六西格玛管理简介(英文版).pptx

《六西格玛管理简介(英文版).pptx》由会员分享，可在线阅读，更多相关《六西格玛管理简介(英文版).pptx（18页珍藏版）》请在淘文阁 - 分享文档赚钱的网站上搜索。

1、4-Block a error a risk Accuracy Active (opportunity or defect) Advocacy Team Alternate Hypothesis ANOVA ANOVA method (Gauge R&R) Assignable cause variation Attribute Chart Attribute data Average Graphical tool to show the relationship between process capability, control & technology.The error made i

2、f difference is claimed, when the reality is sameness (e.g. rejecting good parts; Producers Risk).The risk (probability) of making an a error (frequently set at 5%).How close measurements are, on average, to their target.An opportunity or defect that is being measured (a defect we are looking for).T

3、he group of people who have a stake in the Six Sigma project, including those who must keep it in control.See HaAnalysis of Variance. A statistical method of quantifying contributions of discrete levels of “X”s to the variation in a “Y” response.A Minitab selection for Gauge R&R that includes operat

4、or-part interaction in the calculation of variation contributions. The most accurate method for Gauge R&R.Removable variation in a process; variation due to outside influences. See Black Noise.Statistical Process Control (SPC) chart for discrete data. Includes p, np, c and u charts.Data that can be

5、described by levels, integer values or categories only. See Discrete data.The sum of all data in a sample divided by the number of data points in the sample. See Mean.b error b risk Baselining BenchmarkingBlack Belt Black Noise Boxplot Brainstorming Centring Centring of X variables Central Limit The

6、orem The error made if sameness is claimed, when the reality is difference (e.g. accepting bad parts - Consumers Risk).The risk (probability) of making a beta error (frequently set at 10%).Evaluating the capability of a process as it stands today, without “tweaking” - i.e. passive observation.Evalua

7、ting the capability of similar processes to quantify what constitutes the Best.A person whose full time job consists of application of Six Sigma tools/methods on projects.Process variation due to outside influences. See Assignable Cause Variation.Graph showing the portion of a distribution between t

8、he first and third percentiles within a box. The boxplot also shows the median of the distribution and the extreme values. Often used to compare population.A technique used by an Advocacy Team to, for e.g., develop a list of potential Xs at the beginning of project. A process characteristic describi

9、ng how well the mean of the sample corresponds to the target value.A method used to transform X variables in DoEs that develop higher order (quadratic) models; reduces correlation between Xs.A fundamental statistical theorem stating that the distribution of averages of a characteristic tends to be n

10、ormal, even when the parent population is highly non-normal.Central Composite Design Champion Champion Review Chi-Squared test Classical Yield Common Cause Variation Components Search Confidence Confidence Interval Consumer Continuous Data A Design of Experiments (DoE) method whereeach X is tested a

11、t 5 levels (see Star Points). A CCD provides the capability to model aprocess with a quadratic equation OR a linearequation.Typically a director - someone who can support the Six Sigma project and has the authority to remove barriers and provide resources. Takes an active part in Project Review.A re

12、gular meeting to present Six Sigma projects, share experiences and remove roadblocks.Hypothesis test for discrete data. Evaluates the probability that counts in different cells are dependent on one another, or tests Goodness of Fit to some a priori probability distribution.See “First Pass Yield”. Go

13、od units produced divided by Total Units Produced.See “White Noise”. The inherent variation of a process, free from external influences. Usually measured over a short time period.A method of screening for Vital Few Xs in manufactured assemblies. Also known as Part Swapping.The complement of alpha ri

14、sk. Confidence = 1-a.A range of plausible values for a population parameter, such as mean or standard deviation.The end user of a product (the homeowner, for e.g.). The consumer is external to the business.Data that can be meaningfully broken down into smaller and smaller increments - e.g. length, t

15、emperature etc.)Contour Plot Control Limits Cost of Quality Cp Cpk CQ CTQ Cube Plot Customer Data Window Defect Dependent Variable A graph used to analyze experiments of a Central Composite Design. Two Xs comprise the axes, and levels of constant Y are shown in the body of graph. Resembles a topogra

16、phical map. Lines on a Statistical Process Control (SPC) chart that represent decision criteria for taking action on the process. Lines are drawn +/- 3 standard deviations (s) from the mean. A financial reconciliation of all the costs associated with defects (scrap, rework, concessions etc.) Statist

