Nokia-SPC-Training-Material

,StatisticalProcessControl(SPC)Mechanics-GlobalTrainingMaterial,Owner:VirpiStenmanFunction:MechanicsApprover:MechanicsGlobalProcessSolutionOwnerDocumentID:TBDVersion/Status:V0.1DraftLocation:DateHandledByComments1.029thNov00JohnBrecklineOriginalversion1.115thFeb11MikaPylvninenThoroughreviewandcontentupdate.,NOTE-ALLcommentsandimprovementsproposalsshouldbeaddressedtotheownerofthedocument.NOTE-Elaboratingcommentscanbefoundusingthenotespageviewoptionthroughoutthedocument.,1,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,TableofContent,TheHistoryofStatisticalandProcessThinking3SPCinNokia6IntroductiontoVariationManagement14MeasuresofLocation&Variability23ProcessControlLimits35ProcessControlCharts39Out-of-ControlCriteria50Out-of-ControlActionPlan69Pre-Control79Acknowledgements89Change/IssueNoteV2.090,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,2,EliWhitney1765-1825InventoroftheCottonGinPatentedMarch14,1794,Americaninventor,pioneer,mechanicalengineer,andmanufacturerEliWhitneyisbestrememberedastheinventorofthecottongin.HealsoaffectedtheindustrialdevelopmentoftheUnitedStateswhen,inmanufacturingmusketsforthegovernment,hetranslatedtheconceptofinterchangeablepartsintoamanufacturingsystem,givingbirthtothemass-productionconcept.,TheHistoryofStatisticalandProcessThinking,3,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,TheHistoryofStatisticalandProcessThinking,ThequalitycontrolmethodsandtechniquesusedtodaygottheirstartintheAmericanRevolution,whenEliWhitneytriedinyear1789toproduce10,000riflesbycopyingonerifle,partbypart.Atthattimemostoftheproductswerehandmadebysmallowner-managedshopsandproductpartswerethusnotinterchangeable.TheresultofWhitneysmassproductiontrialwasthattheriflesdidnotworkaswellasthehandmaderifles.Inaddition,thecopiedpartsdidnotfitasexpected.,4,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Thefirsttimethatapresentedmachineproducedpartswas1851attheindustryexhibitionintheCrystalPalaceinLondon.AnAmericangunsmithtook10workingguns,tookthemapart,mixedallthepartsinaboxandre-assembledthemagain.Thiswasfoundaquitesurprising“experiment”.,TheHistoryofStatisticalandProcessThinking,5,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,SPCinNokia-DimensionalControl&CQPProcess,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,6,Black&WhiteDiamonds(FunctionalCharacteristicsforprocesscapability&processcontrol)(ReferalsotoClassification,MarkingandVerificationofFunctionalCharacteristicsDoc.ID.257076399),7,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,3LevelsofCriticality,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,8,1-5PartsMeasured:OKif1stpartiswithinthemiddle50%ofthetolerancespecification.Ifnot,onlyOKifmeanofthe5partsiswithin80%ofthetolerancespecificationandrangenotexceeding30%oftolerancespecification.NoteAllofthemeasurementsmustbeinsidethetolerancearea.,LevelI:Basicdimensions,NOTE:White&BlackdiamondsshouldbeavoidedonGeometricalDimensions&Tolerances(GD&T),duetotheverificationofthesearenotgeneralsuitedforcapabilitystudyandSPC.,TheNokiaProcessVerificationProcess,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,9,BasicDimensions,Tolerancesappliedtodrawing,Whitediamondstobeagreed,Whitediamondstobediscussedwithsupplier,LimitedApproval,MassApproval,ToolingRelease,Marking:Purpose:Where:Whoagrees:Notes:,Indicateacharacteristicwhichisimportantforfunctionalityandthatvariationwithrespecttomanufacturingprocesschangesneedstobeverified.NeedsmonitoringExpecteddifficulttoachievePossiblecandidateforblackdiamondsPartdesigner,SupplierandSourcingagreetogetherRequiresCapabilitystudydonebysupplierWhitediamondsneedtobecarefullychosenandeasilymeasurable(i.e.nosectionsandtobemeasuredinonemeasurementsetup).Themorewhitediamonds,themoreworkforthesupplier,whichcanconflictwithshorttoolapprovaltime.,WhiteDiamond,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,10,Marking:Purpose:Where:Whoagrees:Notes:,Indicatesthecharacteristicwhichgivesthebestimpressionaboutvariationsoccurringduringtheprocess(usedforSPC)Dimensionthatreflectthebiggestshrinkage(mouldedpart)Dimensioninvolvingsliders(mould.