© Statistical Design Institute, LLC. All Rights Reserved. Looking into the Future of Design for Six Sigma (DFSS) Jesse Peplinski January 16, 2012 Description

© Statistical Design Institute, LLC. All Rights Reserved.

Looking into the Futureof Design for Six Sigma (DFSS)

Jesse PeplinskiJanuary 16, 2012

• Description of past deployments

• Comparison and observations• Suggestions for the future

© Statistical Design Institute, LLC. All Rights Reserved. Page 2

Six Sigma Versus DFSS

• You can use a flexibleflexible approach to let each design problem dictate which process is followed

• Use Six Sigma (MAIC)Six Sigma (MAIC) as a data-driven method for design improvements

• Use DFSSDFSS as a rigorous method for creating a design to satisfy multiple requirements

Define theDesign Problem

Capture the Voiceof the Customer

Identify Critical Requirements

Create Design Concept

Build Math Models

Optimizethe Design

Validatethe Design

SelectApproach

Improve ExistingDesign

Improve orCreate NewDesign

DFS

S

MA

IC


What is a “Deployment”?

• A company-specific attempt to inject Six Sigma and/or DFSS into its culture and daily activities

• Typically a customized mixture of:

– Training classes with tailored content

– Structure for projects and “belt” certification

– Supporting software tools

– Strategic communication by management and leadership

• Scope of implementation can vary widely

– All employees vs. targeted teams

– Local vs. global


Past DFSS Deployments

Company Description Status

Automotive 1 • Global deployment• Mandatory training for all

engineers• Projects and certifications

Low level of activity

Automotive 2 • Local deployment• Training and tools for selected

experts based on role or skills

Continued success

Defense 1 • Emphasis on black belts and projects

• DFSS as an afterthought to six sigma


Defense 2 • Leadership evolved a design process intertwined with DFSS tools

Continuing activity


Past DFSS Deployments

Company Description Status

Electronics 1 • Global deployment, mandatory training

• Projects and certifications


Electronics 2 • Local deployment for product teams

• DFSS tools folded into an internal process excellence program

Steady continuing activity

Healthcare 1 • Global deployment with projects and certification

• Significant backlash and years of inactivity

Quiet resurgence through design reviews

Healthcare 2 • DFSS integrated into development process

• Emphasis on providing DFSS tools

Continued activity


Observations

• Pendulum swing– Larger, top-down deployments often end up with lower

levels of long-term practice.

• Backlash against projects and certification– Long-term health of deployment correlated with selective,

low-key implementation

• Challenge of demonstrating DFSS savings– Heroes get visibility for fixing mistakes; cost avoidance is

difficult to recognize.

• Tools stand the test of time– Six Sigma: Gage R&R, SOP’s, DOE, process control

– DFSS: QFD, Pugh Matrix, Monte Carlo, Optimization


Suggestions for the Future

• Design for Six Sigma:– DFSS tools fit naturally within a systems engineering

group. (If you don’t have a systems engineering group, consider starting one.)

– In addition, DFSS tools should be leveraged by your key participants in design reviews. (Principals, architects, etc.)

– DFSS success hinges on modeling and simulation capability. Be prepared for resistance.

• Six Sigma: – Let DMAIC flow naturally from leadership asking questions

and demanding answers with data

• Let plans for training and employee reward be driven by the forces above. (Not vice-versa.)


• First – use the Tools to support the Process

How does DFSS fit within Systems Engineering?

Best Practice Best Practice

Build Models

Voice of the Customer

Design that best meetsall requirements

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

Identify CriticalRequirements

Create Design Concept

Validate the Design

ProductProductDevelopment Development

ProcessProcess

ExplorationExplorationExplorationExploration

DetailDetailDesignDesignDetailDetailDesignDesign

InitialInitialProductionProduction

InitialInitialProductionProduction

ConceptualConceptualDesignDesign

ConceptualConceptualDesignDesign

FinalFinalProductionProduction

FinalFinalProductionProduction

DesignDesignVerificationVerification

DesignDesignVerificationVerification

SSEE

&&

DDFFSSSS

SE/DFSS ProcessSE/DFSS Process

SE/DFSS Enablers & ToolsSE/DFSS Enablers & Tools

TRIZ & Design SelectionTRIZ & Design Selection

DOE and RegressionDOE and Regression

Physics and First PrinciplesPhysics and First Principles

Test Effectiveness AnalysisTest Effectiveness Analysis

ScorecardsScorecards

Quality Function DeploymentQuality Function Deployment

Sensitivity and Monte Carlo Sensitivity and Monte Carlo AnalysisAnalysis

Multi-Objective OptimizationMulti-Objective Optimization

Statistical AllocationStatistical Allocation

Failure Modes & Effects AnalysisFailure Modes & Effects Analysis

FMEA & Fault Tree AnalysisFMEA & Fault Tree Analysis

Cost and Reliability AnalysisCost and Reliability Analysis


Modeling and Analysis within DFSS

Require that this be done everywhere, and if it isn’t, explain why not!Require that this be done everywhere, and if it isn’t, explain why not!

