six sigma测度

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1、Click to edit Master text styles,,Second Level,,Third Level,,Fourth Level,,Fifth Level,,Click to edit Master title style,,Proprietary to Samsung Electronics Company,Six Sigma,测度,-,26,Rev. 2.0,Six Sigma,测度,,(,Metrics),Measure,Define,Analyze,Improve,Control,方法论,,Measure,概要,,,,Project Y,,,基础统计,,,测定,Sys

2、tem,分析,,Six Sigma,测度,,,工程能力分析,,,Process Map,&,特性要因图,,,FDM,,Six Sigma,测度,,学习目标,,理解,Six Sigma,的主要测度,,DPU, DPO, DPMO,,FTY, RTY, Y,NOR,,,理解,,,水准计算方法,DPU,与,DPMO,DPO (,Defects per Opportunity),,,每机会缺陷数,DPU (,Defects per Unit),,,每单位缺陷数,,为了消除缺陷的基准,,工厂单位改善中使用,DPMO (Defects per Million Opportunities),,每百万机会缺

3、陷数,,考虑制品复杂度的比较基准,,企业内或企业之间,Benchmarking,手段,Opportunity,Defects,DPO,=,Unit,Defects,DPO,=,制品,部品,2,部品,1,组装品,,A,组装品,,B,特性,,B,特性,,A,,机会,(Opportunity),,,,,有可能发生缺陷的检查或试验对象,,,发生缺陷的机会在特性、部品、组装品等制品任何阶段 都可能存在。,,,复杂度,(,Complexity ),,总机会数与制品或,Process,复杂度正比。,部品,特性,材料,机械,工具

4、,次序,制品,Process,复杂度,相关部品、特性或材料越多，,,成为越复杂的制品。,相关机械、工具或次序越多，,,成为越复杂的,Process.,,DPU/DPO/DPMO,以下,Drilling,加工的例中计算,DPU,与,,DPMO,.,①,Data,类型是,?,,②,Unit,?,,③,Defect,?,④,Opportunity,?,,⑤,DPU,?,,⑥,DPMO,?,,DPU,计算,总生产单位数,总缺陷数,=,DPU,,DPU,不能考虑一个单位里有多少缺陷机会。,,DPU,=,=,=,DPMO,总缺陷发生机会数,总缺陷数,×1,000,000,=,DPMO,=,,DPMO,计算

5、中心,,一般表示,6,,水准时，比不良率,3.4,ppm,更恰当的是,3.4,DPMO,,DPU/DPO/DPMO –,事例,,使用互相不同的,Process、,制品之 间或制造领域和非领域之间比较时更 恰当。,,DPMO,计算例,,缺陷数,,,某个,PCB,以,800,个焊接点和,200,个部品构成。在这,,PCB,中发现焊接不良,6,处和不良部品,2,个。,,DPMO,是,?,,,DPMO = (6+2)/(800+200),,*,百万,,,= 8,000,DP(M)O,计算时注意事项,,,,例,),某部品的生产工程中不良发生机会数是,100,000,次，但正常作业,,过程中只对

6、其中,1,000,次机会进行评价，结果一个部品里发现,10,个,,缺陷，以下计算中哪个正确？,DPO = 10 / 100,000,DPO = 10 / 1,000,机会是有缺陷发生的可能性，以检查及试验对象被评价时,,Count,,Yield,在工程的各阶段中包括再作业或部品的废弃等不良的管理指标，,,,是良品率的概念。,,初期数率,(,FTY, First Time Yield) :,,决定各别工程的品质水准时使用。再作业,/,不修理的,Process,中适用。,,,,累计数率,(,RTY, Rolled Throughput Yield) :,,表现全体工程的品质水准时使用的指标中的一个

