Engenharia e Gerenciamento de Sistemas Espaciaisperondi/19.10.2009/CSE-314-4_19-10-2009.pdf2009/10/19 · L.F. Perondi CSE -314 4 Planejamento e Gestão da Qualidade 20/10/2009 6

CSE-314-4

Planejamento e Gestão da Qualidade

Engenharia e Tecnologia

Espaciais – ETE

Engenharia e Gerenciamento

de

Sistemas Espaciais

Gestão da Qualidade Total

Ferramentas e Técnicas

19.10.2009

L.F. Perondi

20/10/2009CSE-314-4 Planejamento e Gestão da Qualidade 2

SUMÁRIO

• Ciclo Planejar-fazer-controlar-agir (PDCA)

• Desdobramento da Função Qualidade (QFD)

• Ferramentas para o controle da qualidade

1. Diagramas de Causa-e-Efeito (Cause-and-Effect

Diagrams)

2. Fluxogramas (Flowcharts)

3. Listas de Verificação (Checklists)

4. Gráficos de Controle (Control Charts)

5. Gráficos de Correlação (Scatter Diagrams)

6. Análise de Pareto (Pareto Analysis)

7. Histogramas (Histograms)

L.F. Perondi


BIBLIOGRAFIA

ROZENFELD , H., FORCELLINI , F.A., AMARAL, D.C., TOLEDO, J.C., SILVA, S.L., ALLIPRANDINI,

D.H., SCALICE, R.K., Gestão de Desenvolvimento de Produtos, Saraiva, São Paulo, 2005.

PARK, S.H., Six Sigma for Quality and Productivity Promotion, Asian Productivity

Organization, Tokyo, 2003.

L.F. Perondi


Ferramentas para o controle

da qualidade

Material parcialmente adaptado de: Rozenfeld , H., Forcellini , F.A., Amaral, D.C., Toledo, J.C., Silva, S.L., Alliprandini, D.H.,

Scalice, R.K., GESTÃO DE DESENVOLVIMENTO DE PRODUTOS, Saraiva, São Paulo, 2005. http://www.portaldeconhecimentos.org.br/index.php/por/content/view/full/10294

Material extraído de: PARK, S.H., Six Sigma for Quality and Productivity Promotion, Asian

Productivity Organization, Tokyo, 2003.

L.F. Perondi

20/10/2009 5CSE-314-4 Planejamento e Gestão da Qualidade

Diagramas de Causa-e-Efeito

Denominado diagrama Espinha-de-Peixe (Fishbone Diagram)

Apropriado para a identificação da causa de um problema de qualidade identificado

L.F. Perondi


(1)Diagrama de Causa-e-Efeito

O diagrama de causa-e-efeito se presta ao auxílio de

processos de solução de problemas. Conhecido, também,

como diagrama de Ishikawa ou diagrama espinha-de-peixe

esta técnica estimula o aparecimento de idéias e contribui

para a organização de sessões de brainstroming, onde

pessoas listam as fontes percebidas (causas) de possíveis

problemas (efeitos). Como mostrado na Fig. 4.1, o efeito é

escrito em um retângulo à direita, enquanto que as causas

são escritas em retângulos à esquerda, e são conectados

com setas de modo a mostrar a relação de causa-e-efeito.

Quando construindo um diagrama de causa-e-efeito, é, em

geral, apropriado que se considerem seis possíveis

categorias de fontes para um dado problema : homem,

máquina, material, método, medida e ambiente (5M1A).



L.F. Perondi


Quando preparando um diagrama de causa-e-efeito, o

primeiro passo consiste em acordar sobre a definição do

defeito, para após passar à identificação das principais

fontes responsáveis pelo defeito. As fontes principais,

geralmente, podem ser classificadas no esquema 5M1A, o

que auxilia o início da análise, mas de forma alguma

esgota o estudo. Através do uso de técnicas de

brainstorming, cada possível fonte pode ser analisada,

visando o refino da lista de possíveis causas para esta

fonte, até que se chegue à(s) causa(s) raiz para a fonte em

questão. Na Fig. 4.1, o método consiste em uma fonte

principal para o defeito em estudo, a pressão e a

temperatura são as causas, e “pressão baixa” e

“temperatura muito alta” constituem-se nas causas raiz.



L.F. Perondi


Lista de Verificação

Lista de verificação projetada para registrar o número de

defeitos por categoria, em cada estação de trabalho – por

turno de trabalho, por máquina, por operador.