17、ic used to measure Process Capability. Assumes data is centred on target. Similar in concept to Z.stStatistic used to measure Process Performance. Does not assume centred data. Similar in concept to Z.ltCommercial Quality. Used to categorize non-manufacturing projects that impact the consumer and/or

18、 customer.Critical-to-Quality characteristic. An aspect of the product or service that is important to the customer/consumer.A graph used for analysis of the results of a factorial designed experiment (DoE). Shows test conditions that optimize the response.The recipient of the output of a process. M

19、ay be internal (e.g. Assembly is a customer of finishing shops), or external (e.g. Currys, Belling etc.) who then sell our products to consumers.The spreadsheet window in Minitab where data is entered for analysis.Any aspect of a part or process that does not conform to requirements.The output of a

20、process. The “Y” response.Descriptive Statistics Design of Experiments (DoE) Discrete Data Dotplot DPMO DPO DPU e (Exponential Function) Entitlement Executive Summary F-test Mean, Standard Deviation, Variance and other values calculated from sample characteristics. Also includes assorted graphs.A st

21、atistical field of study where independent variables (Xs) are systematically manipulated and the response observed. Used to demonstrate which Xs are the Vital Few, and to optimize the response.Data that can only be described by levels, i.e. pass/fail, operator a/b/c, integer values (e.g. number of d

22、efects). Data that cannot be broken down into finer increments.Frequency diagram representing data by dots along a horizontal axis. Generally used as an alternative to a histogram for small sample sizes.Defects Per Million Opportunities - 1,000,000 multiplied by total number of defects, divided by t

23、he total number of opportunities. A metric for defects equivalent to ppm used for defectives.Defects Per Opportunity - total number of defects divided by total number of opportunities. Used to enter the Normal Table to obtain Z values.Defects per unit - total number of defects divided by total numbe

24、r of units. Used primarily to calculate Rolled Throughput Yield (Y.rt) through the Poisson formula Y.rt = e-DPU.A mathematic constant roughly equal to 2.718Mathematical identity: ln(e)=1Z.st The best the process can be. What the process would look like if all Assignable Cause Variation was controlle

25、d.The first page of output from the Minitab Process Capability selection.A test to compare variances of 2 or more samples, and to compare the equality of two or more means (in ANOVA).Factorial Experiment Fractional Factorial Experiment.First Pass Yield FMEA Functional Owner GaugeXBR method Gantt Cha

26、rt Gauge R&R Green Belt Ha Ho A designed experiment (DoE) which involves testing of all possible combinations of independent (X) variables.A designed experiment (DoE) which involves testing a fraction of all possible combinations of independent (X) variables in a full Factorial experiment. Results i

27、n fewer test runs.See Classical Yield. Equal to the number of good units produced divided by the total number of units produced.Failure Mode and Effects Analysis - a team-based procedure that identifies and documents all possible failure modes, effects, causes and associated corrective actions.The p

28、erson with financial responsibility for the process under consideration.Gauge R&R method- an option in Minitab.A project management tool that graphs milestones vs. the calendar. Bars are used to indicate both planned and actual duration of tasks.A means of determining the acceptability of the variab

29、ility in the gauging system for use in the process.A person who uses Six Sigma tools and methodology in the course of their work, and who always has a Six Sigma project active in their place of work.Alternate Hypothesis (hypothesis of difference). The hypothesis being proven in a statistical hypothe

30、sis test.Null hypothesis (hypothesis of sameness). The starting assumption in a statistical hypothesis test. NB. The null hypothesis cannot be proved!Histogram Homogeneity of Variance Hypothesis test I/MR Chart Independent Variable Inferential statistics Inherent Process Capability Interaction plot

31、A frequency diagram composed of rectangular bars whose relative heights indicate the number of counts (or relative frequency) at a particular level.A menu selection in Minitab under which the F-test (comparison of variances) is performedAny of several statistical tests of 2 or more samples from popu

32、lations. Used to determine if the observed differences can be attributable to chance alone. The result of the test is to either accept or reject the alternate hypothesis (Ha). (t-test, F-test and Chi-Squared test are examples.)Individual/Moving Range chart - a Statistical Process Control (SPC) chart

33、 in which the upper graph is used to plot individual data points compared to calculated control limits; the lower graph (Moving Range) plots the difference between sequential data as points on the chart. Control limits are also calculated for this chart.Variables (Xs) that influence the response of

