** ** This “mathematics-friendly” volume introduces readers to basic concepts and applications of Statistical Process Control (SPC). Readers 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. ** ** The volume offers an introduction to quality concepts and statistical process control, quality issues, variation and statistics, an introduction to tables, charts, and graphs, probability and the normal distribution, control charts, variables charts for limited data, attributes control charts, problem solving, gauge capability and acceptance sampling. ** ** For quality control trainers and quality management/improvement teams.

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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.

PREFACE

Comments and suggestions from users of the previous editions have been very helpful in the preparation of this fourth edition. The following changes have been made:

The answers to all the odd exercises have been provided in the back of the text. Lab exercises have been developed for each chapter. Definitions and formulas have been boxed to highlight them. Chapter 6 has been split into two chapters. The introduction to control charts and the work with x and R charts have been retained in Chapter 6. The x R, x s,and the small and short run variations have been moved into a new Chapter 7. New exercises have been added. The discussion on linear regression has been expanded to include the application of the linear regression mode on statistical calculators. The discussion on Walter Shewhart's contribution to control charts has been expanded to provide a better understanding of the concepts. A new section on Six Sigma quality concepts has been introduced. NEED

Statistical process control (SPC) is not a new topic in industry: It has been used off and on since its development in the 1920s. However, since the 1970s it has become an extremely important tool. A new economic age is developing in which the demand for quality is rapidly increasing, with a resulting global competition of companies striving to provide that quality. The detection system of final inspection, a costly method of quality control, is giving way to a prevention system that uses in-process inspection and SPC to build quality into a process. This change requires extensive training in SPC. Also, for the most effective application of SPC, management must coordinate a team effort in which everyone in the workforce can contribute meaningfully to the quality effort. PURPOSE

This book was written with the following goals:

To provide an understanding of basic statistical concepts. To present a management philosophy for successful application of statistical process control. To give the student a solid foundation on control charts: setting scales, charting, interpreting, and analyzing process capability. To teach the student the quality concepts and problem-solving techniques associated with statistical process control. To provide a readable source of SPC topics that the student can refer to as the on-the-job need arises. FLEXIBILITY

The book is designed for use in two-year and four-year colleges, as well as industry. The order of the chapters features a low-level mathematics approach so that anyone with a basic mathematics background can learn the control chart concepts in Chapters 1 through 10 and the problem-solving concepts in Chapter 11.

The book is mathematics-friendly:

Only the needed mathematics is presented. The mathematics knowledge that is required for each topic is reviewed at the introduction of the topic.

The entire book contains enough material for a three-credit-hour course. The mathematics prerequisite for someone studying the entire book should be elementary algebra.

The recommended sequence for college is Chapters 1 through 13, with the basic algebra in Appendix A reviewed at the beginning of Chapter 3. The probability section in the Appendix is optional and can be taught with the introduction to probability in Chapter 5 or with the applications of probability in Chapters 8 or 10. One possible variation in the sequence would be to teach Chapter 10 after Chapter 5. Then all of the out-of-control patterns would be available for analyzing the control charts presented in Chapters 6 through 9. The book sequence introduces a few basic out-of-control patterns for use in the presentation of control charts, followed by a more comprehensive analysis after all the control charts have been introduced.

The recommended sequence for industry is Chapters 1 through 11. Chapters 12 and 13 are more job-specific and may be taught to particular groups. The probability in Appendix A can be taught at any time if a more thorough understanding of the probability concepts in chart interpretation or sampling is desired. The basic algebra in Appendix A can be taught at the beginning of Chapter 3. EXAMPLES AND ILLUSTRATIONS

The examples have been carefully chosen to provide a thorough understanding of the concepts involved. A detailed, step-by-step format has been used throughout the book to provide a pattern that can be used effectively, both for the immediate problems and for future reference. The examples feature worksheets and control charts to be filled in by the student and completed worksheets and charts for checking results. Control chart masters have been included at the end of the solutions manual. ACKNOWLEDGMENTS

I thank the following reviewers for their comments and suggestions: Paul Wright, Paul Wright Consulting Services in Ft. Wayne, IN; George E. Brown, Jackson State Community College; Hank Campbell, Ph.D., Illinois State University; David H. Devier, Owens Community College; and Marty Hodges, Colorado Tech.

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