品质管理精华理论Supplier-Summit-07-05-04

UnderstandthedifferencebetweenInspectionandVariationReduction(SPCandProcessCapability)ReviewNewProcessCapability10-StepsandScorecardWhathelpdoyouneedfromDell? QuestionsandAnswersObjectivesProcessCapabilitySummitKirkChiDellInc.WWPBPIBusinessChampionDellBlackBeltASQCertifiedSixSigmaBlackBeltASQCertifiedQualityEngineer7/05/04TraditionalEconomicModelofQualityofConformanceTotalcostCostduetononconformanceCostofqualityassurance“optimallevel”ofquality100%Qualityimprovementbasedon

InspectionModernEconomicModelofQualityofConformanceTotalcostCostduetononconformanceCostofqualityassurance100%QualityimprovementbasedonVariationReduction(SPC/ProcessCapability)Non-ValueAddedoperationsresultin:HigherprocurementcostofproductsHigherprobabilityofdefectsImprovetheProcess

ToReduceNon-Value-AddedOperationsDellVLRR/PID/CNDHiddenCostsWhatisthedifferencebetweenqualitycontrolbasedonInspectionandVariationReduction?InspectionreferstothemanufacturingoperationsbasedonAttributeData(Pass/Fail).CurrentmanufacturingoperationsarefocusedmainlyonPass/Failinspections.Muchofvariabledataismeasured,butthedataisconvertedtoattributedataforPass/Failinspection.Ifyouinspect100%,willyourcustomerexperiencenofailure?Ifyouinspect100%,andinspectagain100%,andinspectagain100%,willyourcustomerexperiencenofailure?CanyouandDellachievethereductionofFIR(andVFIR)basedoninspectionqualitycontrol?Wewillworktogethertounderstandthefollowingpoints:Evenifyouinspect100%,yourcustomerwillstillexperiencefailuresInspectiondoesnotdetecttheprocessandproductmeanshift100%Inspectiondoesnotreducethevariationinyourprocess(andproduct)TraditionalInspectionViewLowerSpec

UpperSpecnoloss

nominaltoleranceTraditionalview:ThereisnofailureaslongasaparameteriswithinspecificationNewView(TaguchiLossFunction)

nominaltoleranceFailurerate,$LowerSpec

UpperSpecnolossNewView:Productsstillfailintimeevenifaparameteriswithinspec.Theprobabilityoffailureincreasesastheparametershiftsawayfromthemean

nominaltoleranceFailurerate,$LowerSpec

UpperSpecnolossWheredowewanttogo?CriticalProcessParameters(x)ProductAttribute(y(x))Metric(Y)VLRRSigmaLevel(Drivefor5)PAPB123YieldCpkMetricWhatMeasuredMetric(Y)SigmaLevel(Drivefor5)VIFIR+90DVFIRKeyMessage:

OnlythereductionofprocessandproductparametervariationswillleadtothereductionofVFIRHighCostofQualityLowestCostofQualityApproachtoProcessCapabilityIdentifyingCauseandEffectWhatisProcessCapability?ProcessCapabilityisthe“VoiceofProcess”tothe“VoiceofCustomer”.ProcessCapabilitystudyprovidesvaluableinsightonhowwellanexistingprocessisperformingwithregardstocustomerrequirements(specifications)whatneedstobedonetoimproveperformanceCapabilitystudiesenablemanufacturerstoimproveproductivity,reducecosts,andenhancetheirstrategicadvantageovercompetitors.ProcessStabilityBeforeCapabilityControlofaprocessmustbeachievedfirst,beforeanyattemptismadetomeasurecapabilityorestimatethepercentageofnonconformingproduct.Whenaprocessisstable,itisrepeatable,well-defined,andpredictable.YoucanpredicttheoutcomeonlyiftheprocessisstabletimePREDICTABLE?UNPREDECTIBLEProcessStability:

