six sigma

2002 Lear Corporation. Unpublished Material © 2002 Statistical Design Institute, LLC. All Rights Reserved.

Revised 17 Dec 03

Module 1.1Introduction to Design for Six Sigma

Lear Corporation Confidential/Proprietary

2002 Lear Corporation. Unpublished Material © 2002 Statistical Design Institute, LLC. All Rights Reserved. Module 1.1 Intro to DFSS - 2

Agenda

Why DFSSWhy DFSSDFSS OverviewDFSS OverviewQuality Function Deployment (QFD)Quality Function Deployment (QFD)BenchmarkingBenchmarkingTRIZ / Trade-Offs (Pugh, and SDI Analysis)TRIZ / Trade-Offs (Pugh, and SDI Analysis)Program Management (LPMP)Program Management (LPMP)Design Failure Mode and Effects Analysis (DFMEA)Design Failure Mode and Effects Analysis (DFMEA)Design for Lean Manufacturing and Assembly (DFMA)Design for Lean Manufacturing and Assembly (DFMA)Design for Reliability (DFR)Design for Reliability (DFR)Regression and Design of Experiments (DOE)Regression and Design of Experiments (DOE)DFSS Tool BoxDFSS Tool Box–– Sensitivity AnalysisSensitivity Analysis–– Monte CarloMonte Carlo–– AllocationAllocation–– OptimizationOptimization–– Other EnablersOther Enablers

Statistical Roll Up and Score CardStatistical Roll Up and Score CardConclusion / Wrap-Up and DiscussionConclusion / Wrap-Up and Discussion


Revised 17 Dec 03

Introductions - Why DFSS?


Lear’s DFSSDFSS deployment is based on an EngineeringEngineering Leadership Leadership focused

Team TrainingTeam Training approach that will achieve improvements during Product DevelopmentProduct Development

in order to provide benefits during Product Launch.Lear Corporation Confidential/Proprietary


$ $ $ $

$ $ $ $

$ $ $ $

$ $ $ $

$ $ $ $

Product DevelopmentProduct Development

Why focus on Product Development activities?Why focus on Product Development activities?

Planning Prototype Pilot Launch Post Launch

Pgm Management

Marketing

Customer Reps

Supplier Mgmt

Quality Engineers

Manuf. Engineers

Design Engineers

Plan

Produc

t Deve

lopment

Problem

s

Result

In Los

t Reve

nue

Actuals



“Golden Opportunities”?

Contributors to Product Contributors to ProductCostCost

Application of Six Sigma Application of Six Sigma

Influences on Product CostInfluences on Product Cost

Synergy with Six SigmaSynergy with Six SigmaAdequate design margins

Stable parts and materials

Good process capabilities

0

10

20

30

40

50

60

70

Infl

uenc

e on

Pro

duct

Cos

t (%

)

Design Material Labor Overhead

Major Cost Contributors

Material OH Labor Engr

Product Cost Elements

50%

30%

15%5%

Follow the money:

Six Sigma

ConceptUtility

Inc

rea

sin

g E

mp

ha

sis

Program Lifecycle Phase

ConceptInitiation

Planning Prototype

Pilot

DFSSTraditionalSix Sigma

LaunchPost

Launch


What is “DFSS”?

Design for Six Sigma IsDesign for Six Sigma Is:– A philosophyphilosophy of how products should be developed– A processprocess that emphasizes designing in quality– A measuremeasure of the level of quality in a product’s performance– A methodmethod for:

analyzing and improving the robustness of a product’sperformance based on the statistical properties andcontribution of each input, component, or parameter.

Why Bother?Why Bother?– The fundamental premise is that customers care about quality

and companies care about profits.– Product Variation ⇒ Poor Quality ⇒ Higher Prices

⇒ Customer Dissatisfaction


Why Change?

Common Engineering Language.Statistical Engineering for Robust Product DesignManufacturing Process Capabilities are a Driving Force in ProductDesignPredictability of Product Performance, Manufacturability, ReliabilityReduced Number of Prototypes, ECN’s, Rework, ScrapFocus on Customer Requirements and Product Quality.Reduced Warranty Expenses.Increase Customer Satisfaction & Loyalty.Gain Market Share


We can’t get there without betterdesigns

The “Five Sigma” Wall

• 70-80% of all qualityproblems aredesigned in

• Manufacturing willnever be able tomake our currentproducts with SixSigma quality

6 Sigma

3 SigmaNew, inherently SixSigma designs are theonly way to reach the

corporate goal.

DFSS is needed

“5 Sigm a W all”

DM

AIC

DM

AIC

Design for Six Sigma


How Other Companies View DFSS

General ElectricGeneral Electric• “Every new GE product and service

will be ‘DFSS’ - Design for SixSigma. These new offerings willtruly take us to a new definition of‘World Class’.”

General Electric Company Annual Report, 1998, p 4

Fruedenberg-NOKFruedenberg-NOK• A systematic methodology, tools,

and techniques which enable us todesign products and processesthat can meet customerexpectations and can be producedat the 6 sigma level.

Drew Algase, “Successful Project Selection forDFSS Operations, IQPC DFSS Conference, Jul2001.

General Domestic Appliance Ltd.,General Domestic Appliance Ltd.,GE-MarconiGE-Marconi

• Vision of product and serviceexcellence.

• Way to manage technical risk.• Gives confidence to decision

making for leading-edge products.• Added value to our customers.• Key element for continued growth.

Phil Rowe, “Training Requirements for DFSS”,IQPCDFSS Conference, Aug 2000.

General MotorsGeneral Motors• "It is expected that our top Interior

Suppliers will be ready to supportthe GM DFSS Initiative for allInterior Integration programsbeginning in 2002!"

Tom Lewandowski,General Motors PurchasingMarch, 2001


What have other Companiesachieved with DFSS

Source: IQPC DFSS Conference Materials, 2000-2001.

General Domestic Appliance Ltd.General Domestic Appliance Ltd.• Revenue growth: volume, market

share, price.• Warranty and manufacturing cost

reductions.• Customers who are delighted with

their products.

A Driver for GrowthA Driver for Growth

Texas Instruments DSEGTexas Instruments DSEG• Capability & Statistical Analyses

resulted in Cost Avoidance• Pgm A - $2K per system• Pgm B - $16.4K per system• Pgm C - $1000 per lot

Lower Production CostsLower Production CostsGeneral Electric Medical SystemsGeneral Electric Medical Systems• Light-Speed Scanner System

• Chest scan from 180 to 17seconds

• Lower cost/scan• $69 million in orders in first

90 days

Customer SatisfactionCustomer Satisfaction

iomegaiomega• Reduced Development Cycle

Time from 12 to 3 months.• Reduced Critical Engineering

Changes from 37 days to 2 days• Reduced Tooling Lead Time from

12 to 6 weeks.• Reduced Component Lead Time

from 36 to 12 weeks.

Improved Time to MarketImproved Time to Market


What is DFSS for Lear?

DFSS….DFSS….

Is a standardized and regimented product developmentprocess that integrates with LPMPintegrates with LPMP

Incorporates process capabilityprocess capability, consumer inputconsumer input and customercustomerrequirementsrequirements into the design process

Provides statistical toolstools to analyze and optimize productdesigns

Is driven by the Engineering organization and requires fullfullproduct teamproduct team involvement

Is geared to Product Development, notnot Process Improvementon existing products (DMAIC)



Agenda

Why DFSSWhy DFSSDFSS OverviewDFSS OverviewQuality Function Deployment (QFD)Quality Function Deployment (QFD)BenchmarkingBenchmarkingTRIZ / Trade-Offs (Pugh, and SDI Analysis)TRIZ / Trade-Offs (Pugh, and SDI Analysis)Program Management (LPMP)Program Management (LPMP)Design Failure Mode and Effects Analysis (DFMEA)Design Failure Mode and Effects Analysis (DFMEA)Design for Manufacturability and Assembly (DFMA)Design for Manufacturability and Assembly (DFMA)Design for Reliability (DFR)Design for Reliability (DFR)Regression and Design of Experiments (DOE)Regression and Design of Experiments (DOE)DFSS Tool BoxDFSS Tool Box

–– Sensitivity AnalysisSensitivity Analysis–– Monte CarloMonte Carlo–– AllocationAllocation–– OptimizationOptimization



Revised 17 Dec 03

DFSS Overview


Concepts forDesign for Six Sigma

DFSS ProcessDFSS Process

Best PracticeBest Practice

Build Models

Voice of the Customer

Design that best meetsall requirements

DFSS Enablers & ToolsDFSS Enablers & Tools

DFSSDFSS

DevelopmentDevelopmentProcessProcessConceptInitiationConceptConceptInitiationInitiation

PrototypePrototypePrototype

PilotPilotPilot

LaunchLaunchLaunch

PlanningPlanningPlanning

PostLaunchPostPost

LaunchLaunch

DOE and RegressionDOE and Regression

Monte Carlo AnalysisMonte Carlo Analysis

Statistical AllocationStatistical Allocation

Multi-Objective OptimizationMulti-Objective Optimization

TRIZ & Design Trade-offTRIZ & Design Trade-off

Quality Function DeploymentQuality Function Deployment

ScorecardsScorecards

Test Effectiveness AnalysisTest Effectiveness Analysis

Optimize the Design• Analyze Variability• Allocate Variability• Optimize Variability

Identify CriticalRequirements

Define Alternatives

Verify & Validate

Sensitivity AnalysisSensitivity Analysis


DFSS ToolApplication Roadmap

Programidentified

Prototypes Simulation/Computer Models

Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx

DescriptiveStatistics

Process data, Supplier data,and Reliability Data[GR&R and Test Effectiveness must be performed]

*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&

Specifications(LL, T, UL)

Analytical Models

µy, σy, PNC

Design ofExperiments

Historical Data

µy, σy, PNCY

ULLL T

PNCPNC

Product ModelProduct Model(equation, (equation, simulation,simulation,workbook,workbook,

hardware, etc.)hardware, etc.)

C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo

Multi-ObjectiveOptimization

Equations

*

ConceptDesign

QFD

Whats/Hows

TRIZExperience &Brainstorming

BenchmarkingTrade-Off

***

DFM/A

DFMEA

DFR


The Language of DFSS

Fundamental metricFundamental metric is the probability of notmeeting a requirement:

the Probability of Non-ComplianceProbability of Non-Compliance ( (PNCPNC))

ComputeCompute PNCPNC by applying statistics to existingengineering analyses . . . during design!

