Six Sigma Glossary Flashcards
3 D’s
Tool for demonstrating the need for change in the areas of Data/Diagnosis, Demonstate and Demand
3P
A 3D model of TQM, having People, Product and Process as the 3 axis.
5S
Sort, shine, set in order, standardize and sustain. A method of creating a clean and orderly workplace that exposes waste and errors.
Affinitize
Methods for reducing the list of ideas by grouping them into categories or voting to identify key ideas.
Baseline
The level of a process performance when a project is initiated.
Black Belt
Leaders of teams responsible for measuring, analyzing, improving and controlling key processes that influence customer satisfaction and/or productivity growth. Black Belts are full time positions.
Output Metrics
Quantify the overall performance of the process
Defect Measurment
Accounting for the number or frequency of defects that cause lapses in product or service quality.
Pareto Diagram
Focuses on efforts or the problems that have the greatest potential for improvment by showing relative frequency and/or size in decending bar graph.
Process Mapping
Illustrated description of how things get done, which enables participants to visualize an entire process and identify areas of strength and weaknesses. It helps reduce sysle time and effects while recognizing the value of individual contributions.
Root Cause Analysis
Study of original reason for nonconformance with a process. When the root cause is removed or corrected the nonconformance will be eliminated.
Statistical Process Control
The application of statistical methods to analyze data, study and monitor process capability and performance.
Tree Diagram
Graphically shows any broad goal broken into different levels of detailed actions. It encourages team members to expand their thinking when creating solutions.
Control
The state of stability, normal variation and predictability. Process of regulating and guiding operations and processes using quantitative data.
Defects
Sources of customer irritation. Defects are costly to both customers and to manufacturers or service providers. Eliminating defects provides cost benefits.
Green Belt
Similar to Black Belt but not a full time position.
Master Black Belt
First and foremost teachers. They also review and mentor Black Belts. Selection criteria for Master Black Belts are quantitative skills and the ability to teach and mentor. Master Black Belts are full-time positions.
Variance
A change in a process or business practice that may alter its expected outcome.
CTQ: Critical to Quality (Critical Y)
Element of a process or practice which has a direct impact on its perceived quality.
Accountability
Conditional personal or professional liability ‘after’ the fact, determined by action or responsibility. Accountability to action assumes the willingness to be held accountable for adequate expertise and capability.
Bar Chart
A bar chart is a graphical comparison of several quanitites in which the lengths of the horizontal or vertical bars represent the relative magnitude of the values.
Pareto Chart
A pareto chart is used to graphically summarize and display the relative importance of the differences between groups of data.
Histogram
A histogram is used to graphically summarize and display the distribution of a process data set.
Alternative Hypothesis (Ha)
The alternate hypotheses (Ha) is a statement that the means, variance, etc. of the samples being tested are not equal. In software program which present a p value in lieu of F Test or T Test when the P value is less than or equal to your agreed upon decision point (typlically 0.05) you accept the Ha as being true and reject the null Ho. (Ho always assumes that they are equal).
Hypothesis Testing
Hypothesis testing refers to the process of using statistical analysis to determine if the observed differences between two or more samples are due to random chance (as stated in the null hypothesis) or to true differences in the samples (as stated in the alternate hypothesis). A null hypothesis (H0) is a stated assumption that there is no difference in parameters (mean, variance, DPMO) for two or more populations. The alternate hypothesis (Ha) is a statement that the observed difference or relationship between two populations is real and not the result of chance or an error in sampling. Hypothesis testing is the process of using a variety of statistical tools to analyze data and, ultimately, to fail to reject or reject the null hypothesis. From a practical point of view, finding statistical evidence that the null hypothesis is false allows you to reject the null hypothesis and accept the alternate hypothesis.
Null Hypothesis (H0)
A null hypothesis is a stated assumption that there is no difference in parameters (mean, variance, DPMO) for two or more populations. Accoriding to the null hypltheses, any oberved difference in samples is due to chance or sampling error.
