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Glossary
The following glossary is a partial extract from the book "Six Sigma Software Quality Improvement - Success Stories from Leaders in the Hi-Tech Industry" printed with permission by the authors

Accuracy: The characteristic of a measurement that tells how close an observed value is to a true value.
Affinity diagram: A management tool used to organize information (usually gathered during a brainstorming activity).
Alignment: The actions taken to ensure that a process or activity supports the organization’s strategy, goals and objectives.
Analysis of variance (ANOVA): A basic statistical technique for analyzing experimental data. It subdivides the total variation of a data set into meaningful component parts associated with specific sources of variation in order to test a hypothesis on the parameters of the model or to estimate variance components. There are three models: fixed, random and mixed.
Assignable cause: A name for the source of variation in a process that is not due to chance and therefore can be identified and eliminated. Also called “special cause.”
Attribute data: Go/no-go information. The control charts based on attribute data include percent chart, number of affected units chart, count chart, count per unit chart, quality score chart and demerit chart. Attributes, method of: Measurement of quality by the method of attributes consists of noting the presence (or absence) of some characteristic (attribute) in each of the units under consideration and counting how many units do (or do not) possess it. Example: go/no-go gauging of a dimension.
Autocorrelation: A technique in time series analysis that checks to see if patterns in a time series repeat themselves over time; i.e., tests whether a data series correlates to itself over different time intervals or delays.
Availability: The ability of a product to be in a state to perform its designated function under stated conditions at a given time.
Baseline measurement: The beginning point, based on an evaluation of the output over a period of time, used to determine the process parameters prior to any improvement effort; the basis against which change is measured.
Benchmarking: An improvement process in which a company measures its performance against that of best in class companies, determines how those companies achieved their performance levels and uses the information to improve its own performance. The subjects that can be benchmarked include strategies, operations, processes and procedures.
Benefit-cost analysis: An examination of the relationship between the monetary cost of implementing an improvement and the monetary value of the benefits achieved by the improvement, both within the same time period.
Best practice: A superior method or innovative practice that contributes to the improved performance of an organization, usually recognized as “best” by other peer organizations.
Big Y,Vital X: A term describe primary business goals (Y’s) and the factors the contribute to these outcomes (X’s).
Black Belt (BB): Full-time team leader responsible for implementing process improvement projects—define, measure, analyze, improve and control (DMAIC) or define, measure, analyze, design and verify (DMADV)—within the business to drive up customer satisfaction levels and business productivity. Blemish: An imperfection severe enough to be noticed but that should not cause any real impairment with respect to intended normal or reasonably foreseeable use.
Bottleneck: Any resource whose capacity is equal to or less than the demand placed on it.
Box plots: A simple graphical technique for showing data sets; the “box plot” shows the mean, 25% and 75% quartiles and 5% and 95% values. Sometimes outliers and median are also show. A graphical method of illustrating basic descriptive statistics
Brainstorming: A technique teams use to generate ideas on a particular subject. Each person in the team is asked to think creatively and write down as many ideas as possible. The ideas are not discussed or reviewed until after the brainstorming session.
Breakthrough event: A dramatic change in process during which a team gets past an old barrier or milestone to achieve a significant increase in efficiency, quality or some other measure.
Burning Platform: A term coined from a true story in which four men were left stranded on the burning platform of an the Piper Alpha oil rig on fire in the North sea in 1967. The men faced the choice of staying where they were and facing certain death- or taking the risky step of jumping into the freezing ocean and risking death from hypothermia. The two men who decided to remain behind perished. There are two elements to the story. The two who stayed put died. The unacceptable option is staying the same and hoping things get better. Against the odds the two who jumped into the sea survived. The message is
sometimes radical risky change is essential.
C chart: A type of SPC charts that displays counts of defects.
Cause and effect diagram: A tool for analyzing process dispersion. It is also referred to as the “Ishikawa diagram,” because Kaoru Ishikawa developed it, and the “fishbone diagram,” because the complete diagram resembles a fish skeleton. The diagram illustrates the main causes and sub-causes leading to an effect (symptom). The cause and effect diagram is one of the “seven tools of quality.”