StatisticalProcessControl

StatisticalProcessControl(SPC)Aprocessoutputisconsideredstablewhenitconsistsofonlycommon-causevariation.SourcesofVariationinProductionProcessesMaterialsToolsOperatorsMethodsMeasurementInstrumentsHumanInspectionPerformanceEnvironmentMachinesINPUTSPROCESSOUTPUTSCommonCausesSpecialCausesTwoFundamentalManagementMistakesTreatingasaspecialcauseanyfault,complaint,mistake,breakdown,accidentorshortagewhenitactuallyisduetocommoncausesAttributingtocommoncausesanyfault,complaint,mistake,breakdown,accidentorshortagewhenitactuallyisduetoaspecialcauseControlChartFocusesattentionondetectingandmonitoringprocessvariationovertimeDistinguishesspecialfromcommoncausesofvariationServesasatoolforon-goingcontrolProvidesacommonlanguagefordiscussionprocessperformance*******CommonlyUsedControlChartsVariablesdatax-barandR-chartsx-barands-chartsChartsforindividuals(x-charts)AttributedataFor“defectives”(p-chart,np-chart)For“defects”(c-chart,u-chart)StatisticalProcessControl(SPC)AmethodologyformonitoringaprocesstoidentifyspecialcausesofvariationandsignaltheneedtotakecorrectiveactionwhenappropriateShiftinProcessAverageIdentifyingPotentialShiftsCyclesTrendQuantitativeComparisonof

TraditionalInspection

vs.

ControlChartReviewofVariable-DataControlChartsμμ+3σμ-3σμ+1σμ+2σμ-1σμ-2σμ

+/-1σ=68%μ+/-2σ=95%μ+/-3σ=99.73%IndividualmeasurementdistributionSPCControlLimits

μx-barμx-bar+3σX-barσx-bar=σ/UCL(UpperControlLimit)=X(doublebar)+3σX-barLCL(LowerControlLimit)=X(doublebar)-3σX-barSamplemeandistributionDistributionofXvs.DistributionofSampleMeans(X-bar)μμ+3σμ-3σμx-barμx-bar+3σX-barσx-bar=σ/IndividualmeasurementdistributionSamplemeandistribution

Thereisnorelationship.SpeclimitsaredeterminedbyengineeringbasedoncustomerrequirementsControllimitsaredeterminedbythecommon-causevariationintheprocessSpeclimitsaretypicallywiderthancontrollimitsSpecLimitvs.ControlLimitμμ+3σμ-3σControlChart:Example:ControlchartismonitoredbyUCLandLCL.(Note:USLandUCLdonothaveanyrelation.)USLLSLμx-barμx-bar+3σX-barUCLLCL*Ifσ=2forInspectionofindividualparts,σofsubgroupsizeof4samples=1.WhyControlChartsAreBetterThanInspection?μμ+3σμ-3σInspection:Example:Onepartischeckedeachhour.Onepartischeckedevery20.Partpassestheinspectionifthepartmeasurementiswithinspec.USLLSL*Assumption:speclimitlis+/-3sigma.μμ+3σμ-3σWhathappenstoInspectionifprocessmeanshifts3sigma?USLLSLAnythingaboveUSLarerejected.50%Becausehalfofthepiecesarestillwithinspec,thereis50%chanceofselectingsuchapartandmistakenlydecidingtocontinuerunningthismodifiedprocess.WhathappenstoControlChartifprocessmeanshifts3sigma?μx-barUCLLCL*Ifσ=2forInspectionofindividualparts,σofsubgroupsizeof4samples=1.99.865%Only0.135%ofthesubgroupaverageswouldfallwithinthecontrollimits.Conversely,99.865%willbeaboveUCL.Thereisalmost100%chancethisshiftintheprocessmeanwillbedetected.+3σshiftPowerofX-barCharttoDetectProcessChangesProducer’sRisk(Alphaerror):RejectingagoodpartAlphaerrormeasuretheprobabilityofrejectinggoodpartsinthefactory.Consumer’sRisk(Betaerror):Shippingbadparts

