated for filling the survey. In the end, twenty
seven experts completed the survey, providing seventy comments. Table 5.4 presents
the survey results.
Convergence was assessed using the median and average to calculate the central
tendency of the responses. The dispersion of responses was calculated using the first
quartile (Q1), third quartile (Q3) and standard deviation (SD).
The calculation of the mean and median time of realization for each survey statement
was weighted with the average year for each option, as in Table 5.3.
9 http://www.calibrum.com/
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132
Table 5.3 - Calculation for time of realization
< 5 years (i.e. 2012 – 2016) 2014
5-10 years (i.e. 2017 – 2021) 2019
10-20 years (2022 – 2031) 2027
> 20 years (2032 – 2041) 2036
Table 5.4 also lists the percentage of participants who chose the option “Never” in each
survey statement.
5.4 Methodology development
In order to reinforce the analytical capability of the Delphi method, a new methodology
is proposed in this section. Its application in the industrial partner of the thesis is
detailed after the description of the methodology.
The logic behind the development of the methodology aims at addressing three
shortcomings of the Delphi method, discussed in section 5.2:
1. need to synthetize information;
2. explore cross events relationships between external factors and technology
diffusion, i.e., how future developments in the economy, in the market and
others might stimulate the diffusion of certain technologies;
3. provide guidance towards strategy making.
Regarding the first shortcoming, it is proposed the use of metrics or indexes that
properly condense the information contained in the experts’ responses in the survey.
Observing Table 5.4, the events are evaluated according to their impact, likelihood and
time of occurrence. These three vectors will be used to provide a metric that represents
the relevance of an event for the organization.
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133
Table 5.4 - Delphi survey results analysis
Median Mean Median Mean Median Mean Median Mean
(Q1 – Q3)(Standard
deviation)(Q1 – Q3)
(Standard
deviation)(Q1 – Q3)
(Standard
deviation)(Q1 – Q3)
(Standard
deviation)
3 2,8 3 2,9 2019 2018,8 3 2,5
(2 - 3) (1,9 - 3,7) (2 - 3,25) (1,9 - 3,8) (2014 - 2019) (2013,5 - 2024,2) (1,75 - 3) (1,4 - 3,5)
2 2,2 3 2,7 2014 2016,8 3 2,6
(1 - 3) (1,1 - 3,2) (2 - 3) (1,7 - 3,6) (2014 - 2019) (2012,8 - 2020,7) (2 - 3) (1,6 - 3,6)
2 2,7 3 2,8 2019 2019,1 2 2,6
(2 - 4) (1,7 - 3,7) (2 - 3) (2 - 3,6) (2014 - 2019) (2013 - 2025,1) (2 - 3) (1,6 - 3,5)
3 2,7 3 3,0 2016,5 2017,9 3 2,9
(2 - 4) (1,7 - 3,8) (2 - 4) (2 - 4) (2014 - 2019) (2012,6 - 2023,3) (2 - 4) (2 - 3,8)
3 2,7 3 3,2 2014 2016,8 3 3,0
(2 - 3) (1,7 - 3,6) (3 - 4) (2,4 - 3,9) (2014 - 2019) (2013 - 2020,7) (3 - 4) (2,1 - 4)
2,5 2,6 3 2,7 2014 2016,9 2 2,2
(2 - 3) (1,8 - 3,5) (2 - 3,5) (1,7 - 3,8) (2014 - 2017,8) (2011 - 2022,7) (1 - 3) (1 - 3,4)
3 3,1 3 3,4 2014 2017,4 4 3,4
(3 - 4) (2,3 - 3,9) (3 - 4) (2,7 - 4,1) (2014 - 2019) (2012,8 - 2022,1) (3 - 4) (2,8 - 4,1)
3 3,2 3 3,0 2019 2018,8 3 3,0
(3 - 4) (2,5 - 4) (3 - 3) (2,2 - 3,7) (2014 - 2019) (2013 - 2024,5) (2,75 - 4) (2,1 - 3,8)
3 2,8 3 2,8 2019 2018,0 3 2,7
(2 - 3) (2 - 3,6) (2 - 3) (2 - 3,7) (2014 - 2019) (2013,3 - 2022,7) (2 - 3) (1,9 - 3,5)
3 2,8 3 3,1 2014 2015,7 3,5 3,3
(2 - 3,5) (1,9 - 3,7) (3 - 4) (2,1 - 4) (2014 - 2019) (2013,3 - 2018,2) (3 - 4) (2,4 - 4,1)
2 2,4 3 2,8 2019 2019,6 3 2,7
(2 - 3) (1,6 - 3,3) (2 - 3) (2,1 - 3,6) (2019 - 2019) (2014,7 - 2024,6) (2 - 3) (1,8 - 3,6)
3 2,5 2 2,4 2023 2021,9 2 2,3
(2 - 3) (1,6 - 3,5) (2 - 3) (1,5 - 3,3) (2016,5 - 2027) (2015,6 - 2028,3) (2 - 3) (1,4 - 3,2)
3 2,8 3 2,9 2014 2017,1 3 2,7
(2 - 4) (1,8 - 3,8) (2,75 - 3,25) (2 - 3,8) (2014 - 2019) (2012,6 - 2021,5) (2 - 3) (1,8 - 3,7)
3 2,7 3 3,0 2019 2019,0 3 2,7
(2 - 3) (1,8 - 3,6) (3 - 3) (2,2 - 3,7) (2014 - 2019) (2013,6 - 2024,5) (2 - 3) (1,7 - 3,7)
3 3,0 3 3,0 2016,5 2017,4 3 3,0
(3 - 4) (2,2 - 3,9) (2 - 4) (2,1 - 3,9) (2014 - 2019) (2012,3 - 2022,5) (2 - 4) (1,9 - 4)
2 2,6 2,5 2,5 2014 2017,2 3 2,7
(2 - 4) (1,4 - 3,8) (2 - 3) (1,5 - 3,6) (2014 - 2019) (2012,5 - 2021,9) (2 - 3,75) (1,6 - 3,8)
2 2,4 2 2,5 2016,5 2018,3 3 2,4
(2 - 3) (1,5 - 3,4) (2 - 3) (1,5 - 3,5) (2014 - 2019) (2013 - 2023,5) (2 - 3) (1,5 - 3,3)
3 2,9 3 2,8 2014 2018,3 3 3,1
(2,25 - 3) (2,1 - 3,7) (2,25 - 3) (1,9 - 3,8) (2014 - 2019) (2011,6 - 2025,1) (2,25 - 4) (2,1 - 4,1)
3 2,9 3 3,1 2014 2017,2 4 3,1
(2 - 4) (1,9 - 4) (2 - 4) (2,2 - 4) (2014 - 2019) (2012,5 - 2021,9) (3 - 4) (2 - 4,3)
2 2,3 3 2,7 2019 2019 3 2,8
(1 - 3) (1,2 - 3,4) (2 - 3) (1,8 - 3,6) (2014 - 2019) (2013,9 - 2024,1) (2,25 - 3) (2 - 3,7)
2 2,4 2 2,6 2019 2023 2 2,4
(2 - 3) (1,5 - 3,4) (2 - 3,5) (1,5 - 3,6) (2019 - 2027) (2016 - 2030) (2 - 3) (1,3 - 3,5)
2 2,4 3 2,8 2014 2017,5 3 2,9
(2 - 3) (1,5 - 3,2) (2 - 3) (1,8 - 3,7) (2014 - 2019) (2012 - 2023) (2 - 4) (2 - 3,8)
21. Interfaces with voice, gestures and
language recognition technologies are applied
in the sheet metal processing machines
7%
22. Reaction times of millisecond are achieved
in the active control of machines3%
19. Massive adoption of sheet processed high-
strength and ultra-high-strength steel3%
20. The sheet processing of new materials
gains 10% of the market share of steel3%
16. Massive adoption of tablets in sheet metal
processing machines7%
17. Polymer concrete becomes the main
structural material for sheet metal machine tools17%
18. Machines for sheet metal processing
incorporate lighter materials in moving parts0%
13. Laser processing machines with CO2
sources loses significant market share0%
14. Hybridization (multiple processes in a single
machine) is massively adopted in sheet metal
processing equipment
3%
15. Trend towards modular architecture in sheet
metal processing machinery0%
10. The metal construction sector adopts sheet
processing machinery which incorporates the
lean philosophy (lean production)
0%
11. Massive adoption of virtual imaging
technology for machine tooling and process
control
0%
12. Massive adoption of laser forming as
complementary process for corrections in hard-
to-form materials
3%
7. The concept of modularity in machine design
is extended to adaptive production systems0%
8. Laser processing replaces traditional sheet
metal cutting processes (punching machines,
shears, etc.)
7%
9. The market prefers laser applications for
remote processing of sheet metal over more
traditional processes
7%
4. Large numbers of countries base their
security rules on the principles set out in the
European law
0%
5. Europe implements stricter machine tool
market surveillance as a consequence of more
stringent environmental regulations
3%
6. Imported and low-cost machinery faces
difficulties entering the European market17%
1. Countries with low labor costs introduce a
growing number of innovations in sheet metal
processing technologies
7%
2. South Korean companies introduce
worldwide innovations in sheet metal
processing technologies
7%
3. Machine orders from low-labor-cost
countries involve greater automation7%
Likelihood
Event
Knowledge level Impact Time of occurrence
Never
(% )
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134
Table 5.4 (continued)
The development of the metric must invariably consider the dispersion of the
judgements given by a panel of experts. This issue is treated resorting to the coefficient
of variation, a metric used in statistics, which is the normalized measure of dispersion of
a sample of numbers. It is calculated as the mean divided by the standard deviation.
In fact, observing the judgements provided by the experts, it becomes evident that
events with higher mean and lower standard deviation are the ones with higher
importance and also the ones where experts agreed more or the uncertainty about an
event is minimum. The coefficient of variation is then used as a proxy for estimating the
relevance of each vector of an event. As such, events with lower mean and higher
standard deviation are then penalized in terms of relevance.
However, and in order to integrate these three vectors into a metric that reflects the
relevance of an event, one needs to consider their units. While the impact and likelihood
of occurrence are assessed using Likert scales, time of occurrence is assessed in years.
The interval years available for experts need then to be converted into corresponding
Likert scales, for consistency reasons. Hereinafter, time of occurrence vector is
converted into the urgency vector, representing how close to the present time an event
is. As such, interval years which are more distant in time will have corresponding lower
Likert scales, while the ones close in time will have greater corresponding Likert scales.
Finally, different weights (wi) can be used for each vector to reproduce different degrees
of importance to the organization. Following the logic suggested previously, the metric
Median Mean Median Mean Median Mean Median Mean
(Q1 – Q3)(Standard
deviation)(Q1 – Q3)
(Standard
deviation)(Q1 – Q3)
(Standard
deviation)(Q1 – Q3)
(Standard
deviation)
3 2,6 3 3,2 2019 2018,8 3 2,8
(2 - 3) (1,6 - 3,5) (3 - 4) (2,5 - 3,9) (2014 - 2019) (2013 - 2024,6) (2 - 4) (1,9 - 3,8)
3 3,0 3 3,2 2014 2017 4 3,2
(2 - 4) (2,1 - 3,9) (3 - 4) (2,3 - 4,1) (2014 - 2019) (2010,7 - 2023,3) (3 - 4) (2,2 - 4,2)
3 2,6 3 2,8 2019 2020,8 3 2,7
(2 - 3) (1,6 - 3,7) (2 - 4) (1,7 - 3,9) (2014 - 2025) (2012,7 - 2028,9) (2 - 4) (1,5 - 3,8)
2 2,4 2,5 2,7 2019 2018,8 2 2,5
(2 - 3) (1,4 - 3,4) (2 - 3,25) (1,7 - 3,6) (2014 - 2019) (2013,6 - 2023,9) (2 - 3) (1,6 - 3,4)
2 2,3 2 2,1 2019 2019,9 2 2,4
(2 - 3) (1,4 - 3,3) (2 - 2) (1,4 - 2,8) (2016,5 - 2019) (2013,4 - 2026,4) (2 - 3) (1,5 - 3,2)
27. Unit cost per linear measurement (meters,
centimeters, etc.) is half of the present-day cost
in sheet bending processes
0%
24. Massive adoption of remote monitoring of
sheet metal machine tools0%
25. Forming forces in hybrid engines (servo
motors and hydraulic systems) exceed the
forces of large hydraulic machines of today
7%
26. Unit cost per linear measurement (meters,
centimeters, etc.) is half of the present-day cost
in sheet metal cutting processes
0%
23. Massive adoption of active monitoring
technologies and intelligent machines with self-
learning capabilities
3%
Likelihood
Event
Knowledge level Impact Time of occurrence
Never
(% )
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135
to estimate the relevance of an event, or event relevance index, is described in equation
(5.1).
Event relevance index
[ ( ( ) ( )
) ( ( ) ( )
) ( ( ) ( )
)]
( )
(5.1)
The second shortcoming deals with the cross relationships between external factors (or
non-technology related events) and the diffusion of technologies. In management
literature, the complex cross relationships analysis between events has relied heavily on
matrix type of tools. They have been employed by consultants and managers in
business, as well as by academics, for its simplicity in communication, flexibility and
easiness to integrate, thus satisfying the generic requirements of a “good” tool for
technology management, as mentioned by Phaal et al. (Phaal et al., 2006).
Among such tools, QFD is a matrix type of tool extensively used in product design
specification, for translating customers’ requirements into technical and engineering
characteristics. As mentioned in the Literature review section, QFD has been used in
foresight studies as well (Lee et al., 2013). For its wide acceptance, an adapted version
of the QFD matrix is used to analyze the relationships between the events from the
Delphi survey.
Observing the Delphi survey, the events indicate different types of predictions. For
example, events numbers 1 and 2 (“Countries with low labor costs introduce a growing
number of innovations in sheet metal processing technologies” and “South Korean
companies introduce worldwide innovations in sheet metal processing technologies”),
indicate future changes in the market, namely in competition. Event number 3 is also
related with market dynamics, but more specifically with emerging customers’ needs.
Events numbers 4, 5 and 6, on the other hand, deal with likely changes in regulatory
issues in the industry. Remaining events are associated with changes in technologies,
either related with adoption (for example, event number 12: “Massive adoption of laser
forming as complementary process for corrections in hard-to-form materials”) or
evolution in technical attributes (for example, event number 25: “Forming forces in
hybrid engines (servo motors and hydraulic systems) exceed the forces of large
hydraulic machines of today”). Therefore, it is possible to group the events of a Delphi
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136
survey in two large sets: technology related events and non-technology related events.
This separation aligns with the emerging paradigm that point to the inclusion of
multiple perspectives in foresight activities.
Non-technology related events represent external conditions which may favor or not the
diffusion of a particular technology or technologies. In other words, the relationships
between these two sets of events should be analyzed in order to point out which are the
most promising technologies of the future. This interacting effect between non-
technology related events and technology related events, a recurrent 2x2 relationship,
can be evaluated via matrix based tool.
Building a matrix as in Figure 5.7 – an adapted QFD matrix - decision makers are able
to analyze the cross relationships analysis between different events. Technology related
events are placed in columns, while non-technology related events in rows. The
calculated event relevance indexes for each event are placed in the first row and column
(grey cells in Figure 5.7), and are normalized for each group of events, since it is
assumed that such events are the most representative of the future, and thus, they are
comprehensive. The relationship between these two groups, or how external events may
influence, are assessed quantitatively using a pre-defined scale (strong, moderate and
weak relationship), just like a typical QFD matrix, for each pair of technology and non-
technology related events. The strength of each relationship is inserted in the
intersecting cells. The strength of each relationship is multiplied by the normalized
event relevance index of the respective technology and non-technology related event, in
order to characterize the interactive effect of the combination of events. Summing all
these results per technology related event, provides the absolute importance of each
technology. The relative importance is the normalized absolute importance. The rank
identifies the most important technologies. Finally, the organizational difficulty
measures how difficult it would be for the organization to develop such technology,
based on their existing internal capabilities and competences, and again using an
appropriate scale for the purpose.
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Figure 5.7 - Adapted QFD matrix for complex events relationship analysis
One may argue that these relationships are already taken into account in the judgments
when experts fill the survey. For example, when an expert states that an event
describing more rigid environmental regulations is highly likely in the future, he/she
will also assert that events describing greater diffusion of environmentally friendly
technologies will also have a high probability of occurrence in the future. However,
these relations are not made explicit in a typical Delphi survey, and therefore there can
be inconsistencies in the relationships between events. The proposed 2x2 cross
relationship analysis corrects any probable inconsistencies. It is understood, then, that a
deeper analysis, namely on the quantification of the relationships between future events
and diffusion of technologies, is needed to complement the Delphi method.
As such, the Delphi method’s deficiencies in point one and two mentioned previously
are addressed. Issue number three (“Provide guidance towards strategy making”)
points towards a need for a better linkage between technology foresight and technology
strategy. Considering the decisions relevant for the formulation of a technology strategy
proposed by Burgelman et al (Burgelman et al., 2004), mentioned in section 5.2.1, it is
argued that the information contained in the Delphi survey potentially answers three
questions: technologies to be developed, required technological competences and
capabilities and timing of introduction of the technologies in the market. The first and
third decisions have straightforward answers from the Delphi survey results: the event
Technology
related events
Non technology
related events
Normalized
relevance
#1
#2 Legend:
#3 ++
Organizational
difficulty+
Absolute score O
Relative score □
Rank ∆
Strong correlation
Correlation
Strong relationship
Moderate relationship
Weak relationship
#1 #2 #3
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138
relevance index for each technology related event reveals the most important
technologies for the future. The time of occurrence reveals when technologies are
massively adopted, and therefore provide an idea of the most appropriate timing for
introducing technologies in the market. The second decision though, does not have a
direct answer and, thus, require some additional analysis.
Product and services technologies are the result of the convergence of different areas of
technical expertise. For instance, event number 7 (“The concept of modularity in
machine design is extended to adaptive production systems”) reflects competences in
machine design, mechatronics and process automation and integration. Event number 11
(“Massive adoption of virtual imaging technology for machine tooling and process
control”) reflects competences in ICT, industrial electronics and shares with event
number 7 competences in mechatronics. Therefore, technology related events portraying
the massive adoption of a certain technology cover at least one technological
competence. Additionally, different technology related events may share competences
too, as illustrated in Figure 5.8.
Technology
related event 1
Technology
related event 2
Technology
related event m...
Technology
competence 1
Technology
competence 2...
Technology
competence n
Figure 5.8 - Relationships between technology-related events and competences
These linkages need to be made explicit in the methodology. This can be done in the
“roof” of the adapted QFD matrix: a strong correlation (++) or correlation (+) between
technology related events that share competences is placed in the intersecting cells of
the roof. The rank of the most important technological competences is done by
summing the absolute importance scores of related technology related events. As such,
this provides basis for answering the question concerning which are the required
technological capabilities and competences.
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The proposed methodology differs from Cross Impact Analysis (Gordon, 1994a) since it
incorporates a multi-dimensional perspective about the relevance of an event (impact,
likelihood and urgency) instead of simply estimating probabilities of occurrence of an
event given the occurrence of another set of events. Moreover, the proposed method is
linked with the technology strategy formulation process. The application of this
methodology in the industrial partner of the thesis is described in the next sub section.
5.5 Methodology application
The method described in the previous section was applied using the results of the Delphi
survey described in section 5.3 as inputs. Through interviews with industrial partner’s
management and engineers that supported the realization of this study, information was
collected about the applications and the potential of the technologies depicted in the
survey, which in turn enabled the cross relationships assessments.
Events 26 and 27 were excluded from the analysis since they do not portray any specific
technology development but rather continuous improvement in technical attributes in
machines. Data collected from the survey was then used to calculate relevance indexes
for each event, which were then normalized for the technology and non-technology
related events. Same weights for each vector in the event relevance indexes were
applied. The cross relationship assessments between these two types of events were
performed using the legend in Figure 5.7: strong, moderate, weak or no relationship
(blank). A score of 9 points was used for a strong relationship, 4 points for moderate, 1
point for poor relationship and 0 point for no relationship. The absolute importance for
each technology related event is calculated by summing the multiplication of the
normalized relevance index of the technology, nontechnology related events –the
“interaction effect”- and the assessment relationships, for each. Then, a rank of the most
important technology events can be done, as depicted in Figure 5.9
Figure 5.9. According to this rank, the top 3 technology related events are events
number 7, 14 and 23 (please refer to Table 5.4).
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140
Figure 5.9 - Events relationship analysis
Event no.
Event
no.
Normalized
relevance
index
1 0.16
2 0.16
3 0.16
4 0.18
5 0.19
6 0.15
Organizational
difficulty
Absolute score
Normalized
score
Rank 12 159 2 5 18 19 161 13 7 8 6 17 1110 14 3 4
0.10 0.04 0.07 0.06 0.07 0.02 0.05 0.10 0.08 0.03 0.09 0.08 0.050.01 0.00 0.03 0.05 0.03 0.05
0.13 0.09 0.25 0.22 0.130.22 0.04 0.01 0.07 0.12 0.070.27 0.12 0.18 0.15 0.19 0.05 0.14 0.27
34 2 3 5 5 4 1 5 4 3 5 14 4 2 3 2 3
O O O ∆ □O O O ∆ □ ∆ □O □ □ O
∆ ∆ □ □ ∆ □ O□ □ □
□ □□ ∆∆ □ O ∆
O □ □ □ ∆ □ O □O □
□ ∆□ O O ∆ ∆ ∆O □ O O □ □O □ O O
∆ ∆ ∆ O □ ∆□ □ □
0.04 0.05 0.05 0.06 0.040.05 0.04 0.04 0.05 0.04 0.04
24 2514 15 16 17 18 19
0.06 0.05 0.05 0.06 0.05 0.04 0.05 0.05
207 8 9 10 11 12 13 21 22 23
+ ++ + + ++ +++ + ++ + + + ++ ++
+++ + + + +++ + ++ ++ +
+ + +
++ ++ +
++
++ ++ + ++ ++ ++
+ ++ ++ ++ + ++ +
++ + ++ ++ ++ +
++
++
+ + ++ + + ++
++ + ++ + ++ ++
+ ++ +
++ +
++ + + ++
++ + ++ + + +
+ + ++ ++ + ++
+ + ++ ++ ++
++ + ++ + +
++ + + ++
++ ++ + ++
++
++ + +
+ +
+ ++
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For the purpose of illustration, the justifications and explanations behind a number of
cross relationships analysis are described next.
Technology related event number 14 ("Hybridization (multiple processes in a single
machine) is adopted massively in sheet metal processing equipment") is strongly
favored by events number 3 and 6 ("Machine orders from low-labor-cost countries
involves greater automation" and "Imported and low-cost machinery faces difficulties
entering the European market"). It is expected that with a demand increase for
automation from countries with lower labor costs, that the market potential for machines
capable of performing multiple manufacturing processes, thus reducing the need for
operators, will also increase. Moreover, from the perspective of a company having
Europe as one of its main markets (as is the case of the industrial partner), greater
restrictions on the entry of low-cost competitors in this market strongly favors the
market for hybrid machines incorporating more traditional sheet forming processes.
Event number 23, in turn, is strongly favored by event number 3, because a higher
request for automation will increase demand for machines with active monitoring and
capacity for self-learning processing. Regarding event number 5, this type of technology
is only moderately favored because its ability to substantially reduce process waste still
needs to be demonstrated.
The “roof” in the adapted QFD matrix portrays the correlation between technology
related events, i.e., the degree to which they are related with common technological
competences. As mentioned earlier, there are nine technological competences regarded
as strategic for the sheet metal processing equipment industry (see Figure 5.6). In this
particular study, pairs of events that have two or more technological competences in
common are strongly correlated (++), one is correlated (+) and none is blank. Summing
the absolute scores of each event related to a technological competence provides a final
score, which then reflects its strategic importance, as shown in Table 5.5. The top 3
most strategic technological competences are “Process Automation and Integration”,
“Machine Design” and “Mechatronics”.
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Table 5.5 - Rank of strategic technological competences
A methodology that combines the Delphi method with QFD was developed with the
goal of assisting companies in identifying and prioritizing the most important future
events and most strategic technological competences. Its application in the industrial
partner of the thesis was described.
5.6 Conclusions
The external analysis activity has borrowed a number of tools from Technology
Foresight field in order to identify the future events of relevance for an industry and
likely technological trajectories. Due to its strategic importance for both private and
public sectors, the field of technology foresight is continuously leading to new
methodological developments. Either through standalone methods or more or less
complex combined methodologies, a considerable evolution is expected in the coming
years.
The Delphi method is one of the most popular technology foresight methods, given its
capability to provide holistic views about future developments. It is also capable of
providing strategic guidelines about the most important technologies of the future. But
this information inevitably needs to be complemented with deeper analyses, such as
adding other technology foresight methods (patent analysis, data mining, etc.) for more
Event no.Machine
designSensing Robotics Mechatronics
Process
automation
and
integration
ICT MaterialsOptics and
Photonics
Industrial
Electronics
7 X X X8 X X9 X X X X X X
10 X X X11 X X X12 X X X X X13 X X14 X X X15 X X X16 X X X17 X X18 X X19 X20 X21 X X X X X22 X X X X X23 X X X X24 X X25 X X X
Total score 1.3 0.6 0.3 1.2 1.6 0.7 0.5 0.5 0.7
Relative score 0.18 0.08 0.04 0.16 0.22 0.09 0.07 0.07 0.10
Rank 2 6 9 3 1 5 8 7 4
Technological competencies
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elaboration needed in R&D project plans. But, in situations when, either for financial
limitations or other relevant reasons, complementary studies can’t be afforded (a
recurrent issue found in smaller business), organizations still need structured ways and
analytical tools to enable them to make strategic decisions. In line with the emerging
paradigm named open foresight, a methodology combining the Delphi method and QFD
is proposed in this study, aimed to overcome such issues.
