项目实例教程

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1、这是黑带如何完成一个项目的实例教程，指导黑带如何更好的完成项目。如何定义一个项目？项目定义是由冠军来完成的。我们简单介绍以下项目是如何定义的。1确定主要商业问题：a目标b目的c可交付使用的2对与生产来说：a循环时间b质量/缺陷水平c耗费3项目的选择a选择项目的工具a1宏观图a2 Pareto图分析a3鱼骨图a4因果矩阵图b项目的标准（评估）b1减少缺陷的70%b2第一年节省 $175Kb3项目完成周期为4个月b4最少的资金总额b5黑带的第一个项目必须满足培训目标6 Sigma项目运作实例-定义阶段-我们在定义阶段做什么-我们在定义阶段需要做什么？1，完成项目陈述。2，完成项目预测节省金额。3，

2、完成问题陈述：3.1问题是什么？3.2在哪里和什么时间发现的？3.3问题将涉及哪些工序？3.4谁将受到影响？3.5问题的严重程度是什么？3.6你是如何得知这些的？4，绘制宏观图。5，描述项目的主线。6，完成目标陈述。7，组成项目小组，列出小组成员。8，完成财务评估。6 Sigma项目运作实例-定义阶段-如何进行项目问题陈述-如何进行问题陈述？分六个方面进行问题陈述：1问题是什么？2在哪里和什么时间发现的？3问题将涉及哪些工序？4谁将受到影响？5问题的严重程度是什么？6你是如何得知这些的？ 6 Sigma项目运作实例-定义阶段-如何绘制宏观图-如何绘制宏观图? 绘制宏观图的顺序：供应商-输入-工

3、序-输出-客户6 Sigma项目运作实例-定义阶段-项目的目标陈述要点- 项目的目标陈述要点: 1,目标陈述2，计算方法3，全年节省额确定Team Members成员:1，小组成员要包括技术人员2，包括维修人员（如果需要）3，包括操作者4，小组人员不超过5人(特殊情况除外)。6 Sigma项目运作实例-测量阶段-如何进行项目描述-如何进行项目描述: 1，目标陈述2，Metric 图3，月节省额如何绘制工艺流程图:召集小组:流程图绘制是集体努力的结果小组包括：流程负责人：项目结果的负责人工程部门-工艺，产品，设计及设备生产部门-操作员，各班次主管，培训员，操作班长，维修技师流程图所需信息脑力风暴

4、观察/经历操作手册工程标准，工作指示六大方面（人，机，方法，测量，材料，环境）确定工艺范围:范围至观重要越窄越好！大量工艺步骤可能表明项目定义不佳或问题源于几个项目问题藏于问题中若问题可以由粗略分析解决，管理层会去做绘制可执行的工艺图你能确认缺陷来源吗？我们能有意识地改变输入指标变量吗？有意识的改变输入指标变量能直接影响输出结果吗？工艺流程图（PFD）:6 Sigma 工艺流程图的要素：所有工艺步骤包括隐形工厂数据采集点所有设备/工具各步骤表明增值性（VA）和非增值性（NVA）控制标准文件用标准符号绘制工艺流程：在Microsoft OfficeTM 等软件中可找到 工艺流程图-程序:绘制工艺

5、记载的工艺步骤包括所有检查点，测量指标和传运步骤确认所有数据采集点标示各工序标准控制文件各步骤标明为增值性（VA）或非增值性（NVA）确认各工艺步骤的 X 和 Y标明可能消除的NVA 步骤加入并标明“隐形工厂”工段标明为VA或NVA，标明可能消除的步骤标明须指定控制文件的步骤加入DUP，RTY，COPQ，循环周期等估计值标明须进行量具和工艺能力研究的步骤通过直接或秘密观察确认准确性文件记录/确认:文件记录的工艺流程首先绘制记录下来的工艺加入并标明隐形工厂步骤当所有步骤展示出来后，流程图就属于实际工艺确认流程图的准确性至关重要项目组必须花时间观察工艺秘密进行。观察导致行为改变确认实际工艺设置与记

