六西格玛046sigma工具其它未分类

6西格玛绿带培训教材-6-4-20246标准偏差第一天(定义阶段):-6西格玛及精简优化与COQ的关系COQ的脑力风暴-FirstPassYieldExerciseIIIPO和流程flowdiagram流程analysisofdroppingcardsontotargetRepeatingtheexerciseResults和discussions-西格玛培训中的某些质量改进工具

脑力风暴技术

第一天wrapup第二天(降低变差):-Thepowerofparetochart和the80/20rulesConstructionofaParetoChartusingcomputer

流程flowdiagram和itsassociatedsymbolsTwo实例of流程flowdiagram(usingacommonscenario)Barriersthathinder6西格玛implementation-----------------------------------Break----------------------------------------------WhatisFMEAExampleofFMEAGroupexerciseonFMEAofbarriersto6西格玛implementationFMEApresentations-----------------------------------Lunch----------------------------------------------第二天(VarianceReduction):-Conceptofprecision和accuracyHowdoesitlinktothemean和标准偏差Precision和accuracyexample(i.e.Selectionoffundmanager)StatapultexerciseI-----------------------------------Break-----------------------------------------------Introducingconceptofvariancereduction(i.e.PF/CE/CNX/FMEA/SOP)-Variancereduction脑力风暴exerciseforStatapultStatapultexerciseIIComputationofStatapultexerciseresultaftervariancereductionDiscussionofvariancecontributors-第二天wrapupDAY3(MeasurePhase):RecapofstatisticalterminologyHistogram和anormaldataConstructionofhistogramTransformationofdataCalculateCp,Cpkfromnon-normaldata-----------------------------------Break----------------------------------------------DAY3(MeasurePhase):

TheimportanceofgoodmeasurementDirect和indirectmeasurement(i.e.Introductiontoscatterdiagram)Riskofwronginterpretation-Underst和varianceconceptinmeasurementsystem-IntroductiontoGaugerepeatability和reproducibility(GR&R)-----------------------------------Lunch-----------------------------------------------GR&RexampleCalculationofmeasurementvarianceRulesofthumbinGR&RCalculationofGR&Rwithcomputer-----------------------------------Break----------------------------------------------GraphicalinterpretationofGR&RBalls’circumferencemeasurementexerciseResult和discussiononmeasurementexerciseTheAnovamethodofGR&RDay3wrapupDAY4(Measure+AnalyzePhase):

IntroductiontoGR&RanalysisonattributedataExampleofattributedataGR&RAttributeGR&RexerciseResult和discussiononexercise-----------------------------------Break----------------------------------------------ComputingattributedataGR&Rusingthecomputer

为什么variabledataisbetterthanattributedataConvertingattributedatatovariabledataExampleofattributedataconversion(i.e.WuFan’sprojectonreducingbubbledefect)Introductiontoprobabilitytheory-----------------------------------Lunch----------------------------------------------Probabilityapproach(classical,relativefrequency)ProbabilityrulesProbabilitiesunderstatisticalindependence(Marginal,Joint,Conditional)Exercise-----------------------------------Break----------------------------------------------ProbabilityunderconditionsofstatisticaldependenceExerciseIntroductiontoprobabilitydistributionsDay4wrapupDAY5(AnalyzePhase):

Whatisabinomialdistribution?ConditionsfortheuseoftheBernoullitrials(流程)GraphicalillustrationofabinomialdistributionMeasuresofcentraltendency和dispersionforbinomialdistributionProbabilitycalculationwithbinomialdistributionBinomialdistributioncasestudiesCharacteristicsofthePoissondistributionProbabilitycalculationusingPoissondistributionPoissondistributionasanapproximationofbinomialdistribution-----------------------------------Break----------------------------------------------PoissondistributioncasestudiesIntroductiontonormaldistributionCharacteristicsofnormaldistributionAreasunderthenormalcurveTheuseofnormalprobabilitydistributiontableProbabilitycalculationusingnormaldistributionNormaldistributioncasestudiesUsingcomputertocalculateprobabilityofdifferentdistribution-----------------------------------Lunch----------------------------------------------DAY5(AnalyzePhase):

Randomsampling:BasisofstatisticalinferenceIntroductiontosamplingdistributionsConceptofstandarderror和samplingfromnormalpopulationProbabilityofthesamplemeanCentrallimittheoremExercisetodemonstratecentrallimittheoremResult和discussion-----------------------------------Break----------------------------------------------Confidenceinterval和populationmeanestimationContinuousdataDiscretedataDeterminingsamplesizeContinuousDataDiscreteDataFinite和infinitepopulation和theassociatedimpacttotheconfidenceintervalExerciseonconfidenceintervalDay5和week1wrapupExercise:SubgroupSize和FrequencySupposeyouhavea流程withCpk=1.33.Youwanttoguardagainsta流程meanshiftthatwillproduce3%defectivewithabout90%confidence.Whatsubgroupsizeshouldyouuse?Youknowthefollowingaboutyour流程Itruns2shifts/day和5days/weekEachshiftproducesabout400partsAscrappedpartrepresentsalossof$5Histo