The events relationships analysis proposed in the adapted QFD matrix may not be
obvious for analysts, at a first sight. The assessment of the impact or influence that non-
technology related events may have on diffusion of technologies will always be subject
to the limitations inherent in qualitative and subjective judgments. A homogeneous and
transversal understanding among the company with respect to the relationships between
influential external factors and technologies is therefore necessary.
Although this was not implemented in this study, this approach could also be integrated
directly on the Delphi survey and thus benefit from a wider pool of knowledge. For
example, after providing their best guesses concerning the impact, likelihood of
occurrence and time of realization, experts could provide, on a second stage, their
assessment on the cross-relationships between events, possibly in a second round of a
typical Delphi, in order to avoid overloading experts with long surveys. Special care
should be given to the inclusion of both technology and non-technology related events
in the surveys, in order to portray the main contextual factors that influence technology
diffusion and thus enable the relationship analysis.
The holistic view concerning the dynamics of technology evolution of an industry,
supported by the methodology, can contribute to an intense organizational debate
around such complex theme, which is of critical relevance in the emerging paradigm of
foresight methodologies. Participants of such analysis generally come from different
departments, which often have conflicting views given their multi-disciplinary
backgrounds. As such, the methodology can contribute to a better homogenization of
the organizational understanding about the influence of external drivers on
technological diffusion.
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The methodology also provides a structure to the analytical process of investigating not
only future scenarios (the ‘external’ perspective), but also in the identification of
strategic technological competences and areas of knowledge (the ‘internal’ perspective)
that deserve careful attention by the organization.
Therefore, the presented methodology can serve as a background platform for
organizations to justify their investments in strategic technological competences, a
critical decision in technology strategy formulation. Conceptually, this study makes a
contribution towards a better linkage between technology foresight and technology
strategy. Further developments are expected resulting from the application of this
technique in other case studies.
CHAPTER 6
R&D project selection incorporating risk
Project selection is one of the most important stages in the technology strategy formulation
process, when decisions are made about the strategic guidelines of the organization for the
future. Resulting from extensive information gathering, analyses and discussions, R&D
projects are generated to address the challenges and opportunities ahead. Resource
limitations impede organizations from engaging in every project, so careful consideration
should be taken in the selection process to ensure that the most promising projects are
selected. The different types of R&D reflect different technology readiness levels, and can
serve multiple purposes: build new or nurture internal competences, develop new
conceptual models, test prototypes, develop technological systems and thus become
platforms for developing new products. Given their diverse nature, the different types of
R&D projects should be addressed separately in the selection process, and compared to
each other using appropriate criteria. R&D projects can also present uncertainty and risk,
since they aim at developing solutions with a degree of novelty. Current R&D project
selection methodologies, although addressing risk and uncertainty, do not take into
consideration different perspectives on risk, driven by the readiness levels of technologies
and the scale of R&D projects, therefore not contributing to a homogenization of
organizational policies towards risk management. Furthermore, project selection
methodologies provide no means for risk assessments made in the selection stage to be used
in later stages of project life cycle, for risk management and control purposes. In order to
address these issues, a new R&D project selection methodology that fills these gaps is
proposed. The proposed methodology is applied in the industrial partner of the thesis.
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6.1 Introduction
As a result of the strategic guidelines defined by internal and external analyses, by
information collected from multiple sources (scientific database, market reports,
competitors’ analysis, etc.) and by collective and creative efforts, a number of strategic
R&D projects are defined within an organization. Due to resources constraints,
organizations often use selection techniques in order to focus on the most promising
projects. Selection is an activity which receives the inputs from the previous three core
activities of the technology strategy process, as depicted in Figure 6.1.
Internal
Analysis
External
Analysis
Generation
Selection
Figure 6.1 – The Selection activity in the technology strategy process
This set of R&D projects can be of various types, depending on the strategic objectives
defined by the organization: competence building and nurturing, expansion of
technologies portfolio for future applications, increase of sales from launching newly
developed products or existing products with improved technologies, etc. (Chiesa, 2001,
Tidd et al., 2005). Therefore, careful consideration should be taken regarding strategic
objectives in different types of R&D projects during the selection process.
Furthermore, the project selection process, which is performed in the early stages of
projects life cycle, is clouded by uncertainties, either originated from incomplete
knowledge about the current shape of the market and the status of scientific and
technological development and/or from questions about which trajectories science and
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technology will follow (Wang et al., 2010). Risk and uncertainty are thus pervasive
throughout all stages of the projects’ life cycle. Decisions made at an early stage may
also undergo several changes throughout the projects’ execution. As such, the
incorporation of uncertainty in the project selection process, as well as a risk control
mechanism able to assist managers in dealing with uncertainty during the execution of
the selected projects, is therefore required.
The strategic nature and irreversibility of this type of investments has stimulated the
development of numerous R&D project selection methodologies. Although risk and
uncertainty has been incorporated in many of proposed methodologies, the
incorporation of risk assessment and control mechanisms early on the projects’ life
cycle based on a defined organizational policy towards risk has not been explored.
Taking into account this research gap, this chapter presents a new project selection
methodology that aims at addressing these issues, through the combination of various
existing tools and techniques. This methodology is applied in the industrial partner of
the thesis.
This chapter is structured as follows: section 6.2 provides a literature review on themes
related to R&D project selection and risk management practices; in section 6.3 the
development of a new methodology is described; the application of the methodology in
the industrial partner of the thesis is presented in section 6.5; and section 6.6 presents
the final discussions and conclusions of this chapter.
6.2 Literature review
This section is divided into two parts. The first presents a review on R&D project
selection methods, with emphasis on the methods that incorporate risk and uncertainty.
The second part presents, with greater detail, risk management tools and practices in
projects
6.2.1 R&D project selection
Executing every single candidate project generated from the strategic guidelines of the
organization is limited by the availability of resources. Investment in the development
of innovative technologies and products is widely recognized as one of the main sources
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for obtaining competitive advantages for organizations. Therefore, selection is a critical
activity in the technology strategy process formulation, because it enables organizations
to focus their efforts on projects that have more chances to succeed.
R&D project selection theme is, clearly, a subset of the project selection problem. And,
as expected, both problems share many traits. Notwithstanding this, some peculiarities
of R&D projects are discussed later in this section.
The topic of project selection or project portfolio selection – addressing the selection of
a group of projects from available projects and projects currently under execution - has
been discussed for decades. Its applicability extends beyond the borders of projects,
including technologies selection (Iamratanakul et al., 2008, Shakhsi-Niaei et al., 2011),
technology acquisition mode (Lee et al., 2009) and its corresponding mode in projects,
project execution mode. In a review on project portfolio selection, Archer and
Ghasmzadeh identified eleven propositions that should be addressed in the development
of an integrated methodology for project selection (Archer and Ghasemzadeh, 1999).
Among such propositions are:
consideration of internal and external business factors prior to project selection
to build strategic directions and focus;
organization into a number of stages to allow decision makers to move logically
towards an integrated approach to project selection;
avoid unnecessary data;
use of common measures, i.e., techniques and indicators that are applicable to
the type of projects under consideration, to ensure that project are compared
equitably during selection;
allow reviews or re-evaluations at milestones or gates of current projects at the
same time new projects are under consideration for selection;
screening should be used before selection, if necessary (i.e., too many projects);
projects dependencies should be considered in selection;
consider the time-dependent nature of project resource consumption, i.e.,
resource competition between projects to be selected and projects under
execution;
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enable controlling mechanisms that provide decision makers with feedback on
the consequences of changes and adjustments made on projects;
should be adaptable to group decision support environments and thus reflect
overall objectives of the organization.
Several project selection methods and techniques have been proposed in literature.
Traditional approaches were based on quantitative and economic tools, such as
discounted cash flow, net present value, return on investment (ROI) and payback period
(Liberatore, 1987). These methods have been criticized for providing one-dimensional
approach to project selection (Shakhsi-Niaei et al., 2011), thus leading to a myopic
decision process (Pinches, 1982). Recent publications have emphasized the importance
of including non-financial criteria into project selection (Meade and Presley, 2002b,
Martinez et al., 2011) in order to cover organizational, managerial, political, social,
environmental and other dimensions (Lopes and Flavell, 1998). In this domain,
subjective (and qualitative) criteria, which relies on managers’ experience, knowledge
and intuition (Tan et al., 2011) have been largely applied.
Operations Research field has contributed substantially to project management (and thus
selection) through mathematical modeling of complex decision problems (Tavares,
2002). Despite its undeniable contribution, some approaches have become so
mathematically intricate that necessitate the support of an expert decision analyst to be
used in practice (Henriksen and Traynor, 1999). Advances in computer technology and
improvements in the sophistication of models developed by academics have not yet
found wide acceptance by managers (Liberatore and Titus, 1983, Fahrni and Spätig,
1990, Shane and Ulrich, 2004).
Nevertheless, and due to the great interest in the area and wide range applicability, a
great variety of methods exist in literature and authors have attempted to cluster or
classify them according to their nature. One of the first classifications is proposed by
Baker and Freeland (Baker and Freeland, 1975). According to their classification, there
are three types of R&D project selection methods: comparative approaches, methods
where managers are supposed to compare project proposals against each other
(examples include Q-sort, ranking, rating, paired comparisons, standard gambles and
others); scoring models, methods based on a relatively small number of decision criteria
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used to assess the desirability of each alternative project proposal; and benefit
contribution models, where projects are evaluated according to their contributions to a
number of objectives or systems’ requirements, examples of such methods include
economic return, cost/benefit, risk analysis and relevance trees.
More recent classifications include the numerous methods applied to the R&D project
selection problem in the last four decades. According to Henriksen and Traynor, there
are eight categories, which are classified according to their underlying theory
(Henriksen and Traynor, 1999): unstructured peer review; scoring; mathematical
programming (integer programming (IP), linear programming (LP), nonlinear
programming (NLP), goal programming (GP) and dynamic programming (DP));
economic models (internal rate of return (IRR), net present value (NPV), return on
investment (ROI), cost-benefit analysis and option pricing theory); decision analysis
(multi attribute utility theory (MAUT), decision trees, risk analysis, and the analytic
hierarchy process (AHP)); interactive models (Delphi method, Q-sort, behavioral
decision aids (BDA), and decentralized hierarchical modeling (DHM); artificial
intelligence (AI) (expert systems and fuzzy sets); and portfolio optimization.
In another study, Iamratanakul et al. classifies project portfolio selection in six
dimensions: benefit measurement methods, mathematical programming approaches,
simulation and heuristics models, cognitive emulation approaches, real options, and ad
hoc models (Iamratanakul et al., 2008). In a brief critical review, the authors argue that
one methodology does not fit all project selection requirements since each methodology
has it owns advantages and disadvantages. The techniques used in each dimension are
portrayed in Figure 6.2.
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Figure 6.2 - A classification of project portfolio selection methods. Source: (Iamratanakul et al., 2008)
Verbano and Nosella adds another class of methods to Henriksen and Traynor’s
classification: strategic models, methods that use subjective input to take into account
multiple strategic aspects in R&D project selection, like the Boston Consulting Group
matrix and strategic buckets (Verbano and Nosella, 2010). In this same publication, and
based on an extensive review of previous studies on project selection methods, a set of
aspects is identified, that needs to be considered during R&D project selection. This
complements the propositions of Archer and Ghasmzadeh: evaluation of both economic
(quantitative) and strategic (qualitative) aspects; strategic coherence within a project
portfolio and interdependency analysis; risk and uncertainty analysis and evaluation of
method implementation characteristics.
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Among such aspects, uncertainty and risk are frequently cited as factors to be
considered in the project selection process (Fahrni and Spätig, 1990, Henriksen and
Traynor, 1999, Ghasemzadeh and Archer, 2000, Poh et al., 2001). The development of
new technologies and products are subjected to uncertainties and risks concerning the
achievement of technical and market goals. Therefore, risk should be managed
throughout all the R&D project stages in order to improve success rates (Wang et al.,
2010). Supporting this perspective, Chiesa argues that projects should be evaluated
according to their characteristics of relevance (or benefit) and risk (Chiesa, 2001).
Given the importance of the theme, a number of project selection methodologies
incorporating uncertainty and risk are reviewed next.
A considerable number of project selection methods that incorporate risk belong to the
class of complex optimization models: Heidenberger presents a mixed integer linear
programming (MILP) model for dynamic project selection and funding problems under
risk, with multiple resources with different qualifications (Heidenberger, 1996);
Medaglia et al. propose an evolutionary method named stochastic parameter space
investigation (PSI) to address the project selection problem with partial funds, multiple
(stochastic) objectives, project interdependencies and resource constraints (Medaglia et
al., 2007); Solak et al. present a multistage stochastic integer model with endogenous
uncertainty for dynamic optimization of project portfolios over a planning period (Solak
et al., 2010); a stochastic optimization model for project portfolio selection is proposed
by Gutjahr and Froeschl, which considers uncertainties about real efforts for the work
packages contained in the projects (Gutjahr and Froeschl, 2013).
Other studies use stand-alone methods that address the dynamic nature of environmental
factors that influence R&D project selection decision process. Fox and Baker use
simulations on a number of selected variables, which are included in two models: the
profitability and project generation models (Fox and Baker, 1985). The outputs of these
two models feed a third one, the decision model, where projects are selected according
to their expected contribution to profitability. A dynamic multi attribute utility decision
model based on simulations made on three project attributes (technological risk, market
risk and economic benefits) is proposed by Zhong el al. (Zhong et al., 2010). A Data
Envelopment Analysis (DEA) model is presented by Ghapanchi et al. that take into
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account project interactions and uncertainties, modeled as fuzzy variables (Ghapanchi et
al., 2012).
More complex frameworks that combine different methods have also been applied. For
example, Gabriel et al. argue that project selection under uncertainty should incorporate
multiple criteria and probabilistic components. As such, they propose a multiobjective
optimization model that maximizes projects ranks (modeled previously via Analytic
hierarchy process - AHP, a multiple criteria method) and minimizes cost distributions,
modeled with Monte Carlo simulations (Gabriel et al., 2006). Another example is
provided by Shakhsi-Niani et al., that uses another multiple criteria method, the
Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)
method (Brans and Vincke, 1985), embedded into a Monte Carlo simulation framework
to rank projects under uncertainty, to analyze the probabilities of achieving different
ranks in each project and the impact of these uncertainties in the final ranking (Shakhsi-
Niaei et al., 2011).
The consideration of strategic factors in conjunction with economic factors has been
largely addressed via multiple criteria and scoring methods. Liberatore presents an AHP
model that links the mission, objectives and strategy of business with criteria used to
select R&D projects (Liberatore, 1988). Henrikssen and Traynor propose a simple
scoring method that accounts with tradeoffs among evaluation criteria through a value
index algorithm that produces a measure of project value (Henriksen and Traynor,
1999). Meade and Presley applies a more generic version of the AHP, the Analytic
Network Process, that considers interrelationships among decision levels and attributes
(Meade and Presley, 2002b). Unlike Henrikssen and Traynor’s scoring model that uses
the same criteria, but with different relative importance for different categories of R&D,
Lawson et al. proposes a scoring model that considers different criteria for different
types of R&D (Lawson et al., 2006), namely basic research, applied research and
experimental development.
The need to consider different R&D project types in the selection process is supported
by many authors (Mitchell, 1990, Coldrick et al., 2005, Tidd et al., 2005, Lawson et al.,
2006, Verbano and Nosella, 2010). According to the Frascati Manual, a document
published by the Organization for Economic Co-operation and Development (OECD)
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that provides the guidelines for collecting statistics about research and development,
there are three types of R&D projects (OECD, 2002):
Basic research: “experimental or theoretical work undertaken primarily to
acquire new knowledge of the underlying foundations of phenomena and
observable facts, without any particular or use in view” (OECD, 2002, p.77);
Applied research: “original investigation undertaken in order to acquire new
knowledge. It is, however, directed primarily towards a specific practical aim or
objective” (OECD, 2002, p. 78);
Advanced technology or experimental development: “systematic work,
drawing on knowledge gained from research and practical experience, that is
directed to producing new materials, products and devices; to installing new
processes, systems and services; or to improving substantially those already
produced or installed” (OECD, 2002, p. 79).
Criteria should be used according to the expected objectives of each project type: early
stage or basic research comprise projects aimed for knowledge building (Tidd et al.,
2005) into areas that can generate future opportunities or threats. Applied research and
advanced technology or experimental development is aimed at testing the feasibility of
early prototypes and versions of technological systems. At this point, possible
applications can be envisioned and thus, market analysis start to play an important role.
Extending beyond this classification, there is another project type, related to business
investments in new products, services and processes, with success criteria depending on
meeting the needs of target groups of users (Tidd et al., 2005). In this thesis, emphasis
is given to product development projects.
Selection criteria for basic research are subjective in nature, while later stages of
development require more pragmatic approaches, more related to expected economic
benefits. Therefore, greater preference has been given to scoring and multiple criteria
methods, which take into consideration qualitative factors, in earlier stages. More
quantitative methods are preferred as market and economic factors become more critical
in later stages, although strategic factors should not be ignored in any way (Chiesa,
2001).
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Perceptions of uncertainty and risk also differ in each type of R&D project: as suggested
by Anderson and Nolte, the technology readiness levels (TRL) or maturation rate of a
technology drives the focus of risk management activities (Anderson and Nolte, 2005).
TRL is a scale developed in the mid-1970s by the National Aeronautics and Space
Administration (NASA) to allow a more effective assessment and communication
regarding the maturity of new technologies (Mankins, 2009). This scale is closely
related to the well know classifications of basic research, applied and technology
development, as depicted in Figure 6.3.
Figure 6.3 - Overview of the technology readiness level scale. Source: (Mankins, 2009)
Perceptions of risk and uncertainty change depending on the magnitude of projects
(Tidd et al., 2005). Basic research projects are reasonably low budget projects, and
“often treated as necessary overhead expense” (Tidd et al., 2005, p.222). Applied
research and technology development projects require greater investments in the
development and feasibility tests of prototypes and technological systems. Product
development projects require investments of an even greater order of magnitude, which
includes the industrialization and commercialization of products. As the investments
levels increase, the perception of risk changes accordingly, since the impact of not
achieving expected technical and market goals increases.
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The various R&D project selection methods reviewed in this section, despite addressing
critical aspects of risk, uncertainty and interdependency of projects, somehow ignore the
integration of risk assessment control mechanisms, which could be based on a defined
organizational policy towards risk. If such integration could be achieved, one could
provide feedback information to managers on the consequences of adjustments
performed in projects and promote a greater homogenization of risk perspectives in the
organization. This could allow continuous re-evaluations, and consider the impact of
selecting new projects in ongoing projects, mentioned by Archer and Ghasmzadeh as a
critical requirement for effective project selection methodologies (Archer and
Ghasemzadeh, 1999). These approaches not only include evaluations performed at early
stages of projects, but also continuous evaluations throughout the lifecycle of projects at
key points, milestones or gate reviews (Cooper, 1990). The integration of front end
activities of projects or “ideation” into portfolio management is mentioned by Heising
as an important factor for sustainable success (Heising, 2012).
Among the various methods for projects’ continuous evaluations are risk management
processes. This type of process also has advantages over others that use deterministic
criteria or indicators, because it recognizes uncertainty as intrinsic to achieving
technical goals and to rapidly changing environments. In fact, considering risks in the
earlier stages of the project life cycle provide managers with more time to act upon risks
(Institute, 2008). None of the reviewed methods present a comprehensive methodology,
which incorporates a risk management process early on project selection stage, that
enables different risk perspectives to be incorporated, and a controlling mechanism that
provides feedback information with respect to changes in risk throughout the execution
of the project. The methodology presented in this chapter aims at addressing these gaps.
Prior to presenting the steps that led to the development of the methodology, project risk
management processes are reviewed in the following section.
6.2.2 Risk management processes
The execution of a project aimed at delivering something new, either a theoretical or
experimental development, a practical application of a concept, a prototype, entire
technological systems or products is inevitably subjected to a certain degree of risks.
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Every type of R&D project and product development project is exposed to risks related
to not achieving specified project goals (duration, budget and quality, namely). In the
specific case of product development projects, because of the dynamic business
environments, there is also the risk of not addressing changing customers’ needs, or
market risk as mentioned by Unger and Eppinger (Unger and Eppinger, 2009).
Although the concepts of risk and uncertainty are often used interchangeably, they are
not synonymous. The researcher adopts the view according to which risk involves
situations where the probability of a particular outcome is known, and uncertainty
occurs in situations when the probability is not known (Horne, 1966). Furthermore, it is
considered that while uncertainty may not necessarily result in undesirable
consequences, risk, on the other hand, is always negative and is manifested in an
unsatisfactory consequence (Lefley, 1997). Recently, a number of authors are
suggesting the incorporation of uncertainty management processes in order to improve
project management performance (Ward and Chapman, 2003, Atkinson et al., 2006,
Perminova et al., 2008). They argue that current risk management processes have solely
focused on managing threats originated from risk, and a more balanced approach to
opportunity and threat management, via uncertainty management, would support
organizations in restricting negative impacts from threats and to leverage positive
impacts originated from opportunities. Despite being a topic of recognized relevance to
project management, uncertainty management processes are still in its infancy.
Therefore, this thesis is focused in risk management processes, without completely
ignoring the role of uncertainty in projects though.
Project risk management processes are defined by the Project Management Institute’s
(PMI) standard Guide to the Project Management Body of Knowledge as the process of
conducting risk management planning, identification, analysis, response planning, and
monitoring and control on a project (Institute, 2008). Its objectives are to increase the
probability and impact of positive events, and to decrease the probability and impact of
negative events in the project. The adequacy of company-wide education on the
concepts of risk management, risk register and risk management plans, and maturity of
an organization’s processes for assigning ownership of risks, are among the success
factors in project management (Cooke-Davies, 2002). An empirical research conducted
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on 176 firms suggests that the integration of risk management into project portfolio
management as having positive impact in risk coping capacity and portfolio success
(Teller and Kock, 2013). Another survey with 84 project managers from the software
and high-tech sectors also revealed that risk management contributes to meeting project
schedules, budget and planned objectives and achieving customer satisfaction (Raz and
Michael, 2001). The importance of project risk management is also supported by the
fact that it belongs to the nine knowledge areas of PMI’s Project Management Body of
Knowledge (PMBOK).
Risks can jeopardize the successful completion of a project, and is formally defined as
the likelihood of an event along with its negative consequence (INCOSE, 2006). There
are four main categories of risk, which are closely related to each other, as portrayed in
Figure 6.4.
Figure 6.4 - Relationships between risk categories. Source: (INCOSE, 2006)
Technical or performance risk is defined as the possibility that a technical or
performance requirement or output of a project may not be achieved; cost risk is the
possibility that available budget or funds set for a project will be exceeded; schedule
risk is the possibility that a project will fail to meet scheduled milestones and duration.
Programmatic risk is produced by events that are beyond the control of the project
manager, normally from decisions made by people with higher level of authority, for
example the reduction in project priority, delayed authorizations and funds, and many
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others. As such, programmatic risks can be a source of risk in any of the other three
categories of risk.
Other forms of risk may exist, for example, risks involved in the collaboration with
project partners, such as the inadequacy of complementary competences, lack of
coordination and others. Any of these risks are expected to increase
technical/performance, cost and schedule risks. A comprehensive categorization of
project risks is, thus, unfeasible, but these four categories are useful for project planning
and controlling purposes (Unger and Eppinger, 2009).
The PMBOK identifies six core activities in the risk management process:
Plan risk management: the process which defines how to conduct risk
management activities for a project, ensuring visibility of the risk management
process, sufficient time and resources and an agreed approach for evaluating
risks;
Identify risks: determination and documentation of the risks that may affect the
project. It is an iterative process since new risks may evolve or become known
along the execution of the project;
Perform qualitative risk analysis: the process where risks are prioritized for
further analysis or action, using their relative probability or likelihood of
occurrence and their impact on project objectives;
Perform quantitative risk analysis: the process of numerically analyzing the
effect of identified risks on overall project performance and objectives, related to
a quantitative approach for decision-making in the presence of uncertainty;
Plan risk responses: the process of developing options and courses of actions to
leverage on opportunities and reduce threats to project objectives, which follows
the qualitative and quantitative risk analysis (if used). The process also includes
the designation of one person (the “risk response owner”) to take responsibility
for each agreed-to and funded risk response;
Monitor and control risks: the process of implementing risk response plans,
tracking identified risks, monitoring residual risks, identifying new risks, and
evaluating the risk process effectiveness throughout the project.
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These processes interact with each other and with the other nine knowledge areas of
PMBOK. A more recent international standard provides additional contributions to this
area: the ISO 31000 “Risk management – Principles and guidelines” (Standardization,
2009a). Despite having many similarities with the process from PMBOK, the ISO
31000 standard observes the risk management process in isolation, thus providing an
easier to understand approach. The process is constituted of seven activities. These
activities and their relationship structure in ISO 31000 are portrayed in Figure 6.5.
Tools and techniques for each activity of the sub process named risk assessment, are
identified in another document from the same family of standards, the Risk management
– Risk assessment techniques (Standardization, 2009b).
Figure 6.5 - Risk Management process. Source: (Standardization, 2009a)
Different and various tools have been used for each phase of the risk management
process. Table 6.1 presents the risk management processes in ISO31000 and PMBOK,
the tools recommended for each activity.
