6SIGMA绿带介绍1.ppt

6 綠帶介紹Introductionto6 GreenBelt Content1 6 QualitySystem Whyneed2 6 Overview3 6 Methodology3 1 6 Define3 2 6 Measure3 3 6 Analysis3 4 6 Improve3 5 6 Control4 Conclusion 1 6 QualitySystem Whyneed1 1 從產品飽受競爭威脅的觀點以美國為例 1975 1985年 日本挾其 反向技術 ReversedTechnology 優勢 將其高品質的產品推向美洲大陸 使得美國一向以經濟與技術領先的盟主地位發生動搖 在1970 1989年間美國的市場佔有率 電視機從50 降至2 收音機從50 降至2 汽車從78 降至28 影印機從90 降至20 照相機從90 降至5 鋼鐵從40 降至30 其結果造成美國貿易赤字每月高達80 100億美元 美國與日本製程能力之比較年代美國日本製程能力品質水準製程能力品質水準19700 672 1 003 1980初4 1980中4 5 1980末 1990初Motorola6 方法 不合格率3 4PPM相當於6 美國企業競爭力衰退的原因美國管理文化中含有不信任的氣氛 這種表現在嚴格的審核 管制 檢查的管理制度上 無形造成過度的管理成本上漲 這種管理監督的作風 大體受到泰勒式科學管理影響的結果 日本經營之神松下幸之助在一次對美國企業界人士發表演說 提到 你們的公司經營是以泰勒法則為基礎 更糟的是頭腦皆已泰勒化 因此堅信正確的管理 應是管理者在一邊 工人在另一邊 一邊的人只管思考 另一邊的只管工作 給你們一句忠告 管理是執行者將觀念轉移到員工身上的一種藝術 因此美國開始檢討其品質 各種品質系統亦相繼提出 其中包含6 品質系統 1 2 由需求的觀點在70年代 產品達到2 便達到標準 在80年代 品質要求已提升至3 但此標準美國會發生以下事件 每年有20 000次配錯藥事件 每年有超過15 000個嬰兒出生時會被拋落地上 每年平均有9小時沒有水 電 暖氣供應 每星期有500宗做錯手術事件 雖然3 合格率已達到99 73 的水平 但相信各位對以上品質要求並不滿意 所以有很多公司已要求 6 的品質管理 其合格品率為99 99966 在3 水準 由1000個零件組成的產品中 每15個產品中只有1個產品是好的 在6 水準則1000個產品卻有996 6好的 傳統以 百分率 水準作為設計品質水準 如今變更為以 百萬分率 ppm 作為衡量品質的水準 1 3 從成本的觀點 1 4 從時代趨勢的觀點 1 1 4 從時代趨勢的觀點 2 1 4 從時代趨勢的觀點 3 1 5 從品管大師的觀點J M Juran 1994年在美國品質管理學會年會會上說 20世紀以生產力的世紀載入史冊 未來21世紀是品質的世紀 1 6 從策點管理的觀點欲建立及維持組織競爭優勢 效率 創新 品質及顧客回應扮演著主要角色 1 7 從近代品質系統的觀點 ISO 9000Effectiveness 5 QS 9000Effectiveness 10 MalcolmBaldrigeGuidelineEffectiveness 25 EuropeanQualityAwardEffectiveness 30 TQMEffectiveness 35 6 TheLittleQEffectiveness 50 TheUltimate6 TheBigQEffectiveness 90 上述品質系統均於80年代末期開展出 6 TheUltimate6 將是一趨勢 2 6 Overview6 thewaytocreateprofit 6 History 1 6 History 2 6 History 3 6 InnovationModeling 3c customer competitor company 3e excitation employment entertainment 3p product process person TheManyAspectsof6 6 asaMetric Thehighlevelofsigma thelowertheprobabilityofproducingadefect 6 asaMetric 如何6 導入一般來說 從3 到4 階段 是企業可以自行改善的範圍 但這樣的品質標準並沒有辦法讓企業變得很有競爭力 從4 到5 階段 就必須找尋可以學習的標竿企業 Benchmarking 當成比較與學習的對象 而5 到6 階段時 品質就已經不是製造出來的 而是設計出來的 DFSS DesignforSixSigma 6 Activity6 activityistofindoutcriticalfactorstoquality CTQ atcustomer spointofviewandtoreducethedefectslessthan3 4DPMO PPM HowDoWeImproveProcessCapability Increasethetolerance Decreasethespreadorvariationoftheprocess Shifttheaverageby Centeringtheaverageifthespec hastwolimits Decreaseorincreasetheaverageforspec withonelimit TheCostOpportunity TheCostofPoorQuality COPQ WhoisImplementing6 Motorola1987TexasInstrument1988ABB AseaBrownBoveri 1993AlliedSignal1995GeneralElectric1995Kodak1995Siemens Nokia Sony1997 6 Benefits Case1 Motorolaover12yrsIncreaseproductivityanaverageof12 3 peryearReducedthecostofpoorqualitybymorethan84 Eliminated99 7 ofinprocessdefectsSavemorethan 11billioninmanufacturingcostRealizedanaverageannualcompoundedgrowthrateof17 inearnings 6 Benefits Case2 6 Benefits Case3 TexasInstrumentsBefore 1988 After 1993 COPQ30 7 DPMO1040009000Scrap 3M 0 3MYield84 5 98 9 CycleTime Week 114InventoryCost 3 9M 1 1M TheFocusof6 6 isApplyingOverallBusinessSystem 6 Methodology Whatisdifferentwith6 6 Organization 3 6 Methodology5StageofDMADVandNewTools DesignForSixSigma DFSS BlackBelt5StageofDMAICmethodology GreenBelt 3 1 DMAICMethodology5StagesDMAICMethodologyandStatisticalToolsPhase DefineSteps WorkBreakdownProjectTeam ProjectCharter COPQ DefineSteps 1 DefineSteps 2 1 Vision 最具競爭力的企業 GE Strategy