六西格玛设计和可靠性设计

Design for SixSigma(DFSS)& Design for Reliability(DFR) 六西格玛设计和可靠性设计The Journey1998 – Seagate adopts Six Sigma defect reduction,cost savings1999 – Lean in Manufacturing &Supply ChainIntro BE July 20102001 – DFSS in Product & ProcessDevelopmentPage 2DFSS in the BeginningIterativeUse of historical requests Test and re-testShort term estimates Isolated CTQ optimizationPredictiveRequirements hierarchy Model buildingLong term estimates System optimizationInitial Approach:Top down Educate the masses in design centers -> “DFSS Certified”• DFSS Foundation – 2 weeks of Statistics • DFSS Project – Systems Engineering – 3 days Train the suppliers and factory BrB/BB/MBBs in DFSSIntro BE July 2010Page 3What Is Design for Six Sigma?Design for Six Sigma (DFSS):• Allows us to set “need-based” requirements for CTQs and to evaluate our capability to meet those requirements.• Is a process that focuses on predictive product design. • Emphasizes the use of statistical methods to predictproduct quality early in the design process.• Is a complement to good engineering/decision making practices.Intro BE July 2010Page 4Six Sigma Improvement Methodology1 ADefineYES2NO1.MeasureIdentify2.YES3NOAnalyzeDesign3.OptimizeYES4NOImprove5YESA4.NOValidate5.ControlA high level Business need is identified(CTQ gap)Does a Current Business Process/Product exist to address the gapAre the Processes/Products that support your key outputs optimized but still not capable of meeting customer requirements?Is the solution or part of the solution a new process, product, or service.Does the capability of one or more KPIV need to be improved to optimize KPOV?Intro BE July 2010Page 5Statistical DesignIdentify DesignOptimize ValidateIntro BE July 2010Identify Customer RequirementsTranslate Into Critical To Quality (CTQ) Measures and Key Process/Product Output Variable (KPOV) LimitsFormulate Designs/Concepts//SolutionsValidate The Measurement Systems Evaluate DesignsFor Each Top Level CTQ, Identify Key Product/Process Input Variables (KPIV’s) Develop Transfer Functions Between KeyInput and Output VariablesOptimize DesignPerform Tradeoffs to Ensure that All CTQ’s Are MetNot OKNot OK OKException ReviewDetermine TolerancesAssess Process Capability to Achieve Critical Design Parameters and Meet CTQ Limits DFSS ScoringTest & ValidationPerform Tradeoffs to Ensure that All CTQ’s Are MetNot OK OKNot OK Exception ReviewAssess Performance, Failure Modes, Reliability and RisksOKFeasibility Point TollgateNot OKPage 6BreakthroughSix Sigma and Design for Six SigmaDesign for Six SigmaDesign robust products so thatspecs can be loosenedDefectsDMAIC Six SigmaFocus on reducing variation around the meanLower Spec LimitUpper Spec Limit• Design for Six Sigma and “Standard” Six Sigma work together!Intro BE July 2010Page 7Design EvolutionFROMEvolving Design requirements Design rework Build and test performance assessment Performance and manufacturability after product is designed Quality is “tested in”REACTIVEIntro BE July 2010TODisciplined CTQ flowdown Controlled design parameters Performance modeled and simulated Design for robust performance and manufacturabilityPREDICTIVEPage 8Key Elements• Systems relationships Transfer Functions, KPIV & KPOV• Statistical Design: Meeting not only target but address variations in design• Identify, Design, Optimize, & Verify (IDOV)Intro BE July 2010Page 9Systems Engineering - FlowdownQFD/FMEASystem CTQsSubsystem CTQsSub-assembly CTQsComponents CTQsProcess CTQsIntro BE July 2010Page 10Systems View Of a Hard Disc Drive38 CTQsCustomer CTQsServo-Mech RSS-H/MMech ServoProcess CTQs7 CTQsElec/InterfaceASIC111 Subsystem CTQs FirmwareAssembly/TestCert/Test>120 Factory CTQsHSA HGA Motor/Base HDA Encl. Head Media Channel/PreampComponent CTQs...Intro BE July 2010Page 11Transfer FunctionWhat is a Transfer Function?X1X2X3f(X1,X2,…, Xn)Y…Xn• It is a relationship of the CTQ (Y) to the key input variables (X’s). • It is not necessarily as rigorous as a process model. • It is key to predicting product performance before buildingprototypes.Intro BE July 2010Page 12Getting to the y = f(x1, x2…)Physical Models - dedicated experts ü Explore design space – run simulations with DOE ü Model management processStatistical Models ü DOE, Regression, Response Surface, etc ü Parametric data analysis – especially for reliability ü MSA“All models are wrong, some are