Quality Management - tfh.phillipkern.de Management.pdf · 12 QUALITY CAPABILITY ... (Total Quality Management) Definition: Degree to which a set of inherent characteristics fulfils

International Technology Transfer Management

Quality Management Prof. Sondermann

Summersemester 2004 Lecture Notes by Phillip Kern

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Quality Management

Content CONTENT ................................................................................................................................ 1

1 TERMS.............................................................................................................................. 3 1.1 QUALITY ..................................................................................................................... 3 1.2 QUALITY MANAGEMENT.............................................................................................. 3

2 QFD – QUALITY FUNCTION DEPLOYMENT......................................................... 4 2.1 HOUSE OF QUALITY .................................................................................................... 4

3 KANO MODEL OF SATISFACTION .......................................................................... 6

4 TQM .................................................................................................................................. 6

5 ISO 9000 ............................................................................................................................ 7 5.1 ISO 9001..................................................................................................................... 7 5.2 9004............................................................................................................................ 9

6 FMEA – FAILURE MODE AND EFFECTS ANALYSIS......................................... 10 6.1 EXAMPLE PROCESS FMEA ........................................................................................ 10 6.2 ASSIGNMENT OF PROBABILITIES................................................................................ 11 6.3 RPN RISE PRIORITY NUMBER ................................................................................... 13 6.4 EXERCISE FOR FMEA ............................................................................................... 13

7 ISO CONTROL CYCLE............................................................................................... 14

8 CONTROL PLAN.......................................................................................................... 15

9 PROCESS CONTROL AND CAPABILITY .............................................................. 15 9.1 PROCESS CAPABILITY ................................................................................................ 16 9.2 MEASUREMENT ......................................................................................................... 18

10 SYSTEMATIC INSPECTION METHODOLOGY AS MEASURE OF QUALITY ASSURANCE ......................................................................................................................... 22

10.1 OPERATIONAL CURVE (OC)....................................................................................... 22 10.2 LARSON – NOMOGRAMM........................................................................................... 23 10.3 DURCHSCHLUPF – CURVE (ESCAPE-CURVE) .............................................................. 24 10.4 ATI (AVERAGE TOTAL INSPECTION)......................................................................... 24

11 CENTRAL LIMITING THEOREM............................................................................ 26 11.1 SHEWART-CONTROL-CHART..................................................................................... 26 11.2 NON-SHEWARD CONTROL CHARTS........................................................................... 27

12 QUALITY CAPABILITY ............................................................................................. 28 12.1 RATING RESULTS & FOLLOWING ACTIVITIES............................................................. 29 12.2 CERTIFICATION AND AUDITING................................................................................. 30




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12.3 POTENTIAL ANALYSIS ............................................................................................... 31 12.4 QUALITY CONTROL CHARTS ..................................................................................... 32

13 BASIC TOOLS FOR QM.............................................................................................. 33 13.1 DATA COLLECTION ................................................................................................... 33 13.2 CHECK SHEETS.......................................................................................................... 33 13.3 HISTOGRAM .............................................................................................................. 33 13.4 PARETODIAGRAM ...................................................................................................... 33 13.5 ISHIKAWA/FISHBONE/CAUSE-EFFECT-DIAGRAM ...................................................... 33 13.6 SCATTERED DIAGRAM............................................................................................... 33 13.7 (CONTROL CHART).................................................................................................... 33 13.8 AFFINITY DIAGRAM .................................................................................................. 33 13.9 RELATION DIAGRAM ................................................................................................. 33 13.10 TREE DIAGRAM..................................................................................................... 33 13.11 MATRIX DIAGRAM ................................................................................................ 33 13.12 RADAR CHART ...................................................................................................... 33

14 USING OF METHODS - MATRIX DIAGRAM ........................................................ 34

15 QUALITY ENGINEERING METHODS STRUCTURE .......................................... 35 15.1 POKA-YOKE .............................................................................................................. 35 15.2 DOE.......................................................................................................................... 36

16 REQUIREMENTS FOR POTENTIAL ANALYSIS (OF SUPPLIERS).................. 37

17 STAGES OF QUALITY................................................................................................ 40

18 SUMMARY FOR EXAM.............................................................................................. 41

LINKS: www.vfh.de/qmuser: QMDMT03 pw: sonn-! automotive recalls: www.nhtsa.dot.gov

http://www.vfh.de/qm




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1 Terms 1.1 Quality concerns:

