SPC_Training.ppt

SPCtraining BeginningsofSPC Dr WalterShewhartpioneeredIndustrialControl SPC inthe1920 s 30 s TheU S usedindustrialcontrolverysuccessfullyduringWWII Afterthewar U S industriesdroppedindustrialcontrolinfavorofareturntomassproduction Japan devastatedbythewar adoptedtheteachingsofDr W EdwardsDemingandDr JosephM Juran Japanesecompaniescontinuedtodeveloptoolsforprocesscontrolsuchascompanywidequalitycontrolandqualitycircles the7QualityToolsandthe7ManagementandPlanningTools DefinitionofQuality Fitnessforpurposeoruse Juran Thetotalityoffeaturesandcharacteristicsofaproductorservicethatbearonitsabilitytosatisfystatedorimpliedneeds ISO8402 1986 Manufacturingbased Qualityisdefinedasthedesirableoutcomeofengineeringandmanufacturingpractice orconformancetospecifications WhatisaProcess Thetransformationofasetofinputs materials methods operations intoasetofoutputs products information services moregenerally results WhatisControl Controlreferstothemeasurementsoftheperformanceofaprocessandfeedbackrequiredforcorrectiveaction Aprocessissaidtobeoperatinginastateofstatisticalcontrolwhencommoncausesaretheonlysourceofvariation StatisticalcontrolisnotanaturalstateformostprocessesItisanachievement onebyone specialcauseeliminationTheinitialfunctionofaprocesscontrolsystemistoprovideastatisticalsignalwhenspecialcausesofvariationarepresent andtoavoidgivingfalsesignalswhentheyarenotpresent QualityToolKit StatisticalProcessControl SPC isamethodologywhichusesthebasicgraphicalandstatisticaltoolstoanalyze controlandreducevariabilitywithinaprocessSPCusesanumberofbasicqualitytoolsRunChartHistogramControlChartDistributionsandconfidenceintervals Capability TheSPCSystem AsystematicapproachtoansweringthefollowingquestionsCanwedothejobcorrectly capability Arewedoingthejobcorrectly control Havewedonethejobcorrectly qualityassurance Couldwedothejobbetter improvement EnhancesprocesscontrolRequiresempoweredemployees SourcesofVariation VariationisafactofnatureandafactoflifeCommoncausevariation withintheprocesstheyareinherentinanyprocessallthetime partofsystemdesign equipmentdesign materialcharacteristics inspectionmethods maintenance theirimpactvariesfromdaytodayindividuallycommoncauseshaveaslighteffectonvariationcollectively commoncausescanadduptosignificantvariationaccountforabout80to90percentoftheobservedvariationSpecialcausevariation outsidetheprocessspecialcausesappearsporadicallyinaprocesstheyoriginateoutsidetheprocess badbatchofmaterial poorlytrainedoperator excessivetoolwear notfollowingprocedure overadjustment cancontributeasmallamountorlargeamountofvariationbuttypicallyhaveamuchbiggerimpactthananyonecommoncause SpecialCause CommonCause SpecialCause CommonCause PrecisionandAccuracy Precision thedegreeofthespreadofthevaluesinaprocessAccuracy theabilityofaprocesstohitthetargetvalue PrecisionandAccuracy Notpreciseandnotaccurate Precisebutnotaccurate Accuratebutnotprecise Accurateandprecise PrecisionandAccuracy ProcesscharacterizationThedistinctionbetweenprecisionandaccuracycanonlybeassessedbylookingatanumberofresultsorvalues notlookingatindividualoneTamperingMakingdecisionsaboutadjustmentstobemadetoaprocessonthebasisofoneindividualresultmaygiveanundesirableoutcomeduetolackofinformationaboutaccuracyandprecisionSpecialcausebeforecommoncauseTheadjustmenttocorrectlackofprocessaccuracy specialcause islikelytobe simpler thanthelargerinvestigationusuallyrequiredtounderstandorcorrectproblemsofspreadorlargevariation commoncause MeasuresofAccuracyorCenteringMean Median Mode Measureofcentering mean Differencebetweenmean medianandmode ArithmeticMean ThesamplemeanisthemeasureofcentraltendencythatmostaccuratelyreflectsthepopulationmeanVerysensitivetoextremescoresDefinedasthepointatwhichthesumofthedeviationsfromthemeanequalstozero Example Median Thepointatwhichonehalf or50 ofthescoresfallaboveandonehalf or50 fallbelow 50thpercentile Listthevaluesinorder eitherfromhighesttolowestorlowesttohighest FindthemiddlemostscoreWhenthereisanevenamountofscores themedianissimplytheaveragebetweenthetwomiddlevalues Example Mode ThemodeisthevaluewhichoccursmostfrequentlyListallofthevaluesinadistribution butlistonlyonceTallythenumberoftimesthateachvalueoccursThevaluethatoccursthemostoftenisthemode MeasuresofSpreadandDeviation Measureofspread range Measureofdeviationfrommean estimatedprocessstandarddeviation CentralLimitTheorem