useful.” - George BoxIntro BE July 2010Page 13Flowdown/Flowup ProcessSystemIdentify Customer CTQs. Translate into System CTQs.Identify Measurement for each system CTQ.Adjust tradeoffs to reduce cost (as new σ improvementsare made).PNCTrade off mean/variance requirements to x1,x2,…,xn to best meet system CTQ need.Determine Specifications for each system CTQ (Y).Identify Transfer FunctionY=f(x1,x2,…,xn)YesCapabilitiesof allNox1,x2,…,xnknown?Obtain process capabilities for those x’s that are not yetknown.Use transfer function and experience/judgement to allocate requirements for x1,x2,…,xn to meet systemCTQ need.SubsystemsIntro BE July 2010Page 14After y = f(x1,x2..), then…Internally developed tool – handles up to 20 transfer functions Ø Runs Sensitivity Analysis, Monte Carlo simulation and determines PNC Ø Optimizes for a Figure of Merit (cost, PNC, Z-score, user specified) Ø Helps set tolerances for all inputsOptimize to a Figure of MeritWhat the customerwantsInput w VariationsIntro BE July 2010Page 15Transfer FunctionsMeeting expectation?Screened Parts?Allocate OptimizedSpecsDesign & Engineering Benefits• KPOVs & KPIVs defined by transfer function • Clear ownership of CTQs • Visibility for trade-off managementIntro BE July 2010Page 16DFSS Process IntegrationCTQ FlowdownCustomer• Marketing Inputs • Product RoadmapsPNCCTQ’sSystem• System Models/Specs • System Eng. RoadmapPNCCTQ’sSubsystems• Subsystem Simulations • Subsystem RoadmapsPNCCTQ’sComponents• Eng. Design Tools • Process CharacterizationPNCCTQ’sParts• Parts CharacterizationParts/Process/Performance Capability FlowupOwnersMarketing /Systems EngineeringSystems EngineeringSubsystem EngineeringDesign Process Centers Mfg/Suppliers/Service Mfg/Suppliers/Sourcing Design TeamsIntro BE July 2010Page 17Prospects• Understanding customer needs • Complete understanding of systems relationships • Considers not only the target but the variation indesign • Integrating models & simulators to estimate Probabilityof Non-Conformance (PNC) • Not about the number 6 but a cultural changeIntro BE July 2010Page 18Design OpportunityMost current Six Sigma effort is here.$Must move quality effort here!Cost to Correct Quality and ReliabilityResearchDesignPrototypeDefects are:Difficult to see/predict Easy to fixProductionCustomerEasy to see Costly to fixIntro BE July 2010Page 19Cost to Design and Manufacture Product6 Sigma vs. Optimal SigmaDESIGN COST MATERIALS COST MANUFACTURING COSTOptimal SettingIntro BE July 2010ZST LEVELPage 20What workedProduct & Process Development culture transformed by DFSS ü More rigorous VOC process ü Doing Systems Engineering vs components (organization change) ü Speaking the “same language” in CTQ flow down (requirements) ü Emphasis on transfer function development - Models, DOE, regression, etc. ü Using statistical thinking vs target only - Monte Carlo simulation, tolerance analysis, etc ü Applying DFR early in product & technology development, FMEAs up front ü More data driven decisionsAvg Development TimeIntro BE July 2010Page 21But Something Still Needs Beefing Up1998 – Seagate adopts Six Sigma1999 – Lean in Manufacturing &Supply ChainIntro BE July 20102001 – DFSS in Product & ProcessDevelopment2006 – Revised Design forReliability (DFR)Page 22Design for ReliabilityDFSSANOVA RegressionHypothesis TestingVOC FlowdownQFD FMEADFREnvironmental & Usage ConditionsLife Data AnalysisPhysics of FailureGeneral Linear Model Control Plans Accelerated Life TestingMSAReliability GrowthSensitivity AnalysisModelingDOEWarranty PredictionsTolerancingFA recognition– Many common tools – DFSS enables achieving high quality at launch with nominal stress conditions – DFR focuses on achieving high quality over time and across stress levelsIntro BE July 2010Page 23Enhanced DFR ProcessUpfront use of DFR Assessment Matrix in the development cycle to identify and address reliability issuesModeling Physics ofFailureDFR Summary page: Key Reliability Risks / Failure ModesIssues from prior productsParetos , Post Mortem, …Competitive AnalysisNew technologiesFMEA’s , brainstorming, …Prioritized list of key reliability risksSys FMEANew market environmental & usage conditionsPotential Failure mode *CFM team?Maturity of physics of failure modelsUnderstand fieldenvironment stressorsEffective Stress testEffective FA recognitionParametric data analysisManufacturing/ supplier controlstrategy/ metrologyDFR TeamDesign OptionsArea Specific RepresentativeFailure Mode 1YesFailure Mode 2YesFailure Mode 3YesFailure Mode 4 NoFailure Mode 5YesFailure Mode 6NoFailure Mode 7YesFailure Mode 8 Yes• The status of the DFR activities will be updated at each progra m phase gate with a DFR review of the activities associated with the stoplight matrix above.