- products - process - company TQM(Total Quality Management)

Definition: Degree to which a set of inherent characteristics fulfils requirements. 1.2 Quality management




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1st layer 2nd layer 3rd layer: Objects 4th layer: Methods 5th layer: documents

House of Quality (HoQ)

2 QFD – Quality Function Deployment www.vfh.de/qm LE08

team-based methods to develop customer focused specification for products and processes

Documentation House of Quality (HoQ) 2.1 House of Quality “Translates the voice of the customer into the voice of the company.”

http://www.vfh.de/qm




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(1) Customer requirements (2) Degree of importance (1..5 or 1...10)

- e.g. by paired comparison (by matrix) (3) Service complaints (4) Customer rating (5) Design requirements (6) Maximize, minimize or achieve target goal (7) Correlation (8) Relation matrix (9) Technical difficulties (10) Technical comparison (11) Technical objectives

- must be measurable Fill in by Symbols. Assign values to the symbols! symbol value Value

(preferred by Prof. Sondermann)




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● Strong relationship 5 9 ○ Medium relationship 3 3 /∇ Weak relationship 1 1

2004-04-01 3 KANO Model of satisfaction

example railway:

- basic: safety - performance: just in time - additional: comfort, food service

4 TQM Formerly(Quality Inspection) Today and Tomorrow (TQM) Company Goals - better products

- production cost reduction

- batch optimization

- better company - customer satisfaction - high flexibility

Basic orientation Product Market Organization of quality assurance

Strong quality department Quality: part of al activities




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2004-04-06 5 ISO 9000

LE02

5.1 ISO 9001

Basis of business certificate for QMS

effectivness “doing the right things” belongs to integrated management Systems

enhanced Management systems

• ISO 14001 ecological management system • working safety & health protection (SCC)

QS9000 VDA 6.1 TS16949

5.1.1 Characteristics of ISO 9001

process orientation

aiming at the ability of an organization to assure conformity of products (to requirements)

striving at fulfilling requirements with customer satisfaction continuous improvement

According to this declarations one need processes!!!

Q-Objectives:

- general o policy

- specific o time & quantity relations




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5.1.2 structure of ISO 9001 0 Einleitung Enthält Hinweise auf:

- Den Prozessorientierten Ansatz - Die Beziehung zur DIN EN ISO 9004 - Die Verträglichkeit mit anderen

Managementsystemen (z.B. für Umwelt, Arbeitssicherheit und Gesundheitsschutz)

- Die Nichtunterstellung, dass einheitlich strukturierte und dokumentierte QM-Systeme verlangt werden

1 Anwendungsbereich

2 Normative Verweisungen

3 Begriffe

4 Qualitätsmanagementsystem Enthält Ausführungen zu: - Dokumentationsanforderungen - Lenkung von Dokumenten und Aufzeichnungen

5 Verantwortung der Leitung Definiert Anforderungen, die die Leitung verpflichten, - Eine Qualitätspolitik und Qualitätsziele festzulegen

und - Mittel und adäquate Organisations- und

Kommunikationsstrukturen bereitzustellen, mit dem Ziel, dass Kundenbedürfnisse und -erwartungen erfüllt werden.

6 Management der Ressourcen Enthält Anforderungen: - Zur Bereitstellung von Mitteln zur Umsetzung und

Verbesserung des QM-Systems - Zu Personalfragen wie adäquate

Aufgabenzuordnung und Schulung

7 Produktrealisierung Anforderungen werden aufgestellt zur: - Planung der Produktrealisierung - Kundenbezogenen Ermittlung der Anforderungen

an das Produkt - Gestaltung des Entwicklungsprozesses mit

Verifizierung und Validierung der Entwicklungsergebnisse

- Beschaffung inklusive der Verifizierung beschaffter Produkte

- Produkt- und Dienstleistungserbringung sowie - zur Lenkung von Überwachungs- und Messmitteln

8 Messung, Analyse und Verbesserung Enthält Anforderungen: - zur Messung der Kundenzufriedenheit - für interne Audits - zur Überwachung und Messung von Produkten und

Prozessen - zur Datenanalyse - zur kontinuierlichen Verbesserung, Korrektur- und

Vorbeugemaßnahmen

standardized is the „WHAT“ not the „HOW“!