Betaerrormeasurestheprobabilityofshipping badpartstocustomers(1-β)Probabilityofdetectingaprocessshiftishigherwithlargern,i.e.Betaerror(consumer’srisk)decreasesasnincreases.ProcessCapabilityProcessCapabilityisdefinedastheabilityofaprocesstosatisfycustomerexpectations.Becausethespecificationlimitsareassumedtoreflectcustomerdesires,capabilitymeasuresaresaidtorelatethe“VoiceoftheProcess”tothe“VoiceoftheCustomer”ProcessCapabilityspecificationspecificationspecificationspecificationnaturalvariationnaturalvariation(a)(b)naturalvariationnaturalvariation(c)(d)ProcessCapabilityIndexCp=USL-LSL6sCpl,Cpu}USL-m3sCpl=m-LSL3sCpk=min{Cpu=

ProcessCapabilityProcessCapabilityIndex,CpCp>1.33,CapableCp=1.00–1.33,CapablewithtightcontrolCp<1.00,IncapableProcessVariationSpecificationsProcessCapabilityCp=1andCpk=1LSLTargetUSLLSLTargetUSLCp=2andCpk=1LSLTargetUSLCp=2andCpk=0NewProcessCapability10-Steps1.Characterizetheentiremanufacturingprocess.ProcessMaptheentiremanufacturingoperationsfromthebeginningtotheend.Laterwhenthecriticalparametersareidentified,locatewhichprocessesarealignedwiththecriticalparameters.Initiateanattributecontrolchart(P-chart)onRolling-Through-PutYield.

Ifasupplierissensitivetosharingtheyielddata,usethecodeddata.ThegoalistomonitorthestabilityofRTY.Thegoalofcontrolchartistoidentifyanyspecial-causes.Identifytherootcausesandtakecorrectiveactionsimmediatelyforanyout-of-controlpoints.DoesthesupplierhavethedocumentedprocessonP-chart,businessowner,andactionstepsforcorrectingout-of-controlpoints?AfterCPK’sofcriticalcharacteristicsareimproved,checkifthereisanyimpacttoRTY.3.Identifythecriticalparameters.Thisisthemostimportantstep.Criticalparameterscanbeidentifiedinmanydifferentways.Criticalparametersshouldbetiedtocustomerexperience.CriticalparameterscanbebothProcessandProductcharacteristics.Onewaytoidentifythecriticalparametersistoanalyzethenonconformingdefectives(fallouts)fromsuppliermanufacturingline,customermanufacturingline,andcustomerfields.Pareto,FMEAandCause-and-Effectdiagramarecommontoolstousefortheanalysis. 3.Identifythecriticalparameters(continued)

AnotherwayofidentifyingcriticalparametersistogetEngineering(DesignandNewProductIntroduction)engagedtoidentifykeycriticalProductcharacteristicsthatimpactcustomerexperience.IdentifyingbothProcessandProductcriticalparametersisdesirable.Identifyingthecriticalparametersfrombothdefectiveanalysisandengineeringinvolvementisdesirable.Reviewthespecificationsofthecriticalcharacteristics.Specificationsshouldbedefinedbasedoncustomerrequirements.Doesthesupplierhavetheprocesstodefinespecsbasedonengineeringandtoleranceanalyses?Ordoesthesupplierdefinesomespecsarbitrarily?Doesthesupplierhavetheprocesstotightenupspecsordotheymaintainthesamespecalways?Thevalueofprocesscapabilitywillbedependentonthespecifications.Quiteoften,veryunreasonablyhighCPKsarereportedbecauseofverywidespecranges.Ifthespecistoowide,productsmaypassthroughsupplier’smanufacturingprocess,butproductswillnotconformtocustomer’sexpectations.DefinetherequiredCPKsforthecriticalcharacteristics.