• New Key Reliability Risks / Failure Modes should be added or pa rked when engineering data justifies that action.© Seagate ConfidentialPage 2Intro BE July 2010Page 24Integration into Product DevelopmentProduct Planning, Design and Development ProcessVOCLessons LearnedRequirements Management Phase-Gates & DeliverablesData Storage DeviceDesign for Design for SixReliabilitySigmaEngineering Models and Six Sigma Tool SetsIntro BE July 2010Page 25The Journey Forward1998 – adopts DMAIC Six SigmaToday – Business Excellence1999 – Lean in Manufacturing &Supply Chain2000 – DFSS in Product & ProcessDevelopment2006 – Integrated DFRwith DFSS2007 – Research ExcellenceIntro BE July 2010Page 26Integration into Product DevelopmentLean Design & DevelopmentProduct Planning, Design and Development ProcessVOCLessons LearnedRequirements Management Phase-Gates & DeliverablesData Storage DeviceDesign for Design for SixReliabilitySigmaEngineering Models and Six Sigma Tool SetsIntro BE July 2010Page 27Tools We UseSIX SIGMA• Traditional DMAIC toolset• Traditional DFSS toolset• DFR tools• Value StreamMapping • Value-add Analysis • Error-proofing • 5S • Cycle time analysis • Benchmarking • 5 why’s • Potential problemanalysis • Work measurement•Setup reduction•Pull systems•Total productive maintenance•Shop floor management• OEE•Lean assessment•Lean diagnostic•48 hour study •Layout optimizationLEAN•Batch size reduction•Time studies•Work sampling•Red flag analysisChange Mgmt•Current reality tree •Future reality tree •Conflict resolutionThroughput focus•Critical chain project mgmt •Prerequisite tree •Transition TreeTOCIntro BE July 2010Page 28Business Excellence“Today” and “Tomorrow” elementsLeanDFSS/DFRDMAIC 6σIntro BE July 2010Research & Technology DevelopmentFutureCommitment to technology developmentAdvanced Drive Integration & PlatformTomorrowStaging, aligning and integrating technologyProduct/ ComponentDesign & Manuf.TodayExecuting to product plansFactory & DeliveryPage 29SLAM II Context DiagramProduct and Technology Portfolio ManagementProduct Planning Process Platform Integration/Technology AlignmentBi-Annual ProcessesFramework Mini MR MRMiniPOREMGen 1 Gen 2Start EM RR Gen 1 RR Gen 2SAD CTU orDRArch.MR Declare Declare Declare Declare ECQPTADrive Development à(Click here forAdvanced Drive Development (ADD) Feasibility Phase 0 DesignIntegration Qualification PilotRampMilestoneDefinitions)FrameMRDrive Development Primary Market Segment-work MRMini MRMini DRADD ExitFeas ExitEMD/ Ph0 ExitProduct Phase-Based Gen1DeclareGen2 DeclareCTU DeclareSADProcPeTAssesEC MarketT-36 T-32T-25T-22T-19T-15T-10T-6T-2T=0T+4PS MarketT-32 T-28T-25T-22T-15T-12T-9T-6T-2T=0T+X# Months prior to SADSeagate ConfidentialIntro BE July 2010Page 30Learning ObjectivesAfter completing this training, the student will be able to:•Tie together the tools and methodology covered in thisclass.•Understand how DFSS, DFR and DMAIC are interrelated.•Apply the knowledge gained to current projects.IDOV ProcessFeasibility Point TollgateException ReviewPerform Tradeoffs to Ensure thatAll CTQ ’s Are MetOKNot OKNot OKNot OKValidateOptimizeDesignIdentifyOKTranslate Into Critical To Quality (CTQ) Measures and Key Process/Product Output Variable (KPOV) LimitsFormulate Designs/Concepts//Solutions Evaluate DesignsFor Each Top Level CTQ, Identify Key Product/Process Input Variables (KPIV ’s)Identify Customer RequirementsDevelop Transfer Functions Between KeyInput and Output VariablesAssess Process Capability to Achieve Critical Design Parameters and Meet CTQ LimitsOptimize Design DFSS ScoringDetermine TolerancesTest & ValidationAssess Performance, Failure Modes,Reliability and Risks Validate The Measurement SystemsNot OKException ReviewOKPerform Tradeoffs to Ensure thatAll CTQ ’s Are MetNot OKStatistical DesignWhat ’s NeededRM Software & Business ProcessIntegration intoProductDevelopment Flow & Phase-Gate ProcessTools Development& Model ManagementIdentify VOC, CTC, Environmental,System Level CTQsRequirement Management common repository, data structure, CTQ dictionary, flowdownDesign & Optimize Transfer FunctionsAllocationsTools Application simulators, models, DOEs, Monte Carlo, optimization, etc.VerifyStress Test, MSAMeasurement Systems & Builds sample sizes, cost, qualification test, etc.Appendix: DFSS Phase ReviewIdentify Phase1. What are you designing?2. Who is the customer?3. What business need will your design fill?4. When is your design needed?5. What does the cost/benefit (effort-to-impact) analysis show?6. What priority does this development effort have in the list of active and future projects?7. Who is going to champion this design effort?8. What are the CTQ requirements for this project?9. How are you sure these are the correct and complete list of requirements? (TTM, technical, environmental, etc)10. How did you determine which requirements are critical and which are non-critical?11. What are the targets and limits for each CTQ requirement?12. How did you determine the limits for each requirement?13. What requirements or limits do you expect to change either before or after project completion? How do you plan to handle this?14. How will you measure the CTQ’s? Who owns the equipment?15. What are the potential technological barriers? Describe your plan to overcome those barriers (alternative technology, costs, etc)?16. What elements of your design will be leveraged from existing designs, and/or will be used in future designs?17. What data do you have on existing similar designs?18. How does your design compare to our competitors?19. What resources are available (both personnel and budget)?20. Who are the critical players who can significantly impact this project? Are they “on board” with the development?21. What is your timeline and milestones?22. What obstacles do you foresee? Describe how you plan to overcome them?23. What does the feasibility / risk assessment indicate? What is your risk mitigation plan?I8-1Design Phase24. What design(s) are you considering?25. Where did the design(s) come from?26. Which design best satisfies the CTQ requirements?27. What existing knowledge are you leveraging into this design?28. What are the most complex elements of your design?29. What are the critical manufacturing/process steps for your design?30. Have you demonstrated technological/manufacturing feasibility?31. What is the risk associated with each design? (risk elements include: time to market, cost, capability, meeting volume,necessary resources, technological barriers, customer receptiveness, environmental regulations and vendor/supplier support)32. What data have you collected on the design(s)?33. How was the data collected?34. What additional output will you need to measure?35. What are the gauge R & R’s for all key measurable inputs and outputs? Who takes the measurements? Who owns the gauging?36. If a better gauge is needed, what would be the cost?37. What are the critical outputs (Vital Few) affecting each CTQ?38. What are the critical inputs (Vital Few) affecting each critical output?39. Who participated in developing the list of ALL (Trivial Many) the inputs/outputs initially analyzed and what were they?40. How were the critical inputs/outputs determined?41. What are the functional relationships between the critical outputs and the CTQ’s?42. What are the functional relationships between the critical inputs and critical outputs43. What are the tentative optimums for the inputs/outputs?44. What data do you have to support your decisions?45. How did you collect your data?46. How many parts and why?47. How do you know that you took enough samples to see a real effect and not just noise? What is your confidence that the effects is real?48. For suppliers, do they agree with your analysis of what the Vital Few are?49. What will be the process flow for your design?50. Who are the potential suppliers?51. What is the supplier’s capacity? Is it sufficient to meet short and long term capacity?I8-1Optimize Phase52. What are the product tolerances for each critical input/output?53. How were the tolerances determined?54. What data do you have to support these tolerances?55. How did you collect your data?56. How many parts and why?57. How do you know that you took enough samples?58. What is the capability for each tolerance?59. Is the capability score based on short or long-term estimates of variability?60. How sensitive is the performance to the critical inputs varying