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Table 6.1 - Risk management tools in PMBOK and ISO 31000. Sources: (Standardization, 2009b)
(Institute, 2008)
Tools PMBOK ISO 31000
PRM IR PQlRA PQnRA PRR MCR RI RA RE
Planning meeting and analysis X
Documentation reviews X
Brainstorming, Delphi technique,
Interviewing, X X
Root cause analysis X X X
Checklists analysis X X
Assumptions analysis X
Cause and effect diagrams X X X
System or process flow charts, influence
diagrams, SWOT analysis X
Expert judgment X X X X
Risk probability and impact assessment,
Risk data quality assessment, Risk
categorization, Risk urgency assessment
X
Probability and impact matrix X X X X
Probability distributions, Sensitivity
analysis, Expected monetary value analysis X
Modeling and simulation X X
Strategies for negative risks or threats
(avoid, transfer, mitigate, accept), Strategies
for positive risks or opportunities (exploit,
share, enhance, accept), Contingent response
strategies
X
Risk assessment, Risk audits, Variance and
trend analysis, Technical performance
measurement, Reserve analysis and Status
meeting
X
Primary hazard analysis, Sneak circuit
analysis X
Hazard and operability studies (HAZOP),
Hazard Analysis and Critical Control Points
(HACCP), Environmental risk assessment,
Structure “What if?” (SWIFT), Scenario
analysis, Business impact analysis, Failure
mode effect analysis, Layer protection
analysis (LOPA), Cost/benefit analysis,
Multi-criteria decision analysis (MCDA),
Risk indices, FN curves, Reliability centered
maintenance, Fault tree analysis, Human
reliability analysis
X X X
Event tree analysis, Markov analysis X X
Decision tree, Bow tie analysis, Bayesian
statistics and Bayes Nets X X
Legend: PRM – Plan Risk Management, IR – Identify Risks, PQlR – Perform Qualitative Risk Analysis,
PQnR – Perform Quantitative Risk Analysis, PRR – Plan Risk Responses, MCR – Monitor and Control
Risks, RI – Risk Identification, RA – Risk Analysis, RE – Risk Evaluation.
The risk management process described in PMBOK and ISO 31000 share some
common activities, namely regarding the identification of risks, the treatment of risks or
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risk response actions and monitoring and review. Other activities have no corresponding
activity in the other risk management process. In PMBOK process, the plan risk
management activity addresses how risk will be managed internally, using for this
purpose a series of documents from other areas of the PMBOK system, defining the set
of tools to be used, data sources, roles and responsibilities, risk categories, budgeting
and timing for the risk management process. On the other hand, establishing the context
activity from ISO 31000, although also addressing how risk will be managed in the
organization, deals with this process in a broader sense, considering internal
(capabilities, information flows, values, culture, etc.) and external (cultural, political,
legal, regulatory and other drivers) parameters relevant to the organization, and the
definition of risk criteria in the process, such as risk acceptance thresholds, nature and
types of impacts, the way probabilities are to be expressed and others.
Risk analysis activity in ISO 31000 is also a broader process, that considers quantitative,
semi-quantitative and qualitative analyses. Quantitative and qualitative analysis in
PMBOK are placed separately, but objectives and purposes are the same as in
ISO31000. The communication and consultation activity is a continuous activity in the
ISO31000 that deals with the development of a communication plan, and is related to all
other activities in the process, while in PMBOK the development of a communication
management plan is one of the inputs of the risk management process.
Table 6.2 - Risk management processes and selected examples from the literature.
PMBOK ISO 31000 Selected examples from the
literature
- Establishing the Context
Plan Risk Management - -
Identify Risks Risk Identification
Perform Qualitative Risk
Analysis Risk Analysis
(Cagno et al., 2007)
Perform Quantitative Risk
Analysis
(Browning, 1998, Wang et al., 2010,
Dey, 2010)
- Risk Evaluation
Plan Risk Responses Risk treatment (Ben-David and Raz, 2001)
(Seyedhoseini et al., 2009)
Monitor and Control Risks Monitoring and review (Kujawski and Angelis, 2010)
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As this literature analysis clarifies, risk assessment is a central issue in project
management. Table 6.2 presents a number of publications that propose methods for each
of the activities portrayed in both risk management processes. Significantly no reviewed
methodology was found which assesses risk in the context of different types of R&D
projects, in the project selection stage, and that provides a link between these early
assessments and risk control and monitoring activities throughout the execution of the
project. It is the researcher’s understanding that such a methodology can provide
valuable assistance to project managers in three areas:
1. consideration of different types of R&D: R&D projects types are characterized
not only by different scopes and objectives, but also by different orders of
magnitude with respect to the duration, cost and quality perspective. For
example, a 1% cost overrun in a basic research project is not the same as a 1%
cost overrun in the Product Development project, given the different levels of
investment of each project (much higher in Product Development projects). A
risk assessment that takes into account risk perspectives in different types of
R&D thus provides a more equitable comparison between projects;
2. risk assessment in project selection: the sooner the risk assessment is made,
more time project managers will have to develop appropriate risk response plans
and mitigate their effect. Furthermore, risk identification and analysis made in
the project selection phase enables risk to be also considered one of the selection
criteria;
3. risk monitoring and control system: linking risk assessments to a control system
enables risk monitoring and control throughout the execution of a project. It also
allows managers to assess how accurate risk estimates made at an early stage of
the projects life cycle (the project selection phase) were, and thus they can
“calibrate" their risk analyses in future projects.
The following section presents the steps taken in the development of a new
methodology that aims to address these gaps.
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6.3 Methodology development
In this section, the building blocks of a new methodology for R&D project selection that
incorporates risk assessment, management and control are presented. This new
methodology incorporates a considerable number of tools, and in order to speed up the
calculations and facilitate its implementation in real settings, software written in VBA
language for Microsoft Excel® was developed.
The new methodology, in addition to incorporating risk, shall meet the critical
requirements for an integrated project selection methodology, proposed by Archer and
Ghasemzadeh, and Verbano and Nosella (Archer and Ghasemzadeh, 1999, Verbano and
Nosella, 2010), which were described earlier in the literature review section. In order to
remind the reader, they are summarized below:
ensure strategic (qualitative) coherence by acknowledging both internal and
external business factors, along with the implications of economic factors
(quantitative) in project selection, where appropriate;
use indicators and criteria that are suitable for the type of R&D project under
consideration, to ensure a more equitable comparison during selection;
organization in a number of stages to enable decision makers with a logical
approach for project selection;
reflect the overall objectives of the organization and perspectives on risk for
different types of R&D;
consider the interdependency between projects;
reflect the effects on resource competition;
incorporate risk controlling or re-evaluation mechanisms at milestones or gates
of projects.
In order to provide a clear description about the methodology development process, the
text that follows is divided into three sections: “Criteria and information requirements”
describing the process by which project selection and execution mode criteria is
mapped; “Risk assessment and management” providing an understanding of the
methods used in incorporating risk assessment and management early in the project
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selection phase and “Methodology for R&D projects selection incorporating risk
management” presenting an overview of the integrated methodology.
6.3.1 Criteria and information requirements
The project selection methodology proposed in this chapter incorporates the three types
of R&D projects, as defined previously: basic research, applied research and advanced
technology development. A fourth type of project is considered, namely product
development, related with development, industrialization and launch of new products.
Beyond selection, another important decision of the technology strategy process, with
clear implications to R&D projects, is related to deciding on the technology acquisition
mode. It is argued that this type of decision is entirely relevant to the project selection
process, since it is intrinsically related to the characterization of projects (cost, duration,
roles and responsibilities, etc.) and, therefore, to the risks involved. Surely, the term
"technology acquisition mode" seems more suitable for advanced technology
development type of R&D projects. In order to extend its meaning to other types of
R&D and to product development, it will be referred hereafter as a project execution
mode. Thus, the proposed methodology considers, in addition to the project selection
decision, the project execution mode decision.
A trend in decision analysis announced four decades ago concerns the transition from
"decision models" towards "decision information systems" (Baker and Freeland, 1975).
There are two reasons for this, as Baker and Freeland pointed out: models are often
incomplete, ignoring important aspects of the R&D environment, which then forces
managers to constantly make adjustments to account for the numerous environmental
conditions not included in the model. The second reason is related with the decision
problem itself, characterized by multiple criteria, many of which are not easily
quantifiable. This requires extra attention in information flows that feed project
proposals at project selection stage, to enable a more transparent and clear comparison
between candidate projects. Nowadays, with the advancement of information
technologies, which enable substantial productivity gains in the management of
information flows, and the importance of knowledge in innovation performance, this
trend becomes even more relevant.
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The information requirements for candidate projects were mapped using a review on the
criteria used in different models proposed in the literature. Thus, one expects to find the
necessary information, whether of quantitative or qualitative nature, to be included in
the characterization of the projects, in a project proposal document, in order to make the
comparison between projects a more transparent task. The relationship between criteria
considered in the methodology and the information contained in the project proposal
document is found in Appendix 4.
Although some studies do not differentiate criteria according to the type of R&D and
product development project (Meade and Presley, 2002b, Henriksen and Traynor,
1999), it is understood that only projects of the same type can be compared against each
other, using adequate criteria for this purpose, as supported by Tidd (Tidd et al., 2005).
Therefore, emphasis was given to publications that used different criteria according to
the type of R&D project considered.
A review on decision criteria for mapping information needs in project execution mode
decision was also performed. The following sections present an analysis conducted on
decision criteria used in project selection and project execution mode.
6.3.1.1 Project selection criteria
The purpose of this section is to identify the most frequently mentioned themes used as
criteria in decision models for selection of different types of R&D projects and product
development. Emphasis was given to publications where criteria were used for each
type of R&D involved, and for product development projects. While acknowledging
that criteria choice in these publications may take into account intrinsic factors to
organizations, it is observed a number of generic themes across these studies, i.e., the
criteria do not differ much from study to study. Those generic themes are embedded in
the methodology as default or built-in criteria, but flexibility is ensured in the way that
managers can delete, add and modify criteria if required. Such flexibility is incorporated
in the software developed for this methodology.
Criteria designed for basic and applied research projects are not abundant in literature.
However, two publications were found that cite specific criteria to these types of
projects.
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Table 6.3 - Review on basic research project selection criteria
Publication, criteria and sub criteria (in parenthesis)
Publication: (Chiesa, 2001)
Criteria: Strategic relevance (Relation with core technologies of the firm, The range of applicability of the project
results, Consistency of the project objectives with business, Relevance of the business(es)); Expected benefits
(Potential applications, Creation of a base of knowledge, Impact on other projects); Time and costs (Project duration,
Project costs); Resource adequacy (Project leadership, Team feasibility, Access to external source); Soundness
(Feasibility, Technical strengths of the project, Peer reviews); Originality (Newness, Patenting); Project definition
(Potential benefits, Soundness of the theoretical background, Awareness of the current knowledge, Project
programming).
Publication: (Coldrick et al., 2005)
Criteria: Technical (Technical risk to project completion, Technical resource availability); Corporate and strategic
(Fit with company business plan, Product range growth potential, Synergy with other products/processes); Regulatory
(Risk in obtaining regulatory clearance, Ability to meet likely future regulations).
As can be seen in Table 6.3, basic research project selection criteria clearly emphasize
the contribution of projects to enhance the knowledge base of the organization, and the
strength of the scientific and theoretical background of the research. Criteria related to
market is not mentioned, since it is a very early stage phase of research. To foresee any
business application at this stage is almost impossible. Other themes are related to the
capability of the organization, reflected in the familiarity with the research topic and
resources (competences, equipment, etc.) to conduct the research, and strategy, namely
concerning the fit with the business strategy of the organization, in observable trends
and their urgency. Project development issues, such as programming or programmatic
risks, interdependencies/synergies with other projects, project risks, and duration and
costs are also cited.
Applied research aims at testing the applicability of theoretical concepts, through early
versions of prototypes, models and devices. Therefore, potential technologies arising
from such applications can be evaluated as well as their patentability. Possible benefits
from standard setting with other compatible technologies, for example in opening new
markets and raising barriers against competitors, should also be included as a criterion
(see Table 6.4).
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Table 6.4 - Review on applied research project selection criteria
Publication, criteria and sub criteria (in parenthesis)
Publication: (Chiesa, 2001)
Criteria: Strategic relevance (Strategic importance of technological area concerned, Range of applicability of project
results, Benefits to the firm's positioning in the business, Relevance of the business(es) where the project results
would be utilized); Economic relevance (Revenues, Costs, Return on investment, Probability of commercial success);
Time-to-market; Robustness (Normative factors, Technological factors, Economic factors, Indirect factors, Industrial
benefits, Environment benefits, Scientific benefits); Resource adequacy (Project leadership, Team specialization,
Integration of R&D with other functions, Availability and appropriateness of the equipment); Soundness and
originality of idea (Technical feasibility, Originality); Project definition (Clarity of the final objective, Clarity of the
intermediate objectives, Market benefits, Patenting); Engineering (Criticality of resources needed in the engineering
phase, Constraints to the industrial exploitation, Firm's strength in the technologies used in the exploitation phase,
Industrialization experience, Transfer to manufacturing and scale up); Willingness to exploit project.
Publication: (Coldrick et al., 2005)
Criteria: Technical (Technical risk to project completion, Technical resource availability); Corporate and strategic
(Fit with company business plan, Product range growth potential, Synergy with other products/processes); Regulatory
(Risk in obtaining regulatory clearance, Ability to meet likely future regulations).
Possible applications of such systems can raise interest in a number of markets as well,
unlike what happened with basic research projects. At this level, market analysis, which
includes knowing the markets size, growth rates, customers’ needs and competitive
intensity, is still broad, meaning that a wide range of applications can be envisioned.
Therefore, market analysis is still more judgmental than pragmatic or quantitative.
As with basic research project selection criteria, themes related to strategy, capability
and project development should also be included.
Advanced technology developments projects bring early prototypes and devices to a
more mature state, likely to be incorporated in a product. Therefore, rather than
assessing potential technologies, as in applied research, in advanced technology
development the assessment should focus on potential products. Furthermore, the stage
in the life cycle of technology - a process that describes the diffusion process of a
technology, normally divided in emerging, mature and in declining technologies - is an
important criterion to evaluate the degree of innovativeness of the technology to be
developed (see Table 6.5).
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Table 6.5 - Review on advanced technology development project selection criteria
Publication, criteria and sub criteria (in parenthesis)
Publication: (Chiesa, 2001)
Criteria: Relevance of the technology (Market potential, Applicability, Customer value creation); Risk associated
with the development of the technology (Technical risk, Commercial risk, Financial risk); Appropriability (Secrecy,
Accumulated tacit knowledge, Lead times and after-sale service, Learning curve, Complementary assets, Product
complexity, Standards, Pioneering radical new products, Strength of patent protection).
Publication: (Coldrick et al., 2005)
Criteria: Technical (Technical risk to project completion, Technical resource availability); Corporate and strategic
(Fit with company business plan, Product range growth potential, Synergy with other products/processes); Regulatory
(Risk in obtaining regulatory clearance, Ability to meet likely future regulations).
Publication: (Cooper and Robert, 2006)
Criteria: Business strategy fit (Congruence, Impact); Strategic leverage (Proprietary position, Platform for growth,
Durability, Synergy with corporate units); Probability of technical success (Technical gap, Project complexity,
Technology skill base, Availability of people and facilities); Probability of commercial success (Market need, Market
maturity, Competitive intensity, Commercial applications development skills, Commercial assumptions, Regulatory
and political impact); Reward (Contribution to profitability, Payback period, Time to commercial start-up).
Publication: (Shehabuddeen et al., 2006)
Criteria: Technical (Quality, Reliability, Flexibility, Repeatability, Volume); Financial (Capital, Sales, Renewal,
Operation); Pressures (Environmental, Regulatory, Standards); Integrability (Compatibility, Impact); Usability
(Usefulness, Utilization); Supplier Suitability (Service, Integrity, Partnership); Strategy Alignment (Support,
Compatibility); Risk (Operational, Technological, Commercial).
Publication: (Huang et al., 2008)
Criteria: Competitiveness of technology (Proprietary technology, Key of technology, Innovation of technology,
Advancement of technology); Relevance of technology (Technological extendibility, Technological connections,
Generics of technology); Economic benefit (Technology spillover effects, The potential size of market, Improvement
on research capability); Social benefit (Improvements on quality, quality, environmental protection, industrial safety,
national image and industrial standards, Coincidence with Science and Technology policy, Benefits for human life,
The contributions to the state of knowledge); Quality of technical plan (Content of technical plan, Capability of
research team, Reasonableness for research period, Reasonableness for research cost, Environmental and safety
consideration); Availability of resource (Technical resource availability, Technical support, Equipment support);
Technical risk (Opportunity of technical success, Evidence of scientific feasibility, Specification of technology);
Development risk (Risk for development cost, Risk for time cost, Timing for project); Commercial risk (Opportunity
of market success, Opportunity of project result implementation).
Publication: (Shen et al., 2010)
Criteria: Technological merit (Advancement of technology, Innovation of technology, Key of technology,
Proprietary technology, Generics of technology, Technological connections, Technological extendibility); Business
effect (Potential return on investment, Effect on existing market share, New market potential, Potential size of
market, Timing for technology); Technology development potential (Technical resources availability, Equipment
support, Opportunity for technical success); Risk (Commercial risk, Technical risk, Technical difficulties).
Publication: (Davoudpour et al., 2012)
Criteria: Market (Span of applications opened by technology, Potential of commercialization, Supporting national
related strategies); Competitiveness (Key of technology, Competitive situation in market, Added value); Technical
factors (Position of the technology in its own life-cycle, Threat of substitution technologies, Ability to result in
technical know-how, Ability to use international cooperation potentials); Capability (Alignment with organization
objective and capability, Value of laboratories, Successful experience accumulated in the field, Registered patents,
Value of equipment); Environmental factors (Impact on environmental factors and energy consumption
improvement).
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Along with these factors and since the maturation rate of the technology is higher,
considerations about the market are even more important at this stage, as the range of
possible applications is narrowed when compared to applied research. Project
development issues such as estimated cost and duration becomes critical given the
larger scale of investment. Interdependencies/synergies with other projects and
programmatic risks should also be considered.
Product development projects aims at bringing technological innovations to the market,
in the form of new products. As such, considerations about the product to be developed
should be included as criterion, namely the degree of product differentiation and
product range growth potential (see Table 6.6).
Table 6.6 - Review on product development project selection criteria
Publication, criteria and sub criteria (in parenthesis)
Publication: (Liberatore, 1988)
Criteria: Manufacturing (Capability, Factory/equipment); Technical (Probability of success, Costs, Time,
Resources); Market/distribution (Potential, Capability, Trends); Financial (Profit, Capital investment, Unit cost).
Publication: (Henig and Katz, 1996)
Criteria: Size of existing market; Competition; Competitive advantage; Patentability; Efficacy; Capability of
development; Production; Cost of development; Time to completion; Toxicity.
Publication: (Calantone et al., 1999)
Criteria: Fit with core marketing competences (The product matches the desired entry timing needed by our target
segments, The product will be priced at or below price points for our target segments, The product fits with our
logistics and distribution strengths, The products fits with channels of distribution where we have strength, The
product fits with current product lines, The product fits our sales force coverage, training, and compensation plans.);
Fit with firm’s core technological competences (The product gives the customer a differential advantage or benefit,
The manufacturing speed will match demands, The product is designed for quality needed by target segments, The
product uses materials of high quality and low rejection, The product fits with our best manufacturing technology,
The product allows us to use the very best suppliers); Total dollar risk profile of the project (Total dollar payoffs in
net present value, Total dollar costs in net present value); Overall management uncertainty about project’s outcomes
(Percentage of loss that cannot be addressed by research, Research and intelligence mitigated uncertainty).
Publication: (Oh et al., 2012)
Criteria: Financial contribution (Net present value, Cost, Revenue, Sales, Quantity); Strategic importance (Fit with
key initiatives and priorities, Innovation related to market, Core competence development); Commercial potential
(Base net present value, Gross profit margin, Use base growth, Proof of concept, Product, process and clinical
development, Intellectual property); Commercial risk (Competitive positioning at launch, Customer preference,
Operational leverage).
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Considerations about the technology(/ies) to be incorporated in the product design
should not be ignored. Patentability and benefits from standards setting might have
greater importance than in previous R&D projects, so as to ensure that the full business
potential of the product can ben grasped. The stage of technology(/ies) life cycle should
also receive greater attention if the objective is to develop a product with a high degree
of innovativeness, i.e., consisting of emerging technologies.
The market(s) where the product will be launched are known at this stage. The timing of
introduction in the market is an important criterion, so managers can assess whether the
expected timing for launching the product is appropriate, since customers’ needs may
change over time.
Product development involves considerable investments, not only in the development of
the product itself, but in industrialization, logistics and distribution networks and in
promotional efforts, such as in fairs and exhibitions. Thus, it requires more rigorous
quantitative criteria, mostly related to the economic benefits of such project, which can
be done using metrics such as net present value (NPV), payback period and internal rate
of return (IRR). As with the other types of R&D projects, strategic issues and project
development should be included as criteria. The capability of the organization should
not only emphasize the resources and competences to develop the product, but also
include the adequacy of complementary assets (Teece, 1986), i.e., the necessary
infrastructure and capabilities to support the production and commercialization of
products, such as appropriate manufacturing equipment, distribution channels, after-
sales services and others.
A recurrent theme in criteria used in the various publications analyzed is associated with
the risks involved in the project, which further reinforces the need to consider the risk as
early as in the project selection stage. As mentioned earlier, risk assessments can be
done either qualitatively, through the description of possible risk events that may cause
an impact on the project, and quantitatively, through a number of tools that were
described in Table 6.1. Both approaches are adopted in the proposed methodology.
Qualitative risk events are described as part of project proposal document. The
quantitative risk assessment of the methodology is described in the following section.
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Based on the ideas mentioned throughout this section, the proposed methodology
includes the following default criteria and sub criteria (in parentheses) for R&D project
and product development selection:
Basic research: Capability (familiarity with research topic, resources and
competences to conduct research); Strategy (strategy fit, observable trends,
urgency); Knowledge creation (learning effects on the organization’s knowledge
base, scientific background, research originality); Project Development
(interdependencies with other projects, estimated cost, estimated duration, cost
risk, schedule risk, performance risk, research risks).
Applied research: Capability (familiarity with research topic, resources and
competences to conduct research); Strategy (strategy fit, observable trends,
urgency); Technology (potential technologies, patentability/design protection,
benefits from standard setting); Market (market size, market growth, clear
market needs, competitive intensity); Project Development (interdependencies
with other projects, estimated cost, estimated duration, cost risk, schedule risk,
performance risk, research risks).
Advanced technology development: Capability (familiarity with technology,
resources and competences to conduct development); Strategy (strategy fit,
observable trends, urgency); Technology (potential products,
patentability/design protection, benefits from standard setting, Stage in
technology life cycle); Market (market size, market growth, clear market needs,
competitive intensity); Project Development (interdependencies with other
projects, estimated cost, estimated duration, cost risk, schedule risk, performance
risk, technology development risks).
Product development: Capability (familiarity with product, resources and
competences to conduct development, complementary assets); Strategy (strategy
fit, observable trends, urgency); technology (patentability/design protection,
benefits from standard setting, stages in technologies life cycles); Product
(product differentiation, product range growth potential); Market (market size,
market growth, clear market needs, competitive intensity, timing of
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introduction); Project Development (interdependencies with other projects,
economic attractiveness, estimated cost, estimated duration, cost risk, schedule
risk, performance risk, product development risks).
6.3.1.2 Execution mode criteria
The criteria reviewed in this section are based on studies that have proposed decision
models for the selection of technology acquisition mode. Studies that propose criteria
for selection of R&D project execution mode are very scarce. Emphasis has been given
to the motivations of organizations in deciding to engage in collaborations and in
outsourcing R&D services (Martinez-Noya et al., 2012, Cruz-Cázares et al., 2013), such
as the desire to share development costs, seek new knowledge and reduce technical
uncertainties. In addition to this, some project execution modes are more common in
certain types of R&D than in others. For example, companies often outsource activities
to universities and research institutes in basic research projects, as they may not have
such scientific competences internally. On the other hand, collaborative and outsource
forms are more rare in product development projects, due to complexities involved in
managing communication channels, how to share revenues and others (Bruce et al.,
1995). Collaborations and outsourcing in product development are seldom focused in
specific activities with partners with which organizations have long standing
relationships.
Notwithstanding this, it is found that criteria for the technology acquisition mode
decision can be easily applicable to the project execution mode, and thus, they are
reviewed in this section. In the proposed methodology, the execution mode decision
precede the project selection decision, so it is assumed that collaborators or
“outsources” are already identified by the time of the decision making process and
included in the project proposal document.
According to Chiesa, there are many technology acquisition modes available to
organizations: license-in, research contract funding, joint ventures, mergers, patent
purchase, alliances, internal development and others (Chiesa, 2001). In order to simplify
this process and extend the scope of this decision to acknowledge project execution
mode decision, the methodology incorporates three generic forms: internal development,
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external acquisition (acquiring technology through purchasing patents or licenses, etc.)
or outsource (the activities that constitute the project) and collaboration. This
simplification was also adopted in a multi criteria model proposed by Lee et al., for the
problem of selecting technology acquisition modes (Lee et al., 2009).
Three publications that propose criteria for the technology acquisition mode decision
are reviewed in this section and portrayed in Table 6.7. This table reads as follows: the
greater value for the criterion, the greater the preference for the corresponding execution
mode column, translated into a higher number of plus signs (+) or asterisks (*).
Table 6.7 - Technology acquisition mode decision criteria. Source: (Lee et al., 2009, Cho and Yu, 2000,
Chiesa, 2001)
Reference Criteria Sub criteria
Internal
development Cooperate
External
acquisition/Outsourc
e
(Cho and
Yu, 2000)
Firm
Technical position Positive
relationship
Positive
relationship
Negative relationship
Research manpower Negative
relationship
Positive
relationship
Negative relationship
R&D experience Positive
relationship
Positive
relationship
Negative relationship
History of in-house
R&D
Positive
relationship
No
relationship
Negative relationship
History of R&D
cooperation
Negative
relationship
Positive
relationship
No relationship
Technology
Level of technology No
relationship
No
relationship
No relationship
Technology
development stage
No
relationship
No
relationship
No relationship
Developing cost Negative
relationship
No
relationship
Positive relationship
Need for
standardization
Negative
relationship
Positive
relationship
Negative relationship
Possibility of
commercial success
No
relationship
No
relationship
No relationship
External
Environment
Market size No
relationship
No
relationship
No relationship
Extent of competition Positive
relationship
Negative
relationship
No relationship
Appropriability
regime
No
relationship
No
relationship
No relationship
Government. support
system
No
relationship
No
relationship
No relationship
(Chiesa,
2001)
Quality of
external sources
- * ** ***
Development
time
- * ** ***
Appropriability - *** ** *
Learning - ** *** *
Development
costs
- * ** ?