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5.2 9004 refers to improvement of efficiency (guideline) approaches

- step to TQM - as addition 6Sigma-Approach




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2004-04-08 6 FMEA – Failure Mode and Effects Analysis

LE09

Failure and defect prevention analytical method team based (inter-disciplinary) used for (single) parts not for (complete) systems

based on preliminary but evaluable designs of processes of products

6.1 example Process FMEA Target: Assembly of a new car System / Operation ↑ ↑ Design Process

search for potential failures (by experience) FMEA doesn’t ask in the first time whether a defect can occur, but how a defect would look like

look for effects of these failures

evaluate the effects




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6.2 Assignment of probabilities

RPZ = A x B x E

Die folgenden Schemata geben praxisrelevante Werte wieder. Die Tabellen sind aber dennoch unternehmensspezifisch zu hinterfragen und ggf. anzupassen.

1. Zuordnungsschema für die Bewertung der Auftretenswahrscheinlichkeit A

Prognostizierte Prozesssituation

(max.) Auftretenswahrscheinlichkeit A

in %

Punktezuordnung A

Fehlerauftreten unwahrscheinlich

gegen 0 1

Beherrschter Prozess mit cp >= 1,33 bzw. Fehleranteil (vor Prüfung) < 1/20000

0,005 2

Beherrschter Prozess mit 1,0 <= cp >= 1,33 bzw. Fehleranteil zw. 1/2000 bis 1/20000

0,05 3

Beherrschter Prozess mit 0,8 <= cp >= 1,00 bzw. Fehleranteil zwischen 1/200 bis 1/1000

0,1 0,2 0,5

4 5 6

Beherrschter Prozess mit cp <= 0,8 bzw. Fehler- anteil zw. 1/100 bis 1/50

1 2

7 8

Nicht beherrschter Prozess mit Fehleranteil zwischen 1/10 bis 1/2

10 50

9 10

2. Zuordnungsschema für die Bewertung der "Bedeutung (Auswirkung, Fehlerfolge)" B

Prognostizierte Bedeutung der Fehlerauswirkung (f. Kunden) Punktezuordnung

Nicht wahrnehmbare Auswirkung 1

Unbedeutende Fehlerauswirkung 2

Geringfügige Beeinträchtigung 3

Geringfügige Belästigung 4




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Unzufriedenheit des Kunden 5

Verärgerung des Kunden (Werkstattbesuch, Reklamation, Beschwerde, nicht fahrbereites Kfz)

6 7 8

Funktionsverlust mit besonderer Verärgerung und Risiken für Kunden (Liegenbleiben mit dem Kfz)

9

Sicherheitsgefährdung 10

3. Zuordnungsschema für die Ermittlung der "Entdeckung" bzw. des "Risikos der Nichtentdeckung" E

Prognostizierte Möglichkeiten der Fehlerentdeckung

(max.) Wahrscheinlichkeit der Entdeckung E in

%

(max.) Nicht- entdeckungs-

wahrscheinlichkeit in %

Punkte- zuordnung

E

Sichere Entdeckens- wahrscheinlichkeit

99,999 0,001 1

Hohe Entdeckens- wahrscheinlichkeit

99,99 99,95 99,9 99,7

0,01 0,05 0,1 0,3

2 3 4 5

Mäßige Entdeckens- wahrscheinlichkeit

99,5 99 98

0,5 1 2

6 7 8

Geringe bis sehr geringe Entdeckens wahrscheinlichkeit

> 90 10 9

Keine Entdeckens- möglichkeit (nicht geprüftes bzw. prüfbares Merkmal)

0 100 10




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2004-04-15 6.3 RPN Rise Priority Number RPN = Occurance * Detectability * Effect(=Severity) (1..10) (1..10) (1..10) Range RPN 1…1000

- no absolute risk measurable - not comparable between teams

Example: Occ. =5 ; Det.= 5; Sev.=5 RPN=125 !!!But RPN is not always objective to judge about failures!!! e.g. O=2 * S=9 * D=3 RPN=54 O=3 * S=6 * D=3 RPN=54 but the first is much more risky because of much higher severity! 6.4 Exercise for FMEA necessary:

- Name of Process - Process Objectives - Process owner (efficiency & effectiveness) - Trigger - Input - Resources - Procedure - Measurements/Controls - Output - Reactionplan in case of failures(irregularities)




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2004-04-20 7 ISO Control Cycle

ISO 9001 5 Management Responsibility 6 Resource Management 7 Product Realization




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2004-04-27 8 Control Plan Ishikawa:

