六西格瑪管理簡介(英文版)(共18頁).ppt

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 erro

2、r made if 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 lookin

3、g for).The 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 inc

4、ludes operator-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.D

5、ata that can be 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 C

6、entral Limit Theorem 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 ob

7、servation.Evaluating 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 distri

8、bution between the 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 charact

9、eristic describing 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 characterist

10、ic tends to be normal, 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 whereea

11、ch X is tested at 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 Pro

12、ject Review.A regular 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

13、 Pass Yield”. Good 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 comple

14、ment of alpha risk. 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

15、- e.g. length, temperature 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. Res

16、embles a topographical 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, concessio

17、ns etc.) Statistic 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

18、 consumer and/or 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

19、 of a process. May 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.

20、The output of a 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 asso

21、rted graphs.A statistical 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 (

22、e.g. number of defects). 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 defec

23、ts, divided by the 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 divide

24、d by total number 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 Variati

25、on was controlled.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

26、method Gantt Chart 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 e

27、xperiment. Results in 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 corr

28、ective actions.The person 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 acc

29、eptability of the variability 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

30、in a statistical hypothesis 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 Capa

31、bility Interaction plot 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 o

32、r more samples from populations. 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 Pro

33、cess Control (SPC) chart 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 in

34、fluence the response of 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.

35、See Entitlement, Z.stA graph 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

36、 Control Limit) Leverage 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 calc

37、ulates subsystem Z values 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 (S

38、PC) chart. A limit calculated 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

39、 the test range.Data obtained 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

40、 fractional factorial designs 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 Minit

41、ab Normal Curve Normal Probability 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

42、 software package containing the 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 a

43、s the perfect normal distribution. 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 o

44、f a well-defined experiment that 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-

45、values are compared to the acceptable 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

46、 Power of the Test ppm Practical 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 p

47、art of the active monitoring process.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, reco

48、rded or not. Usually modelled with 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 th

49、e Z value, centring and spread for 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

50、 operator to take action on a process 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

51、the specification limit(s).Problem Statement Process Capability Process Characterization Process Map Process Optimisation Project Hopper QFD Quartiles R-bar/d Random Cause Variation Range Rational Subgrouping A brief but succinct description of the issue under investigation. Includes the practical a

52、nd business reasons for the project.A statistic that numerically describes how well the process could perform in the absence of black noise. Examples: Z.st, CpUnderstanding the Ys and Xs in a process. Developed through the tools of the Define, Measure and Analyse phases.A problem solving tool that g

53、raphically describes each step or phase in a process.Defining the best operating point for Xs in a process. Developed through tools of the Improve/Control phases.A stack of potential Six Sigma projects, to be picked up by Black Belts or Green Belts when resources allow.Quality Function Deployment. A

54、 rigorous method of determining technical requirements and CTQs from the definition of Consumer Cues.Quarters of a population. 1/4 of the data fall below the first quartile, 1/4 of the data fall above the 3rd quartile.An estimate of standard deviation using the range of the data and tabled adjustmen

55、t factors. Used in calculation of control limits in Minitab Gauge R&R Xbar graphical output.See White Noise. The inherent variation of the process, free from external influences.The largest value in a data set minus the smallest value in the data set.A data collection technique that allows the s

56、eparation of short term variation from long term variation.Regression Repeatability (Gauge) Repetition Reproducibility (Gauge) Response Surface ExperimentResolution (Gauge) Resolution (Fractional Factorial) Rolled Throughput Yield A statistical modelling tool that allows data to be represented by an

57、 equation. Used for continuous Y responses, usually with continuous X inputs. (There is special technique within Minitab called Logistic Regression which handles special forms of discrete Xs.)Ability of a gauge to consistently measure the same part with the same results. Part of the output of a Gaug

58、e R&R study.Collecting multiple data points sequentially from a process, without re-setting the processAbility of operators of a gauge to generate consistent measurements. Part of the output of a Gauge R&R study.A designed experiment (DoE) that allows the Y response to be modelled as a funct

59、ion of continuous X variables. See Regression also.The ability of a gauge to discriminate increments of a continuous measurement. Gauge resolution is usually required to be ten times greater than the measurement of interest; i.e., a feature specified with a specification to one decimal place would r

60、equire a gauge with a resolution of two decimal places etc.A roman numeral that indicates the degree of confounding in a fractional factorial design. Higher resolution indicates less confounding - i.e. less ambiguity in the source of effects.Y.rt The product of yields at each step of a process. Can be estimated using the Poisson Approxima