Technical risk
and familiarity
with technology
- * ** ***
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Table 6.7 (continued)
Reference Criteria Sub criteria Internal
development Cooperate
External
acquisition/Outsource
(Lee et al.,
2009)
Capability
Technological
position
++ +
R&D resources ++ +
R&D manpower ++ +
R&D experience +
Firm size ++ +
Complementary asset +
Strategy
Fit with business
strategy
++ +
Fit with technology
strategy
++ +
Acquisition urgency
+ ++
Importance to a firm ++ +
Technology
Stage in technology
life cycle
0/+ 0/+ ++
Development cost
+
Technological
readiness
++ +
Easiness to imitate
+
Market
Commercial
uncertainty
+ +/++
Market size + ++
Competitive intensity + 0/+ 0/+
Environment
Appropriability
regime
0/++ +
Availability of
external source
+
Quality of external
technology
+ ++
Dynamism
+ ++
A number of criteria can be identified from analyzing Table 6.7. Familiarity with the
research topic, technology or product may favor the internal development mode in order
to take advantage on accumulated knowledge generated internally. Resources and
competences that the organization possesses and that are related to knowledge areas of
the project will favor the internal development mode, since technical risk associated
with the project will be reduced. Collaborations may still be interesting to further reduce
development duration and technical risk.
Environmental factors also play an important role in defining the most suitable
execution mode. A high level of expertise of project partners or technology suppliers
favors the collaboration and external acquisition/outsource as opposed to internal
development. The difference between collaboration and external acquisition/outsource
modes will depend upon the expertise level of the external agents under consideration in
each alternative. Past and positive experiences with external agents will favor the
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external acquisition/outsource and collaboration development modes. The difference
between collaboration and external acquisition development modes will also depend
upon past experiences with the external agents under consideration in each alternative.
The existence and magnitude of stimulus for external acquisition/outsource or
collaboration, of any nature (financial, equipment sharing, etc.), favor these
development modes. Finally, if the outcome of the research is aimed at being
proprietary, the appropriation of the benefits to be generated by the project is affected
by the execution mode. Normally, collaborations and external acquisition/outsource
modes reduce appropriability since the results of the project will be shared. Developing
it internally, on the other hand, ensures that the results of the project will benefit the
organization.
The aforementioned project development criteria also influence execution mode
decision. Collaborations normally reduce the costs to the organization since resources
are shared. On the other hand, this reduction may not be entirely satisfactory if the
duration and resources to set up the collaboration are significant. Costs of external
acquisition/outsource are highly dependent upon the terms and conditions of the
contract. Collaborating in a project normally reduces the project duration, and is
normally faster than internal development if the time to set up is not too long. Still, of
all the three development modes, external acquisition/outsource is the one that
contributes more to shorter project duration. Interdependencies with other projects favor
the internal development since the resources allocated and knowledge generated by the
project remain in-house, thus ensuring a better development of the other projects. Each
development mode may have different risks, and a careful analysis on the risks list is
necessary to determine the most appropriate development mode.
In basic research projects, collaborations normally contribute to a greater and faster
accumulation of knowledge, while in external acquisition/outsource there is none or
reduced sharing of experiences. Notwithstanding this, the knowledge to be assimilated
is also dependent on the expertise and openness of partner(s). The originality of the
research arises interest in the project, and may favor the internal development mode, but
collaborations and outsourcing (normally to research institutions) may be preferred if
the organization does not possess internal competences to execute the project.
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In the remaining types of R&D and product development projects, technological and
market factors influence the decision to whether develop internally, to collaborate, to
acquire technology externally or outsource project activities. Patentability and design
protection of the technology or product to be developed favors internal development
mode, because it ensures that knowledge generated during the development remains in-
house. Collaboration is an intermediate alternative due to a partial loss of control over
the technology, and external acquisition or outsourcing of activities seems to be the least
viable alternative. Benefits can be reaped by launching compatible technologies, in
accessing a wider portion of the market. In these cases, collaborations may be an
interesting solution to boost technology diffusion by setting standard technologies.
External acquisition or outsourcing may also be desirable, at the expense of losing some
technical knowledge. The internal development of technologies positioned in later
stages of their life cycle may not be interesting from a business point of view, since the
useful time period for the commercialization of the technology before its decline is
reduced. Cooperation is a way to reduce this risk by sharing resources and costs. Even if
the technology or product proves to have some economic viability, external acquisition
made by purchasing a patent, for example, seems to be the best alternative to reduce the
time to market and thus ensure a longer time period for technology and product
commercialization.
Organizations may engage in collaboration in order to achieve greater market share that,
by themselves alone, would be difficult to achieve. But, on the other hand, such
collaboration may present risks of deterioration if the scope and responsibilities of each
party are not well defined. The preference for internal development and collaboration
will depend on these factors. External acquisition and outsourcing activities are the least
recommended development modes, as the technical know-how lost by not building
skills and competences internally may hamper the commercialization of the technology
or product. In highly competitive business environments, the value of the intellectual
property generated by the development of a new technology or product is high, so
organizations tend to favor internal development. Collaborations can be interesting but
presents risks with respect to the lack of clarity in the delineation of the relevant
property rights. External acquisition or outsourcing activities appear to be less suitable
for development under these circumstances. Finally, a clear knowledge about market
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needs may favor internal development mode because the organization has greater
control over the definition of product and technologies specifications. Collaborations,
external acquisition or outsourcing activities do not present any apparent benefits,
unless valuable market information is shared.
In the case of product development projects, organizations that possess complementary
assets (manufacturing technology, distribution channels accessibility, after sales
capability and others) may prefer internal development mode in order to take advantage
of these internal capabilities. A highly differentiated product may favor internal
development mode, since the knowledge involved in developing the product is of
strategic nature and supports the creation of a distinguishable market position for the
organization. If the organization possesses compatible or complementary products in its
portfolio, potential for product range growth is high, and therefore internal development
may be preferred.
The criteria and sub criteria (in parenthesis) for selecting project execution mode in
each type of R&D and product development projects in the proposed methodology are
summarized below:
Basic research: Capability (familiarity with research topic, resources and
competences to conduct research); Environment (Expertise level of collaborators
or suppliers, incentives and stimulus for collaboration or outsourcing, experience
with potential collaborators, appropriability regime); Knowledge creation
(learning effects on the organization’s knowledge base, research originality);
Project Development (interdependencies with other projects, estimated cost,
estimated duration, development mode risks).
Applied research: Capability (familiarity with research topic, resources and
competences to conduct research); Environment (expertise level of collaborators
or suppliers, incentives and stimulus for collaboration or outsourcing, experience
with collaborators or suppliers, appropriability regime); Technology (potential
technologies, patentability/design protection, benefits from standard setting);
Market (market size, market growth, clear market needs, competitive intensity);
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Project Development (interdependencies with other projects, estimated cost,
estimated duration, development mode risks).
Advanced technology development: Capability (familiarity with technology,
resources and competences to conduct development); Environment (expertise
level of collaborators or suppliers, incentives and stimulus for collaboration or
outsourcing, experience with collaborators or suppliers, appropriability regime);
Technology (patentability/design protection, benefits from standard setting,
stage in technology life cycle); Market (market size, market growth, clear market
needs, competitive intensity); Project Development (interdependencies with
other projects, estimated cost, estimated duration, development mode risks).
Product development: Capability (familiarity with product, resources and
competences to conduct development, complementary assets); Environment
(expertise level of collaborators or suppliers, incentives and stimulus for
collaboration or outsourcing, experience with collaborators or suppliers,
appropriability regime); Technology (patentability/design protection, benefits
from standard setting, stage in technology life cycle); Product (product
differentiation, product range growth potential); Market (market size, market
growth, clear market needs, competitive intensity); Project Development
(interdependencies with other projects, estimated cost, estimated duration,
development mode risks).
6.3.1.3 Multi criteria method
The AHP is a popular multi criteria method with applicability in a wide range of
situations. A comparative study places the AHP among the top R&D project selection
methodologies (Poh et al., 2001). AHP is transparent, easy to understand method, and is
also capable of handling both quantitative and qualitative criteria. For such advantages,
the AHP is the multi criteria method used in the methodology proposed in this chapter,
for execution mode and project selection.
The AHP is a structured decision making process developed by Thomas Saaty in the
1970s, and is based on mathematics and psychology. Its fundamental reasoning relies on
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the basis that humans are better at comparing successive pairs of alternatives than a high
number of alternatives at once. In this way, AHP differs from scoring models, since
weights are not based on arbitrary scales, but on ratio scales from human judgments,
i.e., on the mathematical synthesis of numerous human judgements about a decision
problem.
The process starts with the definition of a goal. In the case of the proposed
methodology, the goals are “select the best project execution mode” and “select the best
project”. Once the decision alternatives are settled (execution modes and projects), then
a number of criteria and related sub criteria (if necessary) is derived for evaluating the
alternatives with respect to the goal. A hierarchical structure can be used to represent
the problem, such as the one in Figure 6.6.
Goal
Criterion 1 Criterion 2 Criterion 3
Sub
criterion 1.1
Sub
criterion 1.2
Sub
criterion 2.1
Sub
criterion 2.2
Sub
criterion 3.1
Sub
criterion 3.2
Alternative 1 Alternative 2 Alternative 3 Alternative 4
Level I
Level II
Level III
Figure 6.6 - The structure of an AHP hierarchy
Then, priorities are calculated for criteria, sub criteria and alternatives of the decision
hierarchy, through a series of pairwise comparisons at each level, using the judgmental
scales described in Table 6.8. Observing the decision hierarchy above, the process starts
with pairwise comparisons made between criteria depicted at level I, with respect to the
goal, resulting in priority values for each criterion. Then, at level II, pairwise
comparisons are made between sub criteria, with respect to their contribution to their
related criterion, resulting in priority values for each sub criterion. Finally, at the lowest
level of the hierarchy, pairwise comparisons between alternatives are performed, with
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respect to their performance in each sub criterion, resulting again in priority values for
the alternatives. The synthesis of these priorities into an overall priority value for each
alternative provides a ranking of the best alternatives of the decision problem.
The pairwise comparisons are performed on matrix of judgements, and consistency
ratios are calculated throughout the process to ensure consistency in the decision
analysis.
Table 6.8 - The fundamental scale of absolute numbers. Source: (Saaty, 2008)
Intensity of
importance Definition Explanation
1 Equal Importance Two activities contribute equally to the
objective
2 Weak or slight
3 Moderate importance Experience and judgment slightly favor one
activity over another
4 Moderate plus
5 Strong importance Experience and judgment strongly favor one
activity over another
6 Strong plus
7 Very strong or demonstrated
importance
An activity is favored very strongly over
another; its dominance demonstrated in practice
8 Very, very strong
9 Extreme importance The evidence favoring one activity over another
is of the highest possible order of affirmation
Reciprocals
of above
If activity i has one of the above
non-zero numbers assigned to it
when compared with activity j, then
j has the reciprocal value when
compared with i
A reasonable assumption
1.1–1.9 If the activities are very close
May be difficult to assign the best value but
when compared with other contrasting activities
the size of the small numbers would not be too
noticeable, yet they can still indicate the relative
importance of the activities.
6.3.2 Risk assessment and management
This section presents the risk assessment and management mechanism that is
incorporated in the novel project selection methodology.
As mentioned previously, the technology readiness level, which can be easily translated
into the three types of R&D, is an important driver for risk management activities
(Anderson and Nolte, 2005). This suggests that different risk perspectives should be
addressed as the technology follows a path of maturation, from early research and
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prototypes until incorporation into a product for commercialization. The approach to
risk assessment and management proposed in this thesis follows these ideas.
The technology readiness level influence risk in a number of ways. An important one
has to do with a trade-off between uncertainty and impact, with obvious implications to
risk. For example, basic research projects are highly uncertain with regard to achieving
technical objectives set for the project, since work is primarily undertaken on theoretical
concepts. On the other hand, and as basic research tend to be inexpensive projects, the
financial impact of failing technical objectives of the project is reduced.
The transition from basic research to applied research and then advanced technology
development is made through the development of devices and prototyping to test the
feasibility of technological solutions in real-world conditions. Greater knowledge about
the technology is acquired throughout these phases, which then reduces technical risk,
but greater investments are also made, which means that not achieving project goals
may cause severe financial losses. In product development projects, investments are
even higher, since it involves industrialization and the development of an infrastructure
to support the commercialization of the product. Even though technical risk is
supposedly lower in product development, since technologies are already proven
feasible (or at least they should be), risk exists in the form of setting product
specifications or attributes that have low value from the perspective of customers.
Project budget and duration definitely influence technical risk in the way that less funds
or resources and shorter duration diminishes the probability of achieving expected
project goals. Projects need to be delivered under constraints of budget, duration and
scope. A change in one of these constraints has inevitable implications in the other two.
These three constraints represent what is known as project management triangle, and are
often used as measures to project execution. Project scope is usually defined as
statements and quantifiable goals. Thus, project scope can also be understood as
"quality" or "performance" when considering the quantified objectives of the project.
Although there are several classifications of risk, the most commonly used in project
risk management relates to technical risk, cost risk and schedule risk, as portrayed
previously in Figure 6.4. The term “performance risk” will be used hereafter, to include
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not only technical, but also other types of objectives, if quantifiable in some sort of way.
Thus, the concept of risk used in this thesis is related to the probability and impact of
failing targets outlined in terms of performance, cost and duration of projects.
There are two sources of risk: one produced by uncertainty about how much time a
project will take and how much will it cost to reach specified goals (Brigham, 1975) and
the other produced by risk events that may increase or decrease project duration, cost
and performance. Some examples of such events include delays in equipment delivery
from suppliers, which increases schedule risk, raw materials price volatility, which can
both increase and reduce the cost risk and also volatility in product demand, which can
both increase also reduce performance risk.
A class of such risk events is a source of programmatic risks, as defined previously.
These events are usually caused by higher levels of authority, in the context of scientific
and technological development programs, and can be a source of risk in performance,
cost and schedule risk. Modeling the influence of all possible events in the three
categories of events is a task of extreme complexity, and prone to produce unreliable
results, especially in early stages of the project life cycle such as project selection.
However, they definitely cannot be ignored and should be identified as early as possible
so managers have more time to prepare and implement risk response plans. In the
proposed methodology, managers have the opportunity to introduce, in text format, the
events that can be source of risk in the project, and describe their likelihood of
occurrence and impact.
Each individual has different perspectives on risk (Lefley, 1997). The different
perspectives of decision-makers in an organization towards risk tend to make the
process even more difficult to manage. A new approach capable of homogenizing the
organizational policy with regard to managing risk in projects is also proposed in the
methodology.
The modeling of the schedule, cost and performance risk requires proposals to include
project tasks planning and resources to be allocated. Although it considerably increases
the amount of information required, it is justified given the strategic nature of projects
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to be under consideration in the selection process. Realistically, such a procedure would
not be necessary for projects of lesser importance to the organization.
The tools and techniques used for modeling schedule, cost and performance risk in
different R&D and product development projects are presented next.
6.3.2.1 Schedule and cost risk
Among the various existing methods10
, Gantt diagram is chosen to represent project
planning in the methodology, due to its simplicity in use and wide popularity. Gantt
diagrams are a type of bar chart that illustrates project tasks, their durations and
precedence networks. In the software written for the methodology, users are able to
introduce tasks codes, descriptions, durations and precedent tasks (see Appendix 5 to
visualize the forms). Concurrent or parallel tasks in project are also enabled by
introducing start dates for tasks, i.e., without any precedent tasks.
Uncertainty is modelled through the introduction of three estimates for tasks durations -
pessimistic, most likely and optimistic – and Monte Carlo simulation. Monte Carlo
simulation is a computerized mathematical technique used to estimate the probability of
certain outcomes by running multiple trial runs, called simulations, based on random
variations of key parameters within statistical constraints. Many statistical distributions
can be used in Monte Carlo simulation, the most commonly used in project management
are the triangular and beta distributions, since they can be easily modelled using three
estimates, an approximation to pessimistic, most likely and optimistic values commonly
used by managers. Only beta distribution has been implemented in the software, but
additional distributions can be incorporated in the future with few modifications. This
difference between shapes of triangular and beta distributions can be visualized in
Figure 6.7.
10 Some examples include: Graphical Evaluation and Review Technique (GERT), Design and Structure Matrix (DSM), Activity-on-
Arc diagram and Icam DEFinition for Function Modeling (IEDF0) diagram.
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Once project tasks, their three estimates for durations and precedent tasks are inserted,
resources needed to conduct the project are allocated. Resources are of two types:
human and machinery or equipment, and are drawn from a resource pool database,
which contain their operating costs (monetary units/ day). Resources are then allocated
to each task, along with the time fraction (in percentage of total time) dedication to the
task.
(a) (b)
Figure 6.7 - Shapes of triangular (a) and beta (b) distributions
Cost items, such as purchases of specific software, equipment and patent applications,
are inserted for each task, where appropriate. As with task durations, three estimates are
used for cost items. Beta distribution is used as well.
Running a Monte Carlo simulation with the inserted parameters provides distributions
of project duration and cost, as depicted in Figure 6.8.
Figure 6.8 - Project duration (a) and cost (b) distributions from a Monte Carlo simulation
Project target duration and cost, as represented in the dashed lines in the charts above
(102 days and 130000 monetary units), determine the probabilistic component of
schedule and cost risk. In other words, the probability of failing target duration and cost
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
5
10
15
20
25
90 95 100 105 110
Cum
ula
tive
freq
uen
cy
Fre
quen
cy
Duration (days)
(a)
0%
20%
40%
60%
80%
100%
0
5
10
15
20
25
90 110 130 150 170
Cum
ula
tive
freq
uen
cy
Fre
quen
cy
Cost (monetary units)
(b)
Thousands
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can be calculated. The cumulative frequency curve depicted in the charts above
provides an estimation of the probability of project schedule and cost overrun. The
simulation results at the right of (or greater than) the target represent unacceptable
outcomes of project duration and cost. Then, the point where cumulative frequency
curve crosses the target provides the estimates for schedule and cost overrun. In the
example above, schedule overrun is estimated at 19% for cost overrun is estimated at
59%.
As mentioned throughout this chapter, individuals and organizations have different
perspectives on risk. These perspectives are also highly influenced, among other factors,
by the maturation rate of a technology (Anderson and Nolte, 2005). This suggests that
impact suffered from failing project targets have different interpretations depending on
the type of R&D involved. As such, an impact function should be used in order to
translate organizational policy towards risk. The impact component of risk proposed in
this thesis is modelled using the utility based loss function proposed by Ben-Asher
(Ben-Asher, 2008).
Utility theory is frequently used in decision analysis and is essentially based on the idea
that products, policies, outcomes, etc. can be evaluated in terms of utility or value to
their users, customers, recipients, managers, etc.(Keeney and Raiffa, 1993, Browning,
1998). Utility theory also provides a systematic methodology for elicitation and
quantification of relative utility or preference for objects or attributes.
Utility is commonly measured on an ascending scale of preference from zero to one.
The utility based loss function proposed by Ben-Asher is an inversion of this scale. A
value of 1 is assigned to the worst expected impact U(Xworst) and a value 0 to no impact,
U(Xbest). Impact is understood as the difference between actual project’s duration and
cost and their respective targets. The utility based loss function is constructed by asking
managers or the risk management board the following question: “if you have 50:50
chance of having a schedule/cost overrun of [maximum expected impact introduced]
days/monetary units or no overrun, or having a certain schedule/cost overrun of [a high
impact value, but lower than the maximum expected impact] days/monetary units, what
would you prefer?”. Answer options are “take the chance (choose the lottery)”,” the
certain amount” or “indifferent”. Successive questions are made, by alternatively
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changing the certain amount with low and high impact values to reduce the range, until
managers or the risk management board are indifferent. This method of elicitation is
known as certainty equivalent method. The indifference point, U(Xindifferent) has utility
value of 0.5. Then, the utility based loss function can be constructed using the
functional approximation method, which is essentially solving a system of linear
equations, as described by Neufville (Neufville, 1990). A hypothetical example, for the
sole purpose of illustration, is described below:
Utility based loss function – U(X) = a + bXc
Worst expected impact (Xworst) = 200 days
Indifference point (Xi) = 110 days
U(110) = 0.5
U(0) = 0 = a + b(0)c, then a = 0
U(110) = 0.5 = a + b(110)c
U(200) = 1 = a + b(200)c
Solving the system of linear equations, b = 0.002148 and c = 1.159425
Utility based loss function – U(X) = 0.002148(X)1.159425
The impact component can be calculated as the utility of the difference between
duration or cost outcomes that are greater than the target, and the target. Finally, the
formal formula for schedule and cost risk can be written as follows. Only schedule risk
is described in equation (6.1), since a similar equation applies for cost risk.
Schedule risk - ∫ ( )[ ( )]
(6.1)
where,
Ts – target schedule
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f(S0) – probability density function of duration outcomes, from Monte Carlo simulation
S0 – duration outcome (from simulation)
U(S0 - Ts) – utility value of the difference between the duration outcome and the target
duration
The discrete form of the risk equation is calculated in the software application: a
spreadsheet containing the random samples for duration/cost, and a second column for
the impact. If the random sample is lower than target, then the impact is zero. A third
column is a multiplication of each random sample and respective impact. The sum of
this column provides an approximation for the schedule and cost risk, when it is the
case.
The software application of the methodology also provides a Program Evaluation
Research Technique (PERT) analysis. PERT analysis enables the identification of the
minimum duration of the project, or the set of tasks that, if delayed, delays the
completion of the entire project. These tasks are part of the critical path of the project.
Since uncertainty is considered, many critical paths may exist. The software identifies
every possible critical path in the project, and calculates their corresponding probability
of occurrence.
6.3.2.2 Performance risk in basic research, applied research and advanced
technology development projects
The calculation of performance risks in basic research, applied research and advanced
technology development projects borrows the ideas proposed by Browning et al.
(Browning et al., 2002).
Projects are characterized by a number of quantifiable goals, which can be research
objectives, technical specifications in prototypes and entire technological systems,
depending on the type of R&D. These project goals will be mentioned as performance
measures hereafter. Additionally, these goals are of three types: large is better (LIB),
when greater values for project goals are more desirable, small is better (SIB), when
lower values for project goals are preferred, and nominal is best (NIB), when values
near a nominal value are desired.
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As with schedule and cost, uncertainty is considered with three estimates, worst case
value (WCV), most likely value (MLV) and best case value (BCV), and modeled with a
beta distribution. Thus the probability component of performance risk is calculated
using Monte Carlo simulation, as previously described. Random samples, when above
the target in LIB performance, do add to risk. The opposite works for SIB performance
measures. In NIB performance measures, any deviation, greater or lower than the
nominal, add to risk.
The impact component of performance risk is calculated using individual utility curves
for each project goal. The development of utility curves for each performance measure
starts with the definition of a range of possible values, which tend to be equal to the
range defined for the three estimates, i.e., the range between the pessimistic and
optimistic value. Within this range, a utility curve is built, representing different degrees
of preference to each performance measure. Hypothetical examples are given in Figure
6.9. Such information should represent preference levels of customers, designers,
engineers, and others, depending on the situation and the type of R&D. This information
must be gathered through customer surveys or defined internally, through staff meetings
with the team of engineers, managers and designers, and should be available to all
people involved in the projects.
The next step concerns the consideration of possible interactions, relationships and
trade-offs between performance measures. When performance measures are considered
independent from each other, it means that a lower value for a performance measure can
be counterbalanced by a greater value in another performance measure. When this is
observed, the method for performance risk evaluation is the single attribute utility
method. On the other hand, this trade off may not exist, and all performance measures
must be considered together to define the global utility of the system. Observing the
hypothetical performance measures of Figure 6.9, this means, for example, that a lower
performance in processing speed (a lower value) cannot be counterbalanced by a better
performance for set-up time (a lower value) and tolerance (closer to nominal value).
When this is observed, the method for performance risk evaluation is the multi attribute
utility method. Both methods are incorporated into the software developed for the
methodology, and it is the responsibility of the project team to define which type of
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relationship exists in the performance measures of the project. These methods are
described below.
Figure 6.9 - Utility curves for performance measures: large is better (a), small is better (b) and nominal is
best (c)
The single attribute utility method suggests different weights for each performance
measure, in a way that a lower value achieved in performance measure, can be
counterbalanced by a higher value in another performance measure assigned with a high
weight. Thus, different weights, that should sum up to one, are assigned to each
performance measure in order to characterize different degrees of importance to the
global performance of the project.