Cause effect matrix: A matrix variant if an Ishikawa or fishbone chart; useful when there are a large number of factors that are difficult to draw using fishbone diagrams.
CDOV: Concept, Design, Optimize, Verify-another variant of DFSS popular for use in robust product design.
Champion: A business leader or senior manager who ensures that resources are available for training and projects, and who is involved in project tollgate reviews; also an executive who supports and addresses Six Sigma organizational issues.
Change agent: An individual from within or outside an organization who facilitates change within the organization. May or may not be the initiator of the change effort.
Charter: A written commitment approved by management stating the scope of authority for an improvement project or team.
Classification of defects: The listing of possible defects of a unit, classified according to their seriousness. Note: Commonly used classifications: class A, class B, class C, class D; or critical, major, minor and incidental; or critical, major and minor. Definitions of these classifications require careful preparation and tailoring to the product(s) being sampled to enable accurate assignment of a defect to the proper classification. A separate acceptance sampling plan is generally applied to each class of defects.
Common causes: Causes of variation that are inherent in a process over time. They affect every outcome of the process and everyone working in the process (see also “special causes”).
Communication plan: A part of an overall control plan developed in the Control phase of a DMAIC project.
Comparative methods: Different methods of comparing to data sets; including correlation analysis, t-tests, ANOVA, SOV studies, and nonparametric tests.
Conjoint analysis: A statistical technique that requires analysts to make a series of tradeoffs and analyze these trade-offs to determine the relative importance of component attributes.
Constraint: Anything that limits a system from achieving higher performance or throughput; also, the bottleneck that most severely limits the organization’s ability to achieve higher performance relative to its purpose/goal.
Contingency charts: Actually a table, not a chart, which shows the frequency with which two different non-parametric pieces of data are observed together, e.g., defect codes by machine id.
Continuous improvement (CI): Sometimes called continual improvement. The ongoing improvement of products, services or processes through incremental and breakthrough improvements.
Control chart: A chart with upper and lower control limits on which values of some statistical measure for a series of samples or subgroups are plotted. The chart frequently shows a central line to help detect a trend of plotted values toward either control limit.
Control limits: The natural boundaries of a process within specified confidence levels, expressed as the upper control limit (UCL) and the lower control limit (LCL).
Control plan (CP): A document that describes the required characteristics for the quality of a product or service, including measures and control methods.
Corrective action plans: A part of an overall control plan developed in the Control phase of a DMAIC project.
Correlation: The degree to which two factors are related to one another, statistically represented by Pearson’s correlation coefficient. Correlation does not mean causation since two factors may be related by not cause one another (e.g., height and weight).
Cost of poor quality (COPQ): The costs associated with providing poor quality products or services. There are four categories of costs: internal failure costs (costs associated with defects found before the customer receives the product or service), external failure costs (costs associated with defects found after the customer receives the product or service), appraisal costs (costs incurred to determine the degree of conformance to quality requirements) and prevention costs (costs incurred to keep failure and appraisal costs to a minimum).
Cp and Cpk: Capability indices to compare the output of a process to the specification limits. Cp is the ratio of permissible process variability divided by actual process variability. Cpk is a complimentary measure that takes into account the closeness of the mean of the sample to the target.
Critical to quality (CTQ): a method of listing and prioritizing different factors that affect the quality of an product, process, or service. May be determined through use of models or even through VOC surveys and requirements analysis.
Cumulative sum control chart (CUSUM): A control chart on which the plotted value is the cumulative sum of deviations of successive samples from a target value. The ordinate of each plotted point represents the algebraic sum of the previous coordinate and the most recent deviations from the target.
Cycle time: The time required to complete one cycle of an operation. If cycle time for every operation in a complete process can be reduced to equal takt time, products can be made in single-piece flow (see “takt time”).