6、录的设置相同跨班跨机器观察工艺如何绘制工艺流程细图:工艺流程细图：6 Sigma 工艺流程图要素：工艺或产品是输出指标Y和输入指标X标准上下限和标准控制文件所用设备/工具绘制工艺流程细图工艺流程细图必须依工艺流程图而画。更改其一应在另一个中反映出来。应使用最新的控制文件标明所有隐形工厂步骤的输入输出指标工艺流程细图程序：1，从流程图中列出工艺步骤2，加入下列内容输出指标输出指标标准，若存在输入指标输入指标标准，若存在工艺能力或量具能力指标所用设备3，标明隐形工厂步骤4，标明各步骤属于增值性（VA）或非增值性（NVA）5，标明各步骤属于可控性的（C）或噪音性的（N）6，确认各设备的输入指标设置7

7、，确认流程图准确性8，必要时更改及更新流程标准限和工艺能力：工艺及产品标准加入X的工艺设置加入Y 的标准限 标明未记录的Y和可控的X测量系统加入量具重复性及复验性数据标明须做测量系统分析的量具工艺能力展示RTY，DPU，CPK等的估计值标明哪些工艺步骤数据陈旧或不完整而需做工艺能力分析更改及更新：更改记住：6 Sigma 的目标之一是找出：Y=F（X）随着对工艺的深入了解，更新工艺图以反映新的信息更新项目最终成果之一是现有的工艺的流程图更新工艺图以反映任何工艺改变加入测量系统分析及工艺能力分析结果精简制造与5S:精简制造例似于日本的5S精简制造与5S:鱼骨图:鱼骨图一种系统确认所有可能导致问题

8、(后果)产生的原因方法。构造鱼骨图的方法：1. 陈述问题，并置于右边的方框内2. 朝方框画一水平箭头。3. 在箭头上下写上传统因素类型名称*或你怀疑是的类型名称。用 直线连到箭头线上。4.在各主要的类型范围内，集思广益并列出所有可能引起问题发生的因子。5.进一步优化：对各种详细列出的因子再列出其输入变量。*6m-man, machine ,method, measurement, mother nature (environment)(6M:人员，机器，测量方法，原材料，环境) 定性测量系统研究:定性型量具 R&R -术语:检验员分数（%）-在定性型R&R检验过程中，检验员前后一致的比例定性数

9、据-定性（合格/不合格）数据，可用来做记录和分析定性型测量系统-把每个部件与标准进行比较，从而决定部件是否符合标准的测量系统。消费者偏见-员工倾向把合格产品判为废品有效筛选分数（%）-在定性型R&R检验过程中，所有员工本身前后一致且相互之间也一致的比例。标准值-由一个高准确度量具所测的平均值生产者偏差-员工倾向于把不合格（有缺陷的）产品判为合格筛选-用检验方法对产品进行100%的评估筛选有效性-定性量具系统区别合格与不合格的能力使用定性型量具 R&R 的目的:工艺评估评估你的检查标准或工作质量标准与客户要求的一致性确定所有班次，机器等的检查人员是否使用相同标准来决定合格与不合格量化检查人员准确

10、重复其检验结果的能力确定检查人员与“已知标准”的一致性及倾向于消费者偏差还是生产者偏差工艺改进发现是否需要培训，缺少工序或缺乏标准定性型量具 R&R 的方法:准备从工艺中挑选30个部件，50%合格，50%次品可能的话，挑选近乎于合格和不合格样本挑选检查人员-受过完全培训的和有资格的实施要求每一个检查人员随机地检查部件，决定合格与不合格并重复此检查评估将结果载入文件如果必要，采取适当的措施调整测量工艺重做R&R试验，核实调整后的有效性定性型量具 R&R -结论:检查员分数如果大多数员工都是100%，则培训作用极为有限筛选有效分数如果员工本身前后一致但是相互间不一致，则重新培训可帮助减少错误。标准

11、化分数如果员工时常与标准不一致，则需要改变测量系统（或局部标准）工艺能力分析:为何测量工艺能力?使我们根据数据分配资源！ （这可不常见！）缺陷率得以量化确认可以改进机会分析工艺能力可使组织预测其所有产品和服务的真实质量水平确认工艺发生问题的本质-居中程度或分散度工艺能力研究连续数据 离散数据1.确认标准限 1.确认标准限2.收集数据 2.收集数据3.确定短期偏差 3.决定：短期还是长期？4.计算工艺能力指标： （通常是长期）a.短期： 4.计算工艺能力指标： ZU,ZL a.长期： CP PPM CPK Sigma水平ZLT Sigma水平ZST PPKb.长期： b.短期： Sigma水平Z

