6 Sigma GlossaryMaster.ppt

Glossary 4 Block aerror arisk Accuracy Active opportunityordefect AdvocacyTeam AlternateHypothesis ANOVA ANOVAmethod GaugeR R Assignablecausevariation AttributeChart Attributedata Average Graphicaltooltoshowtherelationshipbetweenprocesscapability controlvariationduetooutsideinfluences See BlackNoise StatisticalProcessControl SPC chartfordiscretedata Includesp np canducharts Datathatcanbedescribedbylevels integervaluesorcategoriesonly SeeDiscretedata Thesumofalldatainasampledividedbythenumberofdatapointsinthesample SeeMean berror brisk Baselining Benchmarking BlackBelt BlackNoise Boxplot Brainstorming Centring CentringofXvariables CentralLimitTheorem Theerrormadeifsamenessisclaimed whentherealityisdifference e g acceptingbadparts Consumer sRisk Therisk probability ofmakingabetaerror frequentlysetat10 Evaluatingthecapabilityofaprocessasitstandstoday without tweaking i e passiveobservation Evaluatingthecapabilityofsimilarprocessestoquantifywhatconstitutes theBest ApersonwhosefulltimejobconsistsofapplicationofSixSigmatools methodsonprojects Processvariationdueto outsideinfluences SeeAssignableCauseVariation Graphshowingtheportionofadistributionbetweenthefirstandthirdpercentileswithina box Theboxplotalsoshowsthemedianofthedistributionandtheextremevalues Oftenusedtocomparepopulation AtechniqueusedbyanAdvocacyTeamto fore g developalistofpotentialX satthebeginningofproject Aprocesscharacteristicdescribinghowwellthemeanofthesamplecorrespondstothetargetvalue AmethodusedtotransformXvariablesinDoE sthatdevelophigherorder quadratic models reducescorrelationbetweenX s Afundamentalstatisticaltheoremstatingthatthedistributionofaveragesofacharacteristictendstobenormal evenwhentheparentpopulationishighlynon normal CentralCompositeDesign Champion ChampionReview Chi Squaredtest ClassicalYield CommonCauseVariation ComponentsSearch Confidence ConfidenceInterval Consumer ContinuousData ADesignofExperiments DoE methodwhereeachXistestedat5levels see StarPoints ACCDprovidesthecapabilitytomodelaprocesswithaquadraticequationORalinearequation Typicallyadirector someonewhocansupporttheSixSigmaprojectandhastheauthoritytoremovebarriersandprovideresources TakesanactivepartinProjectReview AregularmeetingtopresentSixSigmaprojects shareexperiencesandremoveroadblocks Hypothesistestfordiscretedata Evaluatestheprobabilitythatcountsindifferentcellsaredependentononeanother ortestsGoodnessofFittosomeaprioriprobabilitydistribution See FirstPassYield GoodunitsproduceddividedbyTotalUnitsProduced See WhiteNoise Theinherentvariationofaprocess freefromexternalinfluences Usuallymeasuredoverashorttimeperiod AmethodofscreeningforVitalFewX sinmanufacturedassemblies Alsoknownas PartSwapping Thecomplementofalpharisk Confidence 1 a Arangeofplausiblevaluesforapopulationparameter suchasmeanorstandarddeviation Theenduserofaproduct thehomeowner fore g Theconsumerisexternaltothebusiness Datathatcanbemeaningfullybrokendownintosmallerandsmallerincrements e g length temperatureetc ContourPlot ControlLimits CostofQuality Cp Cpk CQ CTQ CubePlot Customer DataWindow Defect DependentVariable AgraphusedtoanalyzeexperimentsofaCentralCompositeDesign TwoX scomprisetheaxes andlevelsofconstantYareshowninthebodyofgraph Resemblesatopographicalmap LinesonaStatisticalProcessControl SPC chartthatrepresentdecisioncriteriafortakingactionontheprocess Linesaredrawn 3standarddeviations s fromthemean Afinancialreconciliationofallthecostsassociatedwithdefects scrap rework concessionsetc StatisticusedtomeasureProcessCapability Assumesdataiscentredontarget SimilarinconcepttoZ stStatisticusedtomeasureProcessPerformance