“Non-compliant”

USLLSL T

“Non-compliant”

1 Sigma ⇒ PNC = 0.317

2 Sigma ⇒ PNC = 0.046

3 Sigma ⇒ PNC = 0.0027

4 Sigma ⇒ PNC = 6.33 ×10-5

5 Sigma ⇒ PNC = 5.74 ×10-7

6 Sigma ⇒ PNC = 1.78 ×10-9 (1.5σ Mean Shift not included)

“Compliant”


Fundamentals of DFSS



Y

C

E

A

B

D

ULLL T

PNCPNC

UnderstandingUnderstandingRequirements,Requirements,Specifications,Specifications,& Capabilities& Capabilities

ApplyingApplyingModels &Models &AnalysesAnalyses

PredictingPredictingProbability of Probability of

Non-ComplianceNon-Compliance


Tools for DFSS

Inventive Principle 1

Example 1

Example k

Definition j

Example 1

Example k

Inventive Principle i

Definition 1

Example 1

Example k

Definition j

Example 1

Example k

Flowdown forInventive Principles,Definitions andExamples

Contradiction

Definition 1

TRIZTRIZ

SensitivitySensitivityAnalysisAnalysis

x4 2 0 2 40.20.4

x0 1 2 3

0.5

1

x0 2 4 6 8

0.5

42 44 46 48 50 52

0.1

0.2


AllocationAllocation

High PerformanceHigh PerformanceLow CostLow Cost BalancedBalanced

Plus Outlet Air Req’tPlus Outlet Air Req’t

OptimizationOptimization

It’s all about ...It’s all about ...It’s all about ...LEVEL

1LEVEL

2

LEVEL3

QFDQFD

ScorecardScorecardEvents Good Units Bad Units Total

Unit Fails Test 0.01998 0.00099 0.02097

Unit Passes Test 0.97902 0.00001 0.97903

Total 0.99900 0.001 1.0000

… designing products that work as planned!… designing products that work as planned!… designing products that work as planned!

Test TestEffectivenessEffectiveness


Module 3.3DF Lean M/A (4)

Module 1.1Intro to DFSS (6)

Module 2.1QFD, Inventive Thinking, & Design Trade-off (6)

Module 3.4DFMEA (4)

Module 3.2Reliability, Gage R &

R, & TestEffectiveness (6)

Module 4.3Product Design Variation (4)

Module 4.4Design Optimization (4)

Module 4.2Modeling (TWO DAYS:4hrs then 6 hrs)

Module 4.1Excel / MiniTab Primer (optional) (4)

(Can be taken in any order)

( Shown in recommended order)

I

II

III

IV

Information coveredin Key Stake Holder

Level Course Flow

Module 3.1Basic Statistics(4)

Modular Training


Module 1.1 - Introduction toDesign for Six Sigma (DFSS)(6 Hours)– Why DFSS– DFSS Overview– Quality Function Deployment– Trade-Offs (Pugh, & SDI

Analysis)– SPC– Program Management– Benchmarking– FMEA– DFMA– Design for Reliability– Regression & DOE– Other DFSS Tools– Score Card

Module 2.1 - QFD, InventiveThinking & Design Trade-Off

(6 Hrs)– QFD– Examples and Exercises– Benchmarking– Pugh Techniques– TRIZ

Module 3.1 - Basic Statistics(4 Hrs)- Basic statistics- Probability, Sigma and PNC- Process capability- Scorecard

DFSS Training Modules


Module 3.2 - Reliability, Gage R& R, and Test Effectiveness

(6 Hrs)- Defining Reliability & Failures- Reliability Models- Weibull analysis- Gage R&R + TestingEffectiveness

Module 3.3 - Design for LearnManufacturing and Assembly(DFM/A) (4 Hrs)- Lean Principles- DFM/A Definitions- General Principles and Guidelines- Examples and Case Studies

Module 3.4 - Design FailureMode and Analysis (DFMEA)

(4 Hrs)– FMEA Introduction / Types of

FMEA– What and Why DFMEA– DFMEA Teams and Facilitation– Defining the Design– DFMEA Creation– Taking Action

Module 4.1 - Excel/MinitabPrimer (4 Hrs)– Minitab environment– Excel equations and linking– Discrete Modeling



Module 4.2 - Modeling(Day 1: 4 Hrs + Day 2: 6 Hrs)

– Fundamentals and principles– Analytical Equations– Regression– DOE

Module 4.3 - Product DesignVariation (4 Hrs)– Sensitivity– Monte Carlo Analysis– Allocation Analysis

Module 4.4 -DesignOptimization (4 Hrs)– What is Optimization– Single Objective– Multi-Objective



Agenda





Revised 17 Dec 03

Quality Function Deployment





Build Models




DFSSDFSS



PilotPilotPilot

LaunchLaunchLaunch


PostLaunchPostPost

LaunchLaunch











Define Alternatives

Verify & Validate




Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


Quality Function Deployment:Learning Objectives

At the end of this module, participants will understand the use of . . .

QFDQFD and Voice of the CustomerVoice of the Customer concepts that will be usedby project teams in the requirements development process

House of QualityHouse of Quality for requirements identification and flowdown


SalesQuoted

EngineeringSpecified

Design Modeled Plant Produced Repaired at Dealer

Customerwanted

Communication Breakdown


The House of Qualityprovides ...

… documentation of how the problem is viewed … fromdifferent perspectives

CustomerCustomerNeedNeed

DesignDesignRequirementsRequirements

CompetitiveCompetitiveAssessmentAssessment CorporateCorporate

CompetenciesCompetenciesandand

PlanningPlanning


Quality Function Deployment

Quality Function Deployment (QFD):Quality Function Deployment (QFD): a planningtool for translating the Voice of the Customer(VOC) into explicit design, production, andmanufacturing process requirements.

The QFD House of Quality transfers customers’needs to requirements based on the strengthof the inter-relationships.

HOW Vs. HOW

Relationships

HOW MUCH

HOW

WH

AT

House ofQuality

Requirements,SpecificationsProcesses

HOW

WHAT

QFDQFD

Fuzzy


Fewer Design Changes

21 12 33 ProductionbeginsMonths

Num

ber

of D

esig

n C

hang

es

90% of Totalchangescomplete

Company Using QFD

Company not using QFD

Adapted from L.P Sullivan. “Quality Function Deployment.” Quality Progress, June 1986


The House of Qualitycontains Primary andSecondary elements.Primary elements arerequired to determineCTCs.

Secondary elementsare informational.

House of Quality

CustomerRequirements

(WHATS)

RelationshipMatrix

PerformanceCharacteristics

(HOWS)

InteractionMatrix

Technical Weights

Target Values

CompetitiveBenchmark

CompetitiveBenchmark

Impo

rtan

ce

Com

plet

enes

s

Target Direction

Primary Element

Secondary Element

Req

. P

lann

ing


Customer Need &Design Requirements

95

24

76

63

Faster 5

Lighter 2

+ Operating Time 4

Voice Recognition 3

H

H

L

H

H

H

M

L

M H

H

72 41 18 64 27 3610 GHz 75 Wh 0.5 lb

0.01 C/W

1 kHz 5 W

CP

U S

pee

d

Lo

ng

-Lif

eB

atte

ryL

igh

twei

gh

tC

ase

Hea

t P

ipe

Co

olin

gL

ow

-Pas

sF

ilter

Lo

w-P

ow

erL

CD

Dis

pla

y

/\ /\ \/ \/ O \/

-- +

++ -++ +

1. What the customer wants1. What the customer wants

2. Ranking of customer needs2. Ranking of customer needs

3. Quality Characteristics (Hows)are quantitative and explicit

3. Quality Characteristics (Hows)are quantitative and explicit

4. Typical numerical valuesfor the relationships are:High H 9Medium M 3Low L 1

4. Typical numerical valuesfor the relationships are:High H 9Medium M 3Low L 1 5. Technical Weight5. Technical Weight

6. Completeness6. Completeness

7. Target Direction7. Target Direction

8. Interaction Matrix8. Interaction Matrix


Quality Function Flow

ProductionRequirements

Key

Pro

cess

Op

erat

ion

s

PRODUCTIONPRODUCTIONPLANNINGPLANNING

Key ProcessOperations

Par

t Q

ua

lity

Ch

arac

teri

stic

s

PROCESSPROCESSPLANNINGPLANNING

Part QualityCharacteristics

En

gin

eer

ing

Ch

arac

teri

stic

s

PARTSPARTSDEPLOYMENTDEPLOYMENT

Cu

sto

me

rA

ttri

bu

tes

EngineeringCharacteristics

ENGINEERINGENGINEERINGREQUIREM’TSREQUIREM’TS

Source : Hauser, J. R. and D. Clausing, “The House of Quality,”Harvard Business Review, May-June 1988

VOCVOC

Customer ExpectationsCustomer ExpectationsSolid

4 mm maxmovement Diameter

+/- .1mmCNC

MachiningSPC on

DiameterEverything relates back

to Voice Of the Customer


House of Quality:Tool for Implementation

Triptych - a toolsetthat includes a QFDimplementation tool

Level 1

Level 2

Level 3



CollectCollect as much information as possible before starting toconstruct a QFD

Sources of Information for the Product TeamSources of Information for the Product Team– Requirement Documents and Specifications– Complaints– Recommendations– Data– Internal Information– External Information– Market Research

Customer interviews, questionnaires, surveys, test reports,competitive analyses

– Historical Warranty and Quality Information– Team Experience

One House, One day


Benefits of QFD Process

Dispels the “business as usual” approach to planning anddesign

Revises operational norms– Directors and managers become “team members”

– Functional managers are required to donate the services ofpersonnel

– Decision authority is with teams and not individuals

– Lines of communication are established between functions at theworking level

Provides a structured approachstructured approach for identifying anddocumentingdocumenting product requirements– ‘What’ is required

– ‘How’ can requirements be achieved

– Key relationships and comparative weightings


Quality FunctionDeployment Summary

QFD is a planning tool for translating the Voice of theCustomer (VOC) into explicit design, production, andmanufacturing process requirements.

QFD is a tool and process for listening to customers. QFD isnot a decision maker.

Each House of Quality is used to identify key CTC’s thatmaximize the chance of meeting customer needs at that level.

Information is used define the product design approach andproject development activities.

Team discussions and planning sessions are used to capturethe information.

Triptych QFD tool facilitates the process.


Agenda


–– Sensitivity AnalysisSensitivity Analysis–– Monte CarloMonte Carlo–– AllocationAllocation–– OptimizationOptimization–– Other EnablersOther Enablers



Revised 17 Dec 03

Benchmarking



Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


Learning Objectives


What is Benchmarking?Benchmarking?

DFSS Benchmarking ProcessDFSS Benchmarking Process

–– Activity-Type BenchmarkingActivity-Type Benchmarking

–– Gap Reduction BenchmarkingGap Reduction Benchmarking

BenchmarkingBenchmarking in the Critical-To-Customer (CTC) technicalrequirements development process

Phased Benchmarking approachPhased Benchmarking approach for collecting, prioritizing,and improving key CTC requirements


What is Benchmarking?

