Six Sigma Glossary

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Six Sigma Glossary of Acronyms & Concepts

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<ul><li> 1. Benchmarking An improvement process whereby a company measures its performance against that of best-in-class companies, determines how those companies achieved their performancelevels, and uses the information to improve its own performance. Black Belt Full-time Six Sigma project leader who is certified following a four-month training and application program and successful completion of two Six Sigma Projects, the first under the guidance of a Master Black Belt, the second more autonomously. Breakthrough Strategy The data driven, Six Sigma process improvement strategy involving four phases: Measure, Analyze, Improve and Control. Cause That which produces an effect or brings about change. Cause-And-Effect Diagram A schematic sketch, usually resembling a fishbone, which illustrates the main causes and subcauses leading to an effect (symptom).Also known as Fishbone Diagram. Champion Member of the senior Aircraft Engines staff who has undergone extensive Six Sigma training.Champions provide direction, resources and support to the Six Sigma effort and approve and review projects. Characteristic A definable or measurable feature of a process, product or variable. Control Chart A graphical rendition of a characteristics performance across time in relation to its natural limits and central tendency. Correlation The determination of the effect of one variable upon another in a dependent situation. Cp A widely used capability index for process capability studies.It may range in value from zero to infinity with a larger value indicating a more capable process.Six Sigma represents Cp of 2.0. Page 1</li></ul><p> 2. Cpk An index combining Cp and K (Difference between the process mean and the specification mean) to determine whether the process will produce units within tolerance.Cpk is always less than or equal to Cp.When the process is centered at nominal, Cpk is equal to Cp. Critical To Quality (CTQ) An element of a design or a characteristic of a part that is essential to quality in the eyesof the customer, formerly known as a key quality characteristic (KQC). Data Factual information used as a basis for reasoning, discussion or calculation; often refers to quantitative information. Defect A failure to meet an imposed requirement on a single quality characteristic or a singleinstance of nonconformance to the specification. Defects Per Million Opportunities (DPMO) The number of defects counted, divided by the actual number of opportunities to make adefect, then multiplied by one million.A direct measure of sigma level. Defects Per Unit (DPU) The number of defects counted, divided by the number of products or characteristics produced.A process of counting and reducing defects as an initial step toward Six Sigma quality. Defective A unit of product containing one or more defects. Design For Manufacturability (DFM) A concept in which products are designed within the current manufacturing process capability to ensure that engineering requirements are met during production. Design of Experiments (DOE) Statistical experimental designs to economically improve product and process quality.A major tool used during the Improve Phase of Six Sigma methodology. Distributions Tendency of large numbers of observations to group themselves around some central value with a certain amount of variation or scatter on either side. Page 2 3. Effect That which was produced by a cause. Experiment A test under defined conditions to determine an unknown effect; to illustrate or verify a known law; to test or establish a hypothesis. Experimental Error A test under defined conditions to determine an unknown effect; to illustrate or verify a known law; to test or establish a hypothesis. Factory Processes For Six Sigma purposes, defined as design, manufacturing, assembly or test processes which directly impact hardware (see also transaction processes). Fishbone Diagram A schematic sketch, usually resembling a fishbone, which illustrates the main causes and subcauses leading to an effect (symptom).Also known as Cause-And-Effect Diagram. Failure Mode Effects Analysis (FMEA) A process in which each potential failure mode in every sub-item of an item is analyzed to determine its effect on other sub-items and on the required function of the item. Five Ms Major sources of variation:manpower, machine, method, material and measurement. Additionally, environment is considered to be a source of variation. Frequency Distribution The pattern or shape formed by the group of measurements in a distribution. Gage Repeatability &amp; Reproducibility (Gage R&amp;R) A measurement system evaluation to determine equipment variation and appraiser variation.This study is critical to ensure that the collected data is accurate. Histogram Vertical display of a population distribution in terms of frequencies; a formal method of plotting a frequency distribution. Independent