5.ConductMeasurementSystemAnalysis.ThegoalofrunningGageR&Ristocheckifthedatacollectionisnotcontaminatedbytheinaccuracyandnon-repeatabilityofthemeasuringdevicesandoperators.ImplementX-barandRvariablecontrolchartsonthecriticalparameters.Determinethesubgroupsamplesizeandthefrequencyofsampling.Preferablesubgroupsizeisn=10,inordertohave95%confidenceindetecting1.5sigmaprocessshift.However,ifthecircumstancesdonotallown=10,usesmallersubgroupsizewithmorefrequentsampling.UsetheBeta-errorcurvestofigureouttheprobabilityofdetectingtheprocessshift.Thegoalisnottocollectthedata.Thegoalistoidentifyspecialcausesusingthecontrolcharts.Onceaspecialcauseisidentified,rootcauseanalysisandcorrectiveactionneedtofollowupimmediately.6.ContinuedHavepassionforidentifyingspecialandcommoncausesanddriveactionsforthecontinuousimprovement.TheprocesscanbedeclaredtobeStableif25points(withsubgroupsamplesize)areplottedconsecutivelywithinthecontrollimits.DoesthesupplierhavethedocumentedprocessonX-barandRcharts,businessowner,andactionstepsforcorrectingout-of-controlpoints?Aftertheprocessisstable,calculateCPandCPK.UsethesameX-barandRcharttemplatetocalculateCPandCPK.Checkthenormalityofthedatafirst.WhatisCP?WhatisCPK?DoescalculatedCPKmeettherequiredvalue?

IfCpisnotsameasCPK,plantheactionstomakeCP=CPK. IfCPKisbelow1.33,plantheactionstoraiseCPKto1.33.

Usingtheattachedcontrolplanisoptional.Modifytheformattofityourneeds.ConductstatisticalcorrelationbetweensuppliercriticalparametersandDellqualitymetrics.

Doyoufindanycorrelations?

ConductthecorrelationanalysisbetweencriticalparametersandsupplierRTY.Arethereanycorrelations? Ifthereisnocorrelation,arewemissingtherightcriticalparameterthatimpactcustomerexperience? Workontheactionplansonidentifyingthecriticalparametersthatimpactcustomerexperience.

ImplementProcessCapabilitywithsub-tiersuppliers.Moveupstream.Identifythecoresuppliersthatimpactcustomerexperience.ImplementthesameProcessCapabilityprogramwiththecoresuppliers.

NewScoringCriteriaandScorecard

NewScorecardCriteria

Six-SigmaQualityEnsuringthatprocessvariationishalfthedesigntolerance(Cp=2.0)whileallowingthemeantoshiftasmuchas1.5standarddeviations.MotorolaDefinitionofSixSigmaDefectrategoalislessthan3.4DPPM(with1.5sigmashift)CPgoal=2SixSigmaMetricswith1.5SigmaDynamicShiftSixSigmaMetricswith1.5SigmaDynamicProcessShift(1-β)UsethisplottodeterminewhatsubgroupsizetousefordetectingtheprocessshiftCalculationofSystemLevelYieldCalculatebasedonlowerlimitCPKs.Ifyourfactoryhas:10processeswithCPK=1.33(31dppm)-20processeswithCPK=1(1350dppm)30processeswithCPK=0.83(6200dppm)Predictedsystemlevelyield=(1-.000031)10*(1-.00135)20*(1-.0062)30

=.99969*.97334*.8297=.807Whatifsupplier’sCPKishighandtheirmanufacturingfailurerateislow(i.e.highyield),butDelllineandfieldfailureratesarehigh?Thisshowsthatsupplier’sspecificationsaresettoowide.Therefore,nonconformingproducts(persupplier’srequirements)arepassingthroughsupplier’sfinaltests.HowisProcessCapabilityanalysisgoingtohelpLeadFreetransition?CancomparetheprocesscapabilityofbeforeandafterthetransitionPredictthefailureratebasedonspecchangeandtheexistingvariationBackUpSlidesFPY,RTY,andNAYFPY(YFP),FirstPassYieldorFTY(YFT),FirstTimeYieldGivestheprobabilityofgoingthroughonlyonestepintheprocesswithzerodefectsNumberofgoodunitsproducedthefirsttimethroughtheprocessdividedbythetotalnumberofunitsRTY(YRT)

