奥特利夫精益生产之--6SigmaIntro.ppt

1 APSTrainingModuleQF 6Sigma Autoliv14 April 2006QF TM 004 C2 0 2 3 Whythistraining Duringthistrainingyouwilllearn What6SigmaisAmeasurementofqualityAproblemsolvingmethodExamplesoftoolsin6Sigma 4 Whatis6Sigma SigmaistheGreekletterusedtorepresentvariation AsigmalevelisameasurementofqualityA6SigmalevelprocesscreatesclosetonofailuresAsigmalevelisameasurementofhowwelltheprocessmeetscustomerspecifications6SigmaisaproblemsolvingmethodCustomercenteredSystematicDatadriven 5 AMeasurementofQuality 6 2s308 5373s66 8074s6 2105s2336s3 4 AMeasurementofQuality A6Sigmaprocesshasonlyabout3 4ppm SigmaLevel PPM 7 AMeasurementofQuality Outof365RoundsperYear 2s 6missedputtsperround 3 1missedputtperround 4 1missedputtevery9rounds 5 1missedputtin1 5years 6 1missedputtin45years 8 AProblemSolvingMethod 9 APSToolbox TheAPSToolboxisexpandedwiththe6SigmaToolstosolvemorecomplexproblemswithincreasedefficiency 10 CustomerCentered Whoisthecustomer Whatiscriticaltothequality CTQ forthecustomer Howarethecustomer sexpectationsnotbeingmet ActionsbasedondataarethekeytoCustomerSatisfaction 11 Systematic 6SigmafollowsaroadmapTolinktoolsmorepowerfullytogetherToguidetheteamthroughtheproblem 12 TheDMAICMethod Defineprojectorproblem MeasurecurrentsituationProcessbehaviorchartsCapabilityanalysisParetocharts CreateCause EffectMatrix Selectcorrectiveactions DocumentResults DevelopProjectPlanSelectteamBusinesscaseSMARTobjectiveProjectscopeKeymetricOperationaldefinition ConductMeasurementSystemAnalysis MSA CreateVMEA ImplementCorrectiveactions ImplementcontrolmethodControlplanStandardsTrainingmatrixSWIAuditSPC CreateProcessMaporFishboneDiagram RootcauseanalysisprocessVMEAActionsTestingpotentialrootcausesOFATTestsDOETests VerifycorrectiveactionsCapabilityanalysisProcessbehaviorchartsOFATTestsDOEConfirmatory DocumentLessonsLearned Determinecustomer sCTQs OptimizeProcessDOETestsResponseOptimizerResponsesurfacemodels RSM ConfirmrootcauseReproducefailureOFATTestsDOETests 13 Systematic 6SigmaisaboutreducingvariationUnderstandthesystemY f x DeterminetodominatesourceofvariationControlthedominatesource 14 TheDMAICMethod ProcessMapCollectsallknowninputandoutputvariablesCauseandEffectMatrixPrioritizesthevariablesmostlikelytohaveamajorimpactVMEAStudieshowselectedvariablescancausetheprocesstofailStatisticaltoolsarethenusedtoquantifytherelationshipbetweentheseselectedinputandoutputvariables 15 DataDriven KeyConcepts Variablealwaystrumpscategorical Variabledataismorepowerfulthancategoricalbecauseitcontainsmoreinformation Dataisinnocentuntilprovenguilty Variationinaprocessisconsideredtoberandomuntilevidenceshowsotherwise Agraphisworthathousanddata Thecorrectgraphorstatisticaltoolcanobjectivelymakeconclusionsmuchbetterthanrawortabulateddata 16 DataDriven Attemptingtocontrolvariationwithoutunderstandingandquantifyingit simplyaddsonemoresourcetotheproblem 17 DataDriven Basedonthedata whatisthisprocessdoing GettingbetterGettingworseStayingthesameWhatwillthe scrapbeinthreemonths 18 DataDriven Basedonthechart whatisthisprocessdoing GettingbetterGettingworseStayingthesameWhatwillthe scrapbeinthreemonths 19 DataDriven Basedonthechart whatisthisprocessdoing GettingbetterGettingworseStayingthesameWhatwillthe scrapbeinthreemonths 20 MeasurementSystemAnalysisGageR R