7、。,,表现为初期数率的倍。,,,标准化数率,(,Y,Nor,,,,Normalized Yield) :,,表现全体工程的品质水准时使用的指标中的一个，,,在,Process,中初期数率适用几何平均概念。,Yield(,数率,),的种类,,Yield (,数率,),45,000,ppm,浪费,30,000,ppm,浪费,56,000,ppm,浪费,95.5%,数率,97%,数率,94.4%,数率,RTY = 0.955*0.97*0.944 = 87.4%,不但是最终阶段，其它各阶段的能力,RTY,都重视。,,Hidden,,Factory :,再作业,/,废弃,,Yield,RTY,计算,,

8、已知的,DATA,是缺陷,DATA,时,,是意味着泊松分布中,P[X = 0] (,没有一个缺陷的概率,).,-,e,DPU,,已知的,DATA,是不良率或数率,DATA,时,,Yield,RTY = FTY,1,,FTY,2,,,FTY,3,,……,,Y,Nor,,计算,Y,Nor,= FTY,1,,,FTY,2,,…,,FTY,n,n,,RTY,计算的检讨,,生产一个单位部品所需,3,个工程，各工程的初期数率为,,FTY,i,,.,,,各单位工程中发生的缺陷数，跟随着拥有以下概率函数的泊松分布。,,DPU,dpu,dpu,dpu,e,e,-,+,+,-,=,=,),(

9、,3,2,1,-,但,,,为,,单位工程,,i,的,,平均缺陷数,,因此各单位工程的数率是在工程中一个缺陷都不发生的概率，如下。,i,dpu,e,X,P,-,=,=,=,],0,[,,按以上的结果，可以确认下面的等式。,,3,,RTY,=,,FTY,i,,,,i,= 1,,,Yield,想想下面的工程。,收入检查,,100%,数率,SMD,检查,,95.5%,数率,PBA,检查,,97.0%,数率,ICT,,94.4%,数率,,RTY = 1.00,× 0.955 × 0.970 × 0.944,,= 0.874 = 87.4 %,Y,%,7,.,96,967,.,0,874,.,0,R

10、TY,4,1,4,Nor,=,=,=,=,,Yield (,数率,),,Yield,例,1),现场某一位机士对特定类型,Defect,观察结果,346,个生产单位中发现,1,个。对这特性,(,Characteristic),的,,RTY,?,0.00289,346,1,=,=,DPU,00289,.,0,= 0.99711,=,=,-,-,e,e,RTY,DPU,计算,),例,2),有,3,个工程组成的生产,Line，,各工程通过数率为,,98%,时,Line,的,,RTY,?,计算,),0.98,0.98,0.98,RTY = 0.98,× 0.98 × 0.98 = 0.941192,,

11、RTY,计算 例,,Yield,,,水准,,,水准,:,规格中心和规格上限或下限距离是标准偏差的几倍？,,,,水准的概念,,,品质特性值随正态分布时，从规格中心到规格界限为止距离相当于标准偏差几倍的测度，,Process,散布越小，,,水准越大。,m,1,s,3,s,平均,规格界限,,,测定,Process,可以生产多少均一品质制品能力的测度。,1,s,m,1,s,发生不良限度,3,s,平均,记号前数值,(,Z),,越大，不良发生,,的概率越小。,6,s,规格界限,3,,水准的,,稳定工程,6,,水准的,,稳定工程,,由于作业者倒班、原材料,LOT,变化、设备的保,,养、模具交换

12、等现实性的变化要素而规格中心和,P,rocess,平均常时间维持一致状态是很难。,,,根据经验随着时间过,,去，一般,Process,平均离中心约,,1.5,,左右变动,。,,Process dynamics,LSL,USL,起点,1,起点,2,起点,3,起点,4,Process,,固有能力,长期,,Process,能力,目标值,,,,水准和,,DPMO,,,,,考虑,Process,现实变化，计算对应,,水准的缺陷率或不良率时，,,Process,平均离规格中心标准偏差的,1.5,倍变动为前提计算。,,,在,6,,水准,Process,中，,100,万次机会中能发生,3.4,次左