WhatistheCentralLimitTheorem Ifwedrawsamplesofsizenfromapopulationwithmeanmandstandarddeviations thenasthenumberofsamplesincreasesinsize thedistributionofsamplemeansapproachesanormaldistributionwithameanmandastandarderrorofthemeansofWhatdoesitmean WecandeducestatisticalinformationaboutapopulationbytakingsamplesofthepopulationHowmanysamplesisenough 30appliesforalldistributions normalornotSmallersamplesizesapplyifwebelievethepopulationisnormallydistributed 2 10samples TheNormalDistribution TheNormalDistribution Areaunderthecurveisknown Ifweknowproductspecificationandhavegoodestimatesofthepopulationaverageandstandarddeviation wecanestimatethepercentageofmanufacturedproductsthatfallwithin oroutside therequirements Symbols Example 36sampleshavebeentakenfromapopulationwithameanageof40andastandarddeviationof6years Whatwouldthemeanofthesamplingdistributionbe Answer Whatwouldbethestandarddeviationofthesamplingdistribution Answer Example cont Accordingtotheillustration whatpercentofthetimewouldyouexpectthemeanofonesampletobebetween40and41 Answer Whatpercentofthetimewouldyouexpectthemeanofonesampletobebetween39and41 Answer ZScore AZscoreissimplythenumberofstandarddeviationsawayfromthemeanaparticularmeasurementislocated ProvidesamethodforhowlikelyacertainscorewilloccurItisastandardtocomparedistributions Ifm 15andsX 2 whatzcorrespondstoarawscoreof20 ZScoreExample Apopulationhasameanof20andastandarddeviationof4 Youplantochosearandomsampleof64 Whatistheprobabilityofasamplemeanbetween20and21 Usingatablethatprovidestheprobabilitiesforanormaldistribution TheNormalDistribution ErrorCalculations TypeIerror sayingtheprocessisoutofcontrolwhenitisreallyincontrol calculationintailsofdistributioncurveTypeIIerror sayingtheprocessisincontrolwhenitisreallyoutofcontrol calculationundercurvebetweenlimits ControlCharts ReasonsforControlChartPopularity ProventechniqueforimprovingproductivityEffectivetoolfordefectpreventionPreventsunnecessaryprocessadjustmentProvidesdiagnosticinformationProvidesinformationaboutprocesscapability ControlChartAnatomy UpperControlLimit UCL andLowerControlLimit LCL arecalculatedsothatiftheprocessisincontrol thepointswillfallwithintheselimits CenterLine CL usuallytheaverage ShowswheretheacquireddataiscenteredControlChartsarealwaysTIMEORDEREDplots Thecontrolchartmodel 3 sigmacontrollimitsareusedtominimizetypeIandtypeIIerrors UpperControlLimit UCL Centerline CL andLowerControlLimit LCL GenericformulasthatapplytoallcontrolchartswhereListhedistancefromthecenterline inmostcases3for3sigma ControlLimitsXbarRangeChart ControlLimitsXbarRangeChart WarningLimits ActionLimits Usually3 actionistakentofindanassignablecausewhenapointliesoutsidetheselimits WarningLimits Controllimitsfoundinside3 warnofpossibleupcomingproblems ControlChartsOutofControlConditions Oneofmorepointsfalloutsidethecontrollimits Twopointsoutof3consecutivepoints areonthesamesideoftheaverageinZONEAorbeyond Fourpointsoutof5consecutivepointsareonthesamesideoftheaverageinZONEBorbeyond Nineconsecutivepointsareononesideoftheaverage Therearesixconsecutivepoints increasingordecreasing Thereare14consecutivepointsthatalternateupanddown Thereare15consecutivepointswithinZONEC aboveandbelowtheaverage ControlChartsAnalyzingforAction ControlChartSelection ProcessCapability Importantmeasuresforprocesscapability CapabilityIndexCp IfCp 1thennaturalvariationislessthanEngineeringToleranceTargetCpvalueshouldbeaminimumof1 5RuleofThumb Cpx3 SigmaCapabilityDoesnotconsiderwhethertheprocessaverageiscenteredConsideredtobemoreofaprocesspotential CapabilityRatioCpk IfCpk 1 thentheprocessisjustbarelycapableofproducingproductswithinengineeredtolerances IfCpk 1 thentheprocessismorethancapableofproducingproductswithinengineeredtolerances IfCpk 1 thentheprocessisnotcapableofproducing100 acceptableproducts IfCpkhasanegativevalue thentheoverallprocessaverageisoutsideofoneofthespecificationlimits CpandCpkarethesamevaluewhenevertheprocessiscentered WhenevertheprocessisnotcenteredCpk Cp OneSidedIndexes AMEXBorderExample ProcessCapability ProcessCapability Sigmalevelsvs dpm ControlCharts ThreestepprocessfromcontroltocapabilityCollectionProcessissampledtodefinebaselineControlTrialcontrollimitsarecalculatedbasedonsampledinformation assumesonlycommoncausesofvariationarepresent Theprocessissampledagainandthedatacomparedtothetrialcontrollimitstodetermin