12、LT Sigma水平ZST PPK CPK工艺能力计算实例一位技师负责医院设备的蒸汽杀菌过程。其中一个关键参数是控制“暴露”阶段的温度。设备室温度和在最小饱和蒸汽浓度的周期时间决定杀菌程度在整个设备室维持前后一致的温度范围很重要。第一步：确认标准这一阶段常被忽视。我们如何设定标准？设计部门-设计蓝图设计部门如何得到各项要求？工艺部门-标准由工艺以前能够做到的或开始使用时的能力定这想法有错吗？客户我们总是对客户说可以吗？对上例而言：设备室目标温度是1250C1.50C第二步：采集数据-合理编组应采集数据获得“短期”性能，如可能，“长期”性能通过固定时间区间采集一系列快照型数据应按合理编组采集快照

13、数据什么是合理编组？从流程连续不断产生的零件或产品中合理取样以期捕获最小工艺偏差的方法组内偏差反映一般偏差平均标准差（用一种均方差方法平均）是对工艺应有能力的良好估计第二步：采样-例子例子：技师在暴露周期从控温探针读数中选取五个数据，并从连续七个杀菌运转周期采集数据，数据列在ChamberTemp2.mtw文件的杆ChambTemp栏中第三步：确定短期偏差多数现有数据居于长期和短期之间为了估计真实短期数据：小心设计工艺能力研究方法确保编组策略合理某些工艺无法研究短期数据如低产量和长循环周期工艺采样昂贵或难以取样的工艺第三步：短期还是长期？一个指导思想：如果允许80%的输入指标在其自然范围内浮动

14、，数据就是长期的 短期及长期：组内及组间平均标准差与总标准差对各组方差取平均值可得到组内标准差的平均值总标准差由所有数据算出，不计编组平均标准差不计组间偏差，而总标准差计入组间偏差平均标准差是对组内标准差的最佳估计长期和短期指导思想短期数据在有限的周期或间隔采集数据在有限的机器和员工中采集差不多总是连续变量长期数据在很多的周期，间隔，机器和员工中采集可以是离散或连续数据离散数据几乎都是长期性的第四步： 计算ZU和ZL:Z-分数提供统计数据以便用共同语言交流提供一个与标准上下限相关的工艺性能指标第四步： 计算CP例子工艺平均值为325标准差为15标准上限为380，下限为270CP是多少？若平均值

15、为 355而标准差不变CP又是多少？ Cp与工艺应有能力Cp是工艺应有能力的良好指标工艺应有能力-一个工艺观察到的最好的短期性能机会-工艺长期性能与工艺应有能力间的差距Sigma项目-致力与把长期性能与工艺应有能力的差距缩短定量测量系统研究:定性型量具 R&R -模型测量系统 总和=工艺+测量系统偏离度： 观察值=实际真实值+测量偏移通过“校准计划” 测量偏移来评估 真实值 测量值（准确度）测量系统 2 总合=2工艺+2测量系统偏离度： 观察的偏差=工艺的偏差+测量的偏差通过“校准计划”来评估 真实值 测量值（准确度）测量系统的指标:量具R&R结果-量具偏差（measurement syste

16、m ）真实值 精确度（量具偏差）观察值测量系统的精确度（P）：精确度包括重复性和复制性测量系统的指标-PT:精确度与公差之比-P/T代表量具偏差占公差的部分此部分通常用百分数来表示最好的情形P/T10%-可接受的P/T30%测量系统的测量方法-P/TV:精确度与总偏差之比代表量具偏差占据总偏差的部分此部分通常用百分率来表示最好情形10% 量具可接受条件4 ，可接受的：3-4P/T 和 P/TV 的用处:P/T （% 公差）最常用于测量系统的精确度评估将量具的精确度与公差要求进行对比如果量具用来对生产样品进行分类 P/T 还可以P/SV（%R&R）-6 Sigma 首选测量量具与量具研究偏差相比