34、a dependent variable (Y)Statistical analyses that quantify the risk of statements about populations, based on sample data. Inferential statistics are usually hypothesis tests or confidence intervals.The Best the process can be, with only variation due to white noise present. See Entitlement, Z.stA g

35、raph used to analyse factorial and fractional factorial designs of experiments. Indicates the effect on Y when two Xs are changed simultaneously. The greater the difference in slopes between the Xs, the greater the interaction.Kurtosis L1 Spreadsheet L2 Spreadsheet LCL (Lower Control Limit) Leverage

36、 Variable Linearity (gauge)Long term data LSL m Macro Main Effects Plot Master Black Belt Comparison of the height of the peak of a distribution to the spread of the tails. The kurtosis value is 3 for a perfect normal distribution.Excel spreadsheet for discrete data that calculates subsystem Z value

37、s and rolls them into a system-level Z value. Replaced by Product Report in Minitab release 11.2Excel spreadsheet for continuous data that calculates Z.st and Z.ltReplaced by Process Reports in Minitab release 11.2The lower control boundary on a Statistical Process Control (SPC) chart. A limit calcu

38、lated as the mean minus 3 standard deviations. Note: SEM (Standard Error of the Mean) is used for s; stdev = s/sqrt(n).An X variable with a strong influence on the Y response. One of the Vital Few.The difference in the accuracy of the gauge from the low end to the high end of the test range.Data obt

39、ained in such a way that it contains assignable cause variation (black noise).Lower Specification LimitThe mean or average of a populationA mini program within a software package designed to provide a particular output (e.g. Gauge R&R)A graph used to analyze factorial and fractional factorial design

40、s of experiments. Compares the effect on Y of an X at the high level vs. its effect at the low level. Slope of the line on the graph indicates significance.A coach, mentor and trainer of the Six Sigma methodologies and tools.Mean Measurements Systems Analysis Median Minitab Normal Curve Normal Proba

41、bility Plot Normalize Normalized Average YieldNull Hypothesis Orthogonal p-value Pareto Analysis The average. May be the average of a sample (x-bar), or the average of a population (m).See Gauge R&R.The middle value of a set of data (the 50th percentile).A statistical software package containing the

42、 majority of Six Sigma tools.A widely-used, commonly-seen distribution where data is symmetrically distributed around the mean (bell curve).A graphical hypothesis test in which sample data is compared to a perfect normal distribution. Ho: the sample data is the same as the perfect normal distributio

43、n. Ha: the sample data is different (i.e. non-normal).The process of converting non-normal data through the use of a transformation function.The average yield of a process with multiple steps or operations. Y.na = (Y.rt)1/nSee Ho.Literally, “right angles”. A feature of a well-defined experiment that

44、 allows main effects to be separated from 2-way and higher order interactions, as well as quadratic (squared) terms.The probability of making an alpha (a) error. A value used extensively in hypothesis testing. Also referred to as the observed level of significance. p-values are compared to the accep

45、table level of alpha risk in order to make decisions in hypothesis tests.A problem solving tool that allows characteristics to be ranked in descending order of importance.Pareto Principle Passive (opportunity/defect) Point of Inflexion Poisson Approximation Population Power of the Test ppm Practical

46、 Problem Practical Solution Precision Pre-Control Principle of Reverse Loading .Probability of a defect p(d) The “80-20” rule. The principle that 20% of the variables cause 80% of the variation.A defect or opportunity that is counted upon occurrence, but that is not part of the active monitoring pro

47、cess.Point on the normal curve where it changes from convex to concave. Mathematically defined by setting the third derivative to zero.A mathematical approximation for Rolled Throughput Yield, given DPU: Y.rt = e-DPU.All data of interest for a particular process, recorded or not. Usually modelled wi

48、th samples.The likelihood of detecting beneficial change. Represented as 1-b. The probability of rejecting the null hypothesis.Parts per million defective. A discrete measurement of defectives for long term dataThe output of the Measure phase. A characterization of the Z value, centring and spread f

49、or Y.The output of the Control Phase. The optimised X levels and control plan to maintain the process at its highest Z value.How closely the data is clustered around their mean. Describes the spread of the data.A Statistical Process Control (SPC) method that allows an operator to take action on a pr

50、ocess based on where the part measurements fall in a normal distribution. Parts are coded red, yellow or green.Planning ahead Need to define what do you want to know, so what tool/test should be used, so what data do you need?The tail area of the normal curve, beyond the specification limit(s).Probl