Material parcialmente adaptado e traduzido de: Reid, R.D., Sanders, N.R., Operations Mangement 2a. Ed., Wiley, New York, 2005. Transparências em Inglês, disponíveis a

partir de http://www.wiley.com/college/sc/reid/

L.F. Perondi


(2) Lista de verificação

A lista de verificação é utilizada para a coleta de dados

relativos a características de processos ou produtos que se

deseje aprimora. Por razões práticas, as listas de verificação

são, comumente, organizadas na forma de tabelas. Como

regra geral, a lista de verificação deve ser mantida simples e

sua organização deve ser consistente com o tipo dos dados

sendo coletados. Deve se ter o cuidado de definir quem irá

coletar os dados e quais são os intervalos de medida. Por

exemplo, a Fig. 4.2 apresenta uma lista de verificação para

itens defeituosos em um processo de montagem de câmbio

de automóvel.



L.F. Perondi


Gráfico mostrando o comportamento, ao longo do tempo ou número

de unidades fabricadas, de variáveis físicas do produto.

Ferramenta auxiliar utilizada no Controle Estatístico de Processos

UCL e LCL representam limites de tolerância da variável controlada.

Gráficos de Controle

L.F. Perondi


(3) Gráficos de Controle

(a) Introdução

O gráfico de controle é uma importante ferramenta no ciclo PDSA (plan,

do, study, act) para a melhoria contínua. Gráficos de controle são

utilizados para monitorar processos e atestar se estes são estáveis.

Prestam-se, também, à investigação das possíveis causas da instabilidade

de um processo.

O conceito original de gráfico de controle surgiu em 1924 e se deve a

Walter A. Shewhart, seu proponente. Esta ferramenta tem sido utilizada

na indústria de forma intensiva desde o final da Segunda Guerra

Mundial, especialmente no Japão e Estados Unidos. Prestam-se ao estudo

de variações e à identificação de suas causas; ao monitoramento e

controle; e podem prover indicações para melhorias. Podem, também,

proporcionar a identificação de causas para uma dada variação: se estas

são especiais ou comuns. Finalmente, propiciam a pronta identificação de

causas especiais, de modo que ações remediadoras possam ser logo

implementadas, evitando a produção de grande volume de produtos

defeituosos.Material extraído de: PARK, S.H., Six Sigma for Quality and Productivity Promotion, Asian Productivity Organization, Tokyo, 2003.

L.F. Perondi


Gráficos de controle de Shewhart proporcionam o acompanhamento

temporal de processos, como mostrado na Fig. 4.3. Podem ser plotados

tanto parâmetros variáveis (estatisticamente) quanto fixos. Exemplos de

parâmetros variáveis são a média (x), o range (R) e o desvio padrão (s) de

alguma variável do processo. Exemplos de parâmetros fixos são frações de

itens não-conformes (p), número de itens não-conformes (np), número de

não-conformidades (c), e não-conformidades por unidade de produto (u).

Limites de controle típicos são fixados em mais ou menos 3 desvios

padrão, em torno de um valor nominal. Quando um ponto cai fora destes

limites, é dito que o processo encontra-se fora de controle. Caso contrário,

o processo é dito estar sob controle. Há diferentes tipos de gráficos de

controle, dependendo da natureza e do número de parâmetros que se

queira supervisionar. Os dois principais tipos de gráficos de controle são

aqueles para valores monitorados contínuos ou discretos.

Contínuos: x, R e s.

Discretos: p, np, c e u.

L.F. Perondi


(b) Como é construído um gráfico de controle?

A seguir, é apresentada uma sequência de passos para a construção de

um gráfico de controle genérico.

Passo 1 – Selecione o parâmetro a ser monitorado e o tipo de gráfico

de controle

Inicialmente. deve-se escolher a característica do processo ou produto a

ser monitorada, para após escolher o tipo de gráfico.

Passo 2 – Determine o tamanho da amostra e o intervalo de

amostragem

Gráficos de controle baseiam-se, normalmente, em amostras de mesmo

tamanho, n. Para variáveis contínuas, é comum usarem-se de duas a

seis observações para construir o estimador da variável sob observação.

Para variáveis discretas, n pode ser relativamente grande, da ordem de

100 a 200 observações.