2004-04-29 9 Process control and Capability Processes should be under statistical control and should be capable. Processes can produce parts(components) and services or software etc. Industrial processes should be analysed in order to state the ability to meet requirements. Def.: Industrial processes is under control if

- the parameter of distribution (e.g. µ & δ in case of quality characteristics are following a Gaussian distribution) do not change overtime

or - change in a predictive manner or - vary in know boundaries

in other words: you must be able to assign a so called theoretical process-time-model

to process outcome Process-time-model A1 (according to DIN) samples: Gaussian distributed

process: Gaussian distributed µ & δ: constant

Process-time-model A2 (according to DIN) samples/process non Gaussian characteristic variables: constant




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9.1 Process capability - is capable if quality is guaranteed without e.g. additional sorting processes

case 1: µ, δ are constant upper and lower specification level (USL/LSL) are outside the sample distribution

Process is capable case 2: µ, δ are constant

upper and lower specification level (USL/LSL) are inside the sample distribution

process predictable, theoretical level is assignable process is under control (not directly linked to requirements)

but not capable (directly linked to requirements)

2004-05-04 9.1.1 Process capability index cP, cPK

- machine capability index cm, cmK - measurement (equipment) capability index cg, cgA (g=gauge)

cp = T/p =Tolerance width/Process width

= σ6

LSLUSL − ≥ 1,33(!) (6δ = +/-3δ area)

+/-1δ area corresponds to 68,26% of

all samples +/-2δ area corresponds to 95,4% of

all samples +/-3δ area corresponds to 99,73% of

all samples




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TV – Target Value p=8 (presumption)

difference between minimum & maximum sample is 8

assuming that +/-3δ covers all my samples (99,73%):

δ=p/6=4/3

cp = T/p = 10/8 = 1,25 (too low, should be ≥ 1,33 in automotive industry) cp does not take the position of the process into account (red curve) cpK – Value is taking into account the position(mean) of the process either as deviation from target value or its approximation to the specification limits

(1) ppK ckc ⋅−= )1(

2T

TVµk

−=

(2) ⎭⎬⎫

⎩⎨⎧ −−

=σσ 3

;3

LSLµµUSLMincpK

Cases: cpK < 0 µ^ is outside the specification level cpK = 0 µ^ is equal to USL or LSL cpK = cp k=0 µ=TV centred process




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2004-05-06 9.2 Measurement Characteristics:

1. Resolution R

requirement: T = USL – LSL TR ⋅≤ 05,0 R should be smaller than 5% of your tolerance width

2. Correctness (bias)

xc is a known standard (e.g. sample for calibrating)

e.g. xc = 20,039 x = 20,045 ∆ = 0,006

3. precision, repeatability

measure for precision 4δ area

4. accuracy

correctness + precision

5. reproduceability

Measurement for reproduceabilities xOP1 is measured by an other technique than xOP3




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6. linearity

A measurement is linear if the ∆ between the samples and the means is constant. (in case of different techniques or different samples)

7. stability

A measurement is stable if ∆ is not varying over time.




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2004-05-11 9.2.1 Index Cg

gg S

Tc42,0 ⋅

= !≥ 1,33

gg S

Tc42,0 ⋅

= !≥ 1,33 same as g

g STc

415,0 ⋅

= !≥ 1,0

If not met by measurement device lack of resolution




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g

cggk S

xxTc

2)(1,0 −−⋅

= !≥ 1,33 [ xg – xc = bias ]

example: Weight Measurement Device

- standard 10mg ( xc) - specification 10±0.6[mg] - LSL = 9.4mg - USL = 10.6mg

xg = 10.006 sg=0,04242426 |Bi| = |xg – xc| = 0.00600 cg= 0,2 *1,2/(4 * 0,042426) =1,41 cgk= (0,1 *1,2-0,006) / (2 * 0,042426) =1,34




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2004-05-13 10 Systematic Inspection Methodology as Measure of Quality

Assurance

LE14 Stichprobensysteme

(DIN) ISO 2859 Part 1 … 3 basis for establishing acceptance tests or inspection (lot inspection)

• simple inspection plans o decision of acceptance and rejection based on the results of 1 sample

only o form: n-c (n=sample size; c=acceptance number)

or n-c/d where d = c+1 (d=rejection number) • double inspection plans

o results of sample inspection are treated in following: defects ≤ c1 accept (c1 = acceptance number) defects > d1 reject (d1 = rejection number) defect rate of 1st sample > c1 an < d1