orRolledThroughputYieldProbabilityofaunitpassingthrougheachstepofaprocessdefect-freeRTY=FPY1*FPY2*FPY3NAY(YNA),NormalizedAverageYieldUsedtocharacterizeaprocessusingtheznormaltablesUsedtocomparetwodifferentprocessesNAY=RTY1/numberofopportunitiesorstepsUseNAYtocompareprocesseswithdifferentnumbersofstepsoropportunitiesPChart–PlotFirstPassYieldFractions(AttributeData)ParetoAnalysistoIdentifyMajorDefectCategoriesWorkOn80%IssuesForgetUse80/20ruletofocusonmajorissuesCauseandEffectDiagramEnablesateamtofocusonthecontentofaproblem,notonthehistoryoftheproblemordifferingpersonalinterestsofteammembersCreatesasnapshotofcollectiveknowledgeandconsensusofateam;buildssupportforsolutionsFocusestheteamoncauses,notsymptomsEffectCauseFMEA–Whatisit?Failuremodeeffectsanalysis(FMEA):Atechniquetoidentify,define,andeliminateknownand/orpotentialfailures,problems,errors,andsoonfromthesystem,design,process,and/orservicebeforetheyreachthecustomerProvidedocumentationforsafetyandliabilityissuesFulfillcustomerandlegalrequirementsIdentify,prioritize,andreducepotentialfailuremodesManageandquantifyriskRankorderpotentialdesignandprocessdeficienciesEstablishprioritiesforcontainment,corrective,andpreventiveactionsImprovethereliabilityandsafetyofproductsandservicesSystem/DesignFMEAFocusonaproductpriortoreleaseDecreasedesigndeficienciesandincreasedesignrobustnessProcess/ServiceFMEAFocusonaseriesofsteps(productionoradministrative)IncreaseprocessrobustnessandidentifyhighriskfailurestepsandpointsofcontrolToolforprioritization,prevention,andcorrectionDefineFailureModesAdescriptionofthemannerinwhichafailureoccursAfunctionalrequirementexpressednegativelyAbsenceoffunction:“Doesnot…”DoesnotresolvecustomercomplaintDoesnotpaintfendersFunctiondonepoorly,inadequately,orincompletelyShouldbeexpresseddirectionallyorinactionabletermsIncompleteassemblyExcessivechromeProcessFunctionFailureModeEffectsofFailureSEVCausesofFailureOCCS*OCurrentControlsDETRPNActionsInstallFanFannotseatedproperlyMissingWronginvoiceDamagedHotsurfaceBentpinUndersizeCrackedbezelMeltedplasticShortedcircuitWrongcapacitorWarpedboardOpencircuitSurfacefinishDiscoloredCorrodedLoudfanTightinsertionCreasedScratchWrongdriverOutofpositionLooseDentLowvoltageEjectionpartialHighresistanceFrayedcableBrittleOmittedTightfitMissingpixelMisalignedIdentifyEffectsIdentifytheeffectofthefailuremodeonthefollowing:NextoperationDownstreamusersUltimatecustomerorconsumerRegulatoryagenciesProcessFunctionFailureModeEffectsofFailureSEVCausesofFailureOCCS*OCurrentControlsDETRPNActionsInstallFanFannotseatedproperlyFanfailstestExtrahandlingNoiseanddispatchIdentifyCausesIdentifythecausesofthefailuremode