L.F. Perondi


Passo 3 – Compute a linha central e as linhas de controle

Todo gráfico de controle apresenta limites de controle, UCL e LCL, que

definem a região em que o processo se encontra sob controle. A linha

central (CL) encontra-se entre os limites de controle. A distância de CL

tanto a UCL quanto a LCL é igual a 3 desvios padrão da variável sob

monitoração.

Por exemplo, para n observações x1,x2,...,xn, as seguintes fórmulas são

utilizadas para o cálculo de CL, UCL e LCL para o gráfico da média:

L.F. Perondi


Table 4.1 contains CL, UCL and LCL for the respective control charts.

Step 4. Draw the control chart and check for special causes

The control chart can now be drawn, with CL, UCL and LCL. The

samples used for calculating the control limits are then plotted on the

chart to determine if the samples used to calculate the control limits

embody any special causes of variation. Special causes exist if any of the

following alarm rules apply:

• A single point falls outside the control limits.

• Two out of three consecutive points fall outside the control limits.

• Seven or more consecutive points fall to one side of the center line.

• A run of eight or more consecutive points is up (in increasing trend), or

down (in decreasing trend).

• At least 10 out of 11 consecutive points are on one side of the center

line.

• At least eight consecutive points make a cycle movement, which means

if a point is on one side of the center line, and the next point is on the

other side of the center line.

L.F. Perondi


Um diagrama que mostra a distribuição de freqüência de valores observados de uma dada variável por intervalo de valores da variável.

Constitui-se em uma aproximação para a distribuição de probabilidade de uma variável estocástica.

Histogramas

L.F. Perondi


(4) Histogram

It is meaningful to present data in a form that visually illustrates the

frequency of occurrence of values. In the analysis phase of the Six Sigma

improvement methodology, histograms are commonly applied to learn

about the distribution of the data within the results Ys and the causes Xs

collected in the measure phase and they are also used to obtain an

understanding of the potential for improvements.

To create a histogram when the response only “takes on” certain discrete

values, a tally is simply made each time a discrete value occurs. After a

number of responses are taken, the tally for the grouping of occurrences

can then be plotted in histogram form. For example, Figure 4.3 shows a

histogram of 200 rolls of two dice, where, for instance, the sum of the dice

was two for eight of these rolls. However, when making a histogram of

response data that are continuous, the data need to be placed into

classes or groups. The area of each bar in the histogram is made

proportional to the number of observations within each data value or

interval. The histogram shows both the process variation and the type of

distribution that the collected data entails.

Material extraído de: PARK, S.H., Six Sigma for Quality and Productivity Promotion, Asian Productivity Organization, Tokyo, 2003.

L.F. Perondi


Análise de Pareto

Identifica o percentual de erros devido a diferentes causas (A,B,C…).

Regularmente denominada de regra 80-20, leva o nome de um

economista do século 19.

Princípio afirma que a maioria dos problemas de qualidade advêm de

um pequeno número de causas, e.g., 80% dos problemas causados

por 20% das causas.



L.F. Perondi


(5) Pareto chart

The Pareto chart was introduced in the 1940s by Joseph M. Juran, who

named it after the Italian economist and statistician Vilfredo Pareto,

1848–1923. It is applied to distinguish the “vital few from the trivial

many” as Juran formulated the purpose of the Pareto chart. It is closely

related to the socalled 80/20 rule – “80% of the problems stem from 20%

of the causes,” or in Six Sigma terms “80% of the poor values in Y stem

from 20% of the Xs.”

In the Six Sigma improvement methodology, the Pareto chart has two

primary applications. One is for selecting appropriate improvement

projects in the define phase. Here it offers a very objective basis for

selection, based on, for example, frequency of occurrence, cost saving

and improvement potential in process performance. The other primary

application is in the analyze phase for identifying the vital few causes

(Xs) that will constitute the greatest improvement in Y if appropriate

measures are taken.


L.F. Perondi


A procedure to construct a Pareto chart is as follows:

1) Define the problem and process characteristics to use in the diagram.

2) Define the period of time for the diagram – for example, weekly, daily,

or shift. Quality improvements over time can later be made from the

information determined within this step.

3) Obtain the total number of times each characteristic occurred.

4) Rank the characteristics according to the totals from step 3.

5) Plot the number of occurrences of each characteristic in descending

order in a bar graph along with a cumulative percentage overlay.

6) Trivial columns can be lumped under one column designation;

however, care must be exercised not to omit small but important items.

Table 4.2 shows a summary table in which a total of 50 claims during

the first month of 2002 are classified into six different reasons. Figure

4.4 is the Pareto chart of the data in Table 4.2.