2nd sample needed o form: n-c1/d1-c2/d2 where c2/d2 is the combined defection rate of

1st & 2nd sample example: 20-2/5-4/5 (1st sample:

accept in case of 2 or less failures reject in case of 5 or more failures 2nd sample in case of 3 or 4

2nd sample: accept in case of 4 or less failures at all

(1st + 2nd sample) reject in case of 5 or more failures at all

(1st + 2nd sample) • and others

10.1 Operational curve (OC)

PA Probability of Acceptance p failures per lot size AQL Acceptable Quality Level up to this level you accept (otherwise test 100%) LQ limited Quality causes troubles




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2004-05-18 10.2 Larson – Nomogramm

binomial distribution

inix

ipp

in

pnxG −

=

−⎟⎟⎠

⎞⎜⎜⎝

⎛= ∑ )1(),;(

0




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10.3 Durchschlupf – curve (Escape-curve) D (Durchschlupf) = AOQ (Average Outgoing Quality)

APpAOQ ⋅= example for n-c = 50-1:

Values from Larson - Nomogramm

p PA AOQ

0 1 0 0,011 0,9 0,0099 0,02 0,74 0,0148 0,03 0,56 0,0168 0,04 0,4 0,016 0,05 0,275 0,01375 0,06 0,18 0,0108 0,07 0,12 0,0084

AOQ - curve

00,0020,0040,0060,0080,01

0,0120,0140,0160,018

0 0,011 0,02 0,03 0,04 0,05 0,06 0,07

failure rate p

Aver

age

Out

goin

g Q

ualit

y AO

Q

highest AOQ at p = 3% !!!

operational curve for this example:

Operational Curve

00,10,20,30,40,50,60,70,80,9

11,1

0 0,05 0,1 0,15 0,2

failure rate p[%]

Acce

ptan

ce P

roba

bilit

y P A

[%]

10.4 ATI (Average Total Inspection) Calculated for the average of parts inspected either on sample base or (plus) those lots which are 100% inspected after a lot was stopped.




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lotsPnNnATI A )1()( −⋅−

+= n = sample size; N = lot size




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2004-05-27 11 central limiting theorem based on U-Transformation table

21

180182=

−=u

δ is population standard deviation (over all) s is the standard deviation of one sample S=1

95,4%

178 180 182 [cm]

UCL LCL

σµ ⋅± EAˆ 99% distribution limit of x taken out of a process

u

uAE =

UWL LWL

σ⋅± WAµ 95% distribution limit of x taken out of a process

UCL/LCL Upper/Lower Control Level UWL/LWL Upper/Lower Warning Level example: typical sample sizes n-sample AE AW 3 1,487 1,132 5 1,152 0,877 7 0,974 0,741 10 0,815 0,620 20 0,576 0,438 Out of these values it is possible to establish a control chart.

for a full control chart, additional information are necessary (e.g. frequency of samples) 11.1 Shewart-Control-Chart notes missing! Interaction relevant for exam!!!

Ping fragen! ☺




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α-Risk the risk to interfere a process, where it is not necessary

= 1% because we defined 99% of distribution are inside UCL and LCL β-Risk

2004-06-01

What’s wrong with these samples? They do not follow the distribution on what control and warning levels are based. (There should also be some samples close to the levels.) Possible Reason: The process was improved but the levels were not adopted. This can be a problem because samples that would be outside of new levels will not be discovered by these levels. This process is not capable. 11.2 Non-Sheward Control Charts - no middle line (value) - no Warning-Levels - based on Tolerance Levels not on Levels based on process statistics




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2004-06-03 12 Quality Capability




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2004-06-10 12.1 Rating Results & following activities




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12.2 Certification and Auditing

Documents:




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12.3 Potential Analysis 12.3.1 Auditing and Evaluation process

12.3.2 Evaluation/grading




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12.4 Quality Control Charts Prerequisites for Control Charts:

process must be capable and under control !!! are the limits process(statistical Experience) or product(ToleranceLevel) oriented?

Process distribution Process moved but maybe will not be recognized because most parts are still inside specification levels (only focused on tolerance)

Sample distribution By defining Control Lines/levels you will recognize this process shift!

