Six Sigma Dictionary

Transcription

1 Six Sigma Dictionary # 4M / 5M / 6M Framework for root cause brainstorming. Categorizes root causes by: Man, Methods, Machines, Material, (5M) Mother Nature and (6M) Measurement System Impact 8D Process Team oriented problem solving method for product and process improvement. Structured into 8 steps: Define the problem and prepare for process improvement, establish a team, describe the problem, develop interim containment, define & verify root cause, choose permanent corrective action, implement corrective action, prevent recurrence, recognize and reward the contributors. 80/20 Rule Rule that suggests that 20% of causes (categories) will account for 80% of the trouble A B Acceptable Quality Level (AQL) Acceptance Number Accuracy Affinity Diagram Alpha Analysis of Variance (ANOVA) Assignable Causes Attribute Data Audit Average Outgoing Quality Bar Chart BAU Bathtub Curve Benchmarking Beta Binary Attribute Data Black Belt Maximum proportion of defective units that can be considered satisfactory as the process average. The highest number of nonconforming units or defects found in the sample that permits the acceptance of the lot. Statement about how close the data are to the target A tool used to organize and summarize large amounts of data (ideas, issues, solutions, problems) into logical categories based on user perceived relationships. In hypothesis testing: rejecting the null hypothesis (no difference) erroneously; assuming a relationship where none exists. Also referred to as Type I Error. E.g. convicting an innocent person. A calculation procedure to allocate the amount of variation in a process and determine if it is significant or is caused by random noise. see Special Causes Numerical information at the nominal level. Its subdivision is not conceptually meaningful. It represents the frequency of occurrence within some discrete category. A timely process or system, inspection to ensure that specifications conform to documented quality standards. An Audit also brings out discrepencies between the documented standards and the standards followed and also might show how well or how badly the documented standards support the processes currently followed. The expected quality of outgoing product following the use of an acceptance sampling plan for a given value of incoming product quality. Horizontal or vertical bars that graphically illustrate the magnitude of multiple situations. "Business As Usual" The old way of doing business, considering repetitive tasks with no critical sense of improvement. A curve used to describe the life cycle of a system/device as a function of usage. Initially, when the curve has a downwards slope, the failure rate is decreasing with usage. Then, a constant failure rate is achieved, and finally the curve slopes upward when the failure rate increases with usage. The concept of discovering what is the best performance being achieved, whether in your company, by a competitor, or by an entirely different industry. In hypothesis testing: the failure to reject a false null hypothesis; assume no relationship exists when it does. E.g. failing to convict a guilty person. Data that can only have two values and indicates the presence or absence of some characteristic from the process. E.g. pass/fail Full time team leaders responsible for recommending and implementing process improvement projects (dmaic or dmadv) within the business -- to drive customer satisfaction levels and business productivity up.

2 Six Sigma Dictionary C C chart Statistical Process Control Chart used to determine whether the number of defects/unit is constant over time. Cause and Effect Tool used for brainstorming and categorizing potential causes to a problem. The most commonly used Diagram cause branches are Man, Method, Machine and Material. Also known as the Ishikawa and the Fishbone diagram. Census Collecting information on an entire population Central Limit Theorem The means of samples from a population will tend to be normally distributed around the population mean Central Tendency An indication of the location or centrality of the data. The most common measures of central tendency are: mean, median and the mode. Champion Business leaders and senior managers who ensure that resources are available for training and projects, and who set an maintain broad goals for improvement projects. Chance Causes See Common Causes Check Sheets A data collection form consisting of multiple categories. Each category has an operational definition and can be checked off as it occurs. Properly designed, the Check Sheet helps to summarize the data, which is often displayed in a Pareto Chart. Chi Square Test A statistical goodness-of-fit-test used to test the assumption that the distribution of a set of data is similar to the expected distribution. Chronic Problems See Common Causes Common Causes Small, random forces that act continuously on a process (Also called: by Juran - Chronic Problems, by Deming - System Faults, by Shewhart - Chance Causes) Confidence Interval A region containing the limits of a parameter, with an associated level of confidence that the bounds are large enough to contain the true parameter value Continuous Variable A variable whose possible values consist of an entire interval on the number line,i.e. it can take any value Control Chart A procedure used for tracking a process through time, with the purpose of distinguishing variation that is inherent in the process (common cause) from variation that yield a change to the process(special cause). Cost of Poor Quality Cost of quality issues are often given the broad categories of internal failure costs, external failure (COPQ) costs, appraisal costs, and prevention costs Correlation Denoted by R. Measure of linear relationship between two variables. It can take on any value between 1 and -1. If correlation is 1, the two variables have a perfect positive linear relationship. If correlation is -1, the variables have a perfect negative linear relationship. If correlation >.7, the relationship between the variables is considered strong. Cpk Process Capability index, which takes into account off-centredness. Cpk = min[ (USL-Mean)/(3 x sigma), (Mean-LSL)/(3 x sigma)] (i.e. depending on whether the shift is up or down). A t ypical objective is to have a Cpk > CTC CTQ Cumulative distribution function (CDF) Cumulative sum (CUSUM) control chart Critical to Cost Critical to Quality the calculated integral of the PDF from minus infinity to x. Alternative technique to Shewhart charting. CUSUM charts ccan detect smsall process shifts faster than Shewhart charts.

