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TheBasicSeven(B7)ToolsofQualityAPowerPointTrainingPresentationByKeithH.Cooper"Asmuchas95%ofqualityrelatedproblemsinthefactorycanbesolvedwithsevenfundamentalquantitativetools."-KaoruIshikawa
WhataretheBasicSevenToolsofQuality?FishboneDiagramsHistogramsParetoAnalysisFlowchartsScatterPlotsRunChartsControlChartsWheredidtheBasicSevencomefrom?KaoruIshikawaKnownfor“DemocratizingStatistics”TheBasicSevenToolsmadestatisticalanalysislesscomplicatedfortheaveragepersonGoodVisualAidsmakestatisticalandqualitycontrolmorecomprehendible.TheBasicSeven(B7)ToolsofQualityFishboneDiagramsNostatisticsinvolvedMapsoutaprocess/problemMakesimprovementeasierLookslikea“FishSkeleton”ConstructingaFishboneDiagramStep1-IdentifytheProblemStep2-Draw“spine”and“bones”Example:HighInventoryShrinkageatlocalDrugStoreShrinkageConstructingaFishboneDiagramStep3-IdentifydifferentareaswhereproblemsmayarisefromEx.:HighInventoryShrinkageatlocalDrugStoreShrinkageemployeesshopliftersConstructingaFishboneDiagramStep4-IdentifywhatthesespecificcausescouldbeEx.:HighInventoryShrinkageatlocalDrugStoreShrinkageshopliftersAnti-thefttagspoorlydesignedExpensivemerchandiseoutintheopenNosecurity/surveillanceConstructingaFishboneDiagramEx.:HighInventoryShrinkageatlocalDrugStoreShrinkageshopliftersAnti-thefttagspoorlydesignedExpensivemerchandiseoutintheopenNosecurity/surveillanceemployeesattitudenewtraineetrainingbenefitspracticesConstructingaFishboneDiagramStep5–Usethefinisheddiagramtobrainstormsolutionstothemainproblems.TheBasicSeven(B7)ToolsofQualityHistogramsBarchartUsedtographicallyrepresentgroupsofdataConstructingaHistogramFromasetofdatacomputesummean(x)MaxMinRange(max-min)ConstructingaHistogramUserangetoestimatebeginningandendCalculatethewidthofeachcolumnbydividingtherangebythenumberofcolumnsRange#ofColumns=WidthAcmePizzaExampleLet’ssaytheownerwantsadistributionofAcme’sThursdayNightSalesDataSetfromlastThursday(slices)0212241312122434143223212212214221212212121212121222121211222314223222123224224412223221224212421721223121121222122121222424AcmePizzaExampleMean=2.032258Max=7 Min=0Range=7QuestionFor7columnswhatwouldthewidthbe?Range/Columns=7/7=1sliceAcmePizzaExampleSlicesofPizza#timesorderedHistogramConstructingaHistogramHowisthishelpfultoAcme?2slicesofpizzamostcommonorderplacedDistributionofsalesusefulforforecastingnextThursday’slatenightdemand IfyouwereanAcmemanagerhowcouldyouapplythisinformation?TheBasicSeven(B7)ToolsofQualityParetoAnalysisVerysimilartoHistogramsUseofthe80/20ruleUseofpercentagestoshowimportanceAcmePizza(part2)Slices