at the same time (i.e. interactions) over their tolerance ranges?61. Which environmental factors impact your design the most?62. How will you compensate for environmental influences?63. What are the key reliability issues?64. How did you test for reliability?65. What is your confidence in the predicted level of capability and reliability?66. Who are the suppliers? Have they been qualified? What is their capability?67. How will the parts be inspected?68. Do you have standards to ensure inspection test reproducibility?69. What does the product design / process flow diagram look like?70. Which steps in the process are value added and which are non-value added (rework, testing, inspecting, etc)?71. What is your plan for eliminating non-value added work?72. Are all the CTQ/S limits met or exceeded by using these product/process tolerances? If not, how do you plan to resolve that fact?73. What data do you have to support that all the CTQ/S’s are being met by this design?74. What is the predicted capacity?75. What are the biggest capacity constraints?76. What is the predicted cost?77. What are the areas of greatest risk?78. What is your plan for mitigating the risk? Is the risk acceptable?I8-2Validate Phase79. What is your validation test plan and criteria?80. What data do you have to support that the CTQ’s have been met?81. What is your confidence that the CTQ’s have been met?82. Which variables are the most important to control?83. What type of process control is being implemented?84. What are the action limits and action plans?85. What is the timing of the implementation?86. Who is involved with the implementation?87. Who will take the long-term responsibility for maintaining the controls?88. What plans do you have in place to revisit the process in the future to ensure the capability is being maintained?89. When will you transfer your design?90. How will you verify successful transfer of your design?All Phases91. What success(es) have you had in this phase (beyond what you expected)?92. What roadblocks did you encounter that you needed or still need help with?93. What do you see as your next steps?94. What would you have done differently?I8-2Appendix: MiscAcronyms and SymbolsRSM Response Surface Methodology RSS Root Sum of Squaress standard deviation of a sample s 2Variance of a sample S pSystem Capability IndexSDM Statistical Design Methods SESystems EngineeringSea.DOT Seagate Design Optimization Tool SEI Software Engineering Institute SPC Statistical Process Control SS Sum of SquaresSS p Subsystem Capability Index S/W Software T Target Level TF Transfer FunctionTol ToleranceTTM Time to MarketUCL Upper Confidence Limit (Upper Control Limit in SPC)USL Upper Spec limit VOC Voice of the Customer WC Worst Casex Mean of a sampleZNumber of σ‘s that can fit between Mean and Spec limitI & T Integration & Test Phase of a Program IDOV Identify, Design, Optimize, Validate IV Independent VariableKPIV Key Product/Process Input Variable KPOV Key Product/Process Output Variable KT Kepner-TregoeLCL Lower Confidence Limit (Lower Control Limit in SPC)LSL Lower Spec LimitMAIC Measure, Analyze, Improve, Control MBB Master Black BeltME Mechanical EngineeringMGPD Multi-Generation Product Development MS Mean Sum of SquaresMSA Measurement Systems Analysis MTBF Mean Time Between Failures MTTF Mean Time To Failure p probability of an occurrence PCB Printed Circuit BoardPCD Process Capability Database PCM Process Capability ModelsPNC Probability of Non-Conformance to specificationsPp, Ppk Long term capability measures PPM Parts per MillionQFD Quality Function Deployment R&R Repeatability & Reproducibility RPNRisk Priority NumberµMean of a populationσStandard Deviation of a Population σ2Variance of a population 1-D One dimensional linear stack-up ANOVA Analysis of VarianceBBBlack BeltBOM Bill of MaterialsCp, Cpk Process Capability Index, Short Term CI Confidence Interval COQ Cost of Quality CTQ Critical to Quality df Degrees of Freedom DFA Design for AssemblyDFM Design for Manufacturability DFSS Design for Six Sigma DoEDesign of ExperimentsDPLOC Defects per line of code DPPM Defective Parts per Million DPU Defects Per Unit DV Dependent Variable EE Electrical Engineering ETTR Elapsed Time To RepairFEA Finite Element AnalysisFMEA Failure Modes and Effects Analysis GLM General Linear ModelGR&R Gage Repeatability & Reproducibility H/WHardware。