Benchmarking -Benchmarking -– performance measuring tool– can be used to measure comparative performance– aids in identifying “best-in-class” practices– supports achievement of major improvements

Benchmarking creates value -Benchmarking creates value -– through improvements in performance, reliability, cost and

revenues generated in product developments efforts: - Provides a way to improve customer satisfaction - Identifies key Critical-to-Customer (CTC) performance gaps - Assists in eliminating the “not-invented-here” syndrome - Establishes the state-of-the-art performance in the market place - Allows for making better business decisions based on a larger data base of customer-driven inputs


Benchmarking Types

VisionWorks Benchmarking

– Corporate research group that supports and provides services forLear globally

Project Team Benchmarking

– Activity-Type

For understanding the details of the customer needs and wants andfor developing QFDs for CTC Technical Weight information

– Gap Reduction

For developing and executing a gap reduction plan based on identifiedCTC Technical Weights, gap analysis, and project goals


Activity-TypeBenchmarking Steps

Step 1Step 1

– Use the Quality Functional Deployment (QFD) tool todevelop and prioritize Critical-to-Customer (CTC) technicalweights

Step 2Step 2

– Develop and prioritize key competitors CTC technicalweights (TW) using existing knowledge and same QFDtemplate

Step 3Step 3

– Use TRIZ/Design Trade-off tools to develop new designconcepts and to prioritize new design concepts CTC TWs


Gap-ReductionBenchmarking Steps

Step 1: PlanStep 1: Plan– Identify which CTCs will be

benchmarked– Select product team members

to conduct benchmarking– Determine companies to be

benchmarked

Step 2: PerformStep 2: Perform– Establish benchmarking

agreements with candidatecompanies/organizations insame or other industries.

– Determine key personnel pointsof contact.

– Organize team and conductBenchmarking session usingappropriate means ofcommunication.

Step 3: AnalyzeStep 3: Analyze– Organize collected data– Conduct QFD session using

collected data.– Perform CTC gap analysis

Step 4: DevelopStep 4: Develop– Develop gap-reduction

implementation plan– Communicate plan with

team/management– Obtain agreement for ‘‘go

ahead”

Step 5: ImplementStep 5: Implement– Execute plan– Update progress with reviews– Finalize benchmarking

documentation


Full Interior and Component Teardown

– Feature, Component and Assembly Analysis

– Customized study per each customer request

– ‘A’ side feature & data measurements (non-destructive)

– ‘B’ side data measurements

With components disassembled from the vehicle.

– Digital photos of components

In-vehicle condition

‘A’ & ‘B’ side views of each component disassembled from the

vehicle

VisionWorks Services


Benchmarkingdefined in LPMP


Lear Benchmarking Info


Learnet Benchmarking info

Pending


Learnet Benchmarking info

Pending


VisionWorksBenchmarking Request

Have a list of deliverables and / or a project scope to review with the VW group

List of deliverables should consist ofTarget vehiclesTarget market segmentsOEM recommended vehiclesList of components targetedAny specific issues targeted per vehicle or component (“the why’s”)Program timing dates

Benchmarking study completion timing requirements

Benchmarking output/results format requirementsPresentationHard copyBurned copy on CDElectronic copy


Benchmarking Summary

BenchmarkingBenchmarking– CTCs can be used to identify need for Benchmarking.– QFD tool supports creation of gap analysisgap analysis

with respect to design goals/targetswith respect to competition

– DFSS benchmarking is cost effective and supports productdevelopment activities.

– Gap-reduction implementation is a technique for makingsignificant customer-focusedcustomer-focused improvements


Agenda





Revised 17 Dec 03

TRIZ andTrade-Offs

• Theory of Inventive ProblemSolving

• Pugh Analysis

• SDI Analysis





Build Models




DFSSDFSS



PilotPilotPilot

LaunchLaunchLaunch


PostLaunchPostPost

LaunchLaunch











Define Alternatives

Verify & Validate




Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


TRIZ and Trade-Off:Learning Objectives


MethodsMethods such as TRIZ to promote inventive problem solving

TRIZ methodologyTRIZ methodology as a tool for project teams to solveconflicting requirements

Design Trade-OffDesign Trade-Off approaches to select the best idea orapproach

QualitativeQualitative and QuantitativeQuantitative approaches to design trade-off

Each method and when it should be applied.


Conflicting Requirements

QFDQFD translates the voice of the customer into engineeringrequirements.

Engineering requirements are often conflicting.– Example: The product gets stronger, but the weight increases.

Generating solutions that satisfy conflicting requirements isusually performed using various methods such as:– Free association brainstorming

– Cross fertilization (drawing analogies from other disciplines)

– Futuring (thinking to the future when these constraints may nolonger exist)

The above methods occasionally generate a solution toconflicting requirements, but is there a reliable method forgenerating solutions? YESYES

TRIZTRIZ is a method for generating Inventive Solutions toconflicting engineering requirements that is based on patternsof innovation identified in patents.


Methodology for SolvingTechnical Contradictions

Methodology for resolving Technical Contradictions:1. Identify the Technical Contradiction.2. Determine which Feature in the contradiction is to be Improved

and which one is Degraded (or worsened).3. Choose the Design Parameters that most closely match the

Improving Feature and Degraded Feature in the contradiction.4. Examine the proposed Inventive Principles.5. Select the best Inventive Principle.6. Apply the Inventive Principle to the Technical Contradiction.

SDI’s TRIZ tool (part of the Triptych tool suite) performsSteps 3 and 4 by providing a simple interface to thecontradictions matrix with 130 definitions and 167 examples.

IdentifyContradiction

Choose Best TRIZ General Solution

Translate to TRIZ General Contradiction

Apply Solution to Problem


TRIZ:Tool for Implementation

Select ImprovingFeature from the list of39 Design Parameters

Idea Button is usedto store ideas on aTRIZ worksheet

Select DegradedFeature from the list of39 Design Parameters

Up to four InventivePrinciples are listed here,along with their rank (interms of frequency of use)

One or more definitionsof the selected InventivePrinciple are shown

One or more examplesof the selected InventivePrinciple definition areshown

Arrows cycle throughdefinitions andexamples

Save Button is usedto store selectedFeatures, InventivePrinciple, Definition,and Example on aTRIZ worksheet

Definition Buttonshows definitions forselected DesignParameters

Done Button closesTRIZ Tool

Help Button launchesTRIZ Tool Help

Enter your Idea here.


Revised 17 Dec 03

Trade-Off


Multiple Approacheshave been identified …

If multiple approaches or design alternatives are identified …how do you determine the ‘best’ choice?

Moving a Heavy ObjectPower Assist

Vs...Composite Materials

Vs..Balanced Center of Gravity

Moving a Heavy ObjectPower Assist

Vs...Composite Materials

Vs..Balanced Center of Gravity

Electrical Part CoolingAluminum Heat Sink

Vs..Copper Heat Sink

Vs..Cool Plate

Vs..Heat Pipe

Electrical Part CoolingAluminum Heat Sink

Vs..Copper Heat Sink

Vs..Cool Plate

Vs..Heat PipeSeat Recliner Mechanism

Pawl & Sector, Single SidedVs..

Pawl & Sector, Dual SidedVs..

Integrated AdjusterVs..

Cam Adjuster

Seat Recliner MechanismPawl & Sector, Single Sided

Vs..Pawl & Sector, Dual Sided

Vs..Integrated Adjuster

Vs..Cam Adjuster


Introduction toDesign Trade-Off

Design Trade-OffDesign Trade-Off is a decision-making process that is usedto select a design (among many alternate designs) that mostclosely meets multiple, conflicting CTCs.There are many approaches to performing Design Trade Off,but all have the following characteristics:– Gather Design Options

brainstorming, TRIZ, patent searches– Gather Design Criteria (CTCs)

benchmarking, QFD, surveys– Compare each Design Option against each Design Criterion

qualitative or quantitative scoring– Compare each Design Option against each other

qualitative or quantitative scoring

Triptych uses two approaches:–– Pugh MatrixPugh Matrix for preliminary design phase (Qualitative)–– SDI Trade-Off ToolSDI Trade-Off Tool for detailed design (Quantitative)


Pugh Trade-Off Matrix(Qualitative)

Pugh Matrix is used to make fast qualitative comparisons whenDesign Options and Design Criteria are known.

1. Create matrix with Design Optionsand Criteria

2. Assign Importance to Criteria3. Choose Datum4. Compare Options with Datum

Better (+)Same (S)Worse (-)

5. Add the Weighted Sumsfor Better, Same, andWorse

6. Choose the new datum (thebest design Option for thecurrent iteration cycle)

7. Repeat until best conceptis found.

Weighted Sum of +

Weighted Sum of S

Weighted Sum of -

Cost 2

Heat Transfer 3

Complexity 1

Junction Temp. 2

-

S

S

+

-

+

S

+

S

+

S

+

-

+

-

+

2 5 5 5

4 1 3 0

2 2 0 3

Alu

min

um

Hea

t S

ink

Co

pp

erH

eat

Sin

k

Co

ld P

late

Hea

t P

ipe

Sp

ray

Co

olin

g

Design Options

Crit

eria

Impo

rtan

ce

1st Datum

Next Datum


SDI Trade-Off Matrix(Quantitative)

SDI’s approach to design trade-off is to quantitatively evaluatehow well the Design Option meets each Design Criterion.Specification limits (LSL and/or USL) may be specified alongwith a Target for each Design Criterion.Design Option Scores are calculated for each CTC:Linear variation inside the specificationQuadratic variation inside the specificationInside/Outside of specificationMaximizeMinimize

LSL Target USL

LSL Target USL

LSL Target USL

Min Max


TRIZ and Design Trade Off:Summary

TRIZ - aids in generation of alternativesTRIZ - aids in generation of alternatives– The key concept behind TRIZ is that there is a 95% probability

that someone else, in some other industry has solved the samefundamental technical contradiction.

– TRIZ is a method for creating innovative solutions to engineeringcontradictions.

– TRIZ can be used to generate new ideas.

Design Trade-Off - aids in comparison of alternativesDesign Trade-Off - aids in comparison of alternatives– Design Trade-Off is used to selected Design Options that best

satisfy multiple Design Criteria.

– Pugh Matrix is a qualitative comparison of Design Options againstmultiple Design Criteria

Can be used at any part of the design cycle.

– SDI Trade-Off is a quantitative comparison of Design Optionsagainst multiple Design Criteria

Requires detailed knowledge of the Design Criteria (CTCs).