3 Six Sigma Dictionary D Data Set of measurements obtained from a sample or census Defects Per Million Number of defective parts out of one million. DPM = Fraction Nonconforming * 1 M, where fraction (DPM) non-conforming = 1 - Quality Yield Defects Per Million Number of defective parts per million opportunities. It is used when an inspection unit has 1 or more Opportunities (DPMO) categories of defects. DPMO = Fraction Nonconforming * 1 M, where fraction non-conforming = Total # of defects/total # of Opportunities (TOP), and TOP = # units * # opportunities per unit. E Design of Experiments (DOE) Discrete Variables DMADV DMAIC Dot Plot E(x) Error Experiment Variables whose only possible values are whole units. Methodology used for Design for Six Sigma Projects or new product/service introduction. It stands for Define, Measure, Analyze, Design and Verify. Methodology used for Six Sigma Problem Solving. Its steps are Define, Measure, Analyze, Improve and Control. It is used only when a product or process is in existence at your company but is not meeting customer specification or is not performing adequately. For nominal or ordinal data, a dot plot is similar to a bar chart, with the bars replaced by a series of dots. Each dot represents a fixed number of individuals. For continuous data, the dot plot is similar to a histogram, with the rectangles replaced by dots. A dot plot can also help detect any unusual observations (outliers), or any gaps in the data set. Expected value of the variable x (or the population mean). E(x) = (Σx_i)/n, where n is the sample size Ambiguities during data analysis caused from sources as measurement bias, random measurement error, and mistake. A process undertaken to determine something that is not already known. F F-Test Factorial Experiment Failure Modes and Effects Analysis (FMEA) Fault Tree Analysis Statistical test that utilizes tabular values from the F-distribution to assess significance. Experiment strategy that assesses several factors/variables simultaneously in one test. All possible combinations factors at different levels are examined so that interactions as well as the main effects can be estimated. Analytical approach directed toward problem prevention through which every possible failure mode are identified to determine their effect on the required function of the product or process. A schematic picture using logic symbols of possible failure modes and associated probabilities. First Time Yield (FTY) see Quality Yield Fishbone Force field Analysis Fractional factorial experiment see Cause and Effect Diagram Representation of the forces in an organization that are supporting and driving toward a solution or which are restraining the process. A designed experiment strategy that assesses several factors/variables simultaneously in one test, where only a partial set of all possible combinations of factor levels are tested to more efficiently identify important factors. This type of test is much more efficient than a traditional one-at-a-time test strategy.