L.F. Perondi


Um gráfico mostrando a correlação entre duas variáveis.

Os dados são, normalmente, utilizados em uma análise de regressão para estabelecer uma expressão analítica relacionando as duas variáveis.

Gráficos de Correlação

L.F. Perondi


(6) Scatter diagram

The scatter plot is a useful way to discover the relationship between two

factors, X and Y, i.e., the correlation. An important feature of the scatter

plot is its visualization of the correlation pattern, through which the

relationship can be determined. In the improve phase of the Six Sigma

improvement methodology, one often searches the collected data for Xs

that have a special influence on Y. Knowing the existence of such

relationships, it is possible to identify input variables that cause special

variation of the result variable. It can then be determined how to set the

input variables, if they are controllable, so that the process is improved.


L.F. Perondi


When several Xs may influence the values of Y, one scatter plot should be

drawn for each combination of the Xs and Y. When constructing the

scatter diagram, it is common to place the input variable, X, on the X-axis

and the result variable, Y, on the Y-axis. The two variables can now be

plotted against each other and a scatter of plotted points appears. This

gives us a basic understanding of the relationship between X and Y, and

provides us with a basis for improvement. Table 4.3 shows a set of data

depicting the relationship between the process temperature (X) and the

length of the plastic product (Y) made in the process. Figure 4.5 shows a

scatter diagram of the data in Table 4.3.

L.F. Perondi


(7) Stratification

Stratification is a tool used to split collected data into subgroups in order

to determine if any of them contain special cause variation. Hence, data

from different sources in a process can be separated and analyzed

individually. Stratification is mainly used in the analyze phase to stratify

data in the search for special cause variation in the Six Sigma

improvement methodology.

The most important decision in using stratification is to determine the

criteria by which to stratify. Examples can be machines, material,

suppliers, shifts, day and night, age groups and so on. It is common to

stratify into two groups. If the number of observations is large enough,

more detailed stratification is also possible.


L.F. Perondi


Fluxogramas

Empregado na representação esquemática dos

passos de um processo.

Normalmente, o primeiro passo em um processo

de re-engenharia.

L.F. Perondi


Desdobramento da Função

Qualidade (QFD)



L.F. Perondi


Desdobramento da Função Qualidade (QFD)

- QFD é um método de sistemático de projetar a qualidade de um produto

ou serviço.

- Sistemática que objetiva traduzir as necessidades do cliente em

características (de qualidade, funcionais, de custo e confiabilidade) do

produto ou serviço.

- Sua aplicação pode ser muito mais ampla do que a definição indica.

Princípios são gerais, permitindo vários tipos de aplicações.

FONTE: http://www.portaldeconhecimentos.org.br/index.php/por/content/view/full/10294#eztoc126476_1

L.F. Perondi


QFD is:

•Understanding Customer Requirements

• Entendimento dos requisitos do usuário

•Quality Systems Thinking + Psychology + Knowledge/Epistemology

•Maximizing Positive Quality That Adds Value

•Comprehensive Quality System for Customer Satisfaction

•Strategy to Stay Ahead of The Game

• Understanding 'true' customer needs from the customer's perspective

• What 'value' means to the customer, from the customer's perspective

• Understanding how customers or end users become interested, choose,

and are satisfied

• Analyzing how do we know the needs of the customer

• Deciding what features to include

• Determining what level of performance to deliver

• Intelligently linking the needs of the customer with design,

development, engineering, manufacturing, and service functions

http://www.qfdi.org/

L.F. Perondi


QFD is a comprehensive quality system that systematically links the

needs of the customer with various business functions and

organizational processes, such as marketing, design, quality,

production, manufacturing, sales, etc., aligning the entire company

toward achieving a common goal.

It does so by seeking both spoken and unspoken needs, identifying

positive quality and business opportunities, and translating these into

actions and designs by using transparent analytic and prioritization

methods, empowering organizations to exceed normal expectations and

provide a level of unanticipated excitement that generates value.

The QFD methodology can be used for both tangible products and non-

tangible services, including manufactured goods, service industry,

software products, IT projects, business process development,

government, healthcare, environmental initiatives, and many other

applications.

L.F. Perondi


(1) Four phases of QFD

- Quality Function Deployment (QFD) is a structured technique

to ensure that customer requirements are built into the design of

products and processes.

- QFD is mainly applied in improvement projects on the design of

products and processes. Hence, QFD is perhaps the most important tool

for DFSS (design for Six Sigma).