UseCauseandEffectAnalysis:the4MEPmodelBesurethecausesaddressthefailuremodeStayon-level:avoidinappropriatedeepdivesProcessFunctionFailureModeEffectsofFailureSEVCausesofFailureOCCS*OCurrentControlsDETRPNActionsInstallFanFannotseatedproperlyFanfailstestExtrahandlingNoiseanddispatchContaminationPoortrainingImproperinstallationNotactilefeedbackDefineCurrentControlsAvoidwishfulthinking,especiallywithregardtocurrentcontrolsControlsexisttodetectcausesAuditsandinspectionsarenoteffectivecontrolmethodsInspectionsare,atbest,amethodofdetectionnotcontrolProcessFunctionFailureModeEffectsofFailureSEVCausesofFailureOCCS*OCurrentControlsDETRPNActionsInstallFanFannotseatedproperlyFanfailstestExtrahandlingNoiseanddispatchContaminationPoortrainingImproperinstallationNotactilefeedbackNoneBuildcertificationWI,BOMNoneStatisticalprocesscontrolchartSamplingAuditsandLogsInspectionsRunandtrendchartsPre-controlchartsCapabilitystudiesMilitarystandardsOperatingproceduresChecklistsSetupchecksSendaheadsLaboratorytestsPilotrunsSimulationTestingequipmentPrototypetestingVerificationtestingTolerancestudiesOn-linereliabilitytestingDocumentationFormulasPrintsandspecificationsMeasure:SeverityScaleDirectlyrelatedtofailuremodeandeffectsRatingsof“10and9”arereservedforhazardoussituationsandareobviouslyaddressedimmediately(specialcause)EffectScaleCriteriaHazardouswithoutwarning10Complianceissue,suddenfailureHazardouswithwarning9Safetyrelated,potentiallyhazardous,gradualfailureVeryhigh8Safe,Customerisverydissatisfied,lossofprimaryfunctionHigh7Customerdissatisfied,severeeffectonperformanceModerate6Customerdiscomfort,degradedperformanceLow5Customerannoyed,reducedperformanceVerylow4Customerannoyed,noticeablelossofperformanceMinor3Customerannoyed,slighteffectonperformanceVeryminor2Customermaybeannoyed,cosmeticNone1NoeffectMeasure:OccurrenceandDetectionScalesDirectlyrelatedtocauseOverthedesignlifeoftheproductbeforeanyadditionalprocesscontrolsareappliedDirectlyrelatedtocontrolsAssumeafailurehasoccurredtodeterminethedetectionratingThescaleisreversedRankProbabilityFailureRate10Certain>1in29Veryhigh1in38High1in87Moderatelyhigh1in206Medium1in805Low1in4004Slight1in2K3Veryslight1in15K2Remote1in150K1Impossible1in1.5MRankProbabilityDefectShipped10Never1in109Veryremote1in208Remote1in507Verylow1in1006Low1in2005Moderate1in5004Moderatelyhigh1in1K3High1in2K2Veryhigh1in5K1Certain1in10KMeasure:AssignRatingsandCalculateRiskSeverityisrelatedtofailuremodeandeffectsSeveritycanbeassignaratingbasedoneitherthefailuremode,theworsteffect,oreacheffectseparatelyJustmakesuretostayconsistentthroughouttheentireFMEAanalysisProcessFunctionFailureModeEffectsofFailureSEVCausesofFailureOCCS*OCurrentControlsDETRPNActionsInstallFanFannotseatedproperlyFanfailstestExtrahandlingNoiseanddispatch7Contamination321None10210Poortraining535Buildcertification9315Improperinstallation535WI,BOM7245Notactilefeedback642None10420MeasuresofRiskProcessFunctionFailureModeEffectsofFailureSEVCausesofFailureOCCS*OCurrentControlsDETRPNActionsInstallFanFannotseatedproperlyFanfailstestExtrahandlingNoiseanddispatch7Contamination321None10210Poortraining535Buildcertification9315Improperinstallation535WI,BOM7245Notactilefeedback642None10420MeasureofRiskExamplesCommentsSeverityAfireAninjuryAddresshighseverityimmediatelyS*O,Severity*OccurrenceLotsoffiresMultipleinjuriesOptional;especiallyusefulforDesignRiskPriorityNumber(RPN)Lotsoffiresandwecan’tfindthemWhatinjuries?TraditionalFMEAmeasureofriskApproachestoPrioritizeRiskAddressseverityratingsof9or10immediatelyFocusonhighestRPNfirstUseParetomethodCanbeappliedtobothS*OandRPNUseriskthresholdsRPNsgreaterthan50Definecategoriesofaction:critical/high/minorManufacturingProcessCapabilityNPIProductCapabilityManufacturingProcessIdentifyProduct

CriticalParametersduringDesignandQualificationphases.ImplementSPCandassessCp+CpkinNPIphase.UsetheinfotomakeMassProductDecision.IdentifyProcess