Agenda





Revised 17 Dec 03

Lear’s Program Management Process(LPMP)

• Introduction

• Scope

• Phases

• Deliverables

• Integration of DFSS into LPMP


LPMP

Lear Program Management Processalso

The Process for Product Developmentalso

The Process for Product Launchcould be called

The Cross Functional Team Process

Does Not Work Withoutthe Cross Functional TEAM


Introduction

The Lear Program Management Process is intended toprovide a structured yet flexible methodologyprovide a structured yet flexible methodology to manageproduct development from pre-award of business throughlaunch of production.

The Lear Program Management Process is designed to give athorough overview and template for developing, executingdeveloping, executingand managingand managing a program from early conception throughprogram launch.

This process serves as a planning and managementplanning and management tool toensure that specific activitiesspecific activities occur at the proper time inthe product development process to create products, includingsupportive services that meet and exceed (internal andexternal) customer expectations.


Scope

It provides common strategy, framework, and controls to beutilized through-out Lear Corporation.

Execution of the process is led by a program manager butrequires significant support requires significant support from a cross-functional cross-functionalprogram team.program team.

LPMP is the backbone structurebackbone structure for key deliverables withinDFSS.


LPMP on the LearNet

Click Here



Phases

There are five phases in the LPMP process: Planning,Planning,Prototype, Pilot, Launch, and Post Launch.Prototype, Pilot, Launch, and Post Launch.

– For each phase there is a phase overview on the web site thatprovides a descriptive summary of the scope of activity within therespective phase and highlights key elements of the phase.key elements of the phase.

Each phase overview has a link to a relationship diagramrelationship diagram thatshows the general order and relationship of all therelationship of all thedeliverablesdeliverables within the respective phase.

– The relationship diagramsrelationship diagrams are provided as a tool to help teamsbetter understand how deliverables are related to one another.


LPMP Deliverables

Each phase of LPMP has an associated set of deliverablesset of deliverablesidentified.identified.

For each deliverable there is an associated deliverableinformation sheet that provides the following information:

– Definition: A concise description of the task

– Clarification / Training Point: An expanded explanationexpanded explanation of thedeliverable. May provide more detail on what should or shouldnot be considered in accomplishing a deliverable.

– LPMP Phase: Listing of phases that the respective deliverable isaccomplished.

– Deliverables: A clear statement of the expected outputexpected outputproduced in accomplishing the task.produced in accomplishing the task.

In some cases there is no output such as when the deliverable is amilestone.


LPMP Deliverables (Cont’d)

– Requirements: This section will identify any related/requiredidentify any related/requiredforms or proceduresforms or procedures that are considered mandatory within thecorporation or specified segment of the corporation.

– Best Practices: Best Practices will often provide documents,provide documents,spreadsheets, and formsspreadsheets, and forms that various teams have successfullyused on their program and are considered best practices.

– Reference: Where formal reference documents are available,known, and considered useful, this field will identify them.

At a minimum, there are references to the appropriate sections of theAIAG APQP manualAIAG APQP manual where applicable.


Engineering Deliverables


Integration of DFSS Into LPMP


LPMP Summary

Gate / Phase SystemFlexibleAll ProductsAll CustomersSeries of ReviewsEncompasses APQP

Product Development and LaunchWeb-Based ManualIdentifies and tracks key DFSS deliverables


Agenda





Revised 17 Dec 03

Design Failure Mode and EffectsAnalysis (DFMEA)

• History

• Types of FMEA

• DFMEA Defined

• DFMEA Supports the designprocess

• Role of DFMEA

• The Form



Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


Learning Objectives

At the end of this module, participants will understand . . .

DFMEA and it’s link to the design processDFMEA and it’s link to the design process

Sources of risk

DFMEA concepts that will be used by project teams in thedevelopment process

Why we use DFMEA, and when to use it.Why we use DFMEA, and when to use it.

The DFMEA form.


History

First used in the 1960’s in the Aerospace industry during theApollo missionsIn 1974 the Navy developed MIL-STD-1629 regarding the useof FMEAIn the early to mid 1970’s, automotive applications driven byliability costsCurrent Standard is SAE J-1739


Where Does Risk Come From?

VagueWorkmanship

Standards

Cumulative Risk

Poor controlplans & SOP’s

Raw MaterialVariation

Poorly developedSpecification

LimitsMeasurement

Variation(Online and QC)

MachineReliability

PotentialSafety

HazardsUnclear CustomerExpectations

D. H. Stamatis, FMEA:FMEA from Theory to Practice, Quality Press, 1995

Poor ProcessCapability

Job Assignment

Variation


Type of FMEA’s

System:– Analyzes systems and sub-systems in the early concept and

design stages. Focuses on functions and interactions amongsystems.

Design:– Analyzes product designs before they are released to production.– A DFMEA should always be completed well in advance of a

prototype build.– Focuses on potential failure modes of products due to design

deficiencies or errors.Process:– Analyzes production or administrative processes.– Focuses on potential failure modes of the output caused by

process deficiencies.Machine:– Analyzes a piece of manufacturing equipment prior to its

construction.– Focuses on potential failure modes of the manufacturing

equipment due to design deficiencies or errors.


Type of FMEA’s

Equipment:– Analyzes production or administrative process.– Focuses on potential failure modes of the output caused by

process deficiencies of the manufacturing equipment only.

Change:– Analyzes a design or process change not covered in existing

FMEAs production.

Containment:– Analyzes a containment screen.


DFMEA Defined

A Design FMEA is an analytical technique utilizedanalytical technique utilized primarilyby a Design Responsible Engineer/Team as a means to assurethat, potential failure modes and their associatedcauses/mechanisms have been considered and addressed.

An DFMEA is a summary of an engineer’s and the team’sis a summary of an engineer’s and the team’sthoughtsthoughts (including an analysis of items that could go wrongbased on experience and past concerns) as a component,subsystem or system is designed.

This systematic approach parallels, formalizes and documentsthe mental disciplines that an engineer normally goes throughin any design process.

Source: FMEA manual; Automotive Industry Action Group (AIAG)


DFMEA Supports the DesignProcess

The Design FMEA supports the design processsupports the design process in reducingthe risk failures by:– Aiding in the objective evaluation of design requirementsevaluation of design requirements and

design alternatives.– Aiding the initial design for manufacturing and assemblymanufacturing and assembly

requirementsrequirements–– Increasing the probabilityIncreasing the probability that potential failure modes and

their effects on system and vehicle operation have beenconsidered in the design/development process.

–– Providing potential informationProviding potential information to aid in the planning ofthorough and efficient design test and development programs

– Developing a list of potential failure modespotential failure modes ranked accordingto their effect on the “customer”, thus establishing a prioritysystem for design improvements and development testing.

–– Providing an open issueProviding an open issue format to recommending and trackingrisk reducing actions.


Role of Design FMEA

Key tool for the design team to improve the designimprove the design in a

preemptivepreemptive manner (before failures occur)

Used to prioritize resourcesprioritize resources to insure design improvement

efforts are beneficial to customer

Used to document completiondocument completion of projects

Should be a dynamicdynamic document, continually reviewed,

amended, updated

Used to analyze new design conceptsanalyze new design concepts

EvaluatesEvaluates the risk of design changes


DFMEA Team

Team approach is necessaryResponsible Engineer typically leads the teamDocument should be owned by the process ownerRecommended representatives:– Design– Manufacturing operators / supervisors– Quality– Reliability– Materials– Testing– Supplier


The Form

POTENTIALFAILURE MODE AND EFFECTS ANALYSIS

Print # NUMBER Rev. ECL (DESIGN FMEA) FMEA Number: FILE.XLS

System/Subsystem/Component: Design Responsibility: Prepared by:

Model Year(s)/Vehicle(s) APPLICATION Key Date Page: of

Team: FMEA Date (Orig.) (Rev.)

C Potential O Current Current DItem Potential Potential S l Cause(s)/ c Design Design e R. Recommended Responsibility Action Results

Failure Effect(s) of e a Mechanism(s) c Controls Controls t P. Actions & Target Actions S O D R.Mode Failure v s of Failure u - Prevention - Detection e N. Date Taken e c e P.

Function s r c v c t N.

SUPPLIER

SEVERITY SCALE OCCURENCE SCALE DETECTION SCALE


Summary

DFMEA is a technique utilized primarily by a DesignDesignResponsible Engineer/TeamResponsible Engineer/Team as a means to assure that, tothe extent possible, potential failure modes and theirassociated causes have been considered and addressed.DFMEA:–– Identifies potential failure modesIdentifies potential failure modes early in development.–– Assists in evaluation of product requirementsAssists in evaluation of product requirements and

alternatives.–– Identifies special characteristicsIdentifies special characteristics–– Prioritizes design improvements.Prioritizes design improvements.

Using the team approachteam approach recognizes that a group workingtogether can accomplish more than individuals workingseparately.


Agenda





Revised 17 Dec 03

Design for Manufacturing and Assembly(DFMA)



Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


DFMA:Learning Objectives


Simultaneous Engineering and Cross Functional Teams

Benefits of DFMA, Benefits of DFMA, and its importance in the DFSS process

Methods such as Ten Guidelines for Design for Assembly


Design for Manufacturing andAssembly

The DFMA process answers the question: Is the designIs the designoptimum for manufacturing and assembly?optimum for manufacturing and assembly? DFMA isdefined as follows:

– Is any procedure or design process that considers theconsiders theproduction factorsproduction factors from the beginning of the product design.

– Every design activity, from conceptualization to evaluation, mustfocus on generation of a design that meets marketmeets marketexpectations and can be manufactured successfully.expectations and can be manufactured successfully.

Source: Computer-Integrated Manufacturing; James A. Rehg


Simultaneous Engineering /Cross Functional Teams

Simultaneously design the product and the process

Prevents over-the-wall design

Cross-functional teams continually evaluate each others workand have input on the whole product/process design

Simultaneous decision-making by design teams

Integrates product design & process planning


Breaking Down Barriers


DFMA Benefits

Shorter time to bring the product to marketMajor Concurrent Engineering DriverSmoother transition into productionOptimized manufacturing/assembly methods and processesBetter informed tooling and capital equipment decisionsFewer components in the final productEasier assemblyShorter assembly timeLower costs of productionMajor cost savings (parts & labor)Reduced defectsIncreased product quality and reliability.Greater customer satisfaction


Ten Guidelines forDesign for Assembly

1. Minimize the number of parts: Combine or eliminate parts whenever possible.2. Minimize assembly surfaces.3. Design for top-down assembly.4. Improve assembly access.5. Maximize part compliance.6. Maximize part symmetry.7. Optimize part handling.8. Avoid separate fasteners.9. Provide parts with integral self-locking features.10. Focus on modular design.