13、右缺陷，这相当于,3.4,DPMO,.,USL,LSL,规格中心,3.4,DPMO,4.5,.st,1.5,.st,999,999.6,,999,995,,999,991,,999,987,,999,979,,999,968,,999,952,,999,928,,999,892,,999,841,,999,767,,999,663,,999,517,,999,313,,999,032,,998,650,,998,134,,997,445,,996,533,,995,339,,993,790,,991,802,,989,276,良品数,3.4,,5,,9,,13,,21,,32,,48,,7

14、2,,108,,159,,233,,337,,483,,687,,968,,1,350,,1,866,,2,555,,3,467,,4,661,,6,210,,8,198,,10,724,ＤＰＭＯ,6.0,,5.9,,5.8,,5.7,,5.6,,5.5,,5.4,,5.3,,5.2,,5.1,,5.0,,4.9,,4.8,,4.7,,4.6,,4.5,,4.4,,4.3,,4.2,,4.1,,4.0,,3.9,,3.8,Ｚ.,st,4.5,,4.4,,4.3,,4.2,,4.1,,4.0,,3.9,,3.8,,3.7,,3.6,,3.5,,3.4,,3.3,,3.2,,3.1,,3.0,,2

15、.9,,2.8,,2.7,,2.6,,2.5,,2.4,,2.3,Ｚ.,lt,986,097,,977,250,,971,284,,964,070,,955,435,,945,201,,933,193,,919,243,,903,199,,884,930,,864,334,,841,345,,815,940,,788,145,,758,036,,725,747,,691,462,,655,422,,617,911,,579,260,,539,828,,500,000,,460,172,良品数,13,903,,22,750,,28,716,,35,930,,44,565,,54,799,,66,

16、807,,80,757,,96,801,,115,070,,135,666,,158,655,,184,060,,211,855,,241,964,,274,253,,308,538,,344,578,,382,089,,420,740,,460,172,,500,000,,539,828,ＤＰＭＯ,3.7,,3.5,,3.4,,3.3,,3.2,,3.1,,3.0,,2.9,,2.8,,2.7,,2.6,,2.5,,2.4,,2.3,,2.2,,2.1,,2.0,,1.9,,1.8,,1.7,,1.6,,1.5,,1.4,Ｚ.,st,2.2,,2.0,,1.9,,1.8,,1.7,,1.6,

17、,1.5,,1.4,,1.3,,1.2,,1.1,,1.0,,0.9,,0.8,,0.7,,0.6,,0.5,,0.4,,0.3,,0.2,,0.1,,0.0,,-0.1,Ｚ.,lt,420,740,,382,089,,344,578,,308,538,,274,253,,241,964,,211,855,,184,060,,158,655,,135,666,,115,070,,96,801,,80,757,,66,807,,54,799,,44,565,,35,930,,28,716,,22,750,,17,864,,13,903,,10,724,,8,198,良品数,579,260,,61

18、7,911,,655,422,,691,462,,725,747,,758,036,,788,145,,815,940,,841,345,,864,334,,884,930,,903,199,,919,243,,933,193,,945,201,,955,435,,964,070,,971,284,,977,250,,982,136,,986,097,,989,276,,991,802,ＤＰＭＯ,1.3,,1.2,,1.1,,1.0,,0.9,,0.8,,0.7,,0.6,,0.5,,0.4,,0.3,,0.2,,0.1,,0.0,,-0.1,,-0.2,,-0.3,,-0.4,,-0.5,,

19、-0.6,,-0.7,,-0.8,,-0.9,Ｚ.,st,-0.2,,-0.3,,-0.4,,-0.5,,-0.6,,-0.7,,-0.8,,-0.9,,-1.0,,-1.1,,-1.2,,-1.3,,-1.4,,-1.5,,-1.6,,-1.7,,-1.8,,-1.9,,-2.0,,-2.1,,-2.2,,-2.3,,-2.4,Ｚ.,lt,6,210,,4,661,,3,467,,2,555,,1,866,,1,350,,968,,687,,483,,337,,233,,159,,108,,72,,48,,32,,21,,13,,9,,5,,3,良品数,993,790,,995,339,,9