Decision matrix: A matrix used by teams to evaluate possible solutions and selecting the best one.
Delighter: A feature of a product or service that a customer does not expect to receive but that gives pleasure to the customer when received.
Design for Six Sigma (DFSS): A product and process development methodology related to traditional Six Sigma, focusing on robust design and defect prevention. Rich in the use of quantitative methods, it seeks to develop products and services that provide greater customer satisfaction and increased market share. DFSS is largely a design activity requiring specialized tools including: quality function deployment (QFD), axiomatic design, TRIZ, Design for X, design of experiments (DOE), Taguchi methods, tolerance design, robust design and response surface methodology (see also “DMADV”).
Design of experiments (DOE): A branch of applied statistics dealing with planning, conducting, analyzing and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters.
Deviation: In numerical data sets, the difference or distance of an individual observation or data value from the center point (often the mean) of the set distribution.
Dissatisfiers: The features or functions a customer expects that either are not present or are present but not adequate; also pertains to employees’ expectations.
Distribution (statistical): The amount of potential variation in the outputs of a process, typically expressed by its shape, average or standard deviation.
DMADV: A data driven quality strategy for designing products and processes, it is an integral part of a Six Sigma quality initiative. It consists of five interconnected phases: define, measure, analyze, design and verify.
DMAIC: A data driven quality strategy for improving processes and an integral part of a Six Sigma quality initiative. DMAIC is an acronym for define, measure, analyze, improve and control.
DFMEA: Design FMEA: FMEA performed on new designs or products to proactively identify failure modes and take necessary preventive action.
Effort riskmatrix: A chart similar to a bubble chart that is used to show the tradeoffs between risk and effort when evaluating solutions or alternatives.
Error proofing: A process used to prevent errors from occurring or to immediately point out a defect as it occurs. If defects are not passed down an assembly line, throughput and quality improve (see also “poke-yoke”).
Exciter: See “delighter.”
External failure: Nonconformance identified by the external customers.
Failure cost: The cost resulting from the occurrence of defects.
Failure mode effects analysis (FMEA): A procedure 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.
Fishbone diagram: See “cause and effect diagram.”
Fitness for use: A term used to indicate that a product or service fits the customer’s defined purpose for that product or service.
Five S’s: Five Japanese terms beginning with S (with English translations also beginning with S) used to create a workplace suited for visual control and lean production: Seiri (sort, structure or sift) means to separate needed tools, parts and instructions from unneeded materials and remove the latter; Seiton (set in order or systematize) means to neatly arrange and identify parts and tools for ease of use; Seiso (sanitize or shine) means to conduct a cleanup campaign; Seiketsu (standardize) means to conduct seiri, seiton and seiso at frequent, indeed daily, intervals tomaintain a workplace in perfect condition; and Shitsuke (sustain or self-discipline) means to form the habit of always following the first four S’s. Collectively, they define an orderly, well-inspected, clean and efficient working
environment.
Five whys: A technique for discovering the root causes of a problem and showing the relationship of causes by repeatedly asking the question, “Why?”
Frequency distribution (statistical): A table that graphically presents a large volume of data so the central tendency (such as the average or mean) and distribution are clearly displayed.
Gantt chart: A type of bar chart used in process planning and control to display planned work and finished work in relation to time.
Gap analysis: The comparison of a current condition to the desired state.
Gauge repeatability and reproducibility (GR&R): The evaluation of a gauging instrument’s accuracy by determining whether the measurements taken with it are repeatable (there is close agreement among a number of consecutive measurements of the output for the same value of the input under the same operating conditions) and reproducible (there is close agreement among repeated measurements of the output for the same value of input made under the same operating conditions over a period of time).
Goal: A broad statement describing a desired future condition or achievement without being specific about how much and when.
Goal-Question-Metric paradigm (GQM): A formal technique developed by Victor Basili to ensure selection of appropriate metrics for a process or project. The acronym for “goal, question, metric,” is an approach to software metrics that ensures alignment of metrics to organizational goals.
