Statistical Process Control Demystified

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A practical guide to Statistical Process Control (SPC)

Statistical Process Control DeMYSTiFieD clearly defines the application and interpretation of SPC—a key component of Six Sigma, Quality Engineering, and Quality Assurance practices—within 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 DeMYSTiFieD features:

• 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

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