Design Simplification (DFMAExample)

Design for push-and-snap assembly

(b) Revised design

One-piece base & elimination of fasteners

(a) The original design

Assembly using common fasteners

(c) Final design

Source: Boothroyd/Dewhurst, “ Design for Manufacturing and Assembly,” April 1988, Society of Manufacturing Engineers

Spindle/Housing Assembly


Design a product for easy & economical production (8 month)Baseline: 105 separate parts (reduced to 9 parts)Total calculated time: 1440 seconds (reduced to 258 seconds)Easy to fabricate & assemble componentsIntegrated product design with process planning

DFMA - Case Study

Source: Boothroyd Dewhurst


DFMA Summary

DFMA is recognized as the key to simultaneouslysimultaneouslyminimizing manufacturing cost, assuring productminimizing manufacturing cost, assuring productquality, and increasing productivity.quality, and increasing productivity.

It encourages teamworkencourages teamwork and a dialogue between designersand manufacturing engineers, and any other individual whoplay a part in determining the product costs during the earlystages of design.

The DFMA procedure often produces a considerableconsiderablereduction in part countreduction in part count, resulting in simpler and morereliable products which are less expensive to assemble andmanufacture.


Agenda





Revised 17 Dec 03

Design for Reliability


Learning Objectives


Definition of Reliability

Importance of Reliability

Assessment of Reliability


Definition of Reliability

ReliabilityReliability is a performance attribute of the product orsystem

It is also based on a Probability of SuccessProbability of Success, but is primarilyfocuses on Frequency of FailuresFrequency of Failures

Definition:Definition:

– The probability that an item will perform itsintended function under stated conditions, foreither a specified interval or over its useful life.


Importance of Reliability

Typical performance measures are irrelevant if the productfails

– speed, capacity, range, and other “normal” performancemeasures

Reliability is critical to safety and can be a liability

Reliability is a primary factor in determining operating, repair,and warranty costs

Reliability determines whether or not a product is capable toperform its function


Occurrence of Failures

The “Bathtub Curve”– typically used for electronic equipment– applicable to mechanical equipment with less pronounced

constant failure rate regionFa

ilure

Rat

e ( λ

)

Infant Mortality Useful Life Wearout

Reliability Measure

Durability Measure(Life)

Design RelatedFailures

Quality RelatedFailures

Time


Everything Effects Reliability

PlanningPlanning– Market Survey– Benchmarking– QFD

DesignDesign– Critical Item ID &

Control– Derating– Design Reviews– Environment

Characterization– Fault Tolerance– Parts Application &

Selection– Supplier Control– Thermal Design

AnalysisAnalysis– Allocations– DOE– Dormancy Analysis– Durability Assessment– FMEA– Fault Tree Analysis– Finite Element Analysis– Life Cycle Planning– Modeling & Simulation– Part Obsolescence– Predictions– Repair Strategies– Sneak Circuit Analysis– Thermal Analysis– Translations– Worst Case Analysis– Statistical Analysis

TestTest– Accelerated Life

Test– Environmental

Stress Screening– Reliability Growth

Testing

ManufacturingManufacturing– SPC– Inspection


Prediction withReliability Analysis

Reliability Analysis can not tell you how to FIX the failure.The focus is prediction. Prediction of a potential problem.


Summary

Reliability has a significant impact on our future costs.

Reliability should be considered with all engineering and

design decisions.

Reliability of the design must be predicted and verified.


AgendaWhy DFSSWhy DFSSDFSS OverviewDFSS OverviewQuality Function Deployment (QFD)Quality Function Deployment (QFD)BenchmarkingBenchmarkingTRIZ / Trade-Offs (Pugh, and SDI Analysis)TRIZ / Trade-Offs (Pugh, and SDI Analysis)Program Management (LPMP)Program Management (LPMP)Design Failure Mode and Effects Analysis (DFMEA)Design Failure Mode and Effects Analysis (DFMEA)Design for Manufacturability and Assembly (DFMA)Design for Manufacturability and Assembly (DFMA)Design for Reliability (DFR)Design for Reliability (DFR)Regression and Design of Experiments (DOE)Regression and Design of Experiments (DOE)DFSS Tool BoxDFSS Tool Box




Revised 17 Dec 03

Regression and Design of Experiments


Regression & DOE:Learning Objectives


Models Models in the application of DFSS

Analytical, empirical , and semi-empirical equations asmodels.

DOEDOE and RegressionRegression analysis as tools for developing modelsfrom hardware and simulations





Build Models




DFSSDFSS



PilotPilotPilot

LaunchLaunchLaunch


PostLaunchPostPost

LaunchLaunch











Define Alternatives

Verify & Validate




Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


Fundamentals of DFSS

Product ModelProduct Model((equationequation, , simulation,simulation,workbook,workbook,


Y

C

E

A

B

D

ULLL T

PNCPNC

UnderstandingUnderstandingRequirements,Requirements,Specifications,Specifications,& Capabilities& Capabilities

ApplyingApplyingModels &Models &AnalysesAnalyses

PredictingPredictingProbability of Probability of

Non-ComplianceNon-Compliance


Regression Models:A Case for Action

ModelingModeling is the cornerstone of engineering design. There isno lack of models in any engineering organization.

However, most of these models can be in forms that are verycumbersomecumbersome, slowslow, or expensiveexpensive to use.

A critical enabler for DFSS is having fast, accurate modelsfast, accurate modelsfor analysisfor analysis (Sensitivity Analysis, Monte Carlo), allocationand optimization.

RegressionRegression techniques allow these fast, accurate modelsfast, accurate modelsto be created by approximating the original model.

Multiple types of models can be used in DFSS


The original model may be:The original model may be:

an Equation expert opinion, Engineering textbooks, basic physics

embedded in Data repository of historical data, ongoing data collection

a computer Simulation pSpice, ProEngineer, Matlab, Ansys, etc.

Prototypes physical models or mock-ups, pilot production lines

the Actual System the actual product or process being designed

?X2

X1

Xn

Y

Initial StateInitial State

Original ModelOriginal Model

Y=f(X)X2

X1

Xn

Y

Desired StateDesired State

Fast, Accurate ApproximationFast, Accurate Approximation

StatisticalModelingProcess

Increasing accuracyand expense

Types of Models


Types of Equations

Equations can come from many sources:– Textbooks– Basic Physics– Expert Opinion– Curve-Fits

Equations can be analytical, empirical, or semi-empirical.–– Analytical Equations:Analytical Equations: Relying on or derived from

analysis of elemental parts or basic principles–– Empirical Equations:Empirical Equations: Relying on or derived from

observation or experiment–– Semi-Empirical Equations:Semi-Empirical Equations: The format of the equation

has a basis in physics or first-principles and the coefficientsand/or exponents are fit to experimental data.

All equations are models.


Empirical Equations

Empirical equationsEmpirical equations are typically the result of a regressionprocess where data is fit to a equationExample:Example: An equation was fit to 239 data points taken forthe thermal coefficient of expansion of copper as a function oftemperature.

0

5

10

15

20

25

0 200 400 600 800 1000

Temperature (K)

Co

eff

icie

nt

of

Th

erm

al E

xp

an

sio

n

Empirical Equation

Experimental Data

CTE = (1.08 –0.123 T + 4.09x10-6 T2 – 1.43x10-6 T3) / (1 – 0.00576 T + 2.41x10-4 T2 – 1.23x10-7 T3)

Data Source: NIST standard referencedataset Hahn11.dat


Semi-Empirical Equations

Semi-empirical equationsSemi-empirical equations combine experimental data withphysics-based analytical equations.Example:Example: The heat transferred from the hot walls of a pipe toto a cold fluid flowing through the pipe has the followingphysics-based, analytical relationship:

where a, b, and c are unknown coefficients.Experiments were performed at various flow rates for threefluids:The unknown coefficientsa,b, and c were determinedby fitting the experimentaldata to the equation.

Heat Transferred = a • (Flow Rate)b • (Fluid Property)c

3

6

6

0.7

0.7

Fluid Property

22.2

44.0

31.8

18.6

12.4

Heat Transferred

3400

6000

4000

5000

3000

Flow

Heat Transferred = 0.023 • (Flow Rate)0.8 • (Fluid Property)0.33


Say we have a system, and we don’t have a good understandingof how its outputs are affected by its attributes.

One way to gain understanding is to vary the system attributes andcollect data on the system outputs.DOE is a structured method, based in statistics, for collecting andanalyzing this data.

Design of Experiments (DOE)

SystemSystemOutputsOutputs

(Responses)(Responses)

•• PerformancePerformanceParametersParameters

•• YieldYield•• CostCost•• ScheduleSchedule•• QualityQuality

SYSTEM

• Design Prototypes• Manufacturing Process• Computer Simulation • Purchased Components• Etc.

SystemSystemAttributesAttributes(Factors)(Factors)

•• DimensionsDimensions•• TolerancesTolerances•• ComponentComponent

ValuesValues•• ProcessProcess

ParametersParameters•• MaterialsMaterials


Role of DOE in DFSS

Primary role of DOE is for model creation:model creation:

Results in a fast approximate model that mimicsthe data set.These models are then used in analyzing,allocating and optimizing variability.

Results in a fast approximate model that mimicsthe data set.These models are then used in analyzing,allocating and optimizing variability.

Original(slow,

complex)System

Regression

RepeatedRuns

Data Set

Y = f(x)

Math Model

DOE

Inputs, X Output, Y


Design of Experiments (DOE)

DOE is a structured method, based in statistics, forDOE is a structured method, based in statistics, forrunning a set of tests or analyses on a system, product,running a set of tests or analyses on a system, product,or process.or process.

Two basic applications:Two basic applications: screening and modeling

–– ScreeningScreening experiments help identify the most significantfactors from a larger initial set.

–– ModelingModeling experiments yield equations that approximate thesystem’s behavior.

–– BothBoth applications can indicate directions for applications can indicate directions forimprovement.improvement.

Compared with random or one-factor-at-a-time testing, muchmore information can be extracted from fewer runs.


What Does a RegressionModel Look Like?