ObjectiveEvaluateswhetherameasuringsystemforvariabledataisadequateReportsRepeatabilityAmountofvariationduetogageandmethodReproducibilityAmountofvariationduetochangingoperators StudyVariationPercentofobservedvariationthatisduetothemeasurementsystem ToleranceComparesthemeasurementsystemtothetolerance StudyVar StudyVar ToleranceSourceStdDev SD 5 15 SD SV SV Toler TotalGageR R0 00892860 04598219 379 20Repeatability0 00828860 04268617 988 54Reproducibility0 00331970 0170967 203 42Appraiser0 00331970 0170967 203 42Part To Part0 04522580 23291398 1146 58TotalVariation0 04609870 237408100 0047 48NumberofDistinctCategories 7 21 MeasurementSystemAnalysisKappaTest ObjectiveEvaluateswhetherameasuringsystemforcategoricaldataisadequateReportsKappaScore 7and 9isexcellentPercentAgreementandConfidenceIntervals Fleiss KappaStatisticsResponseKappaSEKappaZP vs 0 Good0 7696560 063245612 16930 0000Short0 7696560 063245612 16930 0000 Fleiss KappaStatisticsResponseKappaSEKappaZP vs 0 Good0 2534720 06324564 007750 0000Short0 2534720 06324564 007750 0000 Before After 22 DataDrivenExample1 You retheplantmanagerYouarereviewinganarea sscraprateWhatshouldyoudo MonthScrapRate Giveareaaplaqueforanall timelowinscrap Wishedyouhadtheplaqueback That sbetter Fivemonthsofsteadilyincreasingscrap 23 DataDrivenExample1 ProcessbehaviorchartsIdentifyspecialcausevariationfromcommoncausevariationCommonCauseTheinherentrandomvariationcausedbymanyinputsSpecialCauseThenon randomchangesinaprocessusuallycausedbyoneinput Allchangesinscrapratewereduetocommoncausevariation 24 DataDrivenExample2 You retheproductionmanagerAmachinesupplieroffersyouanewmachinetoreducecycle timeThenewmachineis 50 000Areductionof2secondsincycle timecouldsave 100 000overayearThefollowingdataisgivenbythemachinesupplier MachineCycleTimesOldNew8 79 419 913 96 35 921 216 812 311 813 6811 56 Averages 25 DataDrivenExample2 TTestDistinguishesifdifferencesbetweensamplesaresignificantorsimplybyrandomchanceIntervalPlotDisplaystheconfidenceintervalsaroundeachsamplemeanandvisuallyrepresentsthettestevaluationOtherStatisticalTestsANOVARegressionChi SquaredProportionTestofEqualVariance TTestResults P Value 0 284Conclusion Thedifferencebetweenmachinesisbyrandomchance 26 DataDrivenExample3 Ateamhasimplementedimprovementsthattheybelievewillreducethevariabilitybyatleast20 Managementwantslessthan5 chanceofmissingthepotentialimprovementManagementalsowantslessthan5 chanceofimplementingthechangesiftheimprovementisnotreal 27 DataDrivenExample3 SamplesizeevaluationDeterminesthesamplesneededtoseechangesbasedon RiskTheprobabilityofmakingawrongdecisionDifferenceThemagnitudeofthechangetobetestedcomparetothenormalvariation Samplesfor20 reductionat95 confidence 220samplesbeforeandafterchanges 28 DataDrivenExample4 ThewebsensitivityforaseatbeltmodelhastoomuchvariationTheteamneedstoknowwheretosetandhowtocontrolthesecomponentdimensions InertialmassweightBearingheightSpringrateLeverlength 29 DataDrivenExample4 Whichfactorshaveanaffect Whatistheoptimalcondition RememberReducingcomponenttolerancescostsmoney Dataarefictionalvalues 30 DataDrivenExample4 DOE DesignofExperiments AsystematicsetofteststhatefficientlydeterminetheeffectofeachinputontheoutputsInteractionHowoneinputchangestheeffectofanotherinputontheoutput notseeninsinglefactortests ResultsTheseresultscanbeusedtodeterminethesettingsoftheinputstooptimizetheoutput Dataarefictionalvalues 31 Control Controlisthelast