QFD enables the translation of customer requirements into product and

process characteristics including target value. The tool is also applied in

Six Sigma to identify the critical-to-customer characteristics which

should be monitored and included in the measurement system.


L.F. Perondi


QFD was developed in Japan during the late 1960s by Shigeru Mizuno

(1910–1989) and Yoji Akao (1928–). It was first applied at the Kobe

shipyard of Mitsubishi Heavy Industry in 1972, with the Japanese car

industry following suit some years later. In the West, the car industry first

applied the tool in the mid 1980s. Since then, it has enjoyed a wide

dispersal across industries in a number of countries.

Although QFD is primarily used to map and systematically transform

customer requirements, this is not its only use. Other possible applications

concern the translation of market price into costs of products and

processes, and company strategies into goals for departments and work

areas.


L.F. Perondi


Basically, QFD can be divided into four phases of transformation

as shown in the figure. These four phases have been applied extensively,

especially in the automobile industry.

Phase 1: Market analysis to establish knowledge about current

customer requirements which are considered as critical for their

satisfaction with the product, competitors’ rating for the same

requirements and the translation into product characteristics.

Phase 2: Translation of critical product characteristics into

component characteristics, i.e., the product’s parts.

Phase 3: Translation of critical component characteristics into

process characteristics.

Phase 4: Translation of critical process characteristics into

production characteristics, i.e., instructions and measurements.

L.F. Perondi


The four phases embody five standard units of analysis always

transformed in the following order:

- customer requirements,

- product characteristics,

- component characteristics,

- process characteristics, and

- production characteristics.

The level of detail hence increases from general customer requirements to

detailed production characteristics. At each phase the main focus is on

the transformation from one of these units of analysis, the so-called

“Whats,” and to the other more detained unit of analysis, the so-called

“Hows.”

At each of the four phases in the figure, the left-hand requirements are

“Whats,” and the upper right hand characteristics are “Hows.”

L.F. Perondi

(2) Eleven elements of house of quality

Of the 11 elements in the basic matrix shown in Figure 4.10, the first

three are concerned with characteristics of the “Whats,” whereas the

remaining eight are concerned with characteristics of the “Hows.” In this

house of quality, identifying

the critical “Hows” which constitute the main result of each matrix is the

essential task. In the following, a generic description of the eleven

elements is given.

1) The “Whats”

The starting point is that the “Whats” are identified and included in the

matrix. If it is the first phase of transformation, customer requirements

will be the “Whats.” Customer requirements are given directly by the

customers, which is sometimes called VOC (voice of customers).


A basic matrix, possessing some resemblances to a house, embodying 11

elements (rooms), is used to document the results of each of the four

phases of transformation in QFD as shown in the figure. Often this

matrix is called the house of quality. The numbers in parentheses

indicate the sequence in which the elements of the matrix are completed.

L.F. Perondi


2) Relative importance

In the first phase of transformation the customer is also asked to attach

relative importance, for example on a scale from

“1” = least to “5” = most,

to each of the requirements they have stated. This holds similarly for the

other phases. This importance is often denoted by Rimp.

3) Competitive assessment

A comparison of how well competitors and one’s own company meet the

individual “Whats” can then be made. If the “Whats” are customer

requirements, it is common that customers give input to this

comparison. For the three other “Whats” – product characteristics,

component characteristics and process characteristics – the comparison

is typically carried out by the team applying QFD.


L.F. Perondi


One way to do the comparison is to evaluate competitors, Ecom, and

one’s own company, Eown, on, for example, a scale from

“1” = very poor to “5” = very good.

Both the ranking of competitors and one’s own company can then be

weighted with relative importance, Rimp, to obtain a better understanding

of the significance of differences in score for the individual “What.” Thus

the weighted evaluation of each “What” for competitors and one’s own

company is obtained by

L.F. Perondi


4) The “Hows”

For every “What,” several corresponding “Hows” should be identified and

described. This is a core part of QFD and needs considerable attention.

For all four phases, the task is conducted by the in-house team applying

the tool. Customers will rarely be able to participate in this transition as

they do not have enough technical knowledge of the processes and

products.

5) Target value

Target values are then set for each of the identified “Hows.” A target

value is a quantified goal value, i.e., the nominal value for the

distribution. It forms the basis for decisions to be made on the need for

improvements.