)ChangeincriticalprocessparametersToolwearingPartdesigner,SupplierandSourcingagreetogetherRequiresstatisticalprocesscontrol(SPC)fromsupplierduringmassproductionandmadeavailabeforNokiauponrequestBlackdiamondsneedtobecarefullychosenandeasilymeasurable(i.e.nosectionsandtobemeasuredinonemeasurementsetup).Ablackdiamondcanbeselectedfromthewhitediamonds,especiallythosethathavehadmarginalcapability.,BlackDiamond,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,11,Approval,EngineeringDesign,ToleranceAnalysis,Drawings,HighcontributionDimensions,Ppk=1.33,Ppk=1.00,Howtochoosethediamonddimensions,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,12,Assumptionsintoleranceanalysis,StatisticalTechniques,TheSuppliershallusestatisticaltechniquestocontrolthemanufacturingprocessesinordertoensurethatprocesscapability(Cpk)andprocessperformance(Ppk)continuouslymeetthesetrequirement.AGaugeRepeatabilityandReproducibility(R&R)studyshallbeperformedtothemeasuringsystemusedinmeasuringR&R-parameterstoensurereliabilityofthedata.RecordsofgaugeR&R-studyshallbesubmittedtoresponsiblePSQMaspartoftheCQPdocumentation.Qualityrecordsofstatisticalmethods,suchasSPC,shallbeavailableforreviewbyNokiauponrequest.,13,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,IntroductiontoVariationManagement,14,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Sampling&selectivemeasurements:ProductProcessStatisticalmethods(incl.SPC),PreventionSampleInspection,15,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,CommonCauses:Causesthatareimplementedintheprocessduetothedesignoftheprocess,andaffectalloutcomesoftheprocess.IdentifyingthesetypesofcausesrequiresDesignofExperiment(DOE)methods.,Shewhart(1931),SpecialCauses:Causesthatarenotpresentintheprocessallthetimeanddonotaffectalloutcomes,butarisebecauseofspecificcircumstances.SpecialcausescanbeidentifiedusingStatisticalProcessControl(SPC).,TheTwoCausesofVariation,16,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,UnstableProcess:Aprocessinwhichvariationisaresultofbothcommonandspecialcauses.,StableProcess:Aprocessinwhichvariationinoutcomesarisesonlyfromcommoncauses.,TheTwoTypesofProcesses,17,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,UCL=uppercontrollimitLCL=lowercontrollimit,WhatisaDefect?,Adefectisanyvariationofarequiredcharacteristicoftheproductoritspart,whichisfarenoughremovedfromitsnominalvaluetopreventtheproductfromfulfillingthephysicalandfunctionalrequirementsofthecustomer.,18,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,TheSourcesofProcess/SystemVariation,19,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,ContinuousProcessImprovementThecontinuousefforttolearnaboutthecausesysteminaprocessandtousethisknowledgetocontrolandeventuallychangetheprocesstoreducevariationandsotoimproveproductqualityandcustomersatisfaction.AProcessDevelopmentConceptTheincorporationofimprovedtechnologyistheendproduct-or,betteryet,thenextstage-ofanongoingprocessoflearningandimprovement.,ProcessControl&ContinuousProcessImprovement,20,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Thecriticalaspectofprocesscontrolandcontinuousprocessimprovementistounderstandthemeaningofvariationintheoutcomeoftheprocess.SixSigmatoolsareusedtomanageprocessvariationandachieveprocesscontrol.,ProcessControl&ContinuousProcessImprovement,21,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,SixSigmahastoolsforvariationmanagement.Analyticallyspeaking,thisunderstandingmaybeexpressedas:,y=f(x1,x2,.,xN),yissomesystem/processcharacteristic,alsocalledthedependentvariable,(x1,x2,.,xN)describesalltheindependentvariablesinthecausesystemThus,wemayinterpretthisexpressiontomeantheoutputvariable(y)isafunction(f)oftheinputvariables(x1,x2,.,xN),TheVariationManagementApproach,22,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,MeasuresofLocationandVariability,23,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,HowWePerceiveaProcess,StabilityHowdoestheprocessperformovertime?Stabilityisrepresentedbyaconstantmeanandpredictablevariabilityovertime.VariabilityIstheprocessontargetwithminimumvariability?Weusethemeantodetermineifprocessisontarget.WeusetheStandardDeviationdeterminevariability,WarmupExerciseAssumemachinesA,B,andCmakeidenticalproducts(w/rangechartsincontrol).Thespecforeachproductoutputvariableis50mm+/-0.05mmAnswerthefollowingquestions:Whichmachine(s)exhibit(s)variation?Whereiseachmachinecentered?Whichmachinesarepredictableovertime?Whichmachineshavespecialcausevariation?Whichmachinewouldyouwantmakingyourproduct?Whichmachinewouldprobablybeeasiesttofix?,24,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,PopulationVs.Sample,PopulationAnentiregroupofobjectsthathavebeenmadeorwillbemadecontainingacharacteristicofinterestHighlyunlikelywecaneverknowthetruepopulationparametersTheaveragewidthofallParkAveairbaghousingsAllregisteredvotersintheU.S.,SampleThegroupofobjectsactuallymeasuredinastatisticalstudyAsampleisusuallyasubsetofthepopulationofinterestTheaveragewidthofairbaghousingsbuilttodayAsurveyof1,000voters,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,25,TypesofData,DiscreteData(Qualitative)Categories(ductgrade)Good/BadTester1,Tester2,Tester3ShiftnumberCountedthings(e.g.#Phonesshipped,#oferrorsonaninvoice)ContinuousData(Quantitative)DatawhichhasdecimalsubdivisionsTime(seconds)Pressure(psi)ConveyorSpeed(ft/min)PowerLevels(db)etc.,26,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,MeasuresofCentralTendency,Mean(,Xbar)ThearithmeticaverageofasetofvaluesUsesthequantitativevalueofeachdatapointIsstronglyinfluencedbyextremevaluesMedian(M)Thenumberthatreflectsthe50%rankofasetofvaluesCanbeeasilyidentifiedasthecenternumberafterallofthevaluesaresortedfromhightolowIsnotaffectedbyextremevaluesModeThemostfrequentlyoccurringvalueinadataset,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,27,AnExample,Forthedatasetsshown:DrawahistogramCalculatethemeanCalculatethemedianCalculatethemode,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,28,WhatAreYourObservations?DoesTheMean=TheMedianForEachDataSet?WhyOrWhyNot?,MeasuresofVariability,Range:Thedistancebetweentheextremevaluesofadataset(Highest-Lowest)Samplevariance(S2):TheAverageSquaredDeviationofeachdatapointfromtheMeanStandardDeviation(S):theSquareRootoftheVarianceIsthemostcommonlyusedmeasurementtoquantifyvariabilityTherangeismoresensitivetooutliersthanthevariance,Range=MaxMinS2=SLT=SST=,29,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,ComputationalEquations,PopulationMean,SampleMean,PopulationStandardDeviation,SampleStandardDeviation,30,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,LongTermVariationVs.ShortTermVariation,31,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Inpractice,ZLT=ZST1.5,LongTermVariationVs.ShortTermVariation,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,32,LongTermVariationVs.ShortTermVariation,Shorttermdata,IsfreeofassignablecausesRepresentstheeffectofrandomcausesonlyIscollectedacrossanarrowinferencespaceAcrossoneshiftofproductionUsingonlyonemachineWithoneoperatorUsingrawcomponentsfromonlyonelotofrawmaterial,etc.,Longtermdata,ReflectstheinfluenceofrandomcausesaswellasassignablephenomenaIstakenacrossabroadinferencespaceAcrossmanyshiftsofproductionUsingmanymachinesWithmanyoperatorsUsingmanylotsofrawmaterial,etc.,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,33,TheDifferenceBetweenSSTandSLT!,ThedifferencebetweenSLTandSSTisonlyinthewaythatthestandarddeviationiscalculatedSLTisalwaysthesameorlargerthanSSTIfSLTequalsSST,thentheprocesscontroloverthelonger-termisthesameastheshort-term,andtheprocesswouldnotbenefitfromSPCIfsLTislargerthanSST,thentheprocesshaslostcontroloverthelonger-term,andtheprocesswouldbenefitfromSPCThereliabilityofSLTisimprovedifthedataistakenoveralongerperiodoftime.AlternativelySLTcanbecalculatedonseveraloccasionsseparatedbytimeandtheresultscomparedtoseewhetherSLTisstable,34,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,ProcessControllimits,35,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Thebasicideaofprocesscontrollimits,average,average+1*s(igma),average-1*s(igma),average+2*s(igma),average-2*s(igma),average-3*s(igma),average+3*s(igma),34.13%,34.13%,13.60%,13.60%,2.14%,2.14%,0.13%,0.13%,Uppernaturallimit(UNL)Uppercontrollimit(UCL),Lowernaturallimit(LNL)Lowercontrollimit(LCL),Variability,36,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Uppercontrollimit(UCL)=grandmean+3*sigmaLowercontrollimit(LCL)=grandmean-3*sigma,Thewarningandcontrollimitsforaprocess,37,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,TheProcessControlLimits,Becausethevariationofaprocessisnotknowninbeforehand,onecannotcalculateordefinethecontrollimitsinadvance.Controllimitsdefines“voiceofprocess”andtheyarecalculatedbasedontheprocesshistoricaldata.NOTE:Controllimitshasnothingtodowithspecificationlimits(tolerances)thatdefines“voiceofcustomer”.Controllimitsboundthevariationoftheprocessthatisduetocommoncauses.Thecalculationofthecontrollimitsshouldbebasedonatleast25subgroupswithminimum5datapointswithineachsubgroupfromaprocessthatisinstatisticalcontrol(stable).NOTE:Iflessthan25subgroupsareusedforcontrollimitscalculationtheconfidencetogetreliablecontrollimitsi.e.“voiceofprocess”islower.Thecontrollimitsshouldnotberecalculatedandmodifiedunlessthereisareasontodoso(e.g.aprocesschange),38,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,ProcessControlcharts,39,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,HowtoselecttherightControlChart?,Typeofdata,Measurements(variabledata),40,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Multi-VariChart,Note:Minitabreferstox/mRasI-MR(individuals),Controlchartsformeasurements:Thex/mR-Chart,Thex-chartisamethodoflookingatvariationinavariabledataormeasurement.Onesourceisthevariationintheindividualsampleresults.Thisrepresents“longterm”variationintheprocess.Thesecondsourceofvariationisthevariationintherangebetweenconsecutivesamples(movingrangeCharti.e.mR-chart).Thisrepresents“shortterm”variation.Individualorx-chartsshouldbeusedwhenthereisonlyonedatapointtorepresentasituationatagiventime,i.e.norationalsub-grouping.Tousethex-chart,theindividualsampleresultsshouldbesufficientlynormallydistributed.Ifnot,thex-chartwillgivemorefalsesignals.,41,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,The(x/mR)-Chart:AnExample,42,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Uppercontrollimit=,Lowercontrollimit=,Uppercontrollimit=,Lowercontrollimit=,Thex-chart,ThemR-chart,wherex1,x2,.,xNarethemeasurements,Nthenumberofmeasurements,Theindividualorx-chartforvariabledata,43,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Controlchartsformeasurements:The(x-bar/R)-Chart,The(x-bar/R)-chartisamethodoflookingattwodifferentsourcesofvariation.Onesourceisthevariationinsubgroupaverages.Theothersourceisthevariationwithinasubgroup(i.e.therangeofthesubgroups)The(x-bar/R)-chartcanbeusedif:TheindividualmeasurementsarenotnormallydistributedOnecanrationallysubgroupthedataandisinterestedindetectingdifferencesbetweenthesubgroupsovertimeThex-bar-chartshowsvariationovertimeandtheR-chartisameasureoftheshort-termvariationintheprocess,44,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,The(x-bar/R)-Chart:Anexample,45,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Uppercontrollimit=,Lowercontrollimit=,TheR-chart,Uppercontrollimit=,Lowercontrollimit=,Thex-bar-chart,wherex-bar1,x-bar2,.,x-barNaretheaveragesofeachsubgroup,nthenumberofitemsinasubgroup,Nthenumberofsubgroups,Thex-barandR-chartforvariabledata,46,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,X-bar-baristhegrandmeani.e.averageofaverages,Controlchartsformeasurements:The(x-bar/s)-Chart,The(x-bar/s)-chartisamethodoflookingatsourcesofvariation.Onechartlooksatvariationinthesubgroupaveragesx-bar.Theotherchartexaminesvariationinthesubgroupsstandarddeviations.The(x-bar/s)-chartcanbeusedwheneveronecanusethe(x-bar/R)-chart.The(x-bar/s)-chartshouldbeusedinsteadthe(x-bar/R)-chartifthesubgroupislargerthan8.Inthiscase,thestandarddeviationisabettermeasurementthantherangeforthevariationbetweenindividualmeasurementsinasubgroup.The(x-bar/s)-chartmustbeusedifthesamplesizeisnotconstant.,47,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,The(x-bar/s)-Chart:AnExample,48,2011NokiaAllRightsReservedSPCTrainingMaterial.ppt/v1.1/180211/MPyCompanyConfidential,Uppercontrollimit=,Lowercontrollimit=