Doesnotassumecentreddata SimilarinconcepttoZ ltCommercialQuality Usedtocategorizenon manufacturingprojectsthatimpacttheconsumerand orcustomer Critical to Qualitycharacteristic Anaspectoftheproductorservicethatisimportanttothecustomer consumer Agraphusedforanalysisoftheresultsofafactorialdesignedexperiment DoE Showstestconditionsthatoptimizetheresponse Therecipientoftheoutputofaprocess Maybeinternal e g Assemblyisacustomeroffinishingshops orexternal e g Currys Bellingetc whothensellourproductstoconsumers ThespreadsheetwindowinMinitabwheredataisenteredforanalysis Anyaspectofapartorprocessthatdoesnotconformtorequirements Theoutputofaprocess The Y response DescriptiveStatistics DesignofExperiments DoE DiscreteData Dotplot DPMO DPO DPU e ExponentialFunction Entitlement ExecutiveSummary F test Mean StandardDeviation Varianceandothervaluescalculatedfromsamplecharacteristics Alsoincludesassortedgraphs Astatisticalfieldofstudywhereindependentvariables X s aresystematicallymanipulatedandtheresponseobserved UsedtodemonstratewhichX saretheVitalFew andtooptimizetheresponse Datathatcanonlybedescribedbylevels i e pass fail operatora b c integervalues e g numberofdefects Datathatcannotbebrokendownintofinerincrements Frequencydiagramrepresentingdataby dots alongahorizontalaxis Generallyusedasanalternativetoahistogramforsmallsamplesizes DefectsPerMillionOpportunities 1 000 000multipliedbytotalnumberofdefects dividedbythetotalnumberofopportunities Ametricfordefectsequivalenttoppmusedfordefectives DefectsPerOpportunity totalnumberofdefectsdividedbytotalnumberofopportunities UsedtoentertheNormalTabletoobtainZvalues Defectsperunit totalnumberofdefectsdividedbytotalnumberofunits UsedprimarilytocalculateRolledThroughputYield Y rt throughthePoissonformulaY rt e DPU Amathematicconstantroughlyequalto2 718Mathematicalidentity ln e 1Z stThebesttheprocesscanbe WhattheprocesswouldlooklikeifallAssignableCauseVariationwascontrolled ThefirstpageofoutputfromtheMinitabProcessCapabilityselection Atesttocomparevariancesof2ormoresamples andtocomparetheequalityoftwoormoremeans inANOVA FactorialExperiment FractionalFactorialExperiment FirstPassYield FMEA FunctionalOwner GaugeXBRmethod GanttChart GaugeR R GreenBelt Ha Ho Adesignedexperiment DoE whichinvolvestestingofallpossiblecombinationsofindependent X variables Adesignedexperiment DoE whichinvolvestestingafractionofallpossiblecombinationsofindependent X variablesinafullFactorialexperiment Resultsinfewertestruns See ClassicalYield Equaltothenumberofgoodunitsproduceddividedbythetotalnumberofunitsproduced FailureModeandEffectsAnalysis ateam basedprocedurethatidentifiesanddocumentsallpossiblefailuremodes effects causesandassociatedcorrectiveactions Thepersonwithfinancialresponsibilityfortheprocessunderconsideration GaugeR Rmethod anoptioninMinitab Aprojectmanagementtoolthatgraphsmilestonesvs thecalendar Barsareusedtoindicatebothplannedandactualdurationoftasks Ameansofdeterminingtheacceptabilityofthevariabilityinthegaugingsystemforuseintheprocess ApersonwhousesSixSigmatoolsandmethodologyinthecourseoftheirwork andwhoalwayshasaSixSigmaprojectactiveintheirplaceofwork AlternateHypothesis hypothesisofdifference Thehypothesisbeingproveninastatisticalhypothesistest Nullhypothesis hypothesisofsameness Thestartingassumptioninastatisticalhypothesistest NB Thenullhypothesiscannotbeproved Histogram HomogeneityofVariance Hypothesistest I MRChart IndependentVariable Inferentialstatistics InherentProcessCapability Interactionplot Afrequencydiagramcomposedofrectangularbarswhoserelativeheightsindicatethenumberofcounts orrelativefrequency