Variable A controlled variable; a variable whose value is independent of the value of another variable. Page 3 4. Interaction When the effects of a factor A are not the same at all levels of another factor B. Lower Control Limit A horizontal dotted line plotted on a control chart which represents the lower process limit capabilities of a process. Master Black Belt An expert in quality techniques specially trained to advise leaders, facilitate quality teams and accelerate process improvement.Master Black Belts select, train and mentor Black Belts; develop and implement the Six Sigma deployment plan; and select and ensurecompletion of Six Sigma projects. Nonconformity A condition within a unit which does not conform to some specification, standard, and/or requirement; often referred to as a defect; any given nonconforming unit can have the potential for more than one nonconformity. Normal Distribution A continuous symmetrical density function characterized by a bell-shaped curve, e.g.,distribution of sampling averages. Pareto Diagram A chart which ranks, or places in order, common occurrences. Primary Control Variables The major independent variables used in the experiment. Probability The chance of something happening; the percent or number of occurrences over a large number of trails. Process A particular method of doing something, generally involving a number of steps oroperations. Process Capability The relative ability of any process to produce consistent results centered on a desired target value when measured over time. Page 4 5. Process Control Chart Any of a number of various types of graphs upon which data are plotted against specific control limits. Process Map Flow chart to analyze a process by breaking it down into its component steps, and then gaining a better understanding of the process, step-by-step. Process Spread The range of values which a given process characteristic displays; this particular term most often applies to the range but may also encompass the variance.The spread may be based on a set of data collected at a specific point in time or may reflect the variability across a given amount of time. Quality Functional Deployment (QFD) Structured methodology to identify and translate customer needs and wants into technical requirements and measurable features and characteristic.This tool is used to identify Critical to Quality Characteristics (CTQCs). Random Selecting a sample so each item in the population has an equal chance of being selected;lack of predictability; without pattern. Random Cause A source of variation which is random; a change in the source (trivial many variables) will not produce a highly predictable change in the response (dependent variable), e.g.,a correlation does not exist; any individual source of variation results in a small amount of variation in the response; cannot be economically eliminated from a process; an inherent natural source of variation. Random Variation Variations in data which result from causes which cannot be pinpointed or controlled. Regression Analysis A statistical technique for determining the relationship between one response and one or more independent variables. Robust The condition or state in which a response parameter exhibits hermetically to external cause of a nonrandom nature; e.g., impervious to perturbing influence. Page 5 6. Rolled Yield The combined resulting quality level, stated as a percent acceptable, that occurs when several processes known to produce defects at some rate are combined to produce aproduct.For example, a product that requires 100 steps, each of which produces a yield of 98.78% will produce a rolled yield of 0%, that is, no acceptable products. Scatter Diagram A diagram that displays the relationships between two variables. Sigma Standard deviation; an empirical measure based on the analysis of random variation in a standard distribution of values; a uniform distance from the mean or average value suchthat 68.26% of all values are within 1 sigma on either side of the mean, 95.44% are within 2 sigma, 99.73% are within 3 sigma, 99.9% are within 4 sigma and so forth. Sigma Level A statistical estimate of the number of defects that any process will produce equivalent to defects per million opportunities for that process. Six Sigma Quality A combination of verified customer requirements reflected in robust designs and matched to the capability of production processes that creates products with fewer then 3.4 defects per million opportunities to make a defect.World-class quality.A collection of tools and techniques for raising quality to worked-class levels. Stable Process A process which i free of assignable causes, e.g., in statistical control. Standard Deviation A statistical index of variability which describes the spread. Statistical Control A quantitative condition which describes a process that is free of assignable/special causes of variation, e.g.,variation in the central tendency and variance.Such a condition is most often evidenced on a control chart. Statistical