Quality Control Charts are an excellent tool to support JIT




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2004-06-08 13 Basic Tools for QM

Copy “BASIC TOOLS FOR QUALITY MANAGEMENT AND PROCESSIMPROVEMENT (ISO 9004-DRAFT)” 13.1 Data Collection

- WHY, WHAT, WHEN, WHO, WHERE, BY WHAT, HOW

13.2 Check Sheets 13.3 Histogram

to check the kind of distribution 13.4 Paretodiagram

sort different characteristics by occurance 13.5 Ishikawa/Fishbone/Cause-Effect-Diagram 13.6 Scattered Diagram 13.7 (Control Chart)

- not really a basic tool, because to complicated 13.8 Affinity Diagram

- e.g. based on Brainstorming to classify the ideas/results 13.9 Relation Diagram to find root cause(s) elements with only outgoing errors 13.10 Tree Diagram 13.11 Matrix Diagram

- e.g. House of Quality 13.12 Radar Chart

- e.g. Mr. Möbius Slides of Part- & Component-Suppliers




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2004-06-15 14 Using of Methods - Matrix Diagram Quality Lead times Productivity Costs Teamwork

Overall Improvements

Improvement steps

Methods

grap

hs

Par

eto

Cau

se-e

ffect

-dia

gram

C

heck

list

s H

isto

gram

C

ontro

l Cha

rt (S

treuu

ngsd

iag.

) B

rain

stto

rmin

g(M

ETA

PLA

N)

Rel

atio

nshi

p di

agra

m

Tree

dia

gram

M

atrix

dia

gram

N

et p

lan

(CPM

)

Understanding the situation Defining problem Assessment of scattering of data Timely scattering Layering and comparision of data

(1) Estimation of Problem size

Discussion of importance of problems Discussion of difference of problems Assessment of importance

(2) Defining Improvement objective

Estimation about possibility of improvement Potential factors (identification) Choose of most important factors selection Test relation

(3) Analysis of influencing factors

Time relation Generation and structuring of ideas Selection of improvements steps/actions Evaluate Estimate of improvement results

(4) Finding & Discussion of improvements

Improvement plan Realize the Plan (execution) (5) Realization of

improvements Collecting data after improvement Check effectiveness Check data variations

(6) Test of improvement results

Confirm results lead improvement to persist Detect deviations

(7) Improvement lasting

Report




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2004-06-17 15 Quality Engineering Methods Structure

FTA = Fault Tree Analysis MEOST to take care of interactions between different environment conditions 15.1 Poka-Yoke Main principle:

Prevent errors to become defects.




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2004-06-22 15.2 DOE

- first define problem - define factors (conditions)

15.2.1 Full Factorial (Vollständiger Versuch) example: 2 parameters A, B

1. Matrix for experiment

A B + + - + + - - -

Conditions for experiment + high level of parameters (e.g. estimated improvement) - low lever of parameters(e.g. actual state of parameter)

No. of experiments Lk (L=no. of levels; k=no. of parameters)

2. Analyse matrix

Result A B AB Trail1 Trail2 + + + 24 22 - + - 11 12 + - - 20 19 - - + 9 11

Result is measuring of the value what you want to improve

3. Impact of the factors Impact of A I(A):

A(+)=24+22+20+19=85 A(-)=11+12+9+11=43 I(A)=|A(+)-A(-)|=42

Impact of B I(B):

B(+)=24+22+11+12=49 B(-)=20+19+9+11=59 I(B)=|B(+)-B(-)|=10

Impact of AB I(AB):

AB(+)=24+22+9+11=66 AB(-)=11+12+20+19=62 I(AB)=|AB(+)-AB(-)|=4




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16 Requirements for Potential Analysis (of suppliers) read pages 43-75 from the “VW.pdf” document!




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2004-06-24 17 stages of quality

see copies! additionally to stage 2:




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2004-06-29 18 Summary for exam Basic principle process under control (see definition) Capable can produce parts to specification without sorting mechanism

1. ISO 9000 o structure o difference 9001 9004 o effectiveness efficiency o QM page 4 o QFD o process approach write down typical ISO process (management

review or supplier evaluation or Internal Audit, Design review) Process Audit (Input Control Plan, Operation/specification documents); Output report

o 2. Process Capability & Control

o calculation of Capability Index o

3. Measurement system capability 4. FMEA 5. paired comparison (open book question)

2004-07-06

6. tree structure page 4 7. what to do to become a potential supplier (p.43-75 in VW.pdf) 8. QFD 9. HoQ 10. Larsson 11. no shewart control chart