Statistical Process Control and Quality Improvement

Statistical Process Control and Quality Improvement

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For freshman/sophomore level introductory courses in SPC (Statistical Process Control), Statistical Quality Control or Quality Control found in two and four-year college curriculums, and in industrial training programs.

This "mathematics-friendly" text introduces students to basic concepts and applications of Statistical Process Control (SPC). Students get a solid foundation in control charts-including setting scales, charting, interpreting, and analyzing process capability. Problem-solving techniques are emphasized, and all learning is linked to the implementation of SPC in the workplace.
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Product details

  • Hardback | 672 pages
  • 178 x 238 x 30mm | 1,102.24g
  • Pearson
  • Upper Saddle River, NJ, United States
  • English
  • 5th edition
  • 0130490369
  • 9780130490360

Back cover copy

This new edition of Statistical Process Control and Quality Improvement has been updated to include a "reader-friendly" environment that allows for quick comprehension of the ideas and concepts covered in this book. With its easy readability, this text sets a foundation for charting, interpreting, and analyzing process capability, while also introducing the reader to other basic concepts and applications of Statistical Process Control (SPC). The problem-solving emphasis of this text is linked to the implementation of SPC in the workplace.

Featuring: New--The incorporation of probability in Chapter 5 now allows for a clear-cut commentary on the importance of probability in and out of the workplace. New--This edition presents a section on Six Sigma that goes into greater depth on the subject than any earlier edition. Completely new exercises help explain and clarify concepts presented in the text.
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Table of contents

1. Introduction to Quality Concepts and Statistical Process Control.

What is Quality? Definitions of Quality. The Need for SPC. Prevention Versus Detection. SPC Goals. The Basic Tools for SPC. Statistical Process Control Techniques. Applying SPC to an Existing Manufacturing Process. Designed Experiments. The Quality Toolbox.

2. Striving for Quality: Management's Problem and Management's Solution.

Management's Problem. Management's Dilemma. Leadership by Management. Deming's Contribution to Quality. Deming's 14 Points for Management. Deming's Seven Deadly Diseases. Crosby's Approach. A Comparison of Deming's 14 Points and Crosby's 14 Steps. Which Way to Top Quality? Pitfalls in the Quest for Quality. Total Quality Management (TQM). The Malcolm Baldrige National Quality Award. Total Customer Satisfaction. ISO-9000. The Service Sector.

3. Introduction to Variation and Statistics.

Measurement Concepts. Special-Cause and Common-Cause Variation. The Variation Concepts. Distributions and SPC Goals. Basic Statistical Concepts. Distributions and Three Standard Deviations.

4. Organization of Data: Introduction to Tables, Charts, and Graphs.

Stemplots. Frequency Distributions and Tally Charts. Histograms. Pareto Charts. Flowcharts. Storyboards. The Cause-and-Effect Diagrams. Checksheets. Scatterplots.

5. Introduction to Probability and the Normal Probability Distribution.

Probability. Compound Probability. Counting with Permutations and Combinations. The Binomial Distribution. The Hypergeometric Distribution. Probability Distributions. The Normal Probability Distribution. The Application of the Central Limit Theorem.

6. Introduction to Control Charts.

The Control Chart Concept. Preparation for Control Charting. Control Charts and Run Charts. The Basic x-bar and R Charts. The x-bar and R Chart Procedure. The Continuation Control Chart. The Capability Analysis. Six-Sigma.

7. Additional Control Charts for Variables.

The Median and Range Chart (x-bar and R). x-bar and s Charts. Coding Data. A Modified x-bar and R Chart for Small Sets of Data. The Nominal x-bar and R Chart. The Transformation x-bar and R Chart. Control Chart Selection.

8. Variables Charts for Limited Data.

Precontrol or Rainbow Charts. A Compound Probability Application. Modified Precontrol for Tight Control. Charts for Individual Measurements.

9. Attributes Control Charts.

The Four Types of Attributes Charts. The p Chart. The np Chart. The c Chart. The u Chart. SPC Applied to the Learning Process. Technology in SPC.

10. Interpreting Control Charts.

The Random Distribution of Points. Freaks. Binomial Distribution Applications. Freak Patterns. Shifts. Runs and Trends. Time and Control Chart Patterns. Cycles. Grouping. Instability. Stable Mixtures. Stratification. Using Control Chart Patterns in Problem Solving.

11. Problem Solving.

The Problem-Solving Sequence. Teamwork for Problem Solving. Brainstorming. Using Problem-Solving Tools. Mistake Proofing. Problem Solving in Management. JIT (Just-in-time). Problem Solving in the Classroom.

12. Gauge Capability.

Preparations for a Gauge Capability Study. The Gauge Capability Procedure. Analysis of R and R with Accuracy and Stability: Maximum Possible Deflection. The Elimination of Gauge Variation From Process Variation. Indecisive Gauge Readings.

13. Acceptance Sampling.

The Sampling Dilemma. Random Sampling. Operating Characteristic Curves. The Average Outgoing Quality Curve. MLT-STD-105D for Inspection by Attributes. The Average Proportion Defective. Vendor Certification and Control Chart Monitoring.

Appendix A: Basic Math Concepts.

Signed Numbers. Variables. Order of Operations. Inequalities. Using the Statistical Calculator.

Appendix B: Charts and Tables.

Formulas and Constants for Control Charts. The G Chart. The Normal Distribution Table (Tail Area). The Normal Distribution Table (Center Area). The Normal Distribution Table (Left Area). Process Capability.

Appendix C: Glossary of Symbols.

Appendix D: Lab Exercises. Answers to Odd Exercises.

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