Green Belt (GB): An employee of an organization who has been trained on the improvement methodology of Six Sigma and will lead a process improvement or quality improvement team as part of his or her full-time job.
Heijunka: Amethod of leveling production, usually at the final assembly line, that makes just-in-time production possible. It involves averaging both the volume and sequence of different model types on a mixed model production line. Using this method avoids excessive batching of different types of product and volume fluctuations in the same product.
Heijunka box: A heijunka box is a visual scheduling tool used in heijunka, a Japanese concept for achieving a smoother production flow. While heijunka refers to the concept of achieving production smoothing, the heijunka box is the name of a specific tool used in achieving the aims of heijunka. It is generally a wall schedule which is divided into a grid of boxes or a set of pigeon-holes (rectangular receptacles). Each column of boxes representing a specific period of time. Lines are drawn down the schedule/grid to visually break the schedule into columns of individual shifts or days or weeks. Colored cards representing individual jobs (referred to as kanban cards) are placed on the heijunka box to provide a visual representation of the upcoming production runs.
Histogram: A graphic summary of variation in a set of data. The pictorial nature of the Histogram lets people see patterns that are difficult to detect in a simple table of numbers. The histogram is one of the “seven tools of quality.”
House of quality: A product planning matrix, somewhat resembling a house, that is developed during quality function deployment and shows the relationship of customer requirements to the means of achieving these requirements.
IDOV: A popular methodology mostly used in the manufacturing industry, particularly with DFSS Six Sigma approaches. The acronym stands for Identify, Design, Optimize and Validate. These development phases are similar to the traditional Six Sigma methodology MAIC (Measure, Analyze, Improve and Control).
In-control process: A process in which the statistical measure being evaluated is in a state of statistical control; in other words, the variations among the observed sampling results can be attributed to a constant system of chance causes (see also “out-of-control process”).
Inputs: The products, services, material and so forth obtained from suppliers and used to produce the outputs delivered to customers.
Internal failure: A product failure that occurs before the product is delivered to external customers.
Ishikawa diagram: See “cause and effect diagram.”
Kaizen: A Japanese term that means gradual unending improvement by doing little things better and setting and achieving increasingly higher standards. Masaaki Imai made the term famous in his book, Kaizen: The Key to Japan’s Competitive Success.
Kaizen blitz: A method of starting and completing process improvement projects in a very short time, often requiring 100 percent dedication of resources to a project until it iscompleted.
Kanban: A Japanese term for one of the primary tools of a just-in-time 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 and quantity.
Kano analysis: The Kano model is a theory of product development and customer satisfaction developed in the 80s by Professor Noriaki Kano which classifies customer preferences into different categories (three to five different types). This analysis is helpful when analyzing and prioritizing requirements for projects.
Key performance indicator (KPI): A statistical measure of how well an organization is doing. A KPI may measure a company’s financial performance or how it is holding up against customer requirements.
Kruskal-Wallis test: The Kruskal-Wallis test is a nonparametric test to compare three or more samples. It tests the null hypothesis that all populations have identical distribution functions against the alternative hypothesis that at least two of the samples differ only with respect to location (median), if at all.
Lead time: The total time a customer must wait to receive a product after placing an order.
Lean: Producing the maximum sellable products or services at the lowest operational cost while optimizing inventory levels.
Line balancing: A process in which work elements are evenly distributed and staffing is balanced to meet takt time (see “takt time”).
Lower control limit (LCL): Control limit for points below the central line in a control chart.Mann-Whitney test: A non-parametric statistical hypothesis test for comparing the medians of two independent samples of observations.
MasterBlackBelt (MBB): Six Sigma or quality experts 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 is a resource for utilizing statistical process control within processes.
Mean: A measure of central tendency; the arithmetic average of all measurements in a data set.
Measurement systems analysis (MSA): An analytical method that examines what is measurable and what is not, e.g., Gage R&R studies.