17、其性能如何最适合进行工艺改进的评估使用时应小心。量具研究偏差并不一定代表真实的工艺偏差P/TV（%R&R）-6 Sigma 首选测量量具与工艺偏差相比其性能如何使用时应小心。量具研究偏差并不一定代表真实的工艺偏差当量具样本中的偏差代表真实工艺偏差时，P/TV等于P/SV定量型量具 R&R -使用方法说明:1，校准量具或确认最近校准仍然有效2，收集10个代表工艺偏差全部范围的样本3，从每日使用这种测量方法的员工中选出检验员4，运用 ClacMake Patterned Data 准备量具研究数据表5，让员工测量所有无标识，随机次序的样本6，分别让另外其他员工测量所有无标识，随机次序的样本7，重复

18、第五步及第六步循环三次。也尽量打乱员工次序8，用 Minitab 作下列两个分析StatQuality ToolsGage R&R Study(Crossed)StatQuality ToolsGage Run Chart9，对测量系统能力研究结果进行分析10，确定适当的后续措施定量型量具 R&R -Minitab 实例:一个黑带想对冶金工艺使用的温度表进行量具研究，他严格按前面一页的方法进行实验，并将数据输进了R&Rexample.xls 中。运用Minitab分析数据并评估量具能力StatQuality ToolsGage R&R Study(Crossed).Minitab 量具R&R研

19、究-选项输入该工艺公差和偏差，如果你想要Minitab帮你计算P/T 和 P/TV的话。Minitab 默认计算P/SV量具R&R结果-ANOVA表P值是变化源在统计上对总偏差影响是否不显著的概率在这个例子中，部件和员工均为显著的偏差源另外，你能用Minitab的计算器计算总的平方和吗？这个值代表什么意思？ 6 Sigma项目运作实例-分析阶段-失效模式及后果分析-失效模式及后果分析: Failure Modes and Effects Analysis (FMEA)Background: Failure Modes and Effects Analysis (FMEA) First deve

20、loped in the 1950s Appropriated by NASA in the 1960s for the space program Ford Motor Company was the first North American company to widelyimplement the use of FMEAs Types of FMEA System Top-level, early stage analysis of complex systems Design Systems, subsystems, parts & components early in desig

21、nstage Process Focuses on process flow, sequence, equipment, tooling,gauges, inputs, outputs, set points, etcWho? When? Who constructs the FMEA? The Black Belt is the team leader. The process owner inherits the finished FMEA. Use the process mapping, C&E matrix team. May need to add a rep from quali

22、ty, a supplier, reliability When should the FMEA be constructed? After the process map & the C&E matrix Before or after the control plan, depending on the maturityof the processWhy?Warm up exercise: You have 60 seconds to document: What would you want to know about a “defect”? For the process: FMEA

23、improves the reliability of the process An FMEA identifies problems before they occur FMEA serves as a record of improvement & knowledge For the future: FMEA helps evaluate the risk of process changes FMEA identifies areas for other studies multi-vari, ANOVA, DOE6s Process FMEA - Terminology FMEA: A

24、 systematic analysis of a process used to identify potentialfailures and to prevent their occurrence Potential Failure mode: The manner in which the process couldpotentially fail to meet the process requirements. Potential Failure Effect: The results of the failure mode on thecustomer. Severity: An

25、assessment of the seriousness of a failure mode.Severity applies to the effects only. Cause: How the failure could occur, described in terms of somethingthat can be corrected or controlled. Occurrence: The likelihood that a specific failure mode is projectedto occur. Detection: The effectiveness of

26、current process controls to identifythe failure mode (or the failure effect) prior to occurring, prior torelease to production, or prior to shipment to the customer. RPN - Risk Priority Number: The product of Severity, Occurrence& DetectionFMEA Examples Plating ExampleAn aerospace plating company wa

27、s shipping product to itscustomers with nickel plating that was too thin. Parts were failingcorrosion testing at the customer. Shipping ExampleThe shipping department of an electronics company is unable toship an assembly without its clam shell protective packaging. Thiscauses occasional late shipme

