Six Sigma_ Project_ Example

xxxProjectpresentation200X-XX-XXWhoXXXXXOrganizationSupplierName:…ProjectLeader:…ProjectSupport:…TeamMembers:…DMAICProjectNoRevisionDateCharterTitleProductImpactedStartDateCompletionDateProjectChampionProjectBeltCategoryBusinessUnitProcessProblemDescriptionTeamMember(s)ProjectScopeBenefitstoExternal/InternalCustomerSupportRequiredProjectInformationProjectCharterDMAICBusinessResultsPUTHEREIFPROJECTHASNON-FINANCIALBENEFITSFINANCIALBENEFITSReductionofCostvs.PreviousYearCostReductionCostCalculationProjectInformationProjectCharterProjectSchedule/KeyMilestoneDateDefineMeasureAnalyzeImproveControlDMAICDefineSIPOCSIPOCSuppliersInputsProcessOutputsCustomersInputboundaryOutputboundaryRequirementsRequirementsDMAICMeasureProcessmapTriggerStep3Step2Step1TerminatorOutputInputOutputInputOutputInputDMAICListallinputs(Xs)andprocessoutputs(ysandYs)MeasureCauseandeffectmatrixImportance32221022334KeyprocessoutputProcessstepProcessinputTotal000000000000000000000000000000PrioritizedXsforFMEA:X1:…X2:…X3:…X4:…X5:…Count2745943191018Percent64,813,910,24,52,44,3Cum%64,878,788,993,495,7100,0CountPercentDefectsOtherIncompletePartDefectiveHousiLeakyGasketMissingClipsMissingScrews4003002001000100806040200ParetoChartofDefectsDMAICPrioritizetheinputs(Xs)basedonthekeyprocessoutputs(ysandYs)MeasureFMEA#ProcessFunction(Step)PotentialFailureModes(whatcangowrong?)PotentialFailureEffects(howdoesitaffectthecustomer?)SEVPotentialFailureCauses(whycoulditgowrong?)OCCCurrentProcessControls(howeasyisittodetectcauses?)DETRPN12Xstoinvestigatefurther:X1:…X2:…X3:…Xstobeadressedimmediately:X4:…X5:…DMAICSeparatethequickwinsfromtheXstoinvestigatefurtherMeasureMSAfortheprojectYGageR&R%ContributionSourceVarComp(ofVarComp)TotalGageR&R0,091437,76Repeatability0,039973,39Reproducibility0,051464,37Operator0,051464,37Part-To-Part1,0864592,24TotalVariation1,17788100,00Processtolerance=8StudyVar%StudyVar%ToleranceSourceStdDev(SD)(6*SD)(%SV)(SV/Toler)TotalGageR&R0,302371,8142327,8622,68Repeatability0,199931,1996018,4214,99Reproducibility0,226841,3610320,9017,01Operator0,226841,3610320,9017,01Part-To-Part1,042336,2539696,0478,17TotalVariation1,085306,51180100,0081,40NumberofDistinctCategories=4PercentPart-to-PartReprodRepeatGageR&R100500%Contribution%StudyVar%ToleranceSampleRange1,00,50,0_R=0,342UCL=0,880LCL=0ABCSampleMean20-2__X=0,001UCL=0,351LCL=-0,348ABCPart1098765432120-2OperatorCBA20-2PartAverage1098765432120-2ABCOperatorGagename:Dateofstudy:Reportedby:Tolerance:Misc:ComponentsofVariationRChartbyOperatorXbarChartbyOperatorMeasurementbyPartMeasurementbyOperatorOperator*PartInteractionGageR&R(ANOVA)forMeasurementMeasurementsystemacceptableDMAICAnalyseBaselineprocessstabilityandcapability282522191613107412826242220ObservationIndividualValue_X=23,53UCL=28,39LCL=18,68282522191613107416,04,53,01,50,0ObservationMovingRange__MR=1,825UCL=5,963LCL=0I-MRChartofData2826242220LSLUSLLSL20Target*USL28SampleMean23,5341SampleN29StDev(Within)1,61789StDev(Overall)1,58651ProcessDataCp0,82CPL0,73CPU0,92Cpk0,73Pp0,84PPL0,74PPU0,94Ppk0,74Cpm*OverallCapabilityPotential(Within)CapabilityPPMLSL0,00PPMUSL0,00PPMTotal0,00ObservedPerformancePPMLSL14466,68PPMUSL2887,35PPMTotal17354,03Exp.WithinPerformancePPMLSL12953,45PPMUSL2439,53PPMTotal15392,98Exp.OverallPerformanceWithinOverallProcessCapabilityofDataBaselinecapability(expectedoverall):DMAICAnalyseHypothesistestTheregressionequationisScore2=1,12+0,218Score1PredictorCoefSECoefTPConstant1,11770,109310,230,000Score10,217670,0174012,510,000S=0,127419R-Sq=95,7%R-Sq(adj)=95,1%AnalysisofVarianceSourceDFSSMSFPRegression12,54192,5419156,560,000ResidualError70,11360,0162Total82,6556UnusualObservationsObsScore1Score2FitSEFitResidualStResid97,502,50002,75020,0519-0,2502-2,15RRdenotesanobservationwithalargestandardizedresidual.ResidualPercent0,300,150,00-0,15-0,30999050101FittedValueResidual3,02,52,01,50,10,0-0,1-0,2-0,3ResidualFrequency0,100,050,00-0,05-0,10-0,15-0,20-0,2543210ObservationOrderResidual9876543210,10,0-0,1-0,2-0,3NormalProbabilityPlotoftheResidualsResidualsVersustheFittedValuesHistogramoftheResidualsResidualsVersustheOrderoftheDataResidualPlotsforScore2ProvestatisticallythattheXsfromtheFMEAhaveaninfuenceontheY!OnlyshowtheresultsoftheimportanttestshereDMAICAnalyseDesignofexperimentDMAICShowhowyouchangedthesettingsofthecriticalXsinordertoimprovetheYBA2001505020CatalystTempTime48,938045,376143,227745,018748,765545,076243,030645,0411CubePlot(datameans)forYield0,500,250,00-0,25-0,50999050101ResidualPercentN16AD1,734P-Value0,0054846440,40,20,0-0,2-0,4FittedValueResidual0,30,20,10,0-0,1-0,2-0,34,83,62,41,20,0ResidualFrequency161514131211109876543210,40,20,0-0,2-0,4ObservationOrderResidualNormalProbabilityPlotVersusFitsHistogramVersusOrderResidualPlotsforYield502047464544200150BA47464544TimeMeanTempCatalystMainEffectsPlotforYieldDataMeansABCACBCCABBA1614121086420TermStandardizedEffect2,36ATimeBTempCCatalystFactorNameParetoChartoftheStandardizedEffects(responseisYield,Alpha=,05)Optimalsettings:Time50Temp200CatalystBImproveActionplan