Median: The middle number or center value of a set of data in which all the data are arranged in sequence.
Mistake proofing: See “poke-yoke.”
Monte Carlo Simulation: A computer based technique that generates pseudo-random data for a model to evaluate outcomes over hundreds or thousands of potential situations
Muda: Japanese for waste. Any activity that consumes resources but creates no value for the customer.
Multivariate charts: A Multivariate chart is a type of SPC control chart for variables data. Multivariate charts are used to detect shifts in the mean or the relationship (covariance) between several related parameters.
Nominal group technique: A technique, similar to brainstorming, used by teams to generate ideas on a particular subject. Team members are asked to silently come up with as many ideas as possible, writing them down. Each member is then asked to share one idea, which is recorded. After all the ideas are recorded, they are discussed and prioritized by the group.
Nonparametric tests: Nonparametric tests are often used in place of their parametric counterparts when certain assumptions about the underlying population are questionable. For example, when comparing two independent samples, the Mann-Whitney test does not assume the difference between the samples is normally distributed, whereas its parametric counterpart, the two-sample t-test, does. Nonparametric tests may be, and often are, more powerful in detecting population differences when certain assumptions are not satisfied. All tests involving ranked data (data that can be put in order) are nonparametric.
Non-value added (NVA): Activities or actions taken that add no real value to a product or service, making such activities or actions a form of waste (see “value added”).
Normal distribution (statistical): The charting of a data set in which most of the data points are concentrated around the average (mean), thus forming a bell shaped curve.
Np-charts: A type of SPC chart that tracks the number of nonconforming units within one subgroup. The number nonconforming units (np), rather than the fraction of nonconforming units (p), is plotted against the control limits (see “P-chart”).
Objective: A specific statement of a desired short-term condition or achievement; includes measurable end results to be accomplished by specific teams or individuals within time limits.
Operational metrics definitions: The formal definition of metrics so that they can be collected, stored, used, and interpreted consistently across an organization and over time.
Orthogonal defect classification: A method of classifying defects that generates rich data for analysis.
Out of specification: A term that indicates a unit does not meet a given requirement.
Out-of-control process: A process in which the statistical measure being evaluated is not in a state of statistical control. In other words, the variations among the observed sampling results can be attributed to a constant system of chance causes (see also “in-control process”).
Outputs: Products, materials, services or information provided to customers (internal or external), from a process.
Pareto chart: A graphical tool for ranking causes from most significant to least significant. It is based on the Pareto principle, which was first defined by J. M. Juran in 1950. The principle suggests most effects come from relatively few causes; that is, 80% of the effects come from 20% of the possible causes.
Payoff tables: A type of quantitative risk analysis that combines possible outcomes and probabilities to determine the best course of action
P-charts: A type of SPC chart that tracks the nonconforming units as a fraction within one subgroup (see “np-chart”).
Pilot: A common practice of testing solutions in a limited venue before widely disseminating to production r across an organization
Poke-yoke: Japanese term that means mistake-proofing. A poke-yoke device is one that prevents incorrect parts from being made or assembled or easily identifies a flaw or error.
Precision: The aspect of measurement that addresses repeatability or consistency when an identical item is measured several times.
Probability (statistical): A term referring to the likelihood of occurrence of an event, action or item.
Process capability index: See “Cp and Cpk.”
Process control: The methodology for keeping a process within boundaries; minimizing the variation of a process.
Process FMEA (PMFEA): An analysis method used to identify potential problems with processes where steps can be skipped, performed out of sequence, or performed improperly.
Process map: A type of flowchart depicting the steps in a process, with identification of responsibility for each step and the key measures.
Process owner: The person who coordinates the various functions and work activities at all levels of a process, has the authority or ability to make changes in the process as required and manages the entire process cycle to ensure performance effectiveness.
Process: A set of interrelated work activities characterized by a set of specific inputs and value added tasks that make up a procedure for a set of specific outputs.
Productivity: A measurement of output for a given amount of input. Increases in productivity are considered critical to raising living standards.