TheThreeCircle GE DefineSteps 2 2 TheThreeCircle GE JackWelch GE SCEO 畫三個圓圈 核心 高科技與服務 GE公司未來都要在其中一個圓圈內 公司任何人不再任一個圓圈內者 未來將會被解雇 圓圈外的的事業將被整頓 關閉或出售 三個圓圈的策略讓GE公司找到焦點 不再是集團 似乎什麼都做 DefineSteps 3 DefineSteps 4 CTQ CriticaltoQuality只要是顧客要求的 就是關鍵品質 CTQ 亦稱為 重要成果 特殊限制 或流程的 Y變數 CTQ係任何會直接影響顧客對產品 服務品質觀感之因素 顧客 在乎 的事 就是企業或組織在乎的事 新點子 要採納 外部觀點 Outside inPerspective 即從顧客的眼光來看企業或組織的一切 3 2 DMAICMethodology5StagesDMAICMethodologyandStatisticalToolsPhase MeasureSteps WorkBreakdownGraphAnalysis CapabilityAnalysis ConfidenceInterval M1SpecifyProjectM1stepcoversthefollowings CTQBreakdownDefineScopeProcessMappingParetoAnalysisDefineperformanceindexandspecifications Thesuccessofany6 activitiesreliesmostlyontheCTQdefinitionandselection DefinePerformanceIndex ExampleofPerformanceIndexYieldCycletimeDefectrateMachinefailurerateCustomerstand byhoursNumberofinvoiceerrorsElapseTimefromloanapplicationtomoneytransfertothecustomeraccountHourstakenfromreceivingordertodeliverytothecustomer M2MeasurementSystemAnalysisM2stepcoversthefollowings StatisticfundamentalsSamplingplanDatacollectionplanVariationofmeasurementsystemGageR RstudyImprovementofmeasurementsystem Withoutaccuratemeasure can tidentifyanysymptomofproblem 6 Metrics DefinitionofDPMOterminologyDPU DefectperUnit Defect Anythingthatresultsincustomerdissatisfaction Anythingthatresultsinnonconformance DPO DefectperOpportunityDPMO DefectperMillionOpportunities ExampleofSigmacalculation DiscreteDataCase 1 Overthelastseveralyears youhavecollecteddataontripstotheairport Ofthe100tripssampled youhavemissedonly5flights IfyouindicatethissituationasSigmalevel whatwillitbe Sol DPU DPO 5 100 0 051 0 05 0 95 Z 1 65 level Z 1 5 1 65 1 5 3 15 Case 2 Anaccountingtermconductedaninternalauditforthefinancialreportofyear2001andtheyobserved25nonconformingrecord Therewere2stepsofprobableincorrectentryidentifiedintheprocessandthetotalobservationwas25 000records WhatisDPO WhatisDPMO WhatisSigmaleveloffinancialreportingprocess Sol DPU 25 25000 DPO 25 2 25000 0 002 DPMO 0 002 10 6 20001 0 002 0 998 Z 2 878 level Z 1 5 2 878 1 5 4 378 ExampleofSigmacalculation ContinuousData 1 ContinuousData 2 1 MathematicalModel 2 Continuous 3 Smooth 4 Symmetrical 5 TailasymptotictoX axis 6 Bellshaped 7 Mean Median Mode 8 Totalareaundercurve 1 1 Thenormaldistributionhasthefollowingproperties 68 27 ofthedatafallwithin 1 95 45 ofthedatafallwithin 2 99 73 ofthedatafallwithin 3 2 Inordertoassessthequalityoftheprocess wemustcomparetheprocesscharacteristics viathelocation spreadandshape tothespecificationlimitsandtargetedvalue ContinuousData 3 StandardizationofNormalDistributionTheSigmaofaProcessisthenumberofstandarddeviationbetweenthemeanandtheSpecificationLimits MeasurementSystemandMeasurementError 1 Measurementsystemisviewingasaprocess Sourcesofvariation 5M1EValidatepossiblesourcesofvariationinthemeasurementprocess VariationinmeasurementSystem MeasurementSystemandMeasurementError 2 ProcessVariance GageR RAnalysisTypesofGageR RAnalysisMethodologyXbar RMethodANOVATypesofVariationestimationbytheGageR REquipmentVariation EVAppraiser Operator AV GR RDecisionandImprovementDirectionGageR RDecisionCriteria GR R 10 Goodmeasurementsystem 10 GR R 30 Maybeused GR R 30 Cannotused GageimprovementdirectionForrepeatabilityerror reproducibilityerror Needtotakenanactiontooperator Forreproducibilityerror