4 G H I J K Gage R&R Gantt Chart Gating Goodness of Fit Green Belt Heteroscedastic Histogram Homogeneous Poisson Process (HPP) Hypergeometric Distribution Hypothesis Testing In Control Inner Array Interquartile Range Ishikawa, Ichiro ISO 9000 Series of Standards JIT (Just In Time) Manufacturing Kaizen Kanban KPOV Kurtosis Six Sigma Dictionary Gage Repeatability and Reproducibility. See Repeatability and Reproducibility Used in project management, it provides a graphical illustration of a schedule, and helps to plan, coordinate, and track specific tasks in a project The limitation of opportunities for deviation from the proven steps in the manufacturing process. The primary objective is to minimize human error. Value determined by using one of many statistical techniques stating probablistically whether data can be shown to fit a theoretical distribution, such as Normal or Poisson. Six sigma player, responsible for deploying six sigma techniques, managing small projects and implementing improvement. Having different variance. In a linear regression model, violation of the assumption of constant variance in the outcome variable (homoscedasticity) is called heteroscedasticity A bar graph of a frequency distribution in which the widths of the bars are proportional to the classes into which the variable has been divided and the heights of the bars are proportional to the class frequencies A model that considers that failure rate does not change with time. Distribution that has similar use to the binomial distrbution; however, the smaple size is "large" relative to the population size. Consists of a null hypothesis (H_o) and an alternative hypothesis (H_a), where, for example, H_o indicates equality between two process outputs, and H_a indicates nonequality. Through a hypothesis test, a decision is made to either reject or fail to reject the null hypothesis. An In-Control" process is one that is free of assignable/special causes of variation. Such a condition is most often evidence on a control chart which displays an absence of nonrandom variation. The structuring in a Taguchi-style fractional factorial experiment of the factors that can be controlled in a process Difference between the 75th percentile and the 25th percentile of a sample or population see Cause and Effect Diagram Series of standards established in the 1980s by countries of Western Europe as a basis for judging the adequacy of the quality control systems of companies A planning system for manufacturing processes that optimizes the needed material inventories at the manufacturing site to only what is needed. JIT is a pull system; the product is pulled along to its finish, rather than conventional mass production, which is a push system. Japanese term that means continuous improvement."kai" means change and "zen" means good Japanese term. It is one of the primary tools of JIT system. It maintains an orderly and efficient flow of materials throughout the entire manufacturing process. It is usually a printed card that contains specific information such as part name, description, quantity, etc Key Process Output Variable Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. For unimodal distributions K=3 is a mesokurtic distribution(normal or bell-shaped); K < 3 is a platykurtic distribution (flatter than normal, with shorter tails); and K > 3 is a leptokurtic distribution (more peaked than normal, with longer tails).

5 L M LCL Lean Manufacturing Least Squares Linear Regression Local Faults Loss function LSL Main effect Malcolm Baldrige National Quality Award Master Black Belt Mean Mean Square Mean Time Between Failure (MTBF) Median Mode Multicollinearity Multimodal distribution Multiple Correlation Coefficient Multi-vari chart Six Sigma Dictionary Lower Control Limit. Usually represents a downwards 3-sigma deviation from the mean. Philosophy developed by Toyota that aims to eliminate waste (non-value added steps, material, etc.) in the system. Method used in regression to estimate the equation coefficients and constant so that the sum of squares of the differences between the individual responses and the fitted model is a minimum. Aims to find a linear relationship between a response variable and a possible predictor variable using the "least squares" method. see Special Causes A continuous "Taguchi" function that measures the cost implications of product variability. Lower Specification Limit. The minimum acceptable value of a variable. An estimate of the effect of a factor measured independently of other factors. The annual self-evaluation covers the following seven categories of criteria: leadership, strategic planning, customer and market focus, information and analysis, human resource focus, process management, and business results. The National Institute of Standards and Technology (NIST), a federal agency within the Department of Commerce, is responsible for managing the Malcolm Baldrige National Quality Award. The American Society for Quality (ASQ) administers the Malcolm Baldrige National Quality Award under a contract with NIST. Master Black Belts are Six Sigma or quality experts and are responsible for strategic implementations within the business. The Master Black Belt is qualified to teach other Six Sigma facilitators the methodologies, tools, and applications in all functions and levels of the company, and are a resource for utilizing statistical process control within processes. Measure of central tendency. It is the average of a set of numbers. See E(x) Sum of squares divided by degrees of freedom. A term that can be used to describe the frequency of failures in a repairable system with a constant failure rate. MTBF is the average time that is expected between failures. MTBF = 1/failure rate. Measure of central tendency. It is calculated by ordering the data from smallest to largest. For an odd sample size, the median is the middle observation. For an even sample size, the median is the average of the middle two values of sample. The value or item occurring most frequently in a series of observations or statistical data. When there exists near linear dependencies between regressors, the problem of multicollinearity is said to exist. It can make the linear regression unstable and/or impossible to accomplish. A combination of more than one distribution that has more than one distinct peak. The square of the correlation (R). It measures the % of the variation in the response variable explained by the variation in the predictor variable. A chart that is constructed to display the variance within units, between units, between samples, and between lots.