Only some model terms aresignificant (implied by P < 0.05)

The output model is:Y = 97.74 - 8.00 X1 + 8.40 X2

- 26.80 X3 – 9.50 X1 X3

Output is from Minitab

Standard Form: Y = β0 + β1x1 + β2x2 + … + β12x1x2 + … + β11x12 +

…

Inputs, X

Interaction between inputs, X1 and X3


Inputs[X’s,Y’s]

Excel-CompatibleModel

Polynomial EquationY=a + b • X1 + c•X1

2

Physics-Based Equatione.g. Y=a • X1

b X2c

Lookup Table[X1 X2 Y1] . . . [Xi Xj Yk]

Linear Regression

Nonlinear Regression

Nonparametric Regression

Regression Techniques


Regression and DOE:Summary

IntroducedIntroduced models as a necessary part of DFSS

HighlightedHighlighted types of equations and compared them

– Analytical, Empirical, and Semi-Empricial

Introduced Introduced how Regression and DOE can be used toproduce models

Discussed Discussed how models are used in design analysis

Opened the door for using Models in DFSSusing Models in DFSS

Key Message … Key Message … Models are a must for DFSSModels are a must for DFSS


Agenda





Revised 17 Dec 03

DFSS Tool Box

• Sensitivity Analysis

• Monte Carlo Analysis

• Statistical Allocation

• Optimization

• Other Enablers to DFSS



Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


Sensitivity Analysis:Learning Objectives


Sensitivity Analysis Sensitivity Analysis as a means of generating µ, σ, and PNCfor a response given:

– an equation or Excel workbook relating a responsevariable to a set of input factors,

– mean and standard deviation (µ and σ) for each inputfactor, and

– upper and lower limits on the response

Sensitivity Analysis WorksheetsWorksheets in design and analysis





Build Models




DFSSDFSS



PilotPilotPilot

LaunchLaunchLaunch


PostLaunchPostPost

LaunchLaunch











Define Alternatives

Verify & Validate



Purpose: Compute variability of an output from a deterministicmodel (i.e. equation)

Information Needed:

Means and standard deviations (µ and σ) for the input factorsA model that relates a response to a set of input factorsSpecification limits (LSL and USL) on the response

Results Obtained:

Response mean and standard deviation (calculation for µ and σ)Relative importance of the factors’ contributions to the responseThe expected Probability of Non-Compliance

Sensitivity AnalysisMethod Overview

Existing ModelExisting Model(equation, (equation, simulation,simulation,

workbook, etc.)workbook, etc.)

Y

USLLSL T

PNCPNC

(µ,σ)

(µ,σ)

(µ,σ)

(µ,σ)

(µ,σ)

(µ,σ)

A

B

D

C

E


Applying Sensitivity Analysis

We have an existing heat sink design that, using a 25 mmfan, looks feasible using nominal analysis.

Will this existing heat sink perform well consideringmanufacturing and processor variation?

If not, what is the probability of non-compliance?



Length = 40 mmWidth = 40 mm

Inlet Temperature = 44 °CHeat Rejected = 34 W

Number of Fins = 16Fin Thickness = 0.5 mm

Fin Height = 15 mmNumber of Fans = 1

Fan Location = 0 (front)

Base Temperature= 99.1 °C

(USL = 100 °C)

Inputs, X Output, YProduct


39.0 39.5 40.0 40.5 41.0

Gather Information for Inputs

µ = 40σ = 0.333

Heat sink fin thickness: ± 0.13 mm

Processor heat rejected: ± 0.5 WHeat sink length & width: ± 1 mm

Heat sink fin height: ± 0.5 mm

14.50 14.75 15.00 15.25 15.50

Consulting supplier data sheets and process data,we identify manufacturing tolerances and variation.We assume tolerances are 3 Sigma.

µ = 15σ = 0.167

0.37 0.44 0.50 0.56 0.63

33.50 33.75 34.00 34.25 34.50

µ = 0.5σ = 0.043

µ = 34σ = 0.289


Sensitivity Analysis Example

Outputs:Outputs: Y Mean Y Mean Y Std Deviation Y Std Deviation PNC PNC

… and … and Contribution Contribution of X’s to of X’s to Variation Variation

Inputs:Inputs: Equation Equation X Values X Values X Variation X Variation

Conclusion: PNC = 16.4%Conclusion: PNC = 16.4%Conclusion: PNC = 16.4%


Sensitivity Analysis:Summary

Quick and easy to use for most functions using theSensitivity Analysis tool in Excel

Allows response variation to be predictedAllows response variation to be predicted fromknowledge of the equation, nominal values, and assumedtolerances.

Provides contribution to varianceProvides contribution to variance for each parameter tosupport cost, process capability, and tolerance decisions.

Accurate for functions with a linear response within therange of variability (≈ ±3σ) for each factor

– non-linear responses can be handled with Monte Carlo analysis


Monte Carlo Analysis:Learning Objectives


Monte Carlo analysis Monte Carlo analysis as a means to generate a probabilitydistribution and PNC for a response given:

– an equation or Excel workbook relating a response variable to aset of input factors,

– a probability distribution for each input factor, and


the Crystal BallCrystal Ball Monte Carlo tool in design and analysis

Monte Carlo techniquesMonte Carlo techniques and when to use them





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Purpose: Compute variability of outputs from a deterministicmodel (i.e. equations or sets of equations)

Information Needed:

A model that relates a response to a set of input factorsProbability distributions for the input factorsSpecification limits (LSL and USL) on the response

Results Obtained:

A probability distribution for the response (statistical data analysis)Relative importance of the factors’ contributions to the responseThe expected Probability of Non-Compliance

Monte Carlo Method Overview



Y

A

B

D

C

EUSLLSL T

PNCPNC


What isMonte Carlo Analysis?

A number of Monte Carlo trials,A number of Monte Carlo trials, n,n, is chosen up front.is chosen up front.

For each trial,For each trial,

– A random value is generated for each input factor that followsits specified distribution.

– A response value is computed using these input factor values.

What results is a samplesample of nn response values.

Sampling statistics are used to compute response mean andstandard deviation, and can also be used to fit a probabilitydistribution to the data.

Proportions and Confidence Intervals are used for computingPNC.

In Summary - Monte Carlo is solving the equations over andover again using randomly selected values and collecting theresults for statistical analysis.


Applying Monte Carlo Analysis

We have an existing heat sink design that, using a 25 mmfan, looks feasible using nominal analysis.





Length = 40 mmWidth = 40 mm


Number of Fins = 16Fin Thickness = 0.5 mm

Fin Height = 15 mmNumber of Fans = 1

Fan Location = 0 (front)

Base Temperature= 99.1 °C

(USL = 100 °C)

Inputs, X Output, YProduct


Perform Monte CarloAnalysis with Crystal Ball

5. Run Simulation and analyzeresults from multiple trials.

2. Set each input factoras an “assumption” cell.

4. Set the responseas a “forecast” cell.

39.0 40.0 41.0

14.50 15.00 15.50

0.37 0.50 0.63

33.50 34.00 34.50

3. Define statisticalproperties for the

input X’s

1. Define ExcelTM

Spreadsheet Model


Monte Carlo Analysis:Summary

Allows Allows a probability distribution and PNC for a responseto be computed given:


– a probability distribution for each input factor, and


Crystal Ball Crystal Ball (Decisioneering) is a Monte Carlo tool that canbe used with any Excel spreadsheet

Monte Carlo techniquesMonte Carlo techniques can be used for any analysis orsimulation

Monte Carlo AnalysisMonte Carlo Analysis provides a “virtual productmanufacturing” process


Statistical Allocation:Learning Objectives

Statistical allocationStatistical allocation as a means to meet a desired level ofresponse PNC given:


– upper and lower limits on the response, and

– a target value for response standard deviation or PNC

Allocation Worksheets Allocation Worksheets for assigning tolerances based oncontribution to variation, process capability, or cost.






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Why is Allocation Needed?

Most Product requirementProduct requirementdefinitiondefinition begins at the TOP

– It must do this …

– We must produce a quantity of …

– The cost must not exceed …

– The production yield must be greater than …

– The profit goal is …

Assembly and ComponentAssembly and Componentrequirementsrequirements flowdown from theProduct requirements

How well the TOP level requirement ismet is dependent upon how well theAssembly and Componentrequirements are met.

Once the Product design is verified,manufacturing variation is usually theproblem

ProductRequirement

Assembly ARequirement

Assembly BRequirement

Comp A

Reqmt

Comp B

Reqmt

Comp C

Reqmt

Comp D

Reqmt


Why is Allocation Needed?

If Assembly and Componentvariation is known, thenANAYLSISANAYLSIS can be used todetermine Product variation

ProductRequirement



Comp A

Reqmt

Comp B

Reqmt

Comp C

Reqmt

Comp D

Reqmt

AnalysisAnalysis ProductRequirement



Comp A

Reqmt

Comp B

Reqmt

Comp C

Reqmt

Comp D

Reqmt

AllocationAllocation

If Product variation is known,then ALLOCATIONALLOCATION can be usedto determine Assembly andComponent variation

Use Sensitivity Analysis Use Sensitivity Analysis and Monte Carlo to do thisand Monte Carlo to do this

Use Statistical AllocationUse Statistical Allocationto do thisto do this


Purpose: Allocate variability to inputs of a deterministic model

Information Needed:A model that relates a response to a set of input factors

Input factor means µµii

Specification limits (LSL and USL) on the response

The desired Probability of Non-Compliance

Results Obtained:Factor standard deviations σσii

Relative importance of the factors’ contributions to the response

Allocation Method Overview



Y

USLLSL T

PNCPNC

(µ,σ)

(µ,σ)

(µ,σ)

(µ,σ)

(µ,σ)

(µ,σ)

A

B

D

C

E


Statistical Allocation:Typical Results

Outputs:Outputs: X Std Deviation X Std Deviation PNC PNC

Contribution ofContribution ofX’s to VariationX’s to Variation

Inputs:Inputs: Equation Equation X Values X Values

Inputs:Inputs: Y Std Deviation Y Std Deviation Allocation Drivers Allocation Drivers

TolerancesTolerances

Heat Sink SA Demo


Statistical Allocation:Summary

Quick and easy to performQuick and easy to perform for most functions using theStatistical Allocation tool in Excel.

Uses budgeted response variationUses budgeted response variation as a requirement fordetermining parameter variations or tolerances.

AllowsAllows the engineer to make component tolerance decisionsbased on contribution to variation, process capability, or cost.

ReplacesReplaces iterative, trial & error methods typically used forassigning tolerances.


Optimization:Learning Objectives


OptimizationOptimization in engineering design:

–– Single-Objective:Single-Objective: automated search of alternatives

–– Multi-Objective:Multi-Objective: trade study tool

Trade Studies Trade Studies to identify designs that meet multiplerequirements and multiple PNC goals simultaneously

ApogeeApogee, a tool for statistical multi-objective optimization




Good PracticesGood Practices

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Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


Traditional View– Maximize (or minimize) a singlesingle objective function f(x)

– Subject to constraints gi(x) < ci , i = 1, 2, ... NDesignvariables {x} are real-valued (continuous or discrete)

– Design variables {x} are real-valued (continuous or discrete)

– Output is a set of values {xi} that is the “optimum”

Working Definition

– Rapid, automated search and evaluation of design alternatives

Implementation– Employ software packages to find “optimum” values for {x}

– Each package uses different algorithms (gradient based, heuristic, etc.)

– Problem formulation is fed to package as input

What is Optimization?