ataparticularlevel AmenuselectioninMinitabunderwhichtheF test comparisonofvariances isperformedAnyofseveralstatisticaltestsof2ormoresamplesfrompopulations Usedtodetermineiftheobserveddifferencescanbeattributabletochancealone Theresultofthetestistoeitheracceptorrejectthealternatehypothesis Ha t test F testandChi Squaredtestareexamples Individual MovingRangechart aStatisticalProcessControl SPC chartinwhichtheuppergraphisusedtoplotindividualdatapointscomparedtocalculatedcontrollimits thelowergraph MovingRange plotsthedifferencebetweensequentialdataaspointsonthechart Controllimitsarealsocalculatedforthischart Variables X s thatinfluencetheresponseofadependentvariable Y Statisticalanalysesthatquantifytheriskofstatementsaboutpopulations basedonsampledata Inferentialstatisticsareusuallyhypothesistestsorconfidenceintervals TheBesttheprocesscanbe withonlyvariationduetowhitenoisepresent SeeEntitlement Z stAgraphusedtoanalysefactorialandfractionalfactorialdesignsofexperiments IndicatestheeffectonYwhentwoX sarechangedsimultaneously ThegreaterthedifferenceinslopesbetweentheX s thegreatertheinteraction Kurtosis L1Spreadsheet L2Spreadsheet LCL LowerControlLimit LeverageVariable Linearity gauge Longtermdata LSL m Macro MainEffectsPlot MasterBlackBelt Comparisonoftheheightofthepeakofadistributiontothespreadofthe tails Thekurtosisvalueis3foraperfectnormaldistribution ExcelspreadsheetfordiscretedatathatcalculatessubsystemZvaluesand rolls themintoasystem levelZvalue ReplacedbyProductReportinMinitabrelease11 2ExcelspreadsheetforcontinuousdatathatcalculatesZ standZ ltReplacedbyProcessReportsinMinitabrelease11 2ThelowercontrolboundaryonaStatisticalProcessControl SPC chart Alimitcalculatedasthemeanminus3standarddeviations Note SEM StandardErroroftheMean isusedfors stdev s sqrt n AnXvariablewithastronginfluenceontheYresponse OneoftheVitalFew Thedifferenceintheaccuracyofthegaugefromthelowendtothehighendofthetestrange Dataobtainedinsuchawaythatitcontainsassignablecausevariation blacknoise LowerSpecificationLimitThemeanoraverageofapopulationAminiprogramwithinasoftwarepackagedesignedtoprovideaparticularoutput e g GaugeR R Agraphusedtoanalyzefactorialandfractionalfactorialdesignsofexperiments ComparestheeffectonYofanXatthe high levelvs itseffectatthe low level Slopeofthelineonthegraphindicatessignificance Acoach mentorandtraineroftheSixSigmamethodologiesandtools Mean MeasurementsSystemsAnalysis Median Minitab NormalCurve NormalProbabilityPlot Normalize NormalizedAverageYield NullHypothesis Orthogonal p value ParetoAnalysis Theaverage Maybetheaverageofasample x bar ortheaverageofapopulation m See GaugeR R Themiddlevalueofasetofdata the50thpercentile AstatisticalsoftwarepackagecontainingthemajorityofSixSigmatools Awidely used commonly seendistributionwheredataissymmetricallydistributedaroundthemean bellcurve Agraphicalhypothesistestinwhichsampledataiscomparedtoa perfectnormal distribution Ho thesampledataisthesameasthe perfectnormal distribution Ha thesampledataisdifferent i e non normal Theprocessofconvertingnon normaldatathroughtheuseofatransformationfunction Theaverageyieldofaprocesswithmultiplestepsoroperations Y na Y rt 1 nSee Ho Literally rightangles Afeatureofawell definedexperimentthatallowsmaineffectstobeseparatedfrom2 wayandhigherorderinteractions aswellasquadratic squared terms Theprobabilityofmakinganalpha a error Avalueusedextensivelyinhypothesistesting Alsoreferredtoasthe observedlevelofsignificance p valuesarecomparedtothe acceptable levelofalphariskinordertomakedecisionsinhypothesistests Aproblemsolvingtoolthatallowscharacteristicstoberankedindescendingorderofimportance