6 N Natural Tolerances Nominal Group Technique (NGT) Nonhomogenous Poisson process (NHPP) Nonstationary Process Non-value added Normal Distribution NP chart Null Hypothesis Six Sigma Dictionary Three standard deviations on either side of the mean. A voting procedure to expedite team consensus on relative importance of problems, issues, or solutions. A mathematical model that can often be used to describe the failure rate of a repairable system that has a decreasing, constant, or increasing rate. A process with a variance that can grow without limit. An activity performed in a process that does not add value to the output product or service, which may or may not have a valid business reason for being performed. A bell-shaped distribution that is often useful to describe various physical, mechanical, electrical, and chemical properties. Statistical Process Control chart where the number of defective items are plotted (like the C chart), but the control limits are calculated using the binomial distribution. This chart can be used if producing defects is not rare. See Hypothesis testing. O Operation Cost Target (OCT) O.E.M. OSHA Output One-sided test One-way analysis of variance Orthogonal Outlier This value represents the maximum expenditure for material, labor, outsourcing, overhead, and all other costs associated with that project. This figure can then be divided between the various operations comprising the manufacturing process, in order to control costs at each step. a company that uses product components from one or more other companies to build a product that it sells under its own company name and brand Occupational Safety and Health Administration The result of a process. Sometimes called the response of the process. The products or services that result from a process. A statistical consideration where, for example, an alternative hypothesis is that the mean of a population is less than a criterion value. see Single Factor Analysis of Variance The property of a fractional factorial experiment that ensures that effects can be determined separately without entanglement. The elements in an orthogonal set are not correlated. A data point that does not fit a model because of an erroneous reading or some other abnormal situation. Value greater than 3-sigma from the mean are considered outliers.

7 P P-chart P-Value Pareto Chart Pareto Principle Performance Ratio (PR) Poka Yoke Point Estimate Poisson Distribution Population Pp Ppk PPM Precision Six Sigma Dictionary Statiscal process control chart used to determine whether the proportion of nonconformities is constant over time. The smallest level of significance at which a null hypothesis would be rejected when a specified test procedure is used on a given data set. Graphical technique used to quantify problems so that effort can be expended in fixing the "vital few" causes, as opposed to the "trivial many". It is a bar chart that displays in descending frequency the number of observed defects in a category. see 80/20 Rule Represents the percent of tolerance width used by the variation. PR = 1/Pp Japanese term which means mistake proofing. A poka yoke device is one that prevents incorrect parts from being made or assembled, or easily identifies a flaw or error. To avoid (yokeru) inadvertent errors (poka) An estimate calculated from sample data without a confidence interval. A distribution that is useful, for exmaple, to design reliability tests where the failure rate is considered to be constant as a function of usage. The entire collection of items that is the focus of concern Represents the capability of the process. It measures the relationship between the tolerance width and the range of variation. Pp = Specification Width / (6-S), where sigma is estimated using the sample standard deviation S. Represents the capability of the process, taking into account any difference between the design target and the actual process mean. Ppk = min(ppk_upper, Ppk_lower), where Ppk_upper = (USL - X-bar)/3- sigma and Ppk_lower = (X-bar - LSL)/3-sigma Parts Per Million The closeness of agreement between randomly selected individual measurements or test results. Probability (P) A numerical expression for the likelihood of an occurrence. Probability Density A mathematical function that can model the probability density reflected in a histogram. Function (PDF) - f(x) Probablity plot Data are plotted on a selected probability paper coordinate system to determine if a particular distribution is appropriate and to make statements about percentiles of the population. Process A method to make or do something that involves a number of steps. A mathematical model such as the HPP (homogeneous Poisson process) Process Management Modifying, altering, reshaping, redesigning any business/production process, work method or management style to deliver greater value. Process Map A visual representation of the work-flow either within a process - or an image of the whole operation. A good Process Map should allow people unfamiliar with the process to understand the interaction of causes during the work-flow. A good Process Map should contain additional information relating to the Six Sigma project i.e. information per critical step about input and output variables, time, cost, DPU value. Q Qualitative Data Quality Function Deployment (QFD) Quality Yield Quantitative Data Data that is non-numerical and allows partitioning of a population. The types of qualitative data are: nominal, ordinal and binary. Quality Function Deployment (QFD) is a systematic process for motivating a business to focus on its customers. It is used by cross-functional teams to identify and resolve issues involved in providing products, processes, services and strategies which will more than satisfy their customers. A prerequisite to QFD is Market Research. This is the process of understanding what the customer wants, how important these benefits are, and how well different providers of products that address these benefits are perceived to perform. This is a prerequisite to QFD because it is impossible to consistently provide products which will attract customers unless you have a very good understanding of what they want. Percentage of products that were not defective. Quality Yield = (1 - fraction defective) *100%. Also called First Time Yield. Numerical data. May allow to uniquely identify each member of a population.