20、96,533,,997,445,,998,134,,998,650,,999,032,,999,313,,999,517,,999,663,,999,767,,999,841,,999,892,,999,928,,999,952,,999,968,,999,979,,999,987,,999,991,,999,995,,999,997,ＤＰＭＯ,-1.0,,-1.1,,-1.2,,-1.3,,-1.4,,-1.5,,-1.6,,-1.7,,-1.8,,-1.9,,-2.0,,-2.1,,-2.2,,-2.3,,-2.4,,-2.5,,-2.6,,-2.7,,-2.8,,-2.9,,-3.0,Ｚ

21、.,st,-2.5,,-2.6,,-2.7,,-2.8,,-2.9,,-3.0,,-3.1,,-3.2,,-3.3,,-3.4,,-3.5,,-3.6,,-3.7,,-3.8,,-3.9,,-4.0,,-4.1,,-4.2,,-4.3,,-4.4,,-4.5,Ｚ.,lt,,,,水准与,,DPMO,表,,水准计算,,,,缺点数,DATA,时,,-,求,DPMO,,,-,表中找出对应,DPMO,的,Z.st,值或利用,,Minitab,求。,,DATA,是不良率时,,-,以不良率可以计算出,PPM,.,,PPM = (,不良率,)×1,000,000,,-,从表中找出与,PPM,值一样的对

22、应,DPMO,的,,Z.st,值或利用,,,Minitab,求。,,DATA,是数率时,,,-,Scrap,、,,再作业等为不良计算数率。,,-,从表中找出对应每百万个良品数,= (,数率,)×1,000,000,,,Z.st,值或利用,Minitab,求。,,,水准的计算,,例,,例题,1.,,,对某工程生产出货的制品特征类型缺陷，,,,经过长期调查结果,346,个制品中发现,1,个缺陷。,,计算在这工程的缺陷相关的,,水准。,,,计算,× 1,000,000,346,1,DPMO,= 2,890,=,从,,表中能查出对应,,DPMO=2,890,的,Z.st,值,,4.26

23、,,,左右,,利用,Minita0b,计算,:,实习,,例题,2,,,某一个工程组成,A, B, C, D, E 5,个作业，各作业数率为,,0.99, 0.95, 0.90, 0.90, 0.95.,A,B,C,D,E,0.99,0.95,0.90,0.90,0.95,,水准是每100万个良品数,= 937,375,对应的,,Z.st,值,,,,3.03,,,左右。,5,),95,.,0,)(,90,.,0,)(,90,.,0,)(,95,.,0,)(,99,.,0,(,=,作业的标准化数率,937375,.,0,=,,计算,,利用,Minitab,的计算,:,实习,,例,,3,,,

24、有一个给顾客提供信用情报的公司，顾客要求事项分为,,,,情报内容、迅速性、正确性、最新性、接触容易性，,,对这些调查重要度和顾客满足度结果如下。,[,表,],项目别重要度及顾客满足度,项目,最新性,接触容易性,计,/,平均,0.60,情报内容,迅速性,正确性,满足,(%),重要度,0.05,0.15,0.15,0.05,1.00,80,90,90,90,95,84.25,,计算,:,确认,,,水准,= 2.5,左右。,,利用,Minitab,的计算：,,实习,,,例题,4,,,某部品销售价格,$120，,制造原价目标值定于,$100，,,,但各种外部及内部原因长期预算时，原价比销售价格高的,,,概率是,5%,左右。,,,计算,:,对应不良率,5% (,PPM = 50,000),的,,,,水准为,3.15,100,120,5%,,利用,Minitab,的计算,:,实习,