The continuous and discrete forms for calculating performance risk for each
performance measure are similar to schedule and cost risk, as described previously. The
utility of each performance measures and their target is defined by the utility curve built
previously for each performance measure. The global performance risk is the weighted
average of all performance risk for each performance measure.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 12 14
Uti
lity
Processing speed (m/s)
(a)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8
Uti
lity
Setup time (hours)
(b)
0
0.2
0.4
0.6
0.8
1
4 6 8 10 12
Uti
lity
Tolerance (x10-2 mm)
(c)
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Performance risk
(LIB) ∫ ( )[ ( ) ( )]
(6.2)
Performance risk
(SIB) ∫ ( )[ ( ) ( )]
(6.3)
Performance risk
(NIB) ∫ ( )| ( ) ( )|
(6.4)
where,
TPM – target performance measure
f(PM0) – probability density function of duration outcomes, from Monte Carlo
simulation
U(PM0) – utility of performance measure outcome
U(TPM) – utility of target performance measure
Global performance (GP)
risk (single attribute
utility method) ∑
(6.5)
where,
wi – weight of the ith performance measure
The multi attribute utility method, on the other hand, requires additional transformations
to account for the relationships between every performance measure. The global
performance utility with i number of performance measures is a composite measure
given by equation (6.6):
( )
∏( ( ) )
(6.6)
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The normalizing factor, K, determines consistency and is determined such that U(GP) =
0 when all U(PMi) = 0 and U(GP) = 1 when all U(PMi) = 1. The scaling factors, ki, are
the multi attribute utility of the best level of its performance measure i, when all other
performance measures PMj, j ≠ i, are on their worst levels. The procedure for estimating
the scaling factor for each attribute suggested by Richard de Neufville (Neufville, 1990)
involves asking a series of questions for each performance measure PMi, similar to the
ones used for the estimation of the utility based loss function. When an indifference
point is reached, that is the scaling factor for the respective performance measure. Such
procedure is implemented in the software.
The normalizing factor K is calculated when all scaling factors ki are known, using
equation (6.7):
∏( ) (6.7)
Solving for K involves trial and error or the Newton’s method. Once all parameters are
calculated, the multi attribute utilities can be calculated. The equation and the discrete
procedure for calculating performance risk are similar to the ones previously described.
The difference is in the number of simulations required: whereas in single attribute
utility method simulations of each performance measure results in simulations of
utilities for each of them, which are then weighted using the weights assigned for each
performance measure, in the multi attribute utility simulations result into an overall
utility value for the project performance.
Although the tools described in this section represent methods for assessing project
performance, some performance measures may be difficult to quantify in the proposed
manner, depending on the type of R&D under consideration. This is the case, for
example, of basic research projects. While still at a very early stage of technological
development, basic research projects’ performance measures tend to be more qualitative
in nature, related to the acquisition of new knowledge, and not to technical
specifications, which is only possible in more advanced types of R&D. Likert scales of
preference can be used for this purpose, but may represent inadequate simplifications. In
addition, and a common practice in many projects, the definition of technical
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specifications of technologies and products are usually made in later tasks of the project
life cycle, normally in the design stages, when enough knowledge about such systems
has been acquired. Therefore, analysis of performance in project selection stage only
makes sense if technical specifications are, somehow, already known.
6.3.2.3 Performance risk in product development projects
The method proposed for calculating performance risk in product development projects
differs from the one used in the other types of R&D. The justification of product
development projects is fundamentally linked with market and economic objectives,
that is, to market share, demand units, sales revenues, profitability and other economic
indicators. As such, it is highly desirable for performance measures of product
development projects to be linked to these sorts of indicators.
This view is also supported by Browning et al., which is then the basis for the
calculation of risks for the methodology proposed in this chapter (Browning et al.,
2002). In Browning et al.’s proposal, the same equation applied for performance risk in
basic research, applied research and advanced technology development is also applied
to product development, but, in this case, is multiplied by a normalizing constant K, for
converting units of utility to more intuitive measures of value, such as number of units
likely to be purchased. While recognizing the need to connect the performance of a
product development project to market objectives, Browning et al. does not propose any
model or mechanism to support this conversion.
Thus, the challenge relies in linking the performance measure of product development
to a demand model, capable of estimating products units likely to be purchased. Existing
demand models, such as Discrete Choice Models, are heavily based on statistical
methods derived from extensive customers’ surveys, which may be infeasible to be
performed in such an early stage as the project selection stage. Performing surveys can
be costlier given the number of projects under consideration and prone to poor results
due to the uncertainty about product future specifications. The product value
methodology proposed by Harry Cook provides a reasonable method for addressing
demand in new products (Cook, 1997). Although developed in the context of the
automobile industry, it has also been applied in the printing industry (Suh et al., 2010)
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and in the construction equipment (Bush, 1998, Freeman, 2000, Herington, 2000),
demonstrating its applicability in other industries. The approach described below
follows the ideas proposed by Suh et al. in estimating the demand of product with a new
technology infused (Suh et al., 2010).
The product value methodology is based on the S-model used for explaining the
diffusion of technologies and new products over their life cycle. Cook’s proposition is
that the value of a product has the same units as price, and is larger than the price if
there is demand for the product, and is also proportional to demand. Using the S-model
based on market equilibrium, the demand of a product is an analytical function of N
competing products’ prices and values (Cook and Wu, 2001):
( ) (6.8)
where
Di – demand for the ith product
N – number of competing products
Vi – value of the ith product
Pi – price of the ith product
The derivations towards the following equations are quite extensive. The interested
reader may consult Harry Cook’s book Product Management: Value, Quality, Cost,
Price, and Organization for more details of such derivations. The equations applied in
the methodology proposed in this chapter are described below.
When prices and values of the products change independently from their levels, it
follows that demand for each product i is provided by the equation (6.9):
{
∑[ ]
} (6.9)
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The constant K is calculated from the following expression:
(6.10)
where,
E – price elasticity of demand
– average demand in the market segment (units/competitor)
– average price in the market segment (monetary units/unit)
If the demands and prices of competing products in a market segment are known from
historical data, the linear set of simultaneous equations represented from equation (6.9)
can be solved, resulting in the following expression:
[ ]
[ ]
(6.11)
where,
DT = total demand for the market segment,
The above expression can be understood as “top-down” approach to quantifying value
of a product, since it can be derived from market data. Another equation provides a
“bottom-up” approach to quantifying product value, based on relevant product
attributes. Equation (6.12) provides the formula for the value of the ith product as a
function of individual product attributes:
( ) ( ) ( ) ( ) ( )
(6.12)
where,
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V = value of the product with j attributes,
V0 = average product value for the market segment,
v(g) = normalized value for attribute g.
Each individual product attribute v(gi) falls within three categories, as with the
performance measures from the other types of R&D projects: LIB, SIB and NIB. The
normalized value for each product attribute g is given by equation (6.13):
( ) [
( ) ( )
( ) ( ) ]
(6.13)
where,
gC = critical level for the product attribute, if the attribute value exceeds, falls below or
deviates from this value, depending on attribute type (LIB, SIB or NIB), the value of the
attribute goes to zero, making the product undesirable,
gI = ideal level for the product attribute beyond which there is no additional gain in
value;
g0 = market segment average level for the product attribute,
γ = time fraction when the attribute is of importance during the utilization of the
product, also the value that controls the slope and shape of the value curve.
In order to determine the demand of a new product, based on the above equations, a
baseline product needs to be identified first. The baseline product is an existing product
in the market, with which the product to be developed in the project is comparable in
terms of relevant attributes and their levels. The total demand for products in the market
segment where the new product will compete, the number of competitors in the
segment, the average market price elasticity, the demand, price and attribute levels of
the baseline product must be known, so that the value of the baseline product can be
calculated using equation (6.11).
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The product development project target performance measures are the target attributes’
levels for the new product. Knowing the target, critical, ideal, market segment average
levels and time fraction for each of the new product’s attribute, then the value of each
individual product attribute can be calculated using equation (6.13). The new product
target attribute levels are assumed to represent incremental improvements from the
attribute levels of the baseline product. Thus, introducing the baseline product value as
V0 and the value of each new products’ attribute target level (calculated from equation
(6.13)) into equation (6.12), then the value of the new product when all its attributes are
on their target levels is calculated. Knowing the price by which the new product will be
sold (Pi), and introducing the product target value (Vi) into equation (6.11), along with
the other parameters (K, N and DT), yields the target demand (Di) for the new product.
As with the performance measures in the other R&D types, uncertainty is modelled by
introducing three estimates for each of the new products’ attributes, i.e., the worst case
value (WCV), the most likely value (MLV) and the best case value (BCV), which yields
three additional estimates for the new products’ value, from equation (6.12), and three
additional estimates for the new products’ demands, from equation (6.11) .
The same calculation can be repeated for each year of the projected product life. For
this, forecasts are required concerning the evolution of the total demand for the products
in the market segment, the number of competitors, the average price and price elasticity
of demand. This results in the forecasted demand for the new product during the product
life.
With this information, the performance risk for product development projects can be
calculated. As with the other risk measures, Monte Carlo simulation based on the three
estimates for each of the new product attribute and the beta distribution is performed,
which results in a simulation of the total demand for the new product along its lifetime.
Simulation results below the target demand (all of the new product’s attributes at their
target levels) contribute to the risk measure.
In order to harmonize with the other risk measures, a utility based loss function is also
used for the impact component. The utility based loss function is built around the lost
units sales. Thus, the worst possible outcome for lost units sales during the projected
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life of the new product must be provided. The utility based loss function is constructed
by asking managers or the risk management board the following question: “if you have
50:50 chance of having a loss of [worst possible impact introduced] unit sales from the
planned target or no loss, or having a certain loss of [a high value, but lower than the
worst possible outcome] units sales from the planned target, what would you prefer?”.
The procedure that follows is the same as described for schedule and cost risk.
Finally, the continuous formula for performance risk in product development project is
provided by equation (6.14):
∫ ( )[ ( )]
(6.14)
where,
TPM – target performance measure (target demand)
f(PM0) – probability density function of demand outcomes, from Monte Carlo
simulation
PM0 –demand outcome
U(TPM - PM0) – utility of lost units sales from target demand
The discrete form of the risk equation above is calculated in the following manner in the
purposely developed software application: a spreadsheet containing the random samples
for demand and a second column for the impact, which is the utility of the difference
between the target demand and the simulated demand. If the random sample for demand
is greater than the target demand, then the impact is zero. A third column is a
multiplication of each random sample and respective impact. The sum of this column
provides an approximation for the performance risk in product development.
The above information can also be used to assess the economic attractiveness of the new
product. The revenues can be calculated by multiplying the demand forecasted by the
price for which the product is sold in each year. Providing estimations for the cost of
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manufacturing the product in each year, and the cost of developing the product at year
0, then a cash-flow analysis for the new product is developed. Establishing a discount
and inflation rate, typical investment appraisal indicators such as the net present value
(NPV), internal rate of return (IRR), payback period and the annualized present value
(ANPV), which is more suitable to compare projects with different durations, can be
calculated. Such indicators are calculated in the software, and are included as built in
criteria for project selection.
Additionally, a sensitivity analysis can be performed on the new product’s attributes.
Sensitivity analysis is used to assess how uncertainty impact key parameters from a
planned target. Uncertainty is modelled in product attributes, with the three estimates
mentioned before. Sensitivity analysis is performed around the target NPV and ANPV,
when all the product attributes are at their target levels. By building tornado charts like
the ones in one can visualize the impact of each product attribute in the target NPV and
ANPV (vertical dark line in charts): when a product attribute is in its worst case value
(left-hand side of bar, in red), NPV and ANPV deviate negatively from the target value,
and when it is in the best case value (right hand side of bar, in blue), NPV and ANPV
deviate positively from the target value. For example, among all product attributes in
Figure 6.10, product attribute 1 seems to be the one to have the highest impact in the
overall product NPV and ANPV. This provides valuable information to designers and
engineers, namely in the prioritization of specific product attributes.
Figure 6.10 – Sensitivity analysis on NPV (a) and ANPV (b)
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Important to highlight that the product value methodology presented in this section is a
theoretical model, only capable of providing rough estimates of performance risk and
economic attractiveness of products. A number of its underlying key parameters are also
dependent on experience of engineers and managers, such as the manufacturing costs
and product selling price. It is highly desirable to update this information in later stages
of the project, possibly using statistical methods and customers’ surveys. Careful
considerations with respect to the quality of data should be taken when using the
product value methodology, to ensure a realistic assessment.
6.4 Methodology for R&D projects selection incorporating risk
management
In addition to characterizing different technology readiness levels, different types of
R&D projects cover different orders of magnitude, in terms of duration and cost.
Adding up to this complexity, there are the numerous perspectives over risk inside an
organization. In order to address these issues, the methodology for R&D project
selection proposed in this thesis presents a new approach towards managing risk, which
is integrated early on projects’ life cycle.
It is proposed that clustering projects proposals estimations of duration and cost into
ranges or “buckets” supports greater homogenization of organizational policies with
respect to project risks. This clustering should take into account the types of R&D
practiced, their impact on the organization, and project execution modes. Basic research
projects are usually inexpensive and short in duration, but as technology matures,
projects tend to be costlier and longer. The perspective on risk is inevitably related to
the size of the organization: 1 000 000 euros projects, lasting two years, are perceived in
different ways by large and small organizations. Projects’ execution modes also
determine the clustering of cost and duration ranges, since collaboration involves
sharing of resources which is expected to reduce project duration and costs to the
organization. Outsourcing involves a third party or parties where duration and cost
outcomes become less controlled by the organization. The definition of project ranges
should take into account these factors, and be widely discussed and disseminated within
the organization.
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Once projects duration and cost ranges are defined inside an organization, a single
utility based loss function is assigned to each one of them. Figure 6.11 illustrates this
process. Important to notice is that the number of duration and cost ranges may not be
the same.
Duration ranges
Cost ranges
Projects “Buckets”Utility based loss
functions
Projects between D1 and D2
Projects between D2 and D3
Projects between D3 and D4
Projects between C1 and C2
Projects between C2 and C3
Legend:
Di – duration i
Ci – cost i
Pi – performance i
Performance ranges
(only in product
development projects)
Projects between P1 and P2
Projects between P3 and P4
Figure 6.11 - Projects clustering into duration, cost and performance (in product development projects)
ranges and utility based loss functions
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The utility based loss function works as an approximation of the risk perspectives of the
organization over the projects’ cost and duration ranges, and is then used for the
calculation of schedule and cost risk when project planning data is introduced. The
mechanism for building the utility based loss function is the following: within each
range, the worst possible impact (cost or schedule overruns, where appropriate) is
defined, and then questions for elicitation of the utility based loss function are asked, as
previously described. An interesting issue, though not tested in a real case, can be
expected: as the ranges and worst possible impact increase, utility based loss function
tend to be more concave, because increasing cost or schedule overruns will be mapped
into higher utility values.
In product development project, performance measures of “total demand for planned
product life” are also clustered into ranges and worst possible impacts (loss of units
sales) defined. A corresponding utility based loss function is assigned to each one of
them.
Projects’ duration, cost and performance (in product development) ranges and
respective utility based loss function should be stored in a database, so that they can be
used in calculating schedule, cost risk and performance risk. A database of the resources
available in the organization and their costs should also be created to support project
planning. With these two databases created, the process for generating proposals for
project selection can be started. The flowchart depicted in Figure 6.12 illustrates the
whole process. The inputs necessary to run the methodology’s underlying models can
be visualized in the forms developed for the software, in Appendix 5.
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Set up new project
(Form 1 )
Strategic justification
(Form 3)
Project relevance (Form
4.1)
More than one
execution mode
considered?
Project proposals
Basic research Applied research Advanced Technology Development Product Development
Project relevance (Form
4.2.1 and 4.2.2) Project relevance (Form
4.3.1 and 4.3.2)
Project relevance (Form
4.4.1 and 4.4.2)
Project execution modes
(Form 5)
Ranking of execution
modesSelected execution mode
Multi criteria
Analysis (spreadsheet)
Project planning
(Form 7 for basic research, applied
research and advacned technology
development)
(Form 8 for product development
NO
Basic Research
Applied research
Advanced technology development
Product development
Cost data (Forms 10.1
and 10.2)
Performance data (Form
11 - single attribute
utility)
Product Development
Performance data (Form
13)
Resources
Pool
Utility based
loss functionsSchedule and cost risk Performance risk
Monte Carlo simulationMonte Carlo simulation
Performance risk
Monte Carlo simulation
Market data
(Form 14)
Economomic
attractiveness
Project selection
(Form 16)
Introduce resources
(Form A)
Ranges definition
(Form B)
Ranking of
projects
Selected projects
Multi criteria
Analysis (spreadsheet)
Financial data
(Form 15)
Scope and goals
(Form 2)
Performance data
(Forms 12.1 and 12.2 -
multi attribute utility)
Monte Carlo simulation
Basic Research
Applied research
Advanced technology development
Interactions between performance
measures DO NOT exist
Interactions between performance
measures DO exist
Sensitivity analysis
Schedule data (Forms
9.1 and 9.2)
Impact function
(Form C)
Rejected
projects
Execution mode
criteria (Form 6)
YES
Figure 6.12 - Methodology for R&D project selection incorporating risk.
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The process starts with filling a number of forms for characterization of the project
proposals, and is related to the identified criteria for each project type, as described
previously. A number of forms are shown below in order to assist the reader in
understanding the steps of the methodology. The remaining forms can be seen in
Appendix 5.
Prior to introducing project data, two databases need to be created: one with resources
to be assigned to projects and the other containing the utility based loss functions for
each defined project ranges. Figure 6.13 depicts the form where users introduce
resource data: resources designation, type (engineer, technician, machine function, etc.)
and standard time durations (month and days). The utility based loss functions are
introduced via two forms. In the form depicted in Figure 6.14, the user introduces the
range or “bucket designation/name, selects the type of R&D project, and then minimum
and maximum value for this range, in terms of schedule, cost and performance (in
product development only). For each range, the worst impact expected is introduced.
The next form, illustrated in Figure 6.15, the user is asked a series of questions which
define the utility based loss function, following the process described in section 6.3.2.1.
Both these databases are stored as files, and need to be created and uploaded when a
new project is introduced.
Figure 6.13 - Resources introduction - Form A
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Figure 6.14 - Project ranges definition – Form B
Figure 6.15 - Utility based loss function definition - Form C
Having introduced these data, the user can initiate the introduction and characterization
of projects. The first form in this process - set up new project, in Figure 6.16 - involves,
among other information, introducing the project type (basic research, applied research,
advanced technology development and product development), the execution modes
under consideration (internal development, collaboration and external
acquisition/outsource) and the paths to the resources and utility based loss function
databases, which will be used later in project planning and risk assessment.
Additionally, more than one execution mode can be selected if the decision maker is not
sure about which is the most appropriate manner for executing the project.
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Figure 6.16 - Set up new project - Form 1
The next two project proposal forms – scope and strategic justification, in Figure 6.17
and Figure 6.18, respectively -, which are common to all project types, were designed
for the introduction of the scope, goals (performance measures), strategic justification,
projected and programmatic risks of the project.
Figure 6.17 - Scope and goals - Form 2
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Figure 6.18 - Strategic justification - Form 3
The fourth form – project relevance - is intrinsic to the project type selected. It is where
information about the relevance of the project is introduced. For example, in basic
research projects, information about how the project will contribute to the knowledge
base of the organization, the scientific and theoretical background, interdependencies
with other projects and risks related to the research are asked. The project relevance
form for basic research projects is shown in Figure 6.19.
The last form before proceeding to project planning is where information about project
execution modes is filled. For each execution mode defined in the first form,
information concerning key stakeholders, risks in the execution mode under
consideration, assumptions and constraints and required resources (competences, skills,
machinery and equipment) is completed. Moreover, and also for each execution mode,
“buckets” or duration and cost ranges and their respective impact functions, are selected
from the utility based loss function database. In this form, the user can create a new
project range and utility based loss function, which is stored in the database – in this
case, a spreadsheet file, for the execution mode under consideration.
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Figure 6.19 - Project relevance – Form 4.1
If more than one project execution mode has been characterized, a multi criteria analysis
based on the AHP is triggered, with built-in criteria for selection of project execution
mode, as described previously. Users can delete, change and add new criteria, as shown
in the form depicted in Figure 6.20. Once the multi criteria analysis is performed, a
ranking of execution modes is produced. Users can continue with the project execution
mode with the highest ranking, or redo the multi criteria analysis if not satisfied with the
results.
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Figure 6.20 - Execution mode criteria - Form 6
The next stage is related to project planning. The forms for introducing schedule and
cost data are the same for all project types. In schedule forms, tasks descriptions, their
three duration estimates, precedents and target schedule are filled. In the cost forms,
target project cost, human and machinery/ equipment resources are assigned to each
task, along with their dedication (in percentage), drawing from the resources database.
Then, cost items are filled, with their three estimates if managers are uncertain about
their value. Figure 6.21 presents the form where project tasks and durations are
introduced, and Figure 6.22 presents the form where resources are assigned to project
tasks.
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Figure 6.21 - Schedule data - Form 9.1
Figure 6.22 - Cost data - Form 10.1
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Performance measures introduced in the second form is retrieved in the performance
data forms, and can be edited at this time if desired. Project performance forms are not
the same for all project types. In basic research, applied research and advanced
technology development projects, managers need to consider whether interactions
between performance measures exist or not. If they do not exist, then the single attribute
utility method should be used. If interactions exist, then the multi attribute utility
method should be used. Figure 6.23 presents the form for single attribute utility method.
Figure 6.23 - Performance data – Form 11
The case of product development projects is different. Besides introducing and/or
editing performance data, market and financial data should also be introduced,
presented in the forms depicted in Figure 6.24 and Figure 6.25, respectively. These data
feed the product value methodology described previously, which then enables
developing an economic attractiveness and sensitivity analysis on product attributes.
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Figure 6.24 - Market data – Form 14
Figure 6.25 - Financial data - Form 15
Once schedule, cost and performance data has been filled for a project, Monte Carlo
simulation can be run for risk analysis. Managers can decide on the size of the random
sample and the bin width. After the simulation is run, schedule, cost and performance
risks are calculated.
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The final project proposal document includes the information filled in the project
characterization forms (scope, strategic justification, relevance and execution mode), the
project planning (estimated cost and duration), and the risk analysis (schedule, cost and
performance). In product development projects, the proposal document also includes
economic attractiveness and sensitivity analysis. Thus, the project proposal document
ensures a mix of qualitative and quantitative criteria, a requirement for integrated
project selection methodologies (Archer and Ghasemzadeh, 1999, Verbano and Nosella,
2010) and a project characterization framework based on benefits and risks (Chiesa,
2001).
The final project proposal document is stored in a folder, and can be used when the
organization engages in the project selection activity. Figure 6.26 presents the form
where projects to be compared are chosen, and selection criteria are defined.
Figure 6.26 - Project selection - Form 16
In the current stage of development, the software still does not convert the information
stored in spreadsheets to a text document, but in the future it is expected that such
feature will be enabled. As with the execution mode selection, the AHP is the multi
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criteria method used for selecting projects. Built-in criteria include the ones described in
sub section 6.3.1.1, the calculated risk levels and, in the case of product development
projects, economic attractiveness of the project, which is based on the investment
appraisal indicators (NPV; ANPV; IRR and payback period).
The models and respective tools and metrics used in the proposed methodology are
summarized in Table 6.9.
Table 6.9 - Summary of models, tools and metrics used in the methodology
Model Tool(s) or metrics
Schedule risk Monte Carlo simulation, utility based loss
function
Cost risk Monte Carlo simulation, utility based loss
function
Performance risk in basic research,
applied research and advanced
technology development projects
Monte Carlo simulation, single and
multi-attribute utility
Performance risk in product development
projects
Monte Carlo simulation, product value
methodology, utility based loss function
Economic attractiveness
Net present value, annualized present
value, internal rate of return and payback
period
Execution mode selection Analytic hierarchy process
Project selection Analytic hierarchy process
6.4.1 Risk management and control
Another contribution of this methodology relates to integrating a risk management and
control mechanism early on the project selection phase. Once risk levels are calculated,
for schedule, cost and performance, the risk levels can be managed throughout the
execution of the selected project. Hypothetical examples for schedule, cost and
performance are provided in Figure 6.27, Figure 6.28 and Figure 6.29. The example for
performance measure shown includes the single attribute utility method. In the multi
attribute utility method a composite measure of performance risk is calculated, and no
individual risk level for each performance measure can be included in a chart.
In each project review, estimates for the remaining tasks durations, costs and the
estimates for performance measures can be updated during project reviews, as new
information is gathered and uncertainty is reduced, which can be observed in the
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reduction of bar sizes in the charts bellow. This means that the maximum and minimum
project’s expected duration, cost and performance are reduced as uncertainty is reduced
along the project execution. Additionally, new targets for schedule, cost and
performance can be set in each project review, and even new performance measures can
be added. As a consequence of uncertainty reduction, and/or target changes and/or new
performance measures, risk levels change in each project review.
The quantification of risk enables an easier interpretation about the current situation of
the project, providing the organization with means for managing risk throughout the life
cycle, i.e., in preparing risk response plans and observing their effectiveness in each
project review.
The risk management and control mechanism has not been developed for the software,
but it can be easily modelled and integrated in future developments.
Figure 6.27 - Chart for schedule risk management and tracking
Figure 6.28 - Chart for cost risk management and tracking
1st project
review
2nd project
review
3rdproject
review
4th project
review
Max 260 230 245 220
Min 121 135 170 181
Average 180.1 190.3 212.7 200.3
Risk level 21.4 15.1 17.2 13.5
0
5
10
15
20
25
0
50
100
150
200
250
Ris
k l
evel
Est
ima
ted
proje
ct
du
ra
tion
1st
project
review
2nd
project
review
3rdpro
ject
review
4th
project
review
Max €1,700,00 €1,800,00 €1,600,00 €1,450,00
Min €850,000. €1,100,00 €1,150,00 €1,220,00
Average €1,236,12 €1,556,78 €1,360,00 €1,370,00
Risk level 32.8 45.9 27.4 14.5
0.05.010.015.020.025.030.035.040.045.050.0
€- €200.00 €400.00 €600.00 €800.00
€1,000.00 €1,200.00 €1,400.00 €1,600.00 €1,800.00 €2,000.00
Ris
k l
evel
Est
ima
ted
pro
ject
co
st
(th
ou
san
ds)
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Figure 6.29 - Chart for performance risk management and tracking
6.4.2 Resource competition
The issue of resource competition is also addressed in the proposed methodology.