8 R S R R^2 R-chart Random Random Effects Random Variable Range Regression Reliability Repeatability Replication Reproducibility Residuals Residual Error Robust Rolled Throughput Yield Root Cause Run Chart Sample Sampling distribution Six Sigma Dictionary see Correlation see Multiple Correlation Coefficient see X-bar and R chart Having no specific pattern. A factorial experiment where the variance of factors is investigated (as opposed to a fixed effects model). A random variable is a function that associates a unique numerical value with every outcome of an experiment. The value of the random variable will vary from trial to trial as the experiment is repeated. There are two types of random variables: discrete and continuous. For a set of numbers, the absolute difference between the largest and smallest value. Data collected from an experiment are used to empirically quanitfy through a mathematical model the relationship that exists between the response variable and influencing factors. The proportion surviving at some point in time during the life of a device. A generic description of tests evaluating failure rates. The variation in measurements obtained with one measurement instrument when used several times by one appraiser while measuring the identical characteristic on the same part. Test trials that are made under identical conditions. The variation in the average of the measurements made by different appraisers using the same measuring instrument when measuring the identical characteric on the same part In an experiment the differences between experimental responses and predicted values that are determined from a model. Experimental error A description of a procedure that is not sensitive to deviations from some of its underlying assumptions. The probability of being able to pass one unit of product or service through the entire process defect free. A factor that caused a non-conformance and should be permanently eliminated through process improvement A time series plot permits the study of observed data for trends or patterns over time, where the x axis is time and the y axis is the measured variable. A selection of items from a population. A distribution derived from a parent distribution by random sampling. Scatter diagram A plot to assess the relationship between two variables. Screening experiment The first step of a multiple factorial experiment strategy, where the experiment primarily assesses the significance of main effects. Two-factor interactions are normally considered in the experiments that follow a screening experiment. Screening experiments should typically consume only 25% of the monies that are allotted for the total experiment effort to solve a problem. Shewhart Control Chart Sigma Sigma level or sigma quality level Significance Single-factor analysis of variance SIPOC diagram Six Sigma Dr. Shewhart is credited with developing the standard control chart test based on 3-sigma limits to separate the steady component of variation from assignable causes. The Greek letter that is often used to describe the standard deviation of a population. A quality that is calculated by some to describe the capability of a process to meet specification. A Six Sigma quality level is said to have a 3.4 ppm rate. Pat Spagon from Motorola University prefers to distinguish between sigma as a measure of spread and sigma used in sigma quality level. A statistical statement indicating that the level of a factor causes a difference in a response with a certain degree of risk of being in error. One-way analysis of varaicne with two levels (or treatments) that is to determine if there is a significant difference between level effects. A SIPOC diagram is a tool used by a team to identify all relevant elements of a process improvement project before work begins, in order to map the process. It is typically employed at the Measure phase of the Six Sigma DMAIC methodology. The categories to include in the process are: Supplier, Inputs, Process, Output and Customer. A term coined by Motorola that emphasizes the improvement of processes for the purpose of reducing variability and making general improvements.