Need modelsfor f(x) and gi(x)

Need modelsfor f(x) and gi(x)

Specificinstances {xi}

Specificinstances {xi}


Single-Objective Vs..Multi-Objective

BenefitsBenefitsBenefits

MathFormulation

MathMathFormulationFormulation

SolutionApproach

SolutionSolutionApproachApproach

Set f1(x) = T1

f2(x) = T2

fk(x) = Tk

Subject to g1(x) < c1

g2(x) < c2

gn(x) < cn

Maximize f(x)

Subject to g1(x) < c1

g2(x) < c2

gn(x) < cn

Find “optimum” values for{x} that maximize f(x)

Find best values for {x} thatbring each fi(x) onto its target Ti

Rapid, automated searchof design space

Trade studies.Trade studies. Exploredifferent priorities forbringing each goal on target


Y=f(xY=f(x11,x,x33,x,x55))PerformancePerformance

Target Target

Yperf 1

Y=g(xY=g(x11,x,x33,x,x22) ) CostCost

Y=h(xY=h(x33,x,x22,x,x55))PerformancePerformance

TargetTarget

Multi - ObjectiveOptimization

USLLSL T

PNCPNC

USLLSL T

PNCPNC

Yperf 2

YCost

X1 (µ,σ)

X3(µ,σ)

X3(µ,σ)

X3(µ,σ)

X1 (µ,σ)

X5 (µ,σ)

X2 (µ,σ)

X2 (µ,σ)

X5 (µ,σ)

USLT


Optimization Example:Heat Sink Design

We discovered that the existing heat sink design hasunacceptable performance when manufacturing variation isconsidered.

Length = 40 to 60 mmWidth = 40 to 60 mm


Number of Fins = 10 to 22Fin Thickness = 0.25 to 1.25 mm

Fin Height = 15 to 25 mmNumber of Fans = 1 or 2

Fan Location = 0 (front) or 1 (back) or 2 (both)

Base Temp < 99.1 °CBase Temp PNC < 0.001

Cost = 0Base Temp USL = 100 °C

Current PNC = 0.16

Can we find a different nominal design that will improve PNCwithout having to tighten tolerances?Can we find a different nominal design that will improve PNCwithout having to tighten tolerances?

Input Factor Ranges(Xmin to Xmax)

Models for CostPerformance,

etc. (Y)Constraints,

Goals,Priorities Length

WidthNo. of Fins

Fin Thickness Fin Height

No. of FansFan Location

SelectedValues (X)


Optimization Formulationusing Apogee

Define inputs, variation, and models that capture system behavior.Define inputs, variation, and models that capture system behavior.


Optimization Formulation

Link to customer requirements.Define constraints, goals, and priorities.

Link to customer requirements.Define constraints, goals, and priorities.

RunOptimizationand ReviewResults

RunOptimizationand ReviewResults


Optimization Results

By exercising the formulation, a familyfamily of solutions is created:

High PerformanceHigh Performance

Low CostLow Cost BalancedBalanced

Plus Outlet Air Req’tPlus Outlet Air Req’t


EXPLORE MORE DESIGN ALTERNATIVESEXPLORE MORE DESIGN ALTERNATIVES– Evaluate hundreds or thousands of options in a few hours– Orders-of-magnitude improvement on standard process

CONSIDER MORE DESIGN ATTRIBUTES: CONSIDER MORE DESIGN ATTRIBUTES: Trade StudiesTrade Studies– Easy to add cost models, quality models, cycle time models– Trade off performance with cost, quality, and schedule

GENERAGENERATE BETTER DESITE BETTER DESIGNSGNS– More designs evaluated across more attributes in less time.

Improved designs are clearly a probable outcome.

IIMPROVE CUSTOMER RELATIONSMPROVE CUSTOMER RELATIONS– Present customer with options

“Your costs can be cut by a third if you’d relax this one requirement...”

– Empower customer to make choices

Benefits of Optimization


Other Enablers to DFSS:Learning Objectives


Process capability data Process capability data in the design process

Gage R & RGage R & R to verify that performance is properly assessed

Test EffectivenessTest Effectiveness calculations to verify that test results arereliable and correct





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Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


15.19 14.79 14.77 15.01 14.9215.09 14.76 14.79 15.14 14.9914.84 15.2 14.99 15.09 15.2515.21 15.25 14.79 14.92 14.8114.83 15.14 14.94 15.13 14.8514.84 14.82 14.97 14.98 14.9515.21 15.01 14.79 14.89 14.8214.77 14.77 14.81 15.27 15.0714.97 14.75 14.99 15.16 15.0115.08 15.03 14.98 14.88 14.89

Use Process Capability Data

Product ModelProduct Model

Y = f(X)Y = f(X)YC

E

A

B

D

ULLL T

PNCPNC

14 15 16

0.05

0.1

CatalogSpecs or Supplier

Estim ates

Sim ilar ProductM easured Data

Process andM aterialDatabase

Perform anceEstim ates

LS US

Potential,Expensive

Catalog,Inexpensive

LS US

LS US

LS US

OneSupplier

with ShiftedM ean

M ultipleSupplierswith ShiftedM eans

ProcessOut ofControl Statistical Methods must be used

to analyze data and test methodsto prevent part and materialrelated contributions to PNC.

Statistical Methods must be usedto analyze data and test methodsto prevent part and materialrelated contributions to PNC.


MeasurementError

Spec Width

Actualoperatingpoint

The Effect ofMeasurement Error

In a perfectperfect world, a test would measure the actualoperating point of a parameter and all tests would beaccurateHowever, in the real world,– If we test a given unit many times, we will probably get a

range of measurements

Testing is a process, just like anyother

All measurements have someerrorDue to measurement error, wecould pass a bad unit or fail agood unit


Measurement system studies assess how much variation isassociated with the measurement system as compared to themanufacturing process variationGage studies assess:

Defining Measurement SystemAccuracy with Gage R & R

RepeatabilityRepeatabilityAccuracyAccuracy

Accuracy

How close is the measurement tothe true value?

True value

ReproducibilityReproducibilityHow close are series of measurementsby several people on the same part on

the same equipment?

Repeatability

True value

How close is a series of measurementson one part by one person?

Reproducibility

True value

Operator A

Operator C

Operator B

Total measurement variance is the sum of the repeatabilityand reproducibility variances

We can estimate these variances using Gage R & R

222& ityrepeatabililityReproducibrR σσσ +=


Understanding Test Effectiveness

In terms of products to be tested, there are two types–– Good UnitsGood Units and Bad UnitsBad Units

In terms of product test results, there are two outcomes–– Passed UnitsPassed Units and Failed UnitsFailed Units

Of the four possible test result outcomes, only two areacceptable– P(Good+Passes) and P(Bad+Fails)

EventsGoodUnits

BadUnits Total

Unit FailsTest

P(G+F) P(B+F) P(F)

Unit PassesTest

P(G+P) P(B+P) P(P)

Total P(G) P(B) P(total)=1


Test EffectivenessAnalysis

USLXbar

ProductVariation

ProductVariation

MeasurementVariation

MeasurementVariation

Probability of Probability of Passing a Bad UnitPassing a Bad Unit

Probability of Probability of Failing a Good UnitFailing a Good Unit

LowerUSLXbar

Integrating the concepts of product and measurementvariability allows us to calculate the true test efficiencytrue test efficiency


We can make thetest effectivenesscalculation using– Excel– Minitab– or a simple

Windows program

Inputs:– Process mean– Process Std

Deviation– Test Error

Outputs:– Overall test

probabilities– Conditional

Pass/FailProbabilities

Test EffectivenessCalculations

Test Effectiveness

Bivariate example


Other Enablers to DFSS:Summary

All measurements include both the real value process resultplus error

Measurement error can have a large influence on the results

We can predict the measured results during the design stageand use this information to further optimize productperformance

Measurement error is obtained using Gage R & R studies

Test Effectiveness Calculations tell us the consequences ofprocess variation and test variation


Agenda





Revised 17 Dec 03

Statistical Roll-up and Score Card





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Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


Statistical Roll-up & Scorecard:Learning Objectives


Model-based approaches to rollup results and generatescorecards:

– Roll up statistical information (µ, σ) to the system level usingexisting engineering models

– Use PNC as the fundamental metric for each requirement

– Use scorecards to evaluate multiple requirements at any level inthe system

Scorecards Scorecards as a method to drive the right DFSS behavior ona program


Metric Simplification

Fundamental metric is the probability of not meeting arequirement: the Probability of Non-ComplianceProbability of Non-Compliance (PNC) (PNC)

ComputeCompute PNC by applying statistics to existing engineeringanalyses.

IncludeInclude all known sources of variation in the distributionabove. Thus avoid the categorization of “short term” and“long term”, and avoid enforcing any arbitrary sigma shifts.

For external reportingexternal reporting, can convert PNC into Z, Sigma, ordpmo values (if truly required).

“Non-compliant”

USLLSL T

“Non-compliant”“Compliant”


Statistical Rollup

Use Sensitivity Analysisor Monte Carlo tocompute statistics forsystem parameters.

Use output of eachanalysis as input to thenext higher level in thesystem.

Compute system PNC bycomparing top-levelsystem parameters withcustomer specifications.

Customer Specifications (LSL, USL, Target)Customer Specifications (LSL, USL, Target)

Manufacturing CapabilitiesManufacturing Capabilities

System Parameter Y1System Parameter Y1

YY11 = f(X) = f(X)

XX11 = f(P) = f(P) XX22 = f(P) = f(P) XXnn = f(P) = f(P)

System ModelSystem Model

SubsystemSubsystemModelsModels

Subsystem Parameters {XSubsystem Parameters {X11, X, X22, ... X, ... Xnn}}

Component Parameters {PComponent Parameters {P11, P, P22, ... P, ... Pmm}}

(µ1, σ1) (µ2, σ2) (µm, σm)

(µ, σ)

(µ, σ)(µ, σ) (µ, σ)

(µ, σ) (µ, σ) (µ, σ)


Implementation ofStatistical Rollup

For any requirement, apply Sensitivity Analysis or Monte Carloto each model in the hierarchy, propagating the outputsupward.

USLLSL T

PNCPNC

A (µ,σ)

B (µ,σ)

D (µ,σ)

C (µ,σ)

E (µ,σ)

(µ,σ)

(µ,σ)

Y(µ,σ)


Program Name:Prepared By: PNC

Date: Identified Goal Met:

Notes: Addressed Goal Unmet:

Allocated # Reqts: 0Enter phase names and dates here: Analyzed

Program Phase: Planning Prototype Pilot Launch Post Launch Measured

Planned Completion: 1-Nov-03 1-Jan-04 1-Mar-04 1-Aug-04 1-Oct-04 Controlled

Name Units Target LSL USL PNC Goal State Distribution Mean StDev Gage R&R PNC123456

2001 Statistical Design Institute, LLC. All Rights Reserved.

Current State

SummaryState Completion Status

Critical Requirements

24-Oct-03

Insert Reqts

Delete Reqts

Use a Scorecard toTrack Multiple Requirements

Gather key requirements at the systemlevel.