ParetoPrinciple Passive opportunity defect PointofInflexion PoissonApproximation Population PoweroftheTest ppm PracticalProblem PracticalSolution Precision Pre Control PrincipleofReverseLoading Probabilityofadefectp d The 80 20 rule Theprinciplethat20 ofthevariablescause80 ofthevariation Adefectoropportunitythatiscounteduponoccurrence butthatisnotpartoftheactivemonitoringprocess Pointonthenormalcurvewhereitchangesfromconvextoconcave Mathematicallydefinedbysettingthethirdderivativetozero AmathematicalapproximationforRolledThroughputYield givenDPU Y rt e DPU Alldataofinterestforaparticularprocess recordedornot Usuallymodelledwithsamples Thelikelihoodofdetectingbeneficialchange Representedas1 b Theprobabilityofrejectingthenullhypothesis Partspermilliondefective AdiscretemeasurementofdefectivesforlongtermdataTheoutputoftheMeasurephase AcharacterizationoftheZvalue centringandspreadforY TheoutputoftheControlPhase TheoptimisedXlevelsandcontrolplantomaintaintheprocessatitshighestZvalue Howcloselythedataisclusteredaroundtheirmean Describesthespreadofthedata AStatisticalProcessControl SPC methodthatallowsanoperatortotakeactiononaprocessbasedonwherethepartmeasurementsfallinanormaldistribution Partsarecodedred yelloworgreen Planningahead Needtodefinewhatdoyouwanttoknow sowhattool testshouldbeused sowhatdatadoyouneed The tail areaofthenormalcurve beyondthespecificationlimit s ProblemStatement ProcessCapability ProcessCharacterization ProcessMap ProcessOptimisation ProjectHopper QFD Quartiles R bar d RandomCauseVariation Range RationalSubgrouping Abriefbutsuccinctdescriptionoftheissueunderinvestigation Includesthepracticalandbusinessreasonsfortheproject Astatisticthatnumericallydescribeshowwelltheprocesscouldperformintheabsenceof blacknoise Examples Z st CpUnderstandingtheY sandX sinaprocess DevelopedthroughthetoolsoftheDefine MeasureandAnalysephases Aproblemsolvingtoolthatgraphicallydescribeseachsteporphaseinaprocess DefiningthebestoperatingpointforX sinaprocess DevelopedthroughtoolsoftheImprove Controlphases AstackofpotentialSixSigmaprojects tobepickedupbyBlackBeltsorGreenBeltswhenresourcesallow QualityFunctionDeployment ArigorousmethodofdeterminingtechnicalrequirementsandCTQ sfromthedefinitionofConsumerCues Quarters ofapopulation 1 4ofthedatafallbelowthefirstquartile 1 4ofthedatafallabovethe3rdquartile Anestimateofstandarddeviationusingtherangeofthedataandtabledadjustmentfactors UsedincalculationofcontrollimitsinMinitabGaugeR RXbargraphicaloutput See WhiteNoise Theinherentvariationoftheprocess freefromexternalinfluences Thelargestvalueinadatasetminusthesmallestvalueinthedataset Adatacollectiontechniquethatallowstheseparationofshorttermvariationfromlongtermvariation Regression Repeatability Gauge Repetition Reproducibility Gauge ResponseSurfaceExperimentResolution Gauge Resolution FractionalFactorial RolledThroughputYield Astatisticalmodellingtoolthatallowsdatatoberepresentedbyanequation UsedforcontinuousYresponses usuallywithcontinuousXinputs ThereisspecialtechniquewithinMinitabcalledLogisticRegressionwhichhandlesspecialformsofdiscreteX s Abilityofagaugetoconsistentlymeasurethesamepartwiththesameresults PartoftheoutputofaGaugeRi e afeaturespecifiedwithaspecificationtoonedecimalplacewouldrequireagaugewitharesolutionoftwodecimalplacesetc Aromannumeralthatindicatesthedegreeofconfoundinginafractionalfactorialdesign Higherresolutionindicateslessconfounding i e lessambiguityinthesourceofeffects Y rtTheproductofyieldsateachstepofaprocess CanbeestimatedusingthePoissonApproximation s Sample SessionWindow Shift Shorttermdata Sigma s