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

ApplyingApplyingModels &Models &AnalysesAnalyses PredictingPredicting

Probability of Probability of Non-ComplianceNon-Compliance

“Non-compliant”

“Non-compliant”

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

hardware, etc.)hardware, etc.)

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

hardware, etc.)hardware, etc.)

Y

C

E

A

B

D

ULLL T

PNCPNC

“Compliant”

Non-Compliance refers to any Non-Compliance refers to any condition that results in Defects condition that results in Defects or Off-Spec conditionsor Off-Spec conditions

Non-Compliance refers to any Non-Compliance refers to any condition that results in Defects condition that results in Defects or Off-Spec conditionsor Off-Spec conditions

The fundamental metric is the The fundamental metric is the Probability of Non-Compliance Probability of Non-Compliance (PNC(PNC))


Modeling: Easier than It May Appear

Identify Existing Models

Perform Regression

Analysis

Perform a Design of

Experiments

Gather Design Parameter

Information

Create New Models

Can equations be developed?

Fast, Accurate Math Model

A simulation of sufficient

accuracy exists?

Prototypesexist?

Historicaldata exists?

Yes

No

Best Design Alternative(s)

Critical Requirements

(Y’s)

Key Design Parameters

(X’s)

No

No

No

Yes

Yes

Yes

Simulation computes

very quickly?

Yes

No


Six Sigma Examples

• What can we do to improve our process yield?

• How can we reduce operating temperatures and fix our thermal issues?

• What can we do to increase sales volume?

• How can we increase the throughput of our call center?

It starts with hard problems:

Our goal is to get solid answers:

~~~~

~~~~

How do we bridge the gap with high levels of confidence based on solid evidence?

How do we bridge the gap with high levels of confidence based on solid evidence?

• Switching from supplier A to supplier B will improve yields by 8%.

• This power supply redesign will reduce operating temperatures by 11 °C.

• A $50 rebate would increase sales by 15%.

• Adding two more operators will increase throughput by 100 calls per day.


Guiding Questions

Answer these questions to bridge the gap:Answer these questions to bridge the gap:

1.1. What is our current state? What is our current state? – Product or process performance in

measurable terms (Y’s)

2.2. What is our desired state?What is our desired state?– How much improvement is needed

in our measurable Y’s?

3.3. How good are our measurement systems?How good are our measurement systems?– If we measure the same thing twice, do we get the same answer?– If we made a process improvement, could we detect it?

4.4. What data do we need to collect?What data do we need to collect?– Responses (Y’s) and Parameters (potential X’s)– How much data? Time period? Shifts?– Existing data? Or new data collection effort?

If we can’t measure it, we If we can’t measure it, we don’t know where we are.don’t know where we are.

If we can’t measure it, we can If we can’t measure it, we can never know if we get there.never know if we get there.


Guiding QuestionsContinued

5.5. If the Y is plotted versus the X’s, is there evidence of If the Y is plotted versus the X’s, is there evidence of correlation (patterns) for some of the X’s? Which ones?correlation (patterns) for some of the X’s? Which ones?

– May begin to indicate the significant drivers for improvement

6.6. Is there statistical evidence that the Y changes when some Is there statistical evidence that the Y changes when some X’s change? Which ones?X’s change? Which ones?

– Type of analysis used (t-Test, F-Test, ANOVA, etc.)

– Confidence level

7.7. What changes in the X’s are needed to achieve the desired What changes in the X’s are needed to achieve the desired state?state?

Implement Six Sigma as a process foranswering these questions.

Implement Six Sigma as a process foranswering these questions.


Thank you…

Questions?

Contact: [email protected]

Documents

© Statistical Design Institute, LLC. All Rights Reserved. Looking into the Future of Design for Six Sigma (DFSS) Jesse Peplinski January 16, 2012 Description