CriticalParametersthatareassociatedwithsupplieryieldfailures,Delllinefailures,andDellfieldfailuresduringmassproduction.ImplementSPCandCapabilityAnalysisonbothProductandProcesscriticalparametersNPIProductCapabilityIdentifyingbothProductandProcessCriticalCharacteristicsQualificationQualificationisdonewithsmallsamplesize NPIandMassProductionSourcesofVariationareadded:NewsubtiersuppliersareaddedNewmanufacturinglinesareaddedNewtoolsareaddedMoreoperatorsareaddedECN’sandPCN’saregeneratedIdentifycriticalproductparameters.ConductStatisticalanalysistodetermineifproductparametersarestable.DoesDesignsupportManufacturability?ContinuetomonitorProductCapabilityIdentifycriticalprocessparameters.ImproveProcessCapabilityImproveThroughputYieldsFromNewProductQualificationtoMassProductionExample:MONDEOCRITICALVARIABLESCharacteristicSpecificationEvaluation/MeasurementSamplingPlanControlMethodProcessCapabilityCommentsNoElectricalFunctionalityProduct1MainOscillatorFrequencyMotherboardAssembly14.318MHZ+/-50ppMFrequencyCounter5/100PCBAXbar&R*RefertoNote1Lessonslearned2VDDQ(2.6V)measurementsMotherboardAssembly2.6V+/-5%DVMorICT5/100PCBAXbar&R*RefertoNote1WillindicatepowersupplyandDCtoDCissues3FunctionalTestTotalTestTime(forafixedconfig)MotherboardAssemblyTBDSeconds+/-TBDFunctionalTestScript(timer)5/100PCBAXbar&R*RefertoNote2Systemwithproblemcanfinishtestbutwillretrymanytimes4BoardThicknessPCB0.062”+/-10%Micrometer5/100Xbar&R*RefertoNote15TraceWidthPCB0.005”+/-0.001”Opticalvenire5/100XBAR&R*RefertoNote16ImpedancemeasurementPCBSE:42,50,60,75DE:70,90,100TDR10forbuildof10020forbuildof1000+XBAR&R*RefertoNote1BesttomonitorresultperbuildfromPCBvendorDEFINE-MONDEOCRITICALVARIABLESCharacteristicSpecificationEvaluation/MeasurementSamplingPlanControlMethodProcessCapabilityCommentsNo.ElectricalFunctionalityProduct75VRippleVoltagePowerSupply70mVppOscilloscopeorEquivalent5/100Xbar&R*RefertoNote1Minimumload85VRippleVoltagePowerSupply70mVppOscilloscopeorEquivalent5/100Xbar&R*RefertoNote1Maximumload912VRailVoltagePowerSupply12V+/-5%DVMorICT5/100UnitsXbar&R*RefertoNote1MinimumLoad1012VRailVoltagePowerSupply12V+/-5%DVMorICT5/100UnitsXbar&R*RefertoNote1MaximumLoadCharacterizingProcessParametersandProductCharacteristicsVariability(Machine,Methods,Measurements,Materials,People,Environment)SupplierCustomerFeedback(Productivity,Timeliness,Financial,Quality)ProcessParametersProduct

CharacteristicsProcessParametersandProductCharacteristicsProductandProcessCriticalParametersOwner:Engineering,NPIProductCriticalParametersSpecificationsCpkRequirementDPPMestimationMB1)

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Owner:Supplier,SQE,ManufacturingProcessCriticalParameterSpecificationsCpkRequirementDPPMestimationMB1)

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ScienceofMeasurementAccuracy-closenessofagreementbetweenanobservedvalueandastandardPrecision-closenessofagreementbetweenrandomlyselectedindividualmeasurementsRepeatabilityandReproducibilityRepeatability(equipmentvariation)

–variationinmultiplemeasurementsbyanindividualusingthesameinstrument.Reproducibility(operatorvariation)-variationinthesamemeasuringinstrumentusedbydifferentindividualsRepeatabilityandReproducibilityStudiesQuantifyandevaluatethecapabilityofameasurementsystemSelectmoperatorsandnpartsCalibratethemeasuringinstrumentRandomlymeasureeachpartbyeachoperat