Resource overload, i.e., when the capacity of a resource is exceeded, happens when
resources are working in multiple tasks (from the same project or from many projects)
in a determined period of time, and the sum of their dedication to the tasks are over
100%. To better deal with this problem, a simple algorithm that warns managers about
the likelihood of resource overload has been developed for the methodology’s software.
For each simulation iteration for a project with N tasks, and beginning at time ti=0 of the
project, the shortest start or finish time of all tasks in the same simulation iteration, ti +1
is searched. Once ti +1 is identified, and for the duration range ti < dj < ti +1, all tasks start
and finish times are checked to see if fall inside this duration range. Every task that
satisfies this condition is stored in a vector vk. If the vector contains more than one task,
then these tasks overlap within the duration dj. The dedication of each resource that is
shared by these tasks is summed and if it exceeds 100%, then resource overload occurs
within the duration dj.
The procedure is repeated for the next duration dj+1 , such that ti+1 < dj+1 < ti +2, and ti +2
is the next shortest start or finish time of all tasks in the same simulation iteration, and is
greater than ti +1. For each simulation iteration, there are Nx2 verifications of duration
ranges and vectors. In a Monte Carlo simulation with 10000 iterations, there are
10000xNx2 verifications. In order to accelerate this process, the software developed for
the methodology only computes average durations, average resource overload and the
1st project
review
2nd project
review
3rdproject
review
4th project
review
Max 210 205 200 200
Min 170 175 190 200
Average 200.2 185.6 195.2 200
Risk level 42.1 37.6 18.9 0
0
5
10
15
20
25
30
35
40
45
0
50
100
150
200
250
Ris
k l
evel
Est
imate
d p
rocess
ing s
peed
(mm
/s)
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217
probability of resource overload, which is taken to be equal to the number resource
overload occurrences divided by the number of iterations. Based on this information,
managers can decide whether to make change and/or reallocate resources to minimize
the chances of resource overload.
Although this mechanism is implemented for resource overloads occurring in single
projects, it can be easily extended to multiple and interdependent projects. A faster
programming language would then be required. Using the software developed for this
thesis, running a Monte Carlo simulation with 10,000 trials which results in the risk
analysis and resource overload verification for a single project, takes approximately two
minutes to complete on a computer with a 2.00GHz processor and 4.00 GB of RAM.
6.5 Methodology application
The methodology proposed for project selection was applied in the industrial partner of
the thesis. A post mortem analysis – i.e., conducted after the completion of the projects
– was conducted on three product development projects. The three projects were
executed in cooperation mode. For confidentiality reasons, they will be denominated
Project A, Project B and Project C.
The application of the methodology was performed in three sessions with the CTO of
the industrial partner. The software developed for the project selection methodology
was used to support the application. In the first session, data and information about the
projects were gathered from internal reports and funding applications. The data and
information in these documents covered most of the themes in the project
characterization forms for product development – forms 1, 2, 3, 4.4.1, 4.4.2 and 5, in
Figure 6.12. Tasks durations and costs described in the projects proposal documents
were used as most likely values for project planning. As suggested by the CTO, tasks
completion delays and cost overruns were used as the pessimistic values, while the
optimistic estimates were around 80% of the tasks durations and costs initially set. The
first targets for projects’ schedules and costs set were used in this analysis. Additionally,
some data about the markets and competitors were available in these documents and fed
the demand model. However, not all the necessary data could be gathered for this
model, and some assumptions needed to be made, as it will be explained later.
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The second session concerned the definition of the utility based functions. First, the
projects’ ranges in terms of schedule, cost and performance needed to be defined. The
CTO provided the minimum and maximum values for each project, which were in line
with the reality of the industrial partner. It was assumed a duration of three years for the
lifetime of the products to be developed in each project. Then, the CTO proceeded and
answered the round of questions to support the definition of the utility based loss
functions. Table 6.10 presents the ranges and the respective worst impact values (WIV),
the indifference values and the utility based loss functions for each project. Figure 6.30
presents the graphic portrayal of the utility based loss functions for Project B.
As it can be observed in Table 6.10, different projects within the same schedule range
may be included in different cost ranges. This is the case of projects B and C, which
belong to the same schedule range (792 – 1048 days) but are included in different cost
ranges (project B: 1,000,000€ - 2,000,000€ and project C: 500,000€ - 1,000,000€). This
can be explained by resource intensive project plans, i.e., the allocation of more
resources to specific projects in order to accelerate development duration to meet the
desired timing of introduction in the market. Another issue of importance is related to
the possibility of the same ranges having different WIV in different projects, such as
performance ranges in projects A and B. The reason for this can be explained by the
difference in cost magnitude, which is higher in Project B since it belongs to a higher
cost range. The execution of a project with a higher development cost can turn the
organization more sensitive to likely losses in units sales, characterized by a lower
WIV, as is the case of the project B in comparison to project A.
Table 6.10 - Ranges, indifference values and utility based loss functions for each project
Project
Ranges and worst impact values (WIV)
Indifference
value
Utility based loss
functions Schedule Cost
Performance (in
three years sales)
Project
A
Min: 528
days
Max: 792
days
WIV: 528
days (delay)
Min: 500,000€
Max:
1,000,000€
WIV:
200,000€ (cost
overrun)
Min: 0 units sales
Max: 15 units
sales
WIV: 6 lost units
sales
Schedule: 106
days (delay)
Cost: 60,000€
(cost overrun)
Performance: 2
lost units sales
Schedule: U(x) =
0.066783^0.431691
Cost: U(x) =
0.000887^0.575717
Performance: U(x) =
0.32288^0.63093
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219
Table 6.10 (continued)
Project
Ranges and worst impact values (WIV)
Indifference
value
Utility based loss
functions Schedule Cost
Performance (in
three years sales)
Project
B
Min: 792
days
Max: 1048
days
WIV: 528
days (delay)
Min:
1,000,000€
Max:
2,000,000€
WIV: 300,000€
(cost overrun)
Min: 0 units sales
Max: 15 units sales
WIV: 3 lost units
sales
Schedule: 106
days (delay)
Cost: 75,000€
(cost overrun)
Performance: 1
lost unit sale
Schedule: U(x) =
0.066783^0.431691
Cost: U(x) =
0.001826^0.500
Performance: U(x) =
0.5^0.63093
Project
C
Min: 792
days
Max: 1048
days
WIV: 528
days (delay)
Min: 500,000€
Max:
1,000,000€
WIV: 200,000€
(cost overrun)
Min: 15 units sales
Max: 30 units sales
WIV: 15 lost units
sales
Schedule: 106
days (delay)
Cost: 60,000€
Performance: 6
lost units sales
Schedule: U(x) =
0.066783^0.431691
Cost: U(x) =
0.000887^0.575717
Performance: U(x) =
0.12892^0.756471
Figure 6.30 – Schedule (a), cost (b) and performance (c) utility based loss functions for Project B
0
0.2
0.4
0.6
0.8
1
0 200 400
Uti
lity
Worst impact value for schedule (days)
(a)
0
0.2
0.4
0.6
0.8
1
0 100000 200000 300000
Uti
lity
Worst impact value for cost (monetary units)
(b)
0
0.2
0.4
0.6
0.8
1
0 1 2 3
Uti
lity
Worst impact value for performance (lost units
sales) …
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220
Having introduced the project characterization data, the project planning, estimates for
tasks durations and costs and the utility based functions, the next stage concerned the
data introduction in the methodology’s demand model. The demand model proposed by
Cook (Cook, 1997) is a theoretical model, based on substantial amount of data, such as
number of competitors in the segment, price elasticity of demand, average market prices
and many others, as described in 6.3.2.3. Although some data could be collected from
projects’ documents, several assumptions needed to be made, with support of engineers
and other collaborators involved in the projects. As such, the industrial partner was
advised to observe the outputs (expected demands for each product, revenues generated
and indicators such as NPV, IRR and Payback period) with extreme caution. As
mentioned before, project performance analysis may be delegated to later stages of
projects’ life cycle, when product specifications are established and more market
information is collected.
The demand model also required estimates (WCV, MLV and BCV) and target values
for each product attribute. For each project, three product attributes were chosen as
representative of the products’ value. With the assumptions made in the demand model
and a products’ lifetime of three years, the demand for these years could be estimated;
all these, along with the prices and manufacturing costs, served to develop a cash flow
analysis. Assuming a 10% discount rate, the NPV, IRR and Payback period (in years)
were calculated. Table 6.11 depicts the target NPV, IRR and Payback period for each
project, which were calculated from the target values for each product attribute.
With these inputs, a Monte Carlo simulation with 100 trials was run to perform the risk
analysis. Schedule targets for projects A, B and C were 748, 1048 and 1048 days,
respectively. Cost targets for projects A, B and C were 720,000€, 1,250,000€ and
825,000€, respectively. Performance targets are based on the target values of each
product’s attributes. The result of this analysis is a distribution of projects’ durations,
costs and performances. For the purpose of illustration, Figure 6.31 depicts the risk
analysis charts for project B. The dotted line in these charts represents the target values.
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Figure 6.31 – Distributions of duration, cost and performance for project B
Table 6.11 - Risk analysis and economic attractiveness indicators for each project
Project Schedule
risk
Cost
risk
Performance
risk Target NPV
Target
IRR
Target
payback
period
Project A 6.3 0.8 226.2 705,451.62 € 63% 2.05
Project B 21.7 17.06 191.9 1,913,955.83 € 83% 1.78
Project C 23.54 9.5 226.2 841,149.06 € 70% 1.75
Finally, in the third section the multi criteria selection of the projects was performed.
First, the CTO was requested to define the selection criteria to use, from the built in
criteria, or adding new ones or changing existing criteria. Figure 6.32 illustrates the
hierarchy model of criteria and sub criteria. Basing the analysis on the characterization
of each project and the outputs from the models and the risk analysis, the CTO
performed the project selection through pairwise comparisons between criteria, then in
sub criteria with respect to corresponding criterion, and then in alternatives with respect
to each sub criterion, following the procedure of the AHP method. The most attractive
0%
20%
40%
60%
80%
100%
0
5
10
15
20
25
950 1000 1050 1100 1150 1200 Cu
mu
lati
ve f
req
uen
cy
Freq
uen
cy
Project duration (days)
(a)
0%
20%
40%
60%
80%
100%
0
5
10
15
20
1220000 1260000 1300000
Cu
mu
lati
ve f
req
uen
cy
Freq
uem
cy
Monetary units
(b)
0%
20%
40%
60%
80%
100%
0
5
10
15
20
25
30
35
0 50 100 150 200
Cu
mu
lati
ve f
req
uen
cy
Freq
uen
cy
Total demand (units)
(c)
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project from this analysis was project C, with a normalized final score of approximately
0.42. The matrices containing the pairwise comparisons can be found in Appendix 6.
The projects considered in this application of the methodology have not yet reached
three years after the completion of each project. As such, it was not possible to verify
whether the results of the methodology corroborate what the industrial partner has been
experiencing with the projects. Overall, the feedback from the CTO was satisfactory,
specifically concerning the criteria proposed for select product development projects,
which were considered as appropriate for comparing such types of projects.
Additionally, the CTO felt that clustering of projects into ranges can contribute to a
more rational project management inside the company.
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Select the most
attractive project
Capability
(C1)
Technology
(C3)
Alternatives
Strategy
(C2)
Product
(C4)
Market
(C5)
Project BProject A
Complementary
assets (C1.2)
Observable
trends
(C2.1)
Patentability/
design protection
(C3.1)
Product
differentiation
(C4.1)
Economic
attractiveness
(C6.1)
Cost risk (C.6.2)
Timing of
introduction
(C5.4)
Competitive
intensity (C5.3)
Clear market
needs (C5.2)
Market growth
(C5.1)
Product range
growth potential
(C4.2)
Project
development
(C5)
Resources and
competences to conduct
development (C1.1)
Project C
Sub
criteria
Criteria
Figure 6.32 - Criteria and sub criteria hierarchy model used in the project selection
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224
6.6 Conclusions
This chapter presents a new methodology for selecting R&D projects that incorporates a
risk management mechanism. By combining series of tools, the proposed methodology
addresses a number of propositions for an integrated project selection framework.
Several managerial implications are envisioned. The early categorization into different
types of R&D and product development projects, in the selection process, allows a more
equitable comparison between projects. Managers are also able to observe a logical
sequence in the project selection process, which involves the characterization, planning,
risk analysis and economic attractiveness (in product development), towards project
selection. Both tangible and intangible, positive (benefits) and negative (risks) aspects
of projects are covered in the whole selection process.
The integration of risk early in the project life cycle enables more time for managers to
mitigate them. The quantification of risks through project buckets and impact functions
contributes to greater homogenization and rationalization of organizational policies and
practices in risk management. Although risk quantification may be done at a very early
stage, and therefore prone to unreliable results, risk levels can be updated throughout
the execution of projects as more information is gathered and uncertainty is reduced,
through a mechanism of risk management and control. In addition to this, managers are
able to calibrate their estimates for future projects.
Despite the listed contributions, some limitations are identified. As with any decision
making methodology depending on human judgments, it may suffer from optimism or
pessimism bias, leading to unrealistic risk assessments and inadequately selected
projects. The extensive data required for the product value methodology may not be
readily available in the organization, which then requires the implementation of an
active business intelligence system, capable of monitoring competitors new offerings,
the market dynamics, and provide more accurate business forecasts.
In highly dynamic environments, the development of utility based loss functions based
on ranges or “buckets” may suffer some drawbacks. In such environments,
organizations engaging in long duration projects may feel that assumptions made earlier
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225
on may not reproduce the “new reality” in times of heavy market turbulence. Financial
limitations experienced by the organization in a determined period of time may change
the perspective over risk in projects under execution. If this happens, then the utility
based loss functions should be revisited. The methodology described does not prescribe
metrics that indicate the need to revisit the functions.
A situation that has not been addressed properly by the methodology, with clear
implications in risk assessments, concerns the collaboration and outsourcing of specific
projects tasks. The methodology assumes uniform cooperation throughout the execution
of the project or full outsource of project execution, which may not always hold true.
This issue should be taken into account in future development of the methodology.
Future work to be conducted in the methodology is essentially related to incorporating
more mechanisms to cover a wider number of situations. Technology valuation methods
in monetary terms, such as the cost, income and market approaches, can be incorporated
in the methodology to provide a more quantitative value of a technology, thus assisting
managers in the decisions involved in what to do with technology once it is developed
(license-out, sell patent, develop product, etc.). In the methodology proposed, the value
of a technology is not assessed in monetary terms, but qualitatively, through the AHP.
Another valuation method, the real options, considers market uncertainty and can thus
be incorporated into the methodology as well. Real options valuation provides a
framework for business to have the right, but not the obligation, to undertake certain
business initiatives (or options), such as deferring, abandoning, expanding, staging
investments in technology, depending on the conditions (favorable or unfavorable) of
the market. Real options provides means for dealing with uncertainty, since exercising
an option supports the minimization of losses when the environment is not favorable
(deferring, abandoning, staging) and leverage gains when is favorable (expanding).
Despite the benefits mentioned, the application of real options in businesses is still
limited, largely due to its complex mathematical structure, which requires managers to
have some background in finance to understand it.
Interdependencies between projects have only been addressed qualitatively in the
methodology proposed. The alternatives considered in the multi criteria method
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integrated in the methodology are restricted to single projects, but future work should be
focused to extend such alternatives to include multiple projects, such as entire programs
consisting of interdependent projects, concurrent or parallel projects. Including multiple
projects as alternatives has, inevitably, implications on how risks are quantified, through
the methods described in this chapter.
Resource competition between the multiple projects and the projects under execution in
the organization should be considered as well. Furthermore, and in order to make
resources management more efficient, incorporation of optimization algorithms for
resource allocation would be highly desirable. The simple mechanism currently
integrated in the software, that warns managers about the possibility of resource
overloading, can be a starting point for the development of this algorithm.
It is hoped that the proposed methodology for project selection provides a significant
contribution towards integrating various practices within an organization. Future work,
as mentioned above, could enhance this integration.
CHAPTER 7
Integrated technology strategy framework
In this chapter, the methodologies presented for the internal analysis,
external analysis and selection activities are integrated into a single
framework to support the formulation of a technology strategy. The
individual contributions of each methodology are summarized, along with a
description of the outputs resulting from their application. Considerations
about the generation activity are also made, in terms of necessary
information and systems to support the generation of new project ideas. A
diagram representing the conceptualization of the integrated framework,
linking the interactions between the proposed methodologies for each
activity, is described. It is expected that the framework contributes towards
a greater understanding about the dynamics between innovation activities
and underlying tools.
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7.1 Introduction
The technology strategy formulation process, as analyzed in Chapter 2, is consolidated
in four core activities: internal analysis, external analysis, generation and selection. The
objective set for this thesis is to propose a novel technology strategy framework with
improved features. The research path followed was to analyze each activity in detail
(excluding generation), namely its purpose in the overall process, commonly used tools
and state of the art with respect to the existing contributions found in literature. As a
consequence, research gaps were identified and addressed through the development and
proposal of new methodologies, which were then tested in the industrial partner of the
thesis. In this chapter, the constituent elements of the methodologies proposed for each
activity are integrated into a single framework to support technology strategy
formulation.
A new audit that considers the social dynamics in organizations was proposed for the
internal analysis activity in Chapter 4, a new methodology that combines the Delphi
method with Quality Function Deployment to support the cross relationships analysis
between future events was proposed for the external analysis activity in Chapter 5, and a
project selection methodology that integrates risk management practices is proposed for
the selection activity in Chapter 6. The generation activity, as it will be explained in
more detail in this chapter, is a very organization-specific activity, and therefore a
generic methodology applicable to any organizational environment is highly
improbable. Despite this, and in an attempt to integrate this activity into the framework,
this chapter reviews current practices and provides a map of the information needed to
support the generation and characterization of projects.
This chapter is organized as follows: section 7.2 briefly reviews the methodologies
proposed in previous chapter and outline their outputs, section 7.3 describes technology
intelligence systems in the context of the generation activity, and provides a map of the
information needs to support the generation of strategic projects, section 7.4 presents the
integrated technology strategy framework resulting from the combination of the
proposed methodologies, and section 7.5 presents the concluding remarks of this
chapter.
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7.2 Outputs from proposed methodologies
The application of the methodologies proposed for the internal analysis, external
analysis and selection activities of the technology strategy formulation process originate
a dossier which reports their results. This information supports organizations to develop
awareness about their technological capabilities and competences, to understand which,
when and how future events may influence their businesses, and to select the projects
that best ensure competitive advantages.
In order to remind the reader, the following sections present a brief summary of the
characteristics of the methodologies developed, in the previous three chapters, for each
core activity, and their main contributions. Moreover, it outlines the outputs from the
application of these methodologies.
7.2.1 Internal analysis
The methodology developed in this thesis for the internal analysis activity is based on
an audit. This audit deals with the identification of available technological competences,
the assessment of the technological innovation management process and the search for
opportunities to improve this process. The audit is composed of two modules: the
capability assessment module and the competences assessment module.
The capability assessment module is based in statements reflecting important traits and
characteristics that organizations must possess to be innovative. The proposed modeling
formalization of the capability assessment module is made by embedding the audit in a
GSS, in order to offset the limitations of face-to-face meetings (Dowling and St. Louis,
2000), which are typically used to perform the audits. It is hoped that two relevant
features of GSS contribute to an improved assessment: 1) asynchronous
communication, which offset the limitations derived from face-to-face meetings’
duration restrictions and, according to Tung ad Turban, also contribute to choice shift,
conflict management and participants focus and 2) anonymity, which reduce the
influence of hierarchical structures on the opinions of individuals with lower positions,
thus contributing to equal participation, less biased and more realistic assessments
(Tung and Turban, 1998).
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During the filling of the audit, participants are asked to assess organizational
performance using a Likert scale on each statement, and also provide comments,
observations and suggestions for improvements. In order to address the internal
dynamics of organizations, i.e., how the capability of the organization to manage
technology and innovation changes over time, the audit is implemented as the Real time
Delphi, thus working as a real time assessment of the “health” of the technological
innovation process inside the organization.
The application of the audit through a structured group management technique, namely
the Real Time Delphi in a GSS also makes the assessment widespread throughout the
organization, engaging individuals and teams involved in the technological innovation
process, from the various departments and organizational functions, and promoting
consensus building and decision quality (Huber, 1982, Beruvides, 1995). It is expected
that this bottom-up approach provides a more realistic assessment of the organization’s
innovation capabilities. The anonymity provided by the platform diminishes possible
negative effects from social pressures, stimulating greater participation. The real time
feature enables participants to converge on their assessments, contrast opinions with
each other and to come up with solutions and improvement actions, working as a
dynamic forum.
Given the features of the GSS, the implementation of the capability assessment module
is proposed to be a continuous activity within the organization. The assessment platform
enables information to be collected at any time, or coinciding with the timing of
strategic decision making process, depending on what suits the organizational
management process best. If made on a periodic basis, the organization can track its
innovation process performance over time.
The competences assessment module aims to contain a compilation of the technical
expertise and knowledge assets in the organization. Because of the intangibility of such
concepts, a quantitative assessment is not performed. Hence, a template is proposed,
that assists the compilation of information on the human resources involved in the
innovation process, on manufacturing processes, intellectual property, products and
technologies. The information requested in the template may be already present in the
company in other forms, such as reports from human resources department, machinery
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231
inventory lists, etc.. The outputs from the internal analysis activity are presented in
Table 7.1. The information should be also updated when appropriate, and disseminated
through the people responsible the strategic decision making process in the
organization.
Table 7.1 - The outputs from the internal analysis activity
Outputs Description
Capabilities
assessment module
Capabilities assessment
Convergence analysis on the
judgements provided by the
experts, on the level or
organizational performance on
each audit statement.
Participants comments
Comments provided by
participants that might include
ideas for improving the
performance of the innovation
process inside the organization.
Competences
assessment module
Human resources
Name, department, position and
technical skills of the people
involved in the innovation
process.
Manufacturing resources
A list of manufacturing process,
technologies and equipment
available in the organization.
Intellectual property
Designation and description of
each of the organization’s patents,
copyrights, industrial design
rights, trademarks and others.
Products and technologies
Description of the products within
the portfolio of the organization
and underlying technologies,
whether developed internally or
outsourced.
7.2.2 External analysis
The proposed methodology for the external analysis activity is aligned with the
emergent paradigm in foresight studies – the Context-based (open) foresight – which is
a response to previous foresight paradigms that were too much focused on extensive
data collection and calculations. The emergent paradigm points towards a more holistic
perspective on the future, and embraces methods that foster open dialogue, divergent
opinions, subjectivity about themes related to the dynamic interactions between social,
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232
technological and economic forces. Technology should be observed in conjunction with
the environment (Reger, 2001, Becker and Lillemark, 2006). As such, it is highly
desirable to involve people with multiple disciplinary backgrounds, from inside and if
possible, outside the organization, in order to cover complementarily points of view in
the discussion about the interactions between the various events in the future.
The methodology proposed is in line with such propositions, which ultimately call for
theoretically supported, adaptable to different contexts, economical, practical, hybrid
methodologies (Phaal et al., 2006) and capable of dealing with multiple perspectives ,
since innovations are increasingly dependent on networks of cooperation (Coates et al.,
2001). The methodology is also intended to support a structured communication,
directed to the identification of the strategic guidelines which the organization should
pursue, as these are the objectives of the external analysis activity (Chiesa, 2001), thus
linking foresight to strategy making (Coates et al., 2001).
The methodology proposed for this activity starts with a Delphi survey, which is
developed by semi-structured interviews with experts. These interviews covers subjects
related to driving forces in the macro-environment (politics, economy, environment,
society and technology) and to the micro-environment (emerging customers’ needs,
entry of new competitors, etc.) of industries, as suggested by Vecchiato and Roveda
(Vecchiato and Roveda, 2010). In this survey, panelists are asked to provide their
judgements concerning the impact, time and likelihood of occurrence on a number of
future events in technology, market, regulations and in other dimensions. Then, through
a structured technique that includes a relevance index for each event, which derives
from the experts judgements with respect to each event’s impact, time and likelihood of
occurrence, and the Quality Function Deployment, the relationships between such
events are analyzed, supporting the identification of strategic technological
competences. The events that have the highest relevance index are events with which
the organization should take greater care in the future. These events may be related to
greater diffusion of certain technologies, which can be translated as opportunities for the
organization, or as threats to their existing products. Events in other dimensions beyond
the technological one should also be considered, such as, for example, stringent
environmental regulation, the emergence of new competitors, etc.. These events, along
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with the strategic technological competences, form the strategic guidelines of the
organization for a determined period, driving the information collection efforts and the
generation of new project ideas, as it will be explained later. The outputs from the
external analysis activity are presented in Table 7.2.
Contrary to the internal analysis, the methodology is proposed to be implemented as a
one-time activity, i.e., whenever the organization engages in the strategic decision
making process. The output of this process is the definition of technology strategy
guidelines, at a broad level, constituted by the most relevant events, based on the
relevance index, and the strategic technological competences, resulting from the cross-
relationship analysis. These guidelines set out the process for collecting data and
information to support the generation of projects.
Table 7.2 - The outputs from the external analysis activity
Outputs Description
Delphi survey analysis
Convergence analysis on the judgements provided by the
experts, on the impact, likelihood and time of occurrence for
each future event contained in the Delphi survey.
Cross relationships
analysis
The cross relationships analysis performed in the Quality
Function Deployment matrix, as well as the supporting
justifications.
Strategic guidelines
A rank of the most important events, based on the relevance
index, and the strategic technological competences, resulting
from the cross relationship analysis. Also includes the
period during which the strategic guidelines are valid.
7.2.3 Selection
The definition of the strategic guidelines for the organization in the external analysis
activity, followed by data and information collection from numerous sources, stimulates
the generation of new project ideas. Due to resource constraints, organizations often put
forward project selection processes. According to Archer and Ghasemzadeh, important
considerations should be embedded in project selection methodologies (Archer and
Ghasemzadeh, 1999), which include, among others: consideration of internal and
external business factors before selection (addressed in this thesis by the internal and
external analysis activities); organization in a structured and logical manner and allow
reviews and controlling mechanisms to provide feedback to decision-makers.