SixSigmaTeamMember Skewness Specification Spread Stability Gauge StandardDeviation Thestandarddeviationofasample Ameasureofspread orvariability ofthedata s sqrt S x xbar n 1 Acollection subset ofdataintendedtorepresentthecharacteristicsoftheparentpopulation Oneofthe4Minitabwindows Usedforcommandentryanddataoutput Thedifferencebetweenshort termandlong termprocessvariation Z shift Z st Z ltDataobtainedinsuchawaythatitcontainsNOassignablecausevariation blacknoise Onlytheinherentprocessvariationisrepresented whichallowscalculationofZ stThestandarddeviationofapopulation AstakeholderintheSixSigmaprocess Apersonwhoneedstohaveanunderstandingofthemethodology butdoesnotformallyusethetools Evaluationofthesymmetryofadistribution Skewness 0forperfectsymmetry skewnessisnegativeifthedistributionisshiftedtotheright positiveifshiftedtotheleft Therequirementsofadesign usuallyexpressedasatarget ornominal valuewithanassociatedallowabletoleranceforvariation e g 5 00cm 0 05cm Howfarthedataisdistributedawayfromtheirmean Consistencyofmeasurementvaluesobtainedwiththesamegaugeonthesamesetofparts withmeasurementstakenatdifferenttimes Gaugeinstabilitycanleadtocalibrationissues Astatisticalmeasureofspreadordispersionfromameanvalue StandardErroroftheMean StandardNormalDeviate StandardOrder StarPoint s StatisticalProblem StatisticalProcessControl StatisticalSolution Statistics StepwiseRegression StructureTree Thestandarddeviationofxbar basedonasamplesizeofn Alsoacorrectionfactorforstandarddeviationofrelativelysmallsamplesizes 30 Reducesthestandarddeviationofthesamplebysqrt n SEM s sqrt n See Ztransform AfeatureoffactorialDesignofExperiments DoE thatdeterminestheorderofthehigh lowsettingsoftheX sforeachrunofanexperimentbyusingapre determinedpatternof 1 sand 1 sforeachX ExtremetestpointsinaCentralCompositeDesignofExperiments Foundbytakingthefourthrootofthenumberof Cubepoints factorialpoints inthedesignandadding subtractingthisvaluefromtheCentrePoint TheoutcomeoftheAnalyzephase Istheproblemcentring spreadorboth SPC Agraphicalmethodofmonitoringaprocessanddeterminingstatisticallywhentheprocessrequiresattentionbycomparingittoahistoricalmeanandcalculatedcontrollimitsat 3sigma OutputoftheImprovephase WheredotheX sneedtobesettocontroltheY Thestudyofvariation includingmethodsofdescribing quantifyingandreducingvariation aswellasestimatingrisks Aregressiontechniquewherethemodelisdevelopedonestepatatime addingXvariablesoneatatimetothemodelinorderoftheircontributiontochangesinY Aproblemsolvingtoollistingthecharacteristicsofinterestononesideofthepage andshowingcontributingfactorstothecharacteristicsasbranches Subgroup SustainedProcessCapabilityt test Target TechnicalRequirement TestSensitivity d s Tolerance TOP TotalOpportunities Transfer Transform TrivialManyX s UCL UpperControlLimit Unit Asampleoflikepartsorrelateddatatakenconsecutivelythatcontainsonlyinherentprocessvariation whitenoise CapabilityofaprocessinthelongtermZ ltAstatisticaltestusedtocomparetwomeans ortocompareameantoastandardvalue ThespecifiedordesiredaverageofaprocessPhysicalorprocesscharacteristicthatmustbecontrolledtoaddressaConsumerCue alsoknownas TheGap Astatisticusedtodeterminesamplesizeforhypothesistesting Comparesthedifferenceinmeanstothespreadofthedata Theamountofvariationallowablebydesigninaprocess Tolerance USL LSL Numberofopportunitiesperunittimesthenumberofunits ThelastphaseofaSixSigmaproject whereknowledgegainedistransferredtoallothersimilarprocesses iesynergy Anymathematicalrelationshipusedtotranslatedataofonespaceintodataofanotherspace e g transformstoconvertnon normaldatatonormaldata log reciprocal powerfunc