repeatabilityerror Needtotakenanactiontogage M3IdentifySigmaLevelM3stepcoversthefollowings DatastratificationGraphicalcauseeliminationUnderstandproductcapabilityandproductperformanceCalculatecurrentsigmalevel GraphicalAnalysisRunChartScatterDiagramBoxPlotHistogramChangestotheprocessmaybemoreeasilyrecognizedgraphicallythantabularly UnderstandingVariationWhatisVariation DifferentoutcomeofaprocessorresultofaproductorserviceMeasurementindexscatteredfromcentervalueVariationwillbeappearedineveryprocessandthetargetofimprovementistoreduceit svariationsWhyavariationmightbeoccurred Byacommoncause Byaspecialcause 5M1EWhatimpactswillbehappenedifavariationbecomebig Cannotpredict forecastoutcomesOccurre inspectionand orreworkDelayschedule Increasecustomerdissatisfaction Short Term Long TermRelationships Overtime a typical processwillshiftanddriftbyapproximately1 5 ProductCapability Cp Cp USL LSL 6 st Zst 3CpCapabilityIndex Cpk Cpu min Cpu Cpl WhereCpu USL 3 st Cpl LSL 3 st Zst 3CpkPerformanceIndex Cpk Cpk min Cpu Cpl WhereCpu USL 3 lt Cpl LSL 3 lt Zst 3Cpk 3 3 DMAICMethodology5StagesDMAICMethodologyandStatisticalToolsPhase AnalysisSteps WorkBreakdownRegression HypothesisTest ANVOA Multi VariAnalysis A1SetUpImprovementGoalInM3thecurrentproductcapabilitiesweredefined StepA1providesthetoolstodefineperformanceobjectives Thepurposeistosetobjectivetoestablishabalancebetweenimprovingcustomersatisfactionandavailableresources A1stepcoversfollowingtopics EstablishperformancegoalsAssessshort termandlong termsigmaintermsofmeasurementbenefits decreaseinCOPQ increaseinrevenue Determineimprovementdirectionbythegapanalysisbetweengoalandcurrentlevel PathstoPerformanceGoals Benchmarking WhyBenchmark 1 Benchmarking WhyBenchmark 2 DevelopandImprovementStrategicGoalsEstablishactionableobjectivesProvidesenseofurgencyEncouragebreakthroughthinkingCreateabetterunderstandingofyourindustry A2IdentifyPotentialCausesFindingmainindependentvariables andmakingthelistY f X ObjectMakelistofpotentialindependentvariablesforchanging improvingYvaluemethodsBrainstorming Cause EffectDiagramMulti voting LogicTree Cause EffectDiagram VariationinmeasurementSystem A3VerifyPotentialCausesOverviewInstepA3 settingapossiblepriorityastotheirimportancetothepotentialvariables X s identifiedinstepA2PurposeGeneralalistofimportantfactors vitalfew fromthepotentialvariables ParetoAnalysis CorrelationAnalysisAstatisticalanalysistoinvestigate measurementofassociationbetweentwovariables X Y iscalledanalysis CorrelationtellsyouthetrendofYwhenXvalueincrease decrease CorrelationAnalysisusingScatterDiagramAnalysis CorrelationcoefficientindicatesclosenessofarelationshipbetweenXandY RegressionAnalysis 1 Y f x1 x2 x3 RegressionAnalysis 2 TypesofRegressionModelSimplelinearregressionMultiplelinearregressionNon linearregression Y f X Y dependentvariableX independentvariable TypesofHypothesisTest 3 4 DMAICMethodology5StagesDMAICMethodologyandStatisticalToolsPhase ImproveSteps WorkBreakdownOperatingWindow ResponsibilityMatrix I1IdentifyVitalFewInanalysisstep potentialX swereidentifiedwithapossibleprioritizationastotheirimportanceincontrollingY ImprovestepprovidestoolstoexploreanduncoverthevitalX sandhowmuchimpacteachXhasontheresponseY OneofthemajortoolsusedforbothexplorationandconfirmationoftheeffectofX sontheY sisexperimentaldesign DOE Bye