Process Control The application of statistical methods and procedures relative to a process and a given set of standards. Page 6 7. Transaction Processes For Six Sigma purposes, defined as any business process that contributes to customer satisfaction or impacts operating efficiency and which is designated by a vice president or by GE Corporate as a focus for process improvement.Such efforts will be led by theprocess owner, with teams being led by specially trained transaction project leaders and/or by certified Black Belts. Transaction Project Leader An individual designated to lead a transaction process improvement project.Transaction project leaders attend a four-day course in specific Six Sigma tools and tactics. Upper Control Limit A horizontal line on a control chart (usually dotted) which represents the upper limits ofprocess capability. Variable A characteristic that may take on different values. Variables Data A numerical measurement made at the interval or ratio level; quantitative data, e.g.., ohms, voltage, diameter; subdivisions, of the measurement scale are conceptually meaningful, e.g.., 1.6478 volts. Variation Any quantifiable difference between individual measurements; such differences can be classified as being due to common causes (random) or special causes (assignable). Xs Designation in Six Sigma terminology for those variables which are independent, root causes; as opposed to Ys which are dependent outputs of a process.Six Sigma focuses on measuring and improving Xs, to see subsequent improvement in Ys. X &amp; R Charts A control chart which is a representation of process capability over time; displays the variability in the process average and range across time. Ys Designation in Six Sigma terminology for those variables which are dependent outputs ofa process, as opposed to Xs which are independent root causes. Page 7 8. 6Ms - Man, Machines, Materials, Methods, Measurement, Mother Nature ANOVA - Analysis of Variance BB - Black Belts C&amp;E Matrix - Cause &amp; Effect Matrix CAP - Change Acceleration Process C&amp;E - Cause &amp; Effect COPQ - Cost of Poor Quality COQ - Cost of Quality Cp - Capability Process Index (Ideal) - Pooled Cpk - Capability Process Index (Real) - Pooled CTQ - Critical to Quality CUSUM - Cumulative Sum DF - Degrees of Freedom DFM - Design for Manufacturing DFSS - Design for Six Sigma DOE - Design of Experiments DPM - Defects per Million DPMO - Defects per Million Opportunities DPO - Defects per Opportunities DPU - Defects per Unit EVOP - Evolutionary Operation EWMA - Exponential Weight Moving Average FMEA - Failure Mode &amp; Effect Analysis GAGEAOV - Gage Analysis of Variance GRR - Gage Repeatability &amp; Reproducibility IDOV - Identify, Design, Optimize, Validate IQR - Inter Quartile Range ISO - International Organization for Standardization KNP -Key Noise Parameters KPI (Factors) - Key Process Inputs KPIV (KCP) - Key Process Input Variable (Key Control Parameter) KPOV or - Key Process Output Variable(Response) LCL - Lower Controls Limits LSL - Lower Specification Limits MAIC - Measurement, Analysis, Improvement, Control MBB - Master Black Belt 9. MBNQA - Malcolm Baldrich National Quality Award MGF - Minitab Graph File MSA - Measurement System Analysis MTB - Minitab MTW - Minitab Worksheet NPI - New Product Introduction OJT - On the Job Training P(ND) - Probability (Not Defective) PEAR - Process, Engineering, Application, Regulatory CTQs Pp - Capability Process Index (Ideal) - Overall Ppk - Capability Process Index (Real) - Overall PPM - Parts per Million QA - Quality Assurance QFD - Quality Functional Deployment P/T Ratio - Precision / Tolerance Ratio ROI - Return of Investment RPN - Risk Priority Number RSM - Response Surface Methodology RTY - Rolled Throughput Yield SOP - Standard Operating Procedure SOV - Source of Variation SPC - Statistical Process Control SQC - Statistical Quality Control T - Target TCS - Total Customer Satisfaction TOP - Total Opportunities TQL - Total Quality Leadership TQM - Total Quality ManagementUCL - Upper Control Limits USL - Upper Specification Limits WIP - Work in Process XLS - Excel Spreadsheet Zlt - Z-long term ZST - Z-short term 10. = Summation; i.e., 1 + 2 + 3 + 4 + 5 = 15 ! = Factorial; i.e. 5! = 5 x 4 x 3 x 2 x 1 = 120 e = Natural constant = 2.7183 g = Total number of subgroups. i = Thei thelement in a string of 1, 2, 3, 4, --i j = The j thelement in a string of 1, 2, 3, 4, -- j n = Subgroup size (for high volume production, the range fornwouldnormally be between 3 and 10. R = Range = difference (subtraction) between the maximum and minimummeasurements observed/recorded for a subgroup R = Average of subgroup ranges =R;g S = Standard deviation = X = A variable measurement made on an individual characteristic and on anindividual unit (often a process output variable) recorded onto a data logor control chart. Note :Xis also used in another sense to denote the variables thatcause process variation. X = Average of theXobservations associated with a subgroup of sizen X = Average of obs...</p>