The term that statisticians often use to indicate the statistical hypotheses being tested.
P- Value
The probability value (p-value) of a statistical hypothesis test is the probability of getting a value of the test statistic as extreme as or more extreme than that observed by chance alone, if the null hypothesis Ho, is true.
It is the probability of wrongly rejecting the null hypothesis if it is in fact true.
It is equal to the significance level of the test for which we would only just reject the null hypothesis. The p-value is compared with the desired significance level of our test and, if it is smaller, the result is significant. That is, if the null hypothesis were to be rejected at the 5% significance level, this would be reported as “p < 0.05”.
Small p-values suggest that the null hypothesis is unlikely to be true. The smaller it is, the more convincing the evidence is that null hypothesis is false. It indicates the strength of evidence for say, rejecting the null hypothesis H0, rather than simply concluding “Reject Ho” or “Do not reject Ho”.
The probability value (p-value) of a statistical hypothesis test is the probability of getting a value of the test statistic as extreme as or more extreme than that observed by chance alone, if the null hypothesis Ho, is true.
It is the probability of wrongly rejecting the null hypothesis if it is in fact true.
It is equal to the significance level of the test for which we would only just reject the null hypothesis. The p-value is compared with the desired significance level of our test and, if it is smaller, the result is significant. That is, if the null hypothesis were to be rejected at the 5% significance level, this would be reported as “p < 0.05”.
Small p-values suggest that the null hypothesis is unlikely to be true. The smaller it is, the more convincing the evidence is that null hypothesis is false. It indicates the strength of evidence for say, rejecting the null hypothesis H0, rather than simply concluding “Reject Ho” or “Do not reject Ho”.
5 Whys
The 5 why’s typically refers to the practice of asking, five times, why the failure has occurred in order to get to the root cause/causes of the problem. There can be more than one cause to a problem as well. In an organizational context, generally root cause analysis is carried out by a team of person related to the problem. No special technique is required.
Benchmarking
The concept of discovering what is the best performance being achieved, whether in your company, by a competitor, or by an entirely different industry.
Benchmarking is an improvement tool whereby a company measures and compares all its functions, systems and practices against strong competitors, identifying quality gaps in the organization, and striving to achieve competitive advantage locally and globally.
Business Value Added
A step or change made to the product which is necessary for future or subsequent steps but is not noticed by the final customer.
Charter
A document or sheet that clearly scopes and identifies the purpose of a Quality Improvement project. Items specified include background case, purpose, team members, scope, timeline.
Chi Square Test
The Chi Square test is a statistical test which consists of three different types of analysis 1) Goodness of fit, 2) test for homogeneity, 3) test of independence.
The test for goodness of fit determines if the sample under analysis was drawn from a population that follows some specified distribution.
The test for homogeneity answers the proposition that several populations are homogeneous with respect to some characteristic.
The test for independence (one of the most frequent uses of Chi Square) is testing the null hypothesis that two criteria of classification, when applied to a population of subjects are independent. If they are not
independent then there is an association between them.
Chi Square is the most popular discrete data hypotheses testing method.
SIPOC
SIPOC stands for suppliers, inputs, process, output, and customers. You obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer’s requirements.
Supplier-Input-Process-Output-Customer: Method that helps you not to forget something when mapping processes.
Process Mapping
Charting a process as it is currently handled
Y
Y is the dependent output variable of a process. It is used to monitor a process to see if it is out of control, or if symptoms are developing within a process. It is a function of the Xs that contribute to the process. Once quantified through Design of Experiment, a transfer function Y=f(X) can be developed to define the relationship of elements and help control a process.
X
Xs are the independent inputs to a process that cause or control a problem to occur in the output (Y) of a process. Once quantified through Design of Experiment, a transfer function Y=f(X) can be developed to define the relationship of elements and help control a process.