28、nts to the customer. In the following examples, a single line from the FMEA is used as anillustration for each of the above examples. 图形技术分析:Graphical MethodsProcess Variation Noise variation from discrete inputs Different operators, machines, setups Different days, shifts Different batches, mixture

29、s, raw materials Noise variation from continuous inputs Ambient temperature, humidity, pressure Wear, drift, erosion, chemical depletion) ,., , ( 2 1 k Process x x x f y =) ,., , ( 2 1 k Noise n n n f +Intentional Unwanted The equation just means that any output isdetermined by the intentional proce

30、ss settingsand the unwanted noise variation.Common Classification of Noise Variables Positional (within part variation) Variation within a single production unit Thickness variation across a plated part Variation across a unit containing many parts Variation across a semiconductor wafer with many di

31、e Variation by position in a batch process Cavity-to-cavity variations in an injection molding operation Cyclical (part-to-part variation) Variation between consecutive production units Batch-to-batch average differences consecutive batches Temporal (time-to-time variation) Shift-to-shift, Day-to-Da

32、y, Setup-to-setup Variation not accounted for by Positional or Cyclical2 2 2 2Temporal Cyclical Positional Noise +=Graphical Analysis Example Injection molding is used to make a type of socket, four pieces at a time, onepiece per slot. Measurements of the sockets consist of thickness values inexcess

33、 of 5.00 millimeters. The gauges measure in hundredths of amillimeter. The specification is 11 6. Four times a day the supervisor would go to the press and gather up theparts produced by five consecutive cycles of the press. Since each cycleproduced four parts, he would have 20 parts to measure ever

34、y two hours.The supervisor kept track of the cycle and the cavity from which each partcame and wrote his twentymeasurements in an array likethis: The supervisor collected samples four times a day for five days (20 samplestotal, 20 parts per sample). Calculate the process capability and use a Multi-V

35、arichart to help determine sources of variation.A BCDES1 18 19 20 19 21S2 13 16 14 13 13S3 10 11 13 10 13S4 11 12 13 13 13Exercise: Determine Capability Using Minitab, analyze the Thick datain SocketData.mtw for process capability Remember, the specifications are: 11 6 What is the short-term process

36、 capability? What is the long-term process capability? Are these good or bad values?Remember, one goal of Six Sigma is toreduce variation, which will increasecapability. It is always important tounderstand the process capability. Preparing Data for Marginal Plot by “Slot” Marginal plots require both

37、 variables to be defined numerically We need to convert “Slot” to a numeric column first Step 1: Convert “Slot” ManipCodeText to NumericManip Code Text to NumericMulti-Vari Analysis Defined A graphical analysis tool Uses logical sub-grouping Analyzes the effects of discrete Xs on continuous Ys A cap

38、ability and process analysis tool Data collected for a relatively short time Data can estimate capability, stability, and y = f(x)s Major focus: study uncontrolled noise variation first Variation in noise variables produces chronic and acutemean shifts, changes in variability, and instability Noise

39、variation must be reduced or eliminated in order toleverage the important controllable variables systematicallyMulti-vari analysis is a very useful toolfor graphically identifying sources ofvariation, especially noise variation. Laterthis week, we will be studying correlation ®ression (an analysi

40、s of the effect ofcontinuous Xs on continuous Ys), analysisof variance (ANOVA) and the General LinearModel (GLM), both numerical analyses ofvariance data.Multi-vari analyses will help identify thevariation sources with the purpose of reducingor eliminating them.A Multi-Vari Plan1. Clearly state the

41、objective2. List the Xs and Ys to be studied3. Ensure measurement system capability4. Describe the sampling plan5. Describe the data collection & storage plan (who, what, when, etc.)6. Describe the procedure and settings used to run the process7. Assemble and train the team. Define responsibilities8

42、. Collect the data9. Analyze the data10. Verify the results11. Draw conclusions. Report results. Make recommendationsInjection Molding Example1. Clearly state the objective Determine the process capability of the injection molding process Determine the major sources of noise variation2. List the Xs

43、and Ys to be studied Output: Thickness Inputs: Cavity (slot), cycle, sample3. Ensure measurement system capability An MSA was conducted and the system was found capable4. Describe the sampling plan One sample from each slot, five consecutive runs, four times aday for five days.5. Describe the data c