L.F. Perondi


6) Relationship matrix

Each “What” is then related to the “Hows.” Each relationship is denoted

by Wij, where i is row number and j is column number in the matrix. A

commonly accepted scale for indicating relationships is to use 9, 3, and1,

where

9 = strong relation

3 = medium relation

1 = poor relation

The relationship matrix is clearly very important as it provides

the links between the “Whats” and the “Hows.”


L.F. Perondi


7) Competitive assessment

Comparison with competitors for each characteristic of the “Hows” can be

made to determine how they are performing. A simple way to rank

competitors, Acom, and one’s own company, Aown, for example, is on a

scale from

“1” = very poor to “5” = very good.

8) Improvement direction

Based on the target value and the competitor assessment, the

improvement direction for each characteristic of the “Hows” can be

identified. It is common to denote increase with “↑,” no change with “”

and decrease with “↓.” This helps to understand the “Hows” better.

9) Correlation matrix

In the correlation matrix, the correlations among the “Hows”

characteristics are identified. Two characteristics at a time are compared

with each other until all possible combinations have been compared.

Positive correlation is commonly denoted by “+1,” and negative

correlation by “–1.” There does not need to exist correlation among all the

characteristics.

L.F. Perondi


10) Sums of correlation

The sum of correlations for each “How,” Sj, can be calculated by summing

the related coordinates as shown in the figure.

11) Importance

The final result is an identification of the “Hows” which are critical. The

critical “Hows” are identified by evaluation and calculation. In general, the

critical “Hows” are those that have a strong relationship with the

improvement potential of the “Whats” compared to competitors and high

positive sum of correlation. The relative importance of each “How,” Irel, is

derived by calculation. This is done by first computing the absolute

importance of each of the “Hows.”


L.F. Perondi


For example, in Figure 4.12, the absolute importance of the first “How,”

Length, becomes

Very often this absolute importance of each “How” is recalculated into

relative importance, Irel. This is done by normalizing the absolute

importance, for example, on a scale from 0 to 10. For example, in Figure 4,

the relative importance of the first “How,” Length, is

L.F. Perondi


The “Hows” with the largest values for relative importance, Irel,

represent critical characteristics. A Pareto chart is sometimes helpful to

apply in this evaluation.

A few critical “Hows” may be selected from this relative importance. In

the selection of critical “Hows,” it can sometimes be useful to also

include the competitor assessment, Acom, and the assessment of one’s

own company, Acom.

The current ability of a company regarding each of the “Hows” can then

be multiplied by the relative importance, Irel, and compared. Some

analysts even include the relative difficulty of improving the various

“Hows” and use this as a further point in the analysis of critical “Hows.”

L.F. Perondi


(3) Ballpoint pen example

Let us take an example of a ballpoint pen made of metal. Customers have

a variety of requirements. The most important requirements, from the

viewpoint of the customers, are brought into Phase 1 of the

transformation as shown in Figure 4.

From Figure 4, it is evident that the shape, material hardness, length,

weight and toxic material are product characteristics (“Hows”) with high

relative importance. It is important that these characteristics should be

improved in order to fulfill customer requirements. The next three phases

help identifying areas of improvement.


L.F. Perondi


QFD - Detalhes

Processo utilizado para garantir, o melhor possível, que o produto

atenda as especificações do usuário.

Voice of the

engineer

Voice

of the

customer

Customer-based

benchmarks



L.F. Perondi


QFD – Casa da Qualidade

Adicionar “trade-offs”, metas e especificações

Trade-offs

Targets

Technical

Benchmarks



L.F. Perondi


PDCA

• Planejar

• avaliar o processo corrente;

• coletar e reunir dados, identificar problemas;

• Desenvolver um plano de melhoria do processo

e fixar objetivos de desempenho.

• Fazer

• Implementar o plano – fase experimental.

• Checar

• coletar e reunir dados, comparação com

objetivos.

• Agir

• comunicar os resultados da fase experimental

• caso haja sucesso, implementar o novo

processo.

L.F. Perondi


PDCA (continuação)

Ciclo é continuamente repetido

◦ Após a realização da fase “Agir”, repetir,

novamente, o ciclo.

Planejar

Fazer

Checar

Agir

L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


L.F. Perondi


Documents

Engenharia e Gerenciamento de Sistemas Espaciaisperondi/19.10.2009/CSE-314-4_19-10-2009.pdf2009/10/19 · L.F. Perondi CSE -314 4 Planejamento e Gestão da Qualidade 20/10/2009 6