Track CTC state and PNC goals.

Use as input for project management.

Gather key requirements at the systemlevel.

Track CTC state and PNC goals.

Use as input for project management.

From StatisticalAnalysis

From StatisticalFrom StatisticalAnalysisAnalysis

From Voice of the Customer

From Voice of From Voice of the Customerthe Customer

From ProgramGoals

From ProgramFrom ProgramGoalsGoals


Driving Program BehaviorWith DFSS Scorecards

Red Red flags:flags:– CTC’s that are lagging behind the

program schedule or are not meetingPNC goals

Actions:Actions:– Escalate issues– Remove barriers

to execution– Manage risks

Program Phase: Planning Prototype Pilot Launch Post LaunchIdentif ied

Addressed

Allocated

Analyzed

Measured

Controlled

Ratings: Requirements in this state at phase completion are acceptable Requirements in this state at phase completion should be examined Requirements in this state at phase completion are lagging

State Definitions: Identified -

Addressed -Allocated -

Analyzed -Measured -Controlled -

2001 S ta tis tica l Des ign Ins titute , LLC. All R ights Res erved.

A requirement that has a PNC computed from measured data.A requirement that has been transitioned to production processes that are in control.

A requirement that doesn't have specifications defined.A requirement that has a specified Target, LSL, and USLA requirement that has an allocated mean and standard deviation to meet a specified PNC Goal. A requirement that has a PNC estimated from statistical analysis.

Expected Statesfor Critical Product Requirements at Phase Completion


Statistical Roll-up & Scorecard:Summary

DiscussedDiscussed statistical rollup using existing SensitivityAnalysis and Monte Carlo techniques

IntroducedIntroduced a scorecard for tracking multiple requirements

OutlinedOutlined how to use scorecards to drive DFSS behavior


DFSS Deployment DFSS Deployment

Things to look for -Things to look for -

– Full team participation in the development of newproducts.

– Activities focused on identifying customer requirements.

– An emphasis on the variation of a Product’s Performance inaddition to the nominal response.

– Attention to Cost Avoidance in addition to Performance andReliability.


DFSS Deployment DFSS Deployment

Questions to ask -Questions to ask -

– How were the requirements for this Product determined?– Have we prioritized the requirements that are Critical to

the Customer?– Have the Performance Parameters for this Product been

Analyzed?– Do we have statistical data for the Product Parameters?– What type of Product Model did you use in the Performance

Analysis?– What other approaches did we consider?– Do we understand the factors that will cause variation in

the Product Response?– Which Parameters are expected to contribute the greatest

to the Response variation?


DFSS DeploymentDFSS Deployment

More Questions to Ask -More Questions to Ask -

– Have Parameter Tolerances been assigned? Are theParameter Tolerances based on Process Capabilities? Priorexperience? Statistical Analysis?

– Have opportunities to relax Parameter Tolerances (andCost) been investigated?

– Which Parts will be measured prior to Product assembly?– Is the quality of the Product ‘designed-in’ or ‘tested-in’?– What is the ‘Probability of Non-Compliance’ for each

requirement?– Have trade studies been performed to optimize

performance, cost, reliability, …?– Have defects due to test variation been considered?– Have FMEA methods been used to identify and reduce

defects?





Build Models




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PilotPilotPilot

LaunchLaunchLaunch


PostLaunchPostPost

LaunchLaunch











Define Alternatives

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Programidentified


Monte CarloAnalysis

µx, σx

µy, σy, PNC

σx



*

Scorecard

RegressionAnalysis

EquationsEquations

Requirements&


Analytical Models

µy, σy, PNC


Historical Data

µy, σy, PNCY

ULLL T

PNCPNC



C

E

A

B

D

SensitivityAnalysis

ToleranceAllocation

MonteCarlo


Equations

*

ConceptDesign

QFD

Whats/Hows



***

DFM/A

DFMEA

DFR


DFSSApplication Assessment

What

do y

ou k

now

?Customer Needs (What)Priority of Needs (Importance)Technical/Capability GapContradictionDesign AlternativesKey CharacteristicsCharacteristic DegradationMeasured DataProduct/Process FactorsExperiment MatrixProduct/Process ResponseProduct Model, Equation, or SimulationInput Parameter StatisticsInput Parameter PDFInput Parameter MeanInput Parameter Std DevResponse ToleranceResponse Std Dev or PNCResponse ToleranceInput Parameter Design SpaceResponse or Sub-Assembly Constraints Response GoalsGoal PrioritiesParameter ConceptsParameter Failure RatesAssembly ConfigurationsFunctional Block DiagramSample Test Results for Units, Operators, EquipTest Result Mean & Std DevTest Upper & Lower Spec LimitsTest ErrorMean ShiftKey Product RequirementsLPMP Phase Completion DatesRequirement Definition & Analysis StateEvaluated Response Mean, Std Dev, PDF

What

do y

ou n

eed t

o k

now

?

Product Requirements (How)Priority of Needs (Tech. Weight)What-How Relationships What-What CorrelationComparative AnalysisImprovement PlanInventive PrinciplesInventive Principle ExamplesRanking of Design AlternativesStatistics (mean, std dev, skew, kurtosis)HistogramProbability Density Function (PDF)Parameter or Factor SignificanceParameter or Factor InteractionsResponse Mean Prediction EquationResponse Std Dev Prediction EquationResponse Sensitivity to Input VariationResponse PNCResponse MeanResponse Std DevResponse Mean Shift due to Input VariationGoal & Constraint Evaluation versus

Mean, Std Dev, or P(fail)Design AlternativesTrade Study ResultsProduct ReliabilityReliability Block DiagramTest RepeatabilityTest ReproducibilityProbability Good Unit Tests BadProbability Bad Unit Tests GoodRequirement PNC & SigmaLPMP Requirement Analysis StatusNumber of Requirement PNCs MetNumber of Requirement PNCs UnMet

What

DFS

S T

ool ca

n b

e use

d?

QFD

Benchmarking

TRIZ

Trade-Off

Descriptive Statistics

DOE

Monte Carlo

Sensitivity Analysis

Tolerance Allocation

Multi-Obj Optimization

Reliability Analysis

Gage R&R

Test Effectiveness

Scorecard


DFSS Method & ToolApplication Guide

Method Inputs OutputsQuality Function Deployment

BenchmarkingTRIZ

Trade-Off

Descriptive Statistics

Design of Experiments

Monte Carlo

Sensitivity Analysis

Tolerance Allocation

Customer Needs (What)Priority of Needs (Importance)

Technical/Capability Gap

Contradiction

Design AlternativesKey CharacteristicsCharacteristic DegradationMeasured Data

Product/Process FactorsExperiment MatrixProduct/Process Response

Product Model, Equation, or SimulationInput Parameter StatisticsInput Parameter PDFProduct EquationInput Parameter MeanInput Parameter Std DevResponse Tolerance

Product EquationInput Parameter MeanResponse Std Dev or PNCResponse Tolerance

Product Requirements (How)Priority of Needs (Tech. Weight)What-How Relationships What-What CorrelationComparative AnalysisImprovement PlanInventive PrinciplesInventive Principle ExamplesRanking of Design Alternatives

Statistics (mean, std dev, skew, kurtosis)HistogramProbability Density Function (PDF)Parameter or Factor SignificanceParameter or Factor InteractionsResponse Mean Prediction EquationResponse Std Dev Prediction EquationResponse StatisticsResponse PDFResponse Sensitivity to Input VariationResponse PNCResponse MeanResponse Std DevResponse Sensitivity to Input VariationResponse Mean Shift due to Input Variation Response PNCResponse MeanInput Parameter Std DevResponse Sensitivity to Input VariationResponse Mean Shift due to Input Variation

Iden

tify

Req

mts

an

d C

on

cep

ts

Mo

del

Data

&

Pro

du

cts

An

aly

ze a

nd

All

oca

te V

ari

ati

on


DFSS Method & ToolApplication Guide

Method Inputs Outputs

Multi-Objective Optimization

Reliability Analysis

Gage R&R

Test Effectiveness

Scorecard

Input Parameter Design SpaceInput Parameter Std DevProduct Model, Equation, or SimulationResponse or Sub-Assembly

ConstraintsResponse GoalsGoal PrioritiesParameter ConceptsParameter Failure RatesAssembly ConfigurationsSample Test Results for Units, Operators, Equipment,

Test Result Mean & Std DevTest Upper & Lower Spec LimitsTest ErrorMean ShiftKey Product RequirementsRequirement Target, LL, ULLPMP Phase Completion DatesRequirement Definition &

Analysis StateEvaluated Response Mean, Std

Dev, PDFEstimated Sigma Shift

Goal & Constraint Evaluation versusMean, Std Dev, or P(fail)

Design AlternativesTrade Study Results

Product ReliabilityReliability Block Diagram

Test RepeatabilityTest Reproducibility

Probability Good Unit Tests BadProbability Bad Unit Tests Good

Requirement PNC & SigmaLPMP Requirement Analysis StatusNumber of Requirement PNCs MetNumber of Requirement PNCs UnMet

Op

tim

ize

Para

mete

rs

& V

ari

ati

on

An

aly

zeTest

Meth

od

s

Rep

ort

P

roje

ctR

esu

lts


Conclusion

DFSS -DFSS -

– Starts with the Voice of the Customer.

– Considers ‘options’ for providing a solution to the Customerneed.

– Makes sure that the solution is represented in ‘engineeringterms’ as a model, equation, or simulation, beforehardware is built.

– Allows analysis of multiple trade-offs to ensure customerrequirements are met while minimizing cost.

– Analyzes capabilities and potential for ‘variation’ in themanufacturing and operational scenarios.

– Seeks to eliminate Launch and Deployment problemsbefore they occur.


This is“Design for Six Sigma”!


Build Models

Optimizethe Design

Verify &Validate



DFSSDFSS

ConceptInitiationConceptConceptInitiationInitiation


PilotPilotPilot

LaunchLaunchLaunch


AnalyzeVariability

AllocateVariability

OptimizeVariability

PostLaunchPostPost

LaunchLaunch Product ModelProduct Model(equation, (equation, simulation,simulation,workbook,workbook,


Y

C

E

A

B

D

ULLL T

PNCPNC


Wrap-Up and Discussion

six sigma

Documents

actual operating

general domestic

potential

historical

design responsible

constraint

design failure

learnet benchmarking