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Project selection methodologies should also consider different types of R&D (Mitchell,
1990, Coldrick et al., 2005, Tidd et al., 2005, Lawson et al., 2006, Verbano and
Nosella, 2010). R&D projects can be classified into three types, depending on the
technology maturity level (OECD, 2002): basic research, applied research, advanced
technology development. In for profit organizations, a fourth category can be included,
namely product development, when technologies are mature enough to be incorporated
into a product to be commercially exploited. Each of these R&D types have different
objectives: basic research aims at knowledge and competence building, applied and
advanced technology development at testing prototypes and technological systems, and
product development at developing a product that is commercially and economically
attractive. Given their different objectives, evaluations of R&D projects should be made
by considering projects of the same type, using appropriate criteria that reflect their
nature (Tidd et al., 2005), gradually considering more market related issues as the
technology matures.
Risk and uncertainty, arising from the unpredictability of the environment and the
technology development capability of the organization should also be taken into
consideration in project selection (Fahrni and Spätig, 1990, Henriksen and Traynor,
1999, Ghasemzadeh and Archer, 2000, Poh et al., 2001). In project management,
downside deviations from planned objectives are manifested in three risk indicators:
schedule risk, cost risk and performance risk (INCOSE, 2006). In order to improve
projects’ success rates, risk should be managed in all stages of R&D projects (Wang et
al., 2010). The methodology proposed for the selection activity addresses this issue, by
incorporating a risk assessment and control mechanism in the project selection phase.
The incorporation of such mechanism on an early phase of the projects’ life cycle, (such
is the selection phase) enables more time for managers to prepare and implement risk
response plans.
As suggested by Anderson and Nolte, the maturation rate of a technology is a driver for
risk management activities (Anderson and Nolte, 2005). In other words and in line with
Tidd et al., projects of different R&D types have different orders of magnitude in terms
of investment, thus the perspective on risk changes as higher investments are made in
technology development (Tidd et al., 2005). In order to address this complexity, the
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project selection methodology proposes a homogenization of the organization’s policy
towards risk management, by clustering projects into ranges (schedule, cost and
performance), according to the organization reality. In each of these ranges, perspective
on risk is modeled through an impact function. As such the impact of not achieving the
goals defined for projects’ schedule, cost and performance is calculated using an utility
based loss function (Ben-Asher, 2008) to reflect the different perspectives according to
the different levels of investment and performance. Uncertainty is modeled in project
planning, through Monte Carlo simulations, which deals the probabilistic component of
the risk calculation. Projects’ schedule and cost data are introduced and assessed using
PERT analysis. Performance in product development is assed using the demand model
proposed by Cook (Cook, 1997).
Finally, the proposed methodology incorporates a multi criteria decision method – the
AHP – to assist managers in the evaluation of projects. Built in criteria for project
selection and execution mode is derived from literature review, and reflect the nature of
each type of R&D project. These criteria reflect both qualitative (strategy alignment,
timing of introduction, etc.) and quantitative (risk, economic attractiveness, project cost,
etc.) aspects of projects. The proposed methodology aligns with the perspective of
Chiesa, who argue that projects should be evaluated according to their relevance (or
benefits) and risks (Chiesa, 2001). The methodology is developed in a logical manner,
and its structure is shown in Figure 6.12. The outputs from the selection activity are
summarized in Table 7.3.
R&D managers, executive board managers such as the Chief Executive Officer (CEO),
the Chief Technology Officer (CTO), Chief Knowledge Officer (CKO), Chief
Marketing Officer (CMO) and others directly responsible for the strategic direction of
the organization are suggested to participate in the project selection activity. Mid-level
managers can also be present, and contribute with a “in the field” or bottom-up
perspective. Finally, the designated risk management board of the organization, if it
exists, should also participate in order to contribute to the projects’ risk analysis.
Similarly to the external analysis, the project selection methodology is supposed to be
implemented by the time when the organization engages in the strategic decision
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making process. The output is a rank of projects, from which the organization must
select, within existing limitations of resources (budget, human resources, etc.).
Table 7.3 - The outputs from the selection activity
Outputs Description
Judgements and
ranking of projects
Judgements provided by decision-makers on the criteria used
for selecting projects, and the resulting ranking of projects
from the multi criteria analysis.
Accepted projects
The project proposals of selected containing the information
that served as basis for comparing the projects. The project
proposals are categorized according to the type of R&D (basic
research, applied research, advanced technology
development) or product development.
Risk analysis
The risk analysis performed on the schedule, cost and
performance of selected projects, to enable risk monitoring
and tracking throughout the execution of these projects.
7.3 Intelligence systems and information requirements for the
generation of projects
The activity that follows the external analysis and precedes selection activities is the
generation of projects. As mentioned in Chapter 2, this activity is shaped by two forces:
1) the data and information collection, analysis and dissemination efforts of the
organization; and 2) the creativity and imagination of the individuals and teams engaged
in the technology strategy formulation process. The first force is related to the
development of technology intelligence systems, as defined by Savioz and colleagues
(Savioz et al., 2001). The second force is intrinsically related to the creative capability
of the organization, which may or not use more structured methods such as
brainstorming, focus groups, etc.. Despite its undeniable relevance, the development of
a methodology for the generation comprises areas of knowledge that are not of object of
analysis for this thesis.
Still, and in an effort to integrate all the methodological developments into a single
framework to support the formulation of technology strategy, a deeper analysis of the
generation activity – the “missing link” - becomes important.
The strategic guidelines defined by external analysis activity, described in Table 7.2, are
insufficient to support the generation of projects ideas. More detailed data and
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information need to be collected and analyzed, not only to stimulate the generation of
new project ideas, but also to support the characterization of projects’ proposals, and
reduce uncertainty and ambiguity in the selection activity. In this sense, the strategic
guidelines resulting from the external analysis activity serve the purpose of preventing
intelligence gathering from becoming a diffuse process, engaging in gathering
information not necessary to the organization's strategy.
The organization of the technology intelligence process has been studied by a number of
authors. According to Norling and colleagues, the technology intelligence process is
composed of four steps (Norling et al., 2000), as described in Figure 7.1:
planning, organizing and directing the competitive intelligence effort;
collecting intelligence information;
analyzing the data;
disseminating the results of intelligence for action.
Figure 7.1 - A generic technology intelligence process. Source: (Norling et al., 2000)
A more in-depth analysis is performed by Lichtenthaler, who studied the technology
intelligence process in twenty five multinationals from the pharmaceutical,
telecommunications and automobile industries. This analysis led to the identification of
three types of technology intelligence process in organizations (Lichtenthaler, 2007):
hierarchical, participatory and hybrid. In the hierarchical type, the process usually starts
with individual researchers becoming aware of a new technological trend, followed by
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the formulation of a project proposal, where the technology is assessed in more detail,
with or without the support of technology intelligence specialists and external experts.
The proposal is then communicated to the top management, who, based on the
assessment of the technology decides on whether to approve or reject the proposal. This
approach is often found in science-driven industries (such as the pharmaceutical
industry), characterized by internal competition of ideas and centralized and formalized
decision-making process. The hierarchical process presents a number of advantages,
such as established communication routines enabling faster decision making and clearly
defined motivation mechanisms. On the other hand, this approach may suffer from
overvaluations or undervaluations of technologies, fundamentally derived from
researchers and specialists lack of competence in assessing the technology holistically.
Figure 7.2 - Three types of organizing technology intelligence process: (a) hierarchical, (b) participatory
and (c) hybrid. Source: (Lichtenthaler, 2007)
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In the participatory approach, the technology is identified by R&D employees, and its
relevance is tested in an exploratory research project. After the test, the technology is
communicated to middle management, leading to intense discussions until a consensual
assessment is reached. Only then, is the technology communicated to top management,
that often bases their decisions by mediating between interest groups rather than relying
on technology assessment reports from intelligence specialists. This type of technology
intelligence approach is typical of a control-oriented, centralized decision-making and
consensus-driven engineering culture, and presents some advantages, among them the
earlier identification and discussion of trends, promoted by the widespread participation
of employees and middle management. Disadvantages include discussions between
interest groups leading to intense conflicts, and suboptimal decisions influenced by
mediation rather than objective assessments.
Finally, in the hybrid approach, technology is identified by individual researchers and
tested in an exploratory research project. Then, with the support of technology
intelligence specialists, the technology is communicated to top management, who make
a broad test of its relevance and initiate a more in-depth assessment process, with the
participation of all interest groups, including middle management and research groups.
Middle management is included because they are regarded as being capable of making
an assessment based on both strategic and scientific aspects of the technology. This type
of technology intelligence is typical of pragmatic innovation environments, with
formalized but decentralized planning. Advantages include early identification of
trends; quick decisions, due to defined communication routines; and effective
participatory assessments. Risks exist if consensus is not achieved between interest
groups.
Despite the fact that the technology intelligence processes described above present a
certain organizational structure, another study, conducted with large corporations,
revealed that the majority of them have unstructured and unsystematic technology
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intelligence processes11
, that is also highly person oriented, very dependent on networks
and on research capabilities of the organizations (Reger, 2001).
Data and information collection and analysis capability is of fundamental importance in
the technology intelligence process. According to Lichtenthaler, this capability is related
to scanning and monitoring the environment for technology opportunities
(Lichtenthaler, 2004). Difference between these two activities is that scanning takes
place before the decision to invest or not on a technology is made, while monitoring
takes place after this decision is made. Both scanning and monitoring can be done either
in a passive manner, as part of the normal job of a researcher, or in an active manner,
which is a deliberate search for new technologies beyond the industry boundaries, and
often includes the participation of dedicated personnel and even external experts.
Basing their analysis on the awareness and provision of the intelligence process in
organizations, Kerr and colleagues conceptualize four data and information collection
types (Kerr et al., 2006):
mine: extracting explicit intelligence information from internal sources;
trawl: making in-house and non-formalized information explicit;
target: monitor the development of new technologies seen as relevant for the
future;
scan: be aware of any technological development that might have an impact on
the business.
Sources of information are various, as outlined by Reger: patents, scientific
publications, magazines, reports, internet searches, trade fairs, workshops, conferences,
personal contacts, networking with suppliers/manufacturers/competitors/universities and
research institutions, customers’ complaints and many others. But intelligence is not
simply collecting information, since a “major portion of technology intelligence is in
the analysis and dissemination of intelligence –the “delivery”, not just the “capture””
11 The term used in Reger’s study is technology foresight instead of technology intelligence, although the process described by the
author closely resembles an intelligence process. Lack of consensus in terminologies has given rise to numerous names, including
technology surveillance, scanning, prognosis and many others.
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(Kerr et al., 2006, p. 75). This analysis should not only focus on technology, but also on
other domains such as the political, regulatory, economic, market, social and
competition domains. Tools that may be used in these analyses are again numerous,
usually related to analyzing large scientific and technology databases, such as data and
text mining, bibliometrics, patent analysis and others (Castellanos and Torres, 2010).
Dissemination of these analyses is often made using databases and repositories,
supported by effective communication channels.
As described above, it can be said that the implementation of technology intelligence
systems, with the purpose of supporting the generation of new project ideas, is a process
that is highly dependent on the characteristics of the organization. This makes the
generalization of an intelligence system applicable and effective in a wide range of
environments a complicated task. This process is still unstructured in many large
corporations, which can be explained by many reasons. One of them is the general
belief that too much structure may cause biased thinking, thus preventing people to
think “outside the box” and breakthrough ideas to emerge. Another reason is to have
reward systems based on the competition of ideas, and motivate people to search
external sources of information and make more use of networking.
However, and in order to present an integrated framework to support the formulation of
a technology strategy, the existence of an intelligence system for data and information
collection and analysis is crucial to link internal and external analyses activities to the
selection activity. A backwards approach is followed, based on the information
contained in the projects proposals, in order to map the information requirements that
must be considered in the intelligence system.
An observation of the information contained in project proposals reveals three different
types: information related to an internal appraisals, for example the familiarity with the
research topic/technology/product, the availability of resources and competences to
conduct the project, interdependencies/synergies with other projects, and alignment with
the business strategy of the organization; related to an external appraisals, primarily
related to knowledge issues (scientific background, research originality), market issues
(size, growth rates, market needs, competition, timing for introduction), technological
issues (potential technologies, patentability/design protection, benefits from standard
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setting and stages in technologies life cycles), product issues (product differentiation
and range growth potential) and environmental issues (expertise level and experience
with collaborators and suppliers and availability of incentives and stimulus); and
information related to both internal and external appraisals, generally related to
projects risks, which can be originated from inside or outside the organization. Based on
the topics included in the project proposals, the information needs as well as
information sources can be identified, as described in Table 7.4.
Table 7.4 – Proposed organization of information needs and sources.
Project proposal topic BR AR ATD PD Information needs Typical information
sources
Familiarity with topic X X X X
Previous experiences with
similar research topics,
technologies or products.
Internal project reports.
Resources and competences
to conduct research X X
Available competences to
the organization.
Competences assessment
module from Internal
analysis activity.
Complementary assets X
Complementary assets
needed to conduct product
development (distribution
channels, manufacturing
processes, etc.).
Networking, industry
publications.
Observable trends/urgency X X X X
Trends (technology,
societal, environmental,
market, and others) and
their time of occurrence
likely to influence the
project.
Scientific publications,
networking with experts,
industry publications,
Delphi surveys, special
reports, magazines, and
others.
Scientific
background/research
originality
X X
Analysis on the scientific
theoretical basis of the
research.
Scientific publications.
Interdependencies/synergies
with other projects X X X X
Availability of
complementary
technologies and products.
Internal projects reports,
competitors’ product
offerings, and patents.
Research risks X X Technical risks in research.
Networking, previous
research conducted in
similar areas.
Technology development
risks X X
Technical risks in the
development of the
technology.
Feasibility tests,
networking with
technology experts.
Product development risks X
Technical risks in the
development of the
product.
Feasibility tests,
networking with experts.
Potential technologies X X
Technologies and
applications that emerge
from research and
development
Patents, customers’
surveys, market reports,
industry publications.
Patentability/design
protection X X X
Registered patents relatable
to the technology under
consideration. Trademarks
and copyrights.
Patents, trademarks and
copyrights database,
competitors
benchmarking reports.
Benefits from standard
setting X X X
Compatibility with other
products, from the
organization or from
others.
Patents, competitors
benchmarking reports.
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Table 7.4 (continued)
Project proposal topic BR AR ATD PD Information needs Typical information
sources
Stage in technology life
cycle X X
Market adoption rate of the
technology to be
developed, or the
technologies in the product
to be developed.
Market reports, industry
publications.
Market size X X X
Market size addressable by
the technologies or
products to be developed.
Market reports, industry
publications.
Market growth rate X X X
Growth rates of the
markets addressable by the
technologies or products to
be developed.
Market reports, industry
publications.
Market needs X X X
Needs of the markets
addressable by
technologies or products to
be developed.
Customers’ surveys,
interviews, networking,
market reports, industry
publications.
Competition X X X
Competitors in each
market, their products,
market share, revenues
Interviews, networking,
market reports, industry
publications.
Timing of introduction X
Product offerings from
competitors, switching cost
to customers, markets
growth rates.
Customers’ surveys,
networking, market
reports, industry
publications.
Economic attractiveness X
Data needed in the demand
model: market size and
forecast (number of units
and value) characteristics
and prices of competitors'
products and their market
shares, manufacturing
costs of the product to be
developed, inflation rate.
Manufacturing costs of
similar products,
customers’ surveys,
networking, market
reports, industry
publications.
Product differentiation X Product offerings from
competitors.
Customers’ surveys,
networking, market
reports, industry
publications.
Product range growth
potential X
Emerging customers’
needs and markets.
Customers’ surveys,
networking, market
reports, industry
publications.
Legend: BR – Basic research, AR – Applied research, ATD – Advanced technology development and PD
– Product development
The data and information needs described above should not only be collected and stored
in a repository, but should also be analyzed in order to better support the decision
making process within the organization. For this purpose, there are several analytical
methods, which should be adapted to the needs and structure of the organization. The
next section describes the integration of the proposed methodologies in a framework to
support the formulation of technology strategy.
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7.4 Integrated technology strategy framework
The framework to support technological strategy formulation, presented in this section,
integrates the methodologies proposed in the previous chapters. It is argued that the
integrated framework presents an improved strategy formulation process, namely due to
the incorporation of the individual contributions from the methodologies proposed for
the internal analysis, external analysis and selection activities.
However, the conceptualization of the integrated technology is not entirely linear as
suggested by the generic structure of Figure 1.2, with internal and external analysis
activities starting and finishing before generation and selection activities. The intrinsic
characteristics of the proposed methodologies require certain adjustments to be made in
order to enable their effective integration into the framework. For example, the
assessment of the capabilities within the internal analysis activity is modeled as a
continuous process in the organization. This process is further detailed below.
Figure 7.3 presents the integrated framework, through a diagram which connects the
different activities and underlying methodologies proposed as part of this thesis.
Attention has been paid to the chronology of activities: the dark arrow at the bottom of
the diagram represents passage of time. The strategy formulation starts with two
processes. The first is the identification of future events and invitations to fill a Delphi
survey and the definition of organizational policies towards risk, under the activity
“Initiate strategy process”. The identification of future events is performed through
semi-structured interviews with industry and academia experts and analysis on selected
publications, a process which is described in Chapter 5. An analysis on these interviews
leads to the identification of relevant future events, which form the output of the first
Delphi study. Then, invitations are sent to experts from inside and outside the
organization. The second process (“organizational policies towards risk”) concerns the
definition of the organization’s perspective on risk among different levels of investment
made in technology development projects. This process is seen as necessary for a
homogenization of the risk policies with regard to projects to be developed during the
strategic cycle. This process consists of defining projects ranges (cost, duration and
performance) and the definition of impact functions for each range that will later serve
as a basis for risk assessments on the projects resulting from the strategy formulation
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process. The development of the impact functions (or utility based loss functions) was
described in Chapter 6.
After the period designated for filling the Delphi survey, an analysis is carried on the
results, under the activity external analysis. The analysis on the convergence of
judgements - which then serves to calculate the relevance index for each event - and the
cross relationships analysis, result in the identification of the most important events of
the future and the strategic technological competences, which should be developed as
part of the technology strategy of the organization. This information is reported in a
dossier and constitutes the guidelines for the next strategic cycle of the organization,
and should be disseminated throughout the departments involved in the innovation
process inside the organization.
These strategic guidelines provide direction and drive the information collection and
analysis efforts, under the generation activity. This prevents the information collection
and analysis from becoming chaotic. This information gathered is stored in an
intelligence database, which is available to any employee involved in the creative or
fuzzy front end of the innovation process. Table 7.4 provides a typology of these
information and typical sources. Based on this information, a creative process, which
can be triggered individually or in group, such as in brainstorming or focus groups
sessions, stimulates the generation of new project ideas. Relevant information for the
generation of projects derives from the competences assessment module, under the
internal analysis activity. New project ideas can also be originated by analyzing
intelligence data coupled with available competences (Chiesa, 2001), in terms of
existing products and technologies, intellectual property of the organization, and
expertise and skills from human resources and existing manufacturing processes. The
competences assessment module is also linked to a resource pool database, from which
resources are allocated to projects during project planning, in the selection activity.
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Capabilities assessment
Participants comments Human resources
Intellectual property Products and technologies
Capabilities assessment module
External analysis
Delphi survey
analysis
Cross relationships
analysis
Strategic technological
competences
Risk assessment
Planning
Selection
Project Proposal
Economic attractiveness*
Internal analysis
Project
proposals
Intelligence
data
Rejected
projects
Sensitivity analysis*
Generation
Strategic Guidelines
Project ideas
Creative
process
Competences assessment module
Multi criteria
analysis
Project 1
Risk control
Resource
pool
.
.
.
Execution mode
Categorization of projects
Identification of events and
invitations to fill Delphi survey
Characterization
Utility based
loss function
Initiate strategy process
Organizational policies towards
risk
Characterization
* only in product development projects
Information
gathering and
analysis
More than one
execution mode?
Selected execution
modeYES
NO
Manufacturing resources
Multi criteria
analysis
Approved projects
Project 2
Project n
Figure 7.3 - Integrated technology strategy framework.
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Once the strategic guidelines are defined and disseminated, generation activity becomes
a continuous activity within the organization until the end of the defined strategic cycle,
meaning that new project ideas may surface at any time. The internal analysis is also a
continuous activity throughout the whole strategy formulation process. The capability
assessment module is based on the combination of Real Time Delphi with the audit, as
described in Chapter 4, and should be always available for participants to evaluate the
innovation process, and to also provide suggestions on how to improve. This real time
information works as an “X-ray” of the innovation process “health”, and can be
collected at any time to enable faster corrections and implementation of improvement
actions. This can be of great interest to improve the execution performance of projects,
for example. The competences assessment module, as mentioned before, should be
updated whenever any change in available competences is made, for example, when
hiring a new engineer, acquiring new manufacturing equipment, registering a new
patent or expanding the products’ portfolio.
The selection activity is the next step in the process. It is triggered when the number of
project ideas is such that the organization is forced to select only the most promising
projects, within budget limitations. When single projects are considered, no selection
procedure is necessary, but a project proposal document still needs to be prepared. The
selection activity follows the same methodology described in Figure 6.12, and is
simplified in Figure 7.3 for space reasons. The new project ideas from the generation
activity result from many information sources (patents, scientific databases, industry
reports, etc.) and may represent different technology maturity levels. Therefore, new
projects ideas are categorized according to the R&D type, so that their characterization
and comparison will follow a different path in the selection activity. Considerations
about the project execution mode are addressed in a first stage, then a project proposal
planning document is prepared, from which decision-makers will base their decisions on
whether to approve or to reject the project. Rejected projects proposal documents are
stored in a database. Approved projects are then initiated, and risk assessments are
updated at each project review, under the activity “Risk control”. Unexpected changes
in the environment experienced during the project execution may alter the
organizational perspective on risk, which may cause the organization to alter its policy
towards risk management. This is represented in Figure 7.3 by the returning arrow from
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“Risk Control” to “Organizational policies towards risk”, which triggers new impact
functions to be used in projects’ risk assessments. Another factor that influences “Risk
Control” activity is related to improvement actions implemented in the innovation
process, resulting from the suggestions made by participants of the real time audit from
the internal analysis activity. These actions may cause efficiency gains in the innovation
process, which in turn influence the projects’ risk assessments through their execution.
The resource pool databases also contain information about resources usage in other
projects under execution. Therefore, and in order to address issues of resource
competition and overloading during the execution of projects, as described in Chapter
6., resource pool databases are also linked to “Risk Control” activity, to address the
issues of resource competition and overloading during the execution of projects.
The generation and selection activities depicted in this framework are therefore
continuous processes, during the period in which the strategic guidelines from the
external analysis activity are valid. The framework’s contributions, when compared
with other proposed frameworks, relates directly to the individual contributions of each
integrated methodology: the real time assessment of organization capabilities, avoidance
of social bias imposed by hierarchy, elimination of time pressures and geographical
constraints, and the conceptualization of a dynamic forum, which enables faster
identification and treatment of problems and implementation of improvement actions (in
internal analysis); the holistic perspective on the future through the analysis of
relationships between events, the background platform to justify investments in
technology development and an improved linkage between technology foresight and
technology strategy (in the external analysis activity). In the selection activity,
contributes relate to integration of risk management practices early on projects’ life
cycle enabling more time for managers to prepare and implement risk responses;
homogenization of an organizational policy towards risk management; a balanced
evaluation of projects, which considers both tangible, intangible selection criteria, as
well as positive (benefits) and negative (risks) aspects of projects and the consideration
of different technology maturity rates and R&D types. The structure of the framework
ensures that the intelligence efforts (“generation”) are aligned with the strategic
guidelines set by the organization.
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The framework also addresses key elements of technology strategy, namely:
Technology selection and technology acquisition mode (Porter, 1985, Hax and
Majluf, 1991, Chiesa, 2001, Lindsay, 2001, Burgelman et al., 2004) – which
was expanded to project selection and execution mode in order to address
different technologies readiness levels
Timing of introduction (Hax and Majluf, 1991, Chiesa, 2001, Burgelman et al.,
2004),
Selection, evaluation, resource allocation and control of projects
(Hax and Majluf, 1991),
Organization and management approach of technology and innovation
(Hax and Majluf, 1991, Burgelman et al., 2004);
required technological competences and capabilities (Burgelman et al., 2004).
Finally, and relating to the framework’s classification models described in the Literature
Review of this thesis (Chapter 2), it can be said that this is an applied framework, since
it deals with implementation issues in real environments. The framework relates to both
internal (competences and capabilities) and external (technology, market trends, and
others), so it addresses both the positioning and resource based schools of strategy. The
framework follows a rational approach, given the structured process, even though it also
acknowledges uncertainty and risk, which is typical of frameworks that advocate an
incremental approach.
7.5 Conclusions
The technology strategy framework proposed in this thesis, which is conceptualized as a
result of the integration of the proposed methods, also incorporates those contributions
as expected. Furthermore, synergies observed between the methodologies can further
enhance individual contributions in the integrated framework. Adopting a more macro
perspective concerning the implications involved in its implementation in organizations,
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three relevant characteristics of the framework are identified: traceability, transparency
and structure.
Traceability is related to the fact that assumptions and premises used in the application
of methodologies, such as judgements made by employees (in the internal analysis) or
middle and top management with the support of external technology experts (in the
external analysis), estimates used in projects planning, risk assessments and others (in
selection), can be traced back. This is of critical importance so managers can check
inconsistencies in the inputs provided in the methods, and thus find ways to correct
them in future strategy formulation cycles.