Prototyping: A common method to test new systems before committing them to production or sending the systems to customers
Push system: The traditional method of manufacturing where material or lots are sent to the next step regardless of whether they are able to process the lots or materials. this type of flow is susceptible to bottlenecks, the formation of large work in process queues, and long cycle times.
Pull system: An alternative to scheduling individual processes, in which the customer process
withdraws the items it needs from a supermarket, and the supplying process produces to
replenish what was withdrawn.
Quality function deployment (QFD): A structured method in which customer requirements are translated into appropriate technical requirements for each stage of product development and production. The QFD process is often referred to as listening to the voice of the customer.
Queue time: The time a product spends in a line awaiting the next design, order processing or fabrication step.
Range (statistical): Themeasure of dispersion in a data set (the difference between the highest and lowest values).
Range chart (R chart): A control chart in which the subgroup range, R, is used to evaluate the stability of the variability within a process.
Regression analysis: A statistical technique for determining the bestmathematical expression describing the functional relationship between one response and one ormore independent variables.
Response plan: a part of an overall control plan developed in the Control phase of a DMAIC project
Rollout/deployment plan: Part of an overall control plan developed in the Control phase of a DMAIC project
Root cause analysis (RCA): Different ways of determining the originating cause of a problem or defect. Sometimes this is achieved by analyzing data preceding an event; sometimes it is achieved by brainstorming.
Root cause: A factor that caused a nonconformance and should be permanently eliminated through process improvement.
Run chart: A chart showing a line connecting numerous data points collected from a process running over a period of time.
Sample standard deviation chart (S chart): A control chart in which the subgroup standard deviation, s, is used to evaluate the stability of the variability within a process.
Satisfier: A term used to describe the quality level received by a customer when a product or service meets expectations.
Scatter diagram (or scatterplot): A graphical technique to analyze the relationship between two variables. Two sets of data are plotted on a graph, with the y-axis being used for the variable to be predicted and the x-axis being used for the variable to make the prediction. The graph will show possible relationships (although two variables might appear to be related, they might not be: those who know most about the variables must make that evaluation).
Sensitivity analysis: An analytical method that changes factors to determine which are the most important to the desired outcome.
Seven tools of quality: Tools that help organizations understand their processes to improve them. The tools are the cause and effect diagram, check sheet, control chart, flowchart, histogram, Pareto chart and scatter diagram.
SIPOC: A process mapping method that examines each step in a process flow and characterizes it by its Inputs, Outputs, Customer (next step or end user), Supplier (source or predecessor), and the Process (the work performed or done at this step); sometimes done in reverse and called COPIS
Six Sigma: A methodology that provides organizations tools to improve the capability of their business processes. This increase in performance and decrease in process variation lead to defect reduction and improvement in profits, employee morale and quality of products or services. Six Sigma quality is a term generally used to indicate a process is well controlled (±6 s from the centerline in a control chart).
Source of variation (SOV): Statistical analysis to determine what factors are the greatest contributors to variation.
Special causes: Causes of variation that arise because of special circumstances. They are not an inherent part of a process. Special causes are also referred to as assignable causes (see also “common causes”).
Stakeholder analysis: the process of identifying the individuals or groups that are likely to affect or be affected by a proposed action, and sorting them according to their impact on the action and the impact the action will have on them. This information is used to assess how the interests of those stakeholders should be addressed in a project plan, policy, program, or other action
Standard deviation (statistical): A computed measure of variability indicating the spread of the data set around the mean.
Statistical process control (SPC): The application of statistical techniques to control a process. The term “statistical quality control” is often used interchangeably with “statistical process control.”
SWOT: A risk analysis technique; acronym stands for Strengths, Weaknesses, Opportunities, and Threats
Takt time: The rate of customer demand, takt time is calculated by dividing production time by the quantity of product the customer requires in that time. Takt, the heartbeat of a lean manufacturing system, is an acronym for a Russian phrase.
