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A practical guide to Statistical Process Control (SPC) Statistical Process Control DeMYSTiFieDclearly defines the application and interpretation of SPCa key component of Six Sigma, Quality Engineering, and Quality Assurance practiceswithin a cross section of industries. Various sampling methods applied in a variety of applications are discussed, including continuous, piece-part, service, and short run processes. The book uses Minitab, Excel and other software to demonstrate application of problem-solving and statistical techniques. You will learn how to solve complicated problems that are generally not addressed in introductory guides. High-level concepts are put it into practical context for ease of understanding. Statistical Process Control DeMYSTiFieDfeatures: Chapter-opening objectives offering insight into what you're going to learn in each step Questions at the end of every chapter to reinforce learning and pinpoint weaknesses "Still Struggling?" icon providing specific recommendations for those having difficulty with certain subtopics A final exam for overall self-assessment "Curriculum Tree" that shows how SPC fits into a larger curriculum Hard stuff made easy! Analyzing process data; Definition of process; Choosing process metrics; Application of SPC to process analysis; Differences between SPC methods and the enumerative methods of statistical inference; Definition and interpretation of common causes and special causes of variation; Difference between statistical process control and process control using specification limits, including the effects of tampering; Misapplication of two-sigma control or warning limits and their implication on the false alarm rate; Establishing and revising control limits. Discussion of different applications of SPC across an organization; Statistical distributions; Types of data (variables vs. attributes); General properties of distributions; The Binomial Distribution; The Poisson Distribution; The Normal Distribution, including the Central Limit Theorem; Non-Normal Distributions; Detection using Normal Probability Plots, Anderson-Darling and K-S statistics. Standardization/translation techniques; Analysis of count data using attribute charts; Definition of attribute data; P charts and Np charts; Assumptions; Calculations; Interpretation, including examples. U charts and C charts; Limitations; The X-Bar Chart; Assumptions, including discussion of subgroup size selection, its influence on detection, and the relevance of rational subgroups; Calculations, including use of Run test rules; Interpretation, including examples; Limitations, including the effects of process autocorrelation, batch processes, multi-stream processes, measurement resolution; Charts for Individuals Data; The individual-X / moving range; Moving average charts; EWMA charts--assumptions; Calculations; Interpretation, including examples; Limitations; Process capability and performance analysis; Assumptions, including the need to establish statistical control, and the comparison of the use of process sigma versus sample standard deviation; Calculations, including for non-normal analysis; Interpretation, including examples; the relation to error rates, dpmo, sigma levels; and the proper reporting of capability indices. Limitations; Measurement systems analysis using control charts; Assumptions; Calculations; Interpretation, including examples; Limitations; Control charts for autocorrelated processes; Assumptions; Calculations; Interpretation, including examples; Limitations
Table of Contents
Chapter 1. Analyzing Process Data Chapter 2. Data Collection Chapter 3. Determining Suitability to Requirements Chapter 4. Attribute Control Charts Chapter 5. X Charts Chapter 6. Charts for Individuals Data Chapter 7. Process Improvement Chapter 8. Measurement Systems Analysis Chapter 9. Questions and Examples from Practitioners Final Exam Answers to Quizzes and Final Exam Appendix: Control Chart Constants References Index