Transparency is reflected in the existing links and information flows between the
activities and tools in the proposed framework. In other words, the inputs and resulting
analyses are made visible to the people involved in the strategy formulation (many from
different hierarchical levels), through a process that is understood within the
organization. The clarity about the definition of underpinning activities also contributes
to an increased understanding among stakeholders. Moreover, one can argue that greater
transparency would contribute to greater motivation since stakeholders would feel as
“part of the process”. In the internal analysis, transparency is ensured by enabling the
real time visualization of the judgements and comments of participants of the audit. In
the external analysis, transparency is ensured through the analysis of the Delphi survey
and the relationship assessments between non-technology related events and technology
related events. The open discussion promoted by the proposed methodologies is likely
to contribute towards more creative and cooperative environments in organizations.
Data and information needs for the generation and selection of projects are made
transparent through the forms that feed the project proposal documents.
Finally, the structure suggested by the framework brings rationality to the strategic
process, through a logical progression towards strategic decision-making, without
neglecting the need to introduce some flexibility, translated in the uncertainty inherent
to technological developments. Its managerial implications are related to presenting a
structured process for technology strategy formulation, where analyses precede
decision-making, thus ensuring strategic alignment and focus throughout the process.
Unlike the formality and rigidity in typical strategic methods, the proposed framework
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incorporates a risk management mechanism that provides control over the selected
projects, in light of the dynamics (internal and external to the organization) that might
affect their return to the organization. Such mechanism provides important feedback to
managers, which can serve as basis and rationale for taking action, such as risk sharing
initiatives or even the termination of the project.
Criticisms may arise, possibly stating that too much structure may prevent breakthrough
ideas to emerge. In order to prevent this to happen, it is highly advisable to include
experts from different (but relatable) backgrounds in the definition of the strategic
guidelines of the organization. This supports the creation of a comprehensive platform,
capable to observe relationships between different events, thus preventing the strategic
guidelines set by the organization from being originated by biased thinking.
Another possible criticism is the lack of considerations about possible organizational
structures that support the implementation of the framework, as well as a greater focus
on generation activity and methodologies for the analysis of strategic information. It is
expected that future developments of the framework address these issues in greater
depth.
The methodologies developed in previous chapters and integrated in the framework
were implemented the industrial partner of the thesis individually, this means, not as
part of the integrated framework. As such, the whole framework presented in this
chapter is a conceptualization, since it has not been implemented, from start to finish, in
a real environment. Future work should focus on implementing such framework in
industrial cases, and in finding ways to assess its validity for use in practice.
CHAPTER 8
Conclusions and future work
This chapter presents the conclusions and guidelines for future work. The
approach followed in the proposal of an improved technology strategy
framework focused on the development of methodologies that addresses
existing research gaps, unlike the approach usually followed mostly focused
on activities. As such, the methodologies proposed for each target activity
were integrated into a framework that brings together their contributions,
thus providing a more transparent and structured process. The proposed
framework has implications for both academia, in deepening the
understanding about management frameworks and applicable tools and
methodologies, and for industry as well, for integrating risk early on the
strategic process and providing a holistic perspective about technology. The
guidelines for future work suggest research to be conducted on a number of
methodologies, which may be carried out in the context of a larger
framework or individually.
.
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8.1 Conclusions
The increasing relevance of technology in the competitiveness of businesses has
stimulated the development of methodologies aimed at improving the analytical and
decision making capabilities of organizations, with regard to the formulation of
guidelines for future technology developments. In this context, the research conducted
and presented in this thesis addresses a challenging topic – technology strategy. The
technology strategy formulation is understood as a fundamental process for
organizations willing to use technological innovations as a basis for differentiation
(Porter, 1983, Chiesa, 2001).
This study was set out to explore the concept of technology strategy, with special
emphasis put in understanding the frameworks proposed to assist organizations in
addressing the decisions involved in the formulation of a technology strategy. This
analysis revealed the existence of a number of frameworks that resulted from the
conceptualization of activities and applicable tools. Despite constituting valid proposals,
existing frameworks merely contribute with general advice and recommendations on the
use application of the tools. Given the growing interest in technology management, and
aligned with the research stream related to development of new tools and methodologies
(Phaal et al., 2006), it was argued that an integrated framework developed from research
gaps identified in tools and methods applicable to each activity can contribute to a
technology strategy formulation process with improved features. This represents a
different approach from what has been done so far in the development of frameworks,
which has been focusing on the conceptualization of activities and processes, and then
on the search for applicable tools. The approach followed in this thesis focuses first on
developing tools and methods to address existing research gaps, and then they are
integrated within the activities that constitute the technology strategy process.
As such, the study sought to answer the following research question: how can different
tools and methods be combined and integrated to improve the process through which
organizations develop their technology strategy?
This research question required a greater understanding of the activities that constitute
this process. The findings from the state of the art analysis revealed a consolidation into
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four core activities: internal analysis, external analysis, generation and selection. The
internal analysis deals with the identification and assessment of internal technological
capabilities and competences. The external analysis aims at identifying future
technological trajectories of the industries that the organization operates and at
analyzing the drivers of technological change. The generation activity is related to the
generation of new project ideas, based upon the strategic guidelines provided in
previous analyses. Finally, selection deals with the selection of the most promising
projects, resulting from the generation activity.
Based on the theoretical basis that postulate that the technology strategy formulation
process is constituted of activities and tools (Centidamar et al., 2010) and, that the
process has been consolidated into the four core activities, the research adopted a
deductive approach and placed the hypotheses that improvements in technology strategy
frameworks could be realized through research on tools and methods underpinning core
activities. Among the four core activities, the generation activity was understood as the
one most dependent on the characteristics and creative capability of organizations,
which precludes generalizations to be made. Additionally, it comprises tools within
areas of knowledge that are out of the scope of this thesis. For these reasons, no
methodology has been proposed for this activity, although considerations about its
possible structure were addressed in order to enable its interaction with the other core
activities in an integrated framework.
A deep examination on tools and methodologies applicable to the internal analysis,
external analysis and selection activities, in order to identify research gaps was then
required. This analysis led to the formulation of three sub research questions for each
targeted core activity. These sub research questions guided the modeling formalizations
performed in each proposed methodology. The idea followed in the development of
these methodologies concerned the combination of stand-alone tools and methods into
methodologies in order to accommodate possible deficiencies and gaps (Liao, 2005,
Phaal et al., 2006). A synthesis of the main contributions from each developed
methodology is provided below.
In the internal analysis activity, the gap identified concerned the lack of approaches in
existing audits (the tool used in this activity) capable of addressing the dynamics of
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organizations and social concerns during self-assessments. The new audit derived from
an extensive review on empirical studies that relate specific organizational
characteristics and traits to the technological innovation capability of the organization
and an analysis on the innovation process of the industrial partner of the thesis. The
audit was embedded in a Group Support System from a web platform which is
accessible by all participants - who were asked to assess and comment about the
organizational performance in each audit statement- at any time. The audit was also
combined with the Real Time Delphi method, in order to deal with multiple
perspectives and facilitate the convergence of judgments.
In the external analysis, the gap identified concerned the inability of the Delphi method,
a tool commonly used by organizations to identify future and relevant events for an
industry, in dealing with three relevant issues: 1) need to synthetize information; 2)
explore cross-relationship analysis between external drivers and technology diffusion
and 3) provide guidance towards strategy formulation. Based on these issues and in line
with the emergent paradigm in foresight studies named Open foresight (von der Gracht
et al., 2010, Miemis et al., 2012), a new methodology was proposed and applied in the
industrial partner of thesis. The process begins with a Delphi survey conducted with a
panel of experts on a number of relevant future events for and industry. The results were
synthetized into a new metric, the event relevance index. This metric was then used as
input for the cross-relationship analysis between technologies (the technology related
events from the survey) and environmental drivers (the non-technology related events),
performed using a Quality Function Deployment matrix. The result of this process is a
set of strategic guidelines, which delineates the process of generating new project ideas.
In the selection activity, the addressed gap is concerned with the lack of approaches that
properly integrate risk management practices in project selection methodologies. The
proposed methodology incorporates, at the beginning of the process, a mechanism
where managers can define the organizational policy towards risk management in
different project ranges (in the form of impact functions) in terms of schedule, cost and
performance. The methodology also takes into account different types of R&D projects,
namely basic research, applied research, advanced technology development and also
product development projects. Monte Carlo simulations along with defined impact
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functions performed against targets result in estimations of schedule and cost risk.
Single and Multi-attribute Utility Analysis were used to measure performance in basic
research, applied research, advanced technology development, while in product
development projects, a demand model based on product attributes (Cook, 1997) is used
to measure the performance of the project, along with typical economic indicators to
assess the business attractiveness of the project.
All these risk measures, indicators and thorough characterization of the projects were
included as criteria in a Multi Criteria Model – the Analytic Hierarchy Process - which
is based on pairwise comparisons of criteria and alternatives. An extensive literature
review was conducted in order to identify applicable criteria for each type of R&D
project. The result of the multi criteria analysis was a rank of projects, which should be
selected within R&D budget limitations. A prototype software written in VBA language
for Microsoft Excel® was developed to support the characterization of projects (based
on identified criteria), project planning, risk assessments and multi criteria analysis.
The combination of tools into methodologies in each activity mentioned above may
contribute to a more transparent and logical organizational process according to the
objectives of each activity. In addition, these methodologies aim to complement the
potentialities of the most commonly used tools in each activity, such as the audits in the
internal analysis and the Delphi method in the external analysis. In order to not only
bring together the contributions from each methodology but to also explore possible
synergies between them, the next step concerned their integration in a technology
strategy framework.
The integrated technology strategy framework provides a novel conceptualization of an
organizational process that supports companies in analyzing and deciding on future
technological developments. Furthermore the integrated framework differs from
frameworks proposed in the literature, whose focus relied too much on the
conceptualization of activities, only slightly addressing the development of methods for
each activity. The proposed methodologies incorporate a number of sub models, and to
ensure a proper integration in the framework, adjustments needed to be made. Figure
8.1 represents a simplification of the integrated framework from Figure 7.3. Figure 8.1
also resembles the generic framework presented in the introductory chapter of this
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thesis, in Figure 1.2. As such, represents an evolution towards a greater integration and
combination of methodologies, rather than the conceptualization of activities. A
synthesis of the interactions between the various methodologies and activities is
provided below.
Inte
rn
al
An
aly
sis
External
Analysis
Gen
eratio
n
Selection
Initiate Strategy Process
Risk control
Risk
management
policies
Delphi survey
Figure 8.1 – Proposed generic technology strategy framework
The integrated framework presents a number of synergies between the activities and
methodologies, when compared to the generic framework from Figure 1.2. These
synergies have implications for both the organization and management of the strategic
process and for enhancing the contributions of the proposed methodologies. The former
is exemplified by the analysis on future events that serves as a background platform to
justify technology investments; the clear communication of strategic guidelines that
drive the generation of projects; the early definition of the organization’s risk
management policies, which are valid during the strategic cycle, in order to facilitate
risk analysis during project selection and risk control during projects execution; the
conceptualization of continuous activities that contribute to a greater flexibility and
faster adoption of corrective and improvements measures. The latter is characterized by
the linkage between the competences assessment module ("internal analysis") with
generation activity that can result in the generation of new project ideas through the
analysis of internal competences; the connection between the resources pool database
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that includes not only the technical expertise of human resources but their availability as
well, with the selection activity and Risk Control, whereas projects planning and their
risk analyses are also dependent on the resources usage in ongoing projects; the link
between Risk control and risk management policies, which allows the update of risk
policies in light of unexpected changes during projects execution and, finally, the
linkage between the capabilities assessment module from the internal analysis activity
with Risk Control, in the sense that the implementation of improvement actions
proposed by participants of the audit may have serious impacts in risk evaluations
throughout the execution of projects.
The proposed methodologies integrated in the framework also stress the increasing
relevance of information technologies in strategy support systems. In the internal and
external analysis activities, the survey was conducted on an online tool that, in addition
to enabling the collection and analysis of responses, included other interesting features,
namely the real-time and anonymous visualization of responses. Embedding these
methodologies in a web platform promotes internal and external networking and the
integration of a wide pool of knowledge in the definition of the strategic guidelines
(from the Delphi survey). In the selection activity, a prototype software written in VBA
language for Microsoft Excel® enabled the automation of a number of processes
involved in the application of the project selection methodology, as described
previously. One can expect that the relevance of the information technologies in
strategy making will only grow in the future. In the case of the proposed framework,
development should focus on the integration with other operational systems of
organizations, such as resource management (for selection), to enable the effects of
resource competition in the execution of projects, and its consequences in risk
assessments, and knowledge management systems (for the generation activity), towards
improved information and data gathering for the characterization of projects.
Despite the numerous contributions, the implementation of the methodologies presented
in this thesis also faces a number of barriers for adoption. The formulation of a
technology strategy is an inherently complex process, which requires methods and tools
capable of dealing with such multifaceted area. The investment level in a number of
systems underlying the framework (software, databases, networks of cooperation and
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260
others) may hamper the justification for investing in its implementation, thus inhibiting
adoption by organizations, particularly smaller ones. The methodologies require
extensive data and information collection, which can substantially increase the costs of
implementation. Even though the framework promotes networking among people of
diverse backgrounds and is supported by a number of communication platforms, its
structure can lead to the belief of a rigid process that restricts creativity. This may lead
to an underutilization of the proposed methodologies.
In the researcher’s understanding the greatest limitation of this work concerns the
validation of the methodologies, as only one implementation case is reported in the
thesis. The deductive approach used in this thesis departed from a theory – technology
strategy frameworks constituted activities and tools – towards conceptualizations aimed
at addressing specific research gaps and needs from practice. Tests were made in a
single case, which is not sufficient to make generalizations and build theories. Testing
the proposed methodologies in a larger number of settings would provide more insights
about their validity. Despite some advances in research methodologies, the issue of
validation of the management frameworks in industrial environments remains a
challenging topic.
Notwithstanding the fact that the application of the methodologies is merely illustrative,
still some important insights about their applicability could be gathered. It was not
possible to implement the proposed methodologies in a continuous strategic process
within the industrial partner, i.e., the application of each methodology was done in
isolated cases.
The multi-disciplinary trait of the theme forced research to be conducted in many areas
of knowledge – Strategic, Technology and Innovation management, Operations
Research, Project and Risk Management. This ensured greater richness and variety in
the research. In fact, the theme is so vast that a number of choices regarding research
focus needed to be made in order to be framed under the time horizon of a doctoral
research.
In the technology management field, the research stream that suggests the combination
of tools as a viable strategy for the development of more robust methodologies opens
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further conceptual opportunities for research. A shift in focus, from research “in”
methods (i.e., development of entirely new techniques) to research “on” methods
(integration and combination of existing techniques), is clearly observed in recent
publications. The idea followed in this thesis, to first restrict the activities in a process to
later investigate gaps and possible improvements in applicable tools and methods
applied may find resonance in other complex and transverse processes in organizations,
such as the innovation management, in a broader context than that presented in this
thesis, i.e., including innovation in services and processes. Furthermore, one can expect
that field based research will be frequently chosen as the primary research strategy to
test and validate such methodologies.
This thesis presents a set of methodological developments hoping to contribute to both
academia and industry. For academia, this thesis deepens the knowledge regarding
technology strategy frameworks, specifically those that are based on management
activities and incorporate a number of analytical and decision support tools.
The work presented in this thesis is also intended to benefit managers responsible for
technology development policy making in organizations, from intermediate levels such
as R&D managers, to top positions such as unit directors, chief technology officers and
others. The goal is for the proposed methodologies to address problems and issues of
particular concern in organizations in the strategic management of technology. For
example, the consideration of risk early in the process and awareness of internal
competences and capabilities may allow managers to have a greater consciousness
about the efforts needed to execute technology development projects and may lead to an
improved management of resources. As such, this also has implications to public
funding institutions in the management of scientific and technology development
programs. The holistic perspective of the framework leads to considerations to be made
not only in the technology but also in the external drivers that influence technological
change. Finally the proposed structure of the framework may contribute to an improved
communication of the strategic guidelines in the organization, transparency and
traceability of the strategic process.
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8.2 Future work
This thesis refers to a number of converging knowledge areas. The knowledge
generated and the existence of these unexplored areas can be converted into
opportunities for future research. The most relevant guidelines for future research
deriving from this thesis are listed below:
test the applicability of the proposed methodologies in other organizations. For
this purpose, a study on specific requirements of the core activities should be
conducted beforehand in order to find out which are the most relevant
requirements, and then validate the proposed methodologies with the
organizations, on how the methodologies meet such requirements;
study the implications of adopting the framework, such as which selection
criteria to use and how to assess risk, in the public sector for scientific and
technology development, such as national laboratories, research institutes and
universities;
explore possible synergies and interfaces of the framework with other operations
within the organization (marketing, manufacturing, etc..), as well as study
possible organizational structures, communication channels and reporting the
most appropriate for the application of the framework;
include broader strategic considerations in the framework, researching
constructs dealing with when to be leader or follower, how to approach the
market, level of specialization among others;
in order to expand the control systems in the present framework, beyond the
projects’ risk monitor mechanism, include a performance measurement system
in the framework, in order to monitor the effectiveness of the investment in
technological innovation. This system can be based on Key Performance
Indicators (KPI), and use typical metrics for measuring innovation performance,
such as average time to market, percentage of project ideas funded, sales from
new products and many others;
research on conflict management strategies to be applied in certain situations
prone to generate serious disagreements in organizations, namely in the
implementation of the audit and in project selection. In project selection, these
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situations include the definition of risk policies of the organization (the utility
based loss functions) and the estimates for duration and cost estimates projects’
risk assessments. In the case of the audit, disagreements may occur during the
organizations’ self-assessment, and a possible solution may be the inclusion of a
facilitator or mediator, whose profile and background is accepted by everyone in
the organization;
research should be conducted on which and how environmental factors influence
the adoption and diffusion of certain technologies. This analysis should
unavoidably take into account the dynamics of the industry, and therefore should
be industry-specific. This is of special relevance for the events relationships
analysis proposed for the external analysis activity;
design and development of the intelligence system for supporting the generation
of new project ideas and its integration in the framework;
in order to facilitate the identification of the technology maturity level, a
mechanism, in the form of a questionnaire for example, could be developed and
included before the characterization of the projects, thus helping managers to
determine the type of R&D project;
deepen the study on criteria for project selection and execution mode, which in
this thesis is based on an extensive review of the literature. This could be done
through surveys targeted at different industrial sectors in order to find the most
relevant criteria for each sector;
expand the capabilities of the project selection methodology to include
concurrent projects and even entire R&D programs consisting of interdependent
projects as alternatives to the multi criteria decision model. This inclusion
should take into account resource competition between projects, and for this
purpose resource allocation optimization algorithms could also be developed and
included;
consider other models that simulate specific events of projects execution
behavior, such as interactions between tasks and rework, and their consequent
impact on risk assessment;
include models for technologies valuation in monetary terms to include more
quantitative criteria in the Multi Criteria Decision Model. This inclusion would
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also enable the inclusion of other kinds of technological innovations, such as
processes and services. Selection criteria for these types of innovation should
also be investigated accordingly;
study easier-to-understand and more intuitive mechanisms to support the
definition of risk policies, which in the proposed framework is made through
successive questions, directed towards the development of utility based loss
functions.
The work developed in this thesis presents new perspectives for future research on a
number of methodologies, which may be carried out in the context of a larger
framework or individually. In this sense, one can expect that important steps have been
taken towards promising future developments.
265
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APPENDICES
288
289
Appendix 1
General information
Company Name ………………………………………………………
Sector ………………………………………………………
Address ………………………………………………………
Tel. ………………………………………………………
Fax. ………………………………………………………
Responsible ………………………………………………………
Position ………………………………………………………
Date ………………………………………………………
Technological competences
Human resources
Name Department Position Technical skills
290
Manufacturing processes
Process Technologies Equipment
Intellectual property
Designation/Name Description
291
Products and technologies
Product Technologies developed internally Outsourced technologies
292
293
Appendix 2
1 - Macro trends in the sheet metal forming equipment industry
i. Which generic macro trends can you observe in the sheet metal forming equipment
industry for the next ten years?
2 - Markets and needs
ii. Which emerging needs and trends in the use of sheet metal machinery can you
identify in the following sectors (if possible):
a. Automotive
b. Aeronautics
c. Shipyard industries
d. Renewable energies
e. Household appliances
f. Metallic buildings
g. Furniture
3 Technological evolution
iii. Can you identify technologies with relevant market potential in the sheet metal
machinery industry for the next 10 years?
iv. How do you foresee the development and adoption rate of the technologies you just
mentioned in the next 10 years? In which industries and markets would they have
greater impact?
v. Which drivers would increase the adoption of the technologies you just mentioned?
On the other hand, which barriers would limit a greater adoption by the market?
vi. Is there any chance that existing technologies will be replaced by the ones you just
mentioned?
vii. Can you foresee the state level of some technological variables, for the next 10
years?
294
295
Appendix 3
296
297
Appendix 4
Project proposal document template – Basic research
298
299
300
301
Project proposal document template –Applied research
302
303
304
Project proposal document template – Advanced technology
development
305
306
307
308
309
310
Project proposal document template – Product development
311
312
313
314
315
316
317
Appendix 5
Project relevance (Form 4.2.1)
318
Project relevance (Form 4.2.2)
319
Project relevance (Form 4.3.1)
320
Project relevance (Form 4.3.2)
321
Project relevance (Form 4.4.1)
322
Project relevance (Form 4.4.2)
323
Project execution modes (Form 5)
324
Project planning (Form 7)
Project planning (Form 8)
325
Schedule data (Form 9.2)
Cost data (Form 10.2)
326
Performance data (Form 12.1)
Performance data (Form 12.2)
327
Performance data (Form 13)
328
329
Appendix 6
Pairwise comparisons - Criteria
Capability Strategy Technology Product Market Project Development Priority vector
1 0.5 0.5 0.5 0.5 3 0.114654952
2 1 2 1 0.5 4 0.214325709
2 0.5 1 0.5 0.5 0.5 0.107162854
2 1 2 1 1 2 0.214325709
2 2 2 1 1 4 0.270033472
0.333333333 0.25 2 0.5 0.25 1 0.079497304
Pairwise comparisons – “Capability” sub criteria
Resources and competences to
conduct development Complementary assets Priority vector
1 1 0.5
1 1 0.5
Pairwise comparisons – “Product” sub criteria
Product differentiation Product range growth potential Priority vector
1 4 0.8
0.25 1 0.2
Pairwise comparisons – “Market” sub criteria
Market growth Clear market needs
Competitive
intensity
Timing of
introduction Priority vector
1 2 2 2 0.390524292
0.5 1 2 2 0.276142375
0.5 0.5 1 2 0.195262146
0.5 0.5 0.5 1 0.138071187
330
Pairwise comparisons – “Project Development” sub criteria
Economic attractiveness Cost risk Priority vector
1 2 0.666666667
0.5 1 0.333333333
Pairwise comparisons – Alternatives versus “Resources and competences to
conduct development” sub criterion
Project A Project B Project C Priority vector
1 2 2 0.493385967
0.5 1 0.5 0.195800351
0.5 2 1 0.310813683
Pairwise comparisons – Alternatives versus “Complementary assets” sub criterion
Project A Project B Project C Priority vector
1 9 3 0.671625453
0.111111111 1 0.2 0.062941205
0.333333333 5 1 0.265433342
Pairwise comparisons – Alternatives versus “Observable trends” sub criterion
Project A Project B Project C Priority vector
1 0.333333333 0.333333333 0.142857143
3 1 1 0.428571429
3 1 1 0.428571429
331
Pairwise comparisons – Alternatives versus “Patentability/design protection” sub
criterion
Project A Project B Project C Priority vector
1 1 0.25 0.174371455
1 1 0.333333333 0.19192062
4 3 1 0.633707925
Pairwise comparisons – Alternatives versus “Product differentiation” sub criterion
Project A Project B Project C Priority vector
1 0.333333333 0.333333333 0.139647939
3 1 0.5 0.332515928
3 2 1 0.527836133
Pairwise comparisons – Alternatives versus “Product range growth potential” sub
criterion
Project A Project B Project C Priority vector
1 1 1 0.333333333
1 1 1 0.333333333
1 1 1 0.333333333
Pairwise comparisons – Alternatives versus “Market growth” sub criterion
Project A Project B Project C Priority vector
1 2 1 0.4
0.5 1 0.5 0.2
1 2 1 0.4
332
Pairwise comparisons – Alternatives versus “Clear market needs” sub criterion
Project A Project B Project C Priority vector
1 0.5 0.333333333 0.157055789
2 1 0.333333333 0.249310525
3 3 1 0.593633685
Pairwise comparisons – Alternatives versus “Competitive intensity” sub criterion
Project A Project B Project C Priority vector
1 3 3 0.593633685
0.333333333 1 2 0.249310525
0.333333333 0.5 1 0.157055789
Pairwise comparisons – Alternatives versus “Timing of introduction” sub criterion
Project A Project B Project C Priority vector
1 1 1 0.333333333
1 1 1 0.333333333
1 1 1 0.333333333
Pairwise comparisons – Alternatives versus “Economic attractiveness” sub criterion
Project A Project B Project C Priority vector
1 0.25 0.333333333 0.117220771
4 1 3 0.614410656
3 0.333333333 1 0.268368573
Pairwise comparisons – Alternatives versus “Cost risk” sub criterion
Project A Project B Project C Priority vector
333
1 5 3 0.636985572
0.2 1 0.333333333 0.104729434
0.333333333 3 1 0.258284994
Alternatives Final and Normalized scores
Project Final score Normalized score
Project A 0.555616099 0.272099496
Project B 0.626608468 0.306866285
Project C 0.859734744 0.421034219
334
335
Appendix 7
CD-ROM:
Experts interviews transcripts
Installation file for project selection software
PDF file of the thesis