Frequency
%0 1 .3
1 33 13.09
2 65 25.79 3 8 3.17
4 12 4.76 5 0 0 6 0 0 7 1 .3AcmePizza(part2)ThecompletedParetoAnalysisresultsinthefollowinggraph:SlicesofPizza#timesordered2143756AcmePizza(part2)CriticalThinkingHowdoestheParetoAnalysisdifferfromtheHistogram?HowcanthisbeausefultooltotheAcmeboss?The咽Basi朴cSe车ven敲(B7)殿Too卷lso趣fQu态alit诊yFlow注char裤tsAgr血aphi融cal选pict胶ure笔ofa饼PRO思CESSProc环essDeci镇sionThe贩proc阻ess助flowFlow衫char塔tsDon螺’t壁For茅get乔to花:Defi屡nes贷ymbo洋lsb妻efor昆ebe着ginn蕉ingStay级con必sist稼entChec步kth皆atp腔roce谁ssi恒sac骗cura浊teAcm急eP艳izz仪aE隶xam腰ple悉(Fl钱owc友har患t)Win坑dow旁T萝ake窃Cu输sto狠merMone换y?(sta究rt)姨Orde淹rGet辩Pi江zzaLock加upPut京More贺inOve双n补2P礼iesAva陡ila趴ble绕?Tim喂etoc币lose炭?Tak迫et丸oC性ust芦ome浆rnoyesnoyesnoyesHow瓜ca笼nw富eu忘se仍the借fl汽owc撞har赴tt闹oa驱nal课yze留im服pro袭vem俗ent欢id刃eas很fr旱om捉the删Hi施sto孔gra驳m?Win招dow能T因ake俘Cu鸡sto鲁merMone皱y?(st侨art叹)瓣Or仿derGet倘Pizz销aLoc随kupPut倒Mo红re厨inOven季2P但iesAvai你labl悉e?Timetoc粪lose胳?Take贫to诊Cust村omernoyesnoyesnoyesWan听ts肢ome单pr角act饥ice掠?Mak株ea斧fl榨owc地har终tf仁or:Taki呈nga崭sho邪werCoo户kin淘gd秤inn扛erDri蜓vin勤ga蹄ca铺rHav劲ing薪a住par鞋tyCre恶ati搭ng闸aF迎low笋cha运rtAny海othe惜rpr容oces茂ses偶you境can腰thin挣kof怨?The拥Ba偏sic消Se护ven眠(B倡7)嚼To齐ols薯of及Qu场ali撒tySca躬tte朴rP枣lot畏s2Di按mens钓iona球lX/生Ypl筒otsUse亦dt拾os撞how怪re把lat璃ion芒shi湾pb语etw费een如in捧dep唇end谢ent炮(x)挖an调dd默epe磨nde肉nt(毯y)弦var什iab雷lesAcme咽Piz皂za积(Sc雅atte百rDi寄agra亏m)Min钞ute彻sC锁ook草ing柏De色fec周tiv强eP郊ies10脆145青830吸575落2060触1420钩425融6In搏thi尿ss万imp历le写exa翼mpl锦e,旷you父ca滨nf误ind死th傍ee砖xis栋tin践gr枯ela仇tio肯nsh咸ip段wit毕hou论tm绒uch塘di缺ffi屑cul揉ty叛but雀…Sca见tte汉rD明iag镇ram仓sEas挪ier办to盛se托ed样ire伪ct性rel组ati怀ons起hipTim顿eC刃ook经ing岭(m宿inu嚷tes好)Def煎ect谣ive棒Pi悟zza桌sSca片tte以rD旁iag押ram匙sAs耻aq解ual伴ity仁to颂olWha嘱td跌oes孟th漂is劳tel挣lA勾cme嚷ma凑nag降eme抽nt横abo疯ut倒the潜ir烧pro崖ces矮ses足?Imp敞rov迎eme睬nts皱?Time诱Coo帅king俭(mi即nute芽s)Defe归ctiv尤ePi晓zzasThe师Ba罪sic壮Se慕ven坦(B覆7)捎To很ols童of叔Qu侵ali升tyRun劳char另tsTime辰-bas朗ed(雄x-ax脑is)Cycl您icalLook疯for痕pat迈tern断sRun胡Char寻ts89毕10奴11彼12采1复23撒489堡101暮112旺12开3489永101塔112薯12移34PM-蔑AMPM-钢AMPM-泊AMThur文sdayWee拘k1Thu王rsd滨ayWee动k2Thu界rsd商ayWeek枕3510雷15括202怀530蚕35暖404篇550庸55粉606境570捡75岛808闷590棵95朴100Sli池ces远/ho崇urTimeThe稠Ba乔sic挥Se惨ven膛(B辅7)恳To冻ols傲of所Qu袖ali吧tyCon矿tro斤lC办har语tsDev敲iat钱ion样fr俊om免Mea焦nUppe晒ran墙dLo斜wer霸Spec赶’sRan内geCont府rol带Char姜tsUppe拖rLi爱mitLowe丑rLi漠mitUna累cce婚pta典ble纺de堪via府tio配nXCont怠rol寇Char席tsAcm抽eP隆izz羽aM砌ana愈gem众ent秒wa旅nts恳to膝ge露ti膝no仿nt捕he腾con赛tro炭lc们har疗ta颜cti醒onAver眠age变Diam哭eter悦=1慕6in肥chesUppe角rLi津mit易=17奖inc乏hesLow浊er椒Lim题it惭=1脉5i数nch越esAcme准exa花mpleCont症rol律Char骡tsUppe完rLi错mit17i
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