Tests of normality: Statistical tests like Levine’s test or Bartletts test that test a data set to determine if they have a normal or Gaussian distribution.
Theory of constraints (TOC): Also called constraints management, TOC is a lean management
philosophy that stresses removal of constraints to increase throughput while decreasing inventory and operating expenses.
Time series analysis: Analysis that examines data for patterns over time (see “autocorrelation”).
Tolerance: The maximum and minimum limit values a product may have and still meet customer requirements.
Training plan: A part of the overall control plan developed in the Control phase of a DMAIC project.
Tree diagram: A management tool that depicts the hierarchy of tasks and subtasks needed to complete an objective. The finished diagram bears a resemblance to a tree.
Trend control chart: A control chart in which the deviation of the subgroup average, X-bar, from an expected trend in the process level is used to evaluate the stability of a process.
Trend: The graphical representation of a variable’s tendency, over time, to increase, decrease or remain unchanged.
TRIZ: Russian acronym for the “Theory of Inventive ProblemSolving.”TRIZ is an international science of creativity that relies on the study of the patterns of problems and solutions; more than three million patents have been analyzed to discover the patterns that predict breakthrough solutions to problems, and these have been codified within TRIZ.
T-test: A statistical technique used to determine whether two different data sets are statistically different or whether the differences could have occurred by chance alone. Used for small sized data sets.
U chart: A type of SPC chart used to track defects or non-conformances per unit within one subgroup.
Unit: An object on which a measurement or observation can be made. Note: Commonly used in the sense of a “unit of product, ”the entity of product inspected in order to determine whether it is defective.
Upper control limit (UCL): Control limit for points above the central line in a control chart.
Value added: Activities that transform input into a customer usable output. The customer can be internal or external to the organization.
Value stream mapping: A pencil and paper tool used in two stages: 1. Follow a product’s production path from beginning to end and draw a visual representation of every process in the material and information flows. 2. Then draw a future state map of how value should flow. The most important map is the future state map.
Value stream: All activities, both value added and non-value added, required to bring a product from raw material state into the hands of the customer, bring a customer requirement from order to delivery and bring a design from concept to launch.
Variable data: Measurement information. Control charts based on variable data include average (X-bar) chart, range (R) chart, and sample standard deviation (s) chart.
Variation: A change in data, characteristic or function caused by one of four factors: special causes, common causes, tampering, or structural variation.
Vision: An overarching statement of the way an organization wants to be; an ideal state of being at a future point.
Vital few, useful many: A term used by Joseph M. Juran to describe his use of the Pareto principle, which he first defined in 1950. (The principal was used much earlier in economics and inventory control methodologies.) The principle suggests most effects come from relatively few causes; that is, 80%of the effects come from20%of the possible causes. The 20% of the possible causes are referred to as the “vital few”; the remaining causes are referred to as the “useful many” When Juran first defined this principle, he referred to the remaining causes as the “trivial many,” but realizing that no problems are trivial in quality assurance, he changed it to “useful many.”
Voice of the customer (VOC): The expressed requirements and expectations of customers relative to products or services, as documented and disseminated to the members of the providing organization.
Waste: Any activity that consumes resources and produces no added value to the product or service a customer receives (see “muda”).
Weighted attribute analysis: A method of evaluating the preference or priority of different solutions based upon weightings that are assigned to the different factors that contribute to the overall system or solution (also called net attribute analysis).
Work in process (WIP): Items between machines or equipment waiting to be processed.World-class quality: A term used to indicate a standard of excellence: best of the best.
Xmoving average charts: A type of SPC chart that plots averages across multiple data points instead of individual values
Xbar-R charts: A type of two panel SPC chart that shows both average measures of samples and ranges of those samples
Xbar-S charts: A type of two panel SPC chart that shows both average measures of samples and the standard deviation of those samples
Zero defects: A performance standard and methodology developed by Philip B. Crosby that states if people commit themselves to watching details and avoiding errors, they can move closer to the goal of zero defects.

 

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