44、ollection & storage plan (who, what, when, where,etc.) The supervisor collected the data and entered it in a worksheet6. Describe the procedure and settings used to run the process Standard, constant process settings.7. Assemble and train the team. Define responsibilities. For a small project, the s

45、upervisor did all the work8. Collect the data. The data are in Minitab worksheet SocketData.mtw9. Analyze the data Analysis is on the following slides 中心限理论:Central Limit TheoremQ: Why Are So Many Distributions Normal? Why is something thiscomplicated socommon?Science has shown us that variables tha

46、tvary randomly are distributed normally. Soa normal distribution is actually a randomdistribution.Another reason why some distributionsare normally distributed is becausemeasurements are actually averages overtime of many sub-measurements. Thesingle measurement that we think we aremaking is actually

47、 the average (or sum) ofmany measurements. The Central LimitTheorem, discussed in the following slides,provides an explanation of why averages ofnon-normal data appear normal.Dice Demonstration (Integer Distribution) What does a probability distributionfrom a single die look like? What is the mean?

48、What is the standard deviation? Construct a dataset in Minitab Select Calc Random Data Integer from the mainmenu Generate 1,000 rows of data in C1: Min = 1, Max = 6 Use Minitabs Graphical Summary routine for analysis Stat Basic Statistics Display Descriptive Statistics Minitab Output (Typical)The pr

49、obability distribution of thepossible outcomes of the roll of a single dieis obviously non-normal.A perfect distribution would have hadall six bars exactly equal, but even with10,000 data points, there is still somedifferences in the histogram. If a betterestimate is required, a different data setco

50、uld be constructed with exactly equalcounts of each possible outcome. Try itand see if the numbers are any different. Sampling a Non-normal Distribution Exercise Each person in the class is to toss a single die sixteentimes and record the data. Calculate the mean and standard deviation of eachsample

51、 of sixteen Record the means and standard deviations from eachperson in the class in a Minitab worksheet Use Minitabs Graphical Summary routine for analysis Stat Basic Statistics Display Descriptive StatisticsAlternately, a sample of sixteen throwsof the dice can be simulated in Minitab asfollows:Se

52、lect: Calc Random Data Integer fromthe main menuGenerate 16 rows of data in C1: Min = 1, Max= 6Analyze the Sample Data What is the mean of the sample averages? Mean 3.5 What is the standard deviation of the sample averages? Sigma 0.4 Is the distribution normal? What is the p-value? What is the relat

53、ionship between the average of thesample means and the population average? What is the relationship between the sigma of theaverages and the sigma of the individuals?The Central Limit Theorem Formal Definition: If random samples of n measurements are repeatedlydrawn from a population with a finite m

54、ean and a standarddeviation , then, when n is large, the relative frequencyhistogram for the sample means (calculated from therepeated samples) will be approximately normal with amean and a standard deviation equal to the populationstandard deviation, , divided by the square root of n.(Note: The app

55、roximation becomes more precise as nincreases.)Central Limit Theorem Exercise From a Minitab analysis of the uniformly distributeddata: For an exercise, verify that the Central Limit Theorem isvalid for this uniform dataVariable N Mean StDevn=1 (Individuals) 10000 -0.00331 0.57918n=2 (Means) 10000 0

56、.00259 0.40613n=5 (Means) 10000 -0.00113 0.25953n=30 (Means) 10000 -0.00237 0.10559相关性及简单线性回归:Regression & CorrelationIntroduction Used for quantitative variables (Xs and Ys) For review: What is the focus of Six Sigma?Q. What does this equation represent?A. A mathematical model of a process Purpose

57、of Regression: to predict Y from a setting of x Examples: Distance = f(acceleration, initial velocity, time) Product yield = f(concentrations of reactants) Hardness = f(alloy, anneal temperature) ( x f Y =Remember, the focus of Six Sigma is todetermine the defining equation of theprocess. It is to i

58、dentify the important inputvariables, determine the relationship to theoutputs, determine the optimum values of thecritical inputs and then control the inputs atthe optimum settings.To do this, the Black Belt must know therelationship between the inputs and theoutputs. This module discusses linearmodeling techniques for identifying therelationship between continuous variab