• Book Name: Introduction to Statistical Quality Control Sixth Edition by Douglas C. Montgomery
  • Author: Douglas C. Montgomery
  • Pages: 754
  • Size: 6 MB

Introduction to Statistical Quality Control PDF


This book is about the use of modern statistical methods for quality control and improvement. It provides comprehensive coverage of the subject from basic principles to state-of-the-art concepts and applications. The objective is to give the reader a sound understanding of the principles and the basis for applying them in a variety of situations. Although statistical techniques are emphasized throughout, the book has a strong engineering and management orientation. Extensive knowledge of statistics is not a prerequisite for using this book. Readers whose background includes a basic course in statistical methods will find much of the material in this book easily accessible.


The book is an outgrowth of more than 35 years of teaching, research, and consulting in the application of statistical methods for industrial problems. It is designed as a textbook for students enrolled in colleges and universities, who are studying engineering, statistics, management, and related fields and are taking a first course in statistical quality control. The basic quality-control course is often taught at the junior or senior level. All of the standard topics for this course are covered in detail. Some more advanced material is also available in the book, and this could be used with advanced undergraduates who have had some previous exposure to the basics or in a course aimed at graduate students. I have also used the text materials extensively in programs for professional practitioners, including quality and reliability engineers, manufacturing and development engineers, product designers, managers, procurement specialists, marketing personnel, technicians and laboratory analysts, inspectors, and operators. Many professionals have also used the material for self-study.

Chapter Organization and Topical Coverage

The book contains five parts. Part I is introductory. The first chapter is an introduction to the philosophy and basic concepts of quality improvement. It notes that quality has become a major business strategy and that organizations that successfully improve quality can increase their productivity, enhance their market penetration, and achieve greater profitability and a strong competitive advantage. Some of the managerial and implementation aspects of quality improvement are included. Chapter 2 describes DMAIC, an acronym for define, measure, analyze, improve, and control. The DMAIC process is an excellent framework to use in conducting quality improvement projects. DMAIC often is associated with six-sigma, but regardless of the approach taken by an organization strategically, DMAIC is an excellent tactical tool for quality professionals to employ. Part II is a description of statistical methods useful in quality improvement. Topics include sampling and descriptive statistics, the basic notions of probability and probability distributions, point and interval estimation of parameters, and statistical hypothesis testing. These topics are usually covered in a basic course in statistical methods; however, their presentation in this text is from the quality-engineering viewpoint. My experience has been that even readers with a strong statistical background will find the approach to this material useful and somewhat different from a standard statistics textbook.

Part III contains four chapters covering the basic methods of statistical process control (SPC) and methods for process capability analysis. Even though several SPC problem-solving tools are discussed (including Pareto charts and cause-and-effect diagrams, for example), the primary focus in this section is on the Shewhart control chart. The Shewhart control chart certainly is not new, but its use in modern-day business and industry is of tremendous value.

Introduction to Statistical Quality Control PDF

There are four chapters in Part IV that present more advanced SPC methods. Included are the cumulative sum and exponentially weighted moving average control charts (Chapter 9), several important univariate control charts such as procedures for short production runs, autocorrelated data, and multiple stream processes (Chapter 10), multivariate process monitoring and control (Chapter 11), and feedback adjustment techniques (Chapter 12). Some of this material is at a higher level than Part III, but much of it is accessible by advanced undergraduates or firstyear graduate students. This material forms the basis of a second course in statistical quality control and improvement for this audience.

Part V contains two chapters that show how statistically designed experiments can be used for process design, development, and improvement. Chapter 13 presents the fundamental concepts of designed experiments and introduces factorial and fractional factorial designs, with particular emphasis on the two-level system of designs. These designs are used extensively in the industry for factor screening and process characterization. Although the treatment of the subject is not extensive and is no substitute for a formal course in experimental design, it will enable the reader to appreciate more sophisticated examples of experimental design. Chapter 14 introduces response surface methods and designs, illustrates evolutionary operation (EVOP) for process monitoring, and shows how statistically designed experiments can be used for process robustness studies. Chapters 13 and 14 emphasize the important interrelationship between statistical process control and experimental design for process improvement.

Two chapters deal with acceptance sampling in Part VI. The focus is on lot-by-lot acceptance sampling, although there is some discussion of continuous sampling and MIL STD 1235C in Chapter 14. Other sampling topics presented include various aspects of the design of acceptance-sampling plans, a discussion of MIL STD 105E, MIL STD 414 (and their civilian counterparts, ANSI/ASQC ZI.4 and ANSI/ASQC ZI.9), and other techniques such as chain sampling and skip-lot sampling.

Throughout the book, guidelines are given for selecting the proper type of statistical technique to use in a wide variety of situations. Additionally, extensive references to journal articles and other technical literature should assist the reader in applying the methods described. I also have showed how the different techniques presented are used in the DMAIC process.

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