9 Six Sigma Dictionary Skew Special Causes Specification Sporadic Problem Stability Stable Process Standard Deviation Standard Error Stationary process Statistical process control (SPC) Statistical quality control (SQC) Skewness is defined as asymmetry in the distribution of the sample data values. Values on one side of the distribution tend to be further from the 'middle' than values on the other side. Distributions of data that are positively skewed have a tail to the right; negatively skewed data have a tail to the left. A problem that occurs in a process because of an unusual condition. An out-of-control condition in a process control chart. (Also called: by Juran - Sporadic problems, by Deming - Local Faults, by Shewhart - Assignable Causes) A criterion that is to be met by a part or product. see Special Causes The total variation in the measurements obtained with a measurement system on the same master or parts when measuring a single characterisitc over an extended time period. A process that does not contain any special cause variation, ie. It is in statistical control A mathematical quantity that describes the variability of a response. It equals the square root of the variance. The square root of the variance of the sampling distribution of a statistic. A process with an ultimate constant variance. The application of statistical techniques in the control of processes. SPC is often considered a subset of SQC, where the emphasis in SPC is on the tools associated with the process but not product acceptance techniques. The application of statistical techniques in the control of quality. SQC includes the use of regressions analysis, tests of significance, acceptance sampling, control charts, distributions, and so on. Stratification Analysis Decomposing a variable into groups in order to identify or narrow the search for root causes. T Sum of Squares System System Faults t-test Taguchi philosophy Tolerance Total Quality Management Trend chart Trimmed mean Two-sided Test Type I Error Type II Error The summation of the squared deviations relative to zero, to level means, or the grand mean of an experiment. Devices that collectively perform a function. Within this text, systems are considered repairable, where a failure is caused by failure of a device(s). System failure rates can either be constant or change as a function of usage (time). see Common Causes A statistical test that utilizes tabular values from the t distribution to assess, for example, whether two population means are different. Basic philosophy of reducing product/process variability for the purpose of improving quality and decreasing the loss to society; however the procedures used to achieve this objective often are different. Specifies an allowable deviation from a target value where a characteristic is still acceptable. It is the difference between the upper specification limit (USL) and the lower specification limit (LSL). TQM is management and control activities based on the leadership of top management and based on the involvement of all employees and all departments from planning and development to sales and service. These management and control activities focus on quality assurance by which those qualities which satisfy the customer are built into products and services during the above processes and then offered to consumers A chart to view the resultant effect of a known variable on the response of a process. Measure of central tendency. It is the average of a sample with a % of the smallest and largest observations discarded. For example, a 10% trimmed mean discards the smallest 10% and the largest 10% observations. A statistical consideration where, for example, the mean of a population is to be equal to a criterion, as stated in a null hypothesis. see Alpha see Beta

10 Six Sigma Dictionary U V W X U Chart UCL Unbiased Statistic Unimodal USL Value-Added Value Stream Mapping Variance Weibull Distribution White Noise X-Bar X-Bar Chart and R chart A statistical control chart for the average defect rate. Upper Control Limit. Usually represents a 3-sigma deviation from the mean value. A statistic is an unbiased estimate of a given parameter when the mean of the sampling distribution of that statistic can be shown to be equal to the parameter being estimated. A distribution that only has one peak (ex: Normal distribution). Upper Specification Limit. A value below which performance of a product is acceptable. Any action within a process that adds value to the product from the customer's point of view. A visual picture of how material and information flows from suppliers, through manufacturing, to the customer. It includes calculations of total cycle time and value-added time. Typically written for the current state of the value chain and the future, to indicate where the business is going. Measure of dispersion; deviation from the process mean. It is the square of standard deviation. Var(x) = [ Σ(xi x-bar)^2 ] / (n 1) Widely used distribution because it has a density function that has many possible shapes. The twoparameter distribution is described by the shape and location parameters. Common cause variation in a process Also known as the sample mean A pair of control charts used with processes that have subgroups of 2 or more. The standard charts help determine whether a process is stable and predictable. The X-bar chart shows how the average changes over time, and the R chart shows how the range of the subgroup changes over time. Y Z Yield Z Score see Quality Yield A measure of the distance in standard deviations of a sample from the mean, assuming a standard normal process.