Statistical Methods for Quality Improvement

Thomas P. Ryan

• Publisher: Wiley
• Year: 2011
• Print ISBN: 9780470590744
• eBook ISBN: 9781118058107
• Edition: 3
• Topic: Technology & Engineering
• Subtopic: Quality Control in Engineering
• Index: Technology & Engineering

Praise for the Second Edition

"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."
— Technometrics

This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement

The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods.

In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include:

• Updated coverage of control charts, with newly added tools
• The latest research on the monitoring of linear profiles and other types of profiles
• Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures
• New discussions on design of experiments that include conditional effects and fraction of design space plots
• New material on Lean Six Sigma and Six Sigma programs and training

Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic.

Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

PART I

Fundamental Quality Improvement and Statistical Concepts

CHAPTER 1

Introduction

This is a book about using statistical methods to improve quality. It is not a book about Total Quality Management (TQM), Total Quality Assurance (TQA), just-in-time (JIT) manufacturing, benchmarking, QS-9000, or the ISO 9000 series. In other words, the scope of the book is essentially restricted to statistical techniques. Although standards such as QS-9000 and ISO 9000 are potentially useful, they are oriented toward the documentation of quality problems, not the identification or eradication of problems. Furthermore, many people feel that companies tend to believe that all they need to do is acquire ISO 9000 certification, thus satisfying only a minimum requirement.

Statistical techniques, on the other hand, are useful for identifying trouble spots and their causes, as well as predicting major problems before they occur. Then it is up to the appropriate personnel to take the proper corrective action.

The emphasis is on quality improvement, not quality control. On July 1, 1997 the American Society for Quality Control (ASQC) became simply the American Society for Quality (ASQ). The best choice for a new name is arguable, as some would undoubtedly prefer American Society for Quality Improvement (the choice of the late Bill Hunter, former professor of statistics at the University of Wisconsin). Nevertheless, the name change reflects an appropriate movement away from quality control. George Box has emphasized that systems are not stationary and that improvements should constantly be sought. In defending his statement in Box ((1997a) that there are “not truths, only major steps in a never-ending (and diverging) process that helped predict natural phenomena,” Box (1997b) pointed out that “Orville and Wilber Wright undoubtedly had profound knowledge about the design of flying machines” in 1903, but their plane looks primitive now.

What is quality? How do we know when we have it? Can we have too much quality? The “fitness for use” criterion is usually given in defining quality. Specifically, a quality product is defined as a product that meets the needs of the marketplace. Those needs are not likely to be static, however, and will certainly be a function of product quality. For example, if automakers build cars that are free from major repairs for 5 years, the marketplace is likely to accept this as a quality standard. However, if another automaker builds its cars in such a way that they will probably be trouble free for 7 years, the quality standard is likely to shift upward. This is what happened in the Western world some years ago as the marketplace discovered that Japanese products, in particular, are of high quality.

A company will know that it is producing high-quality products if those products satisfy the demands of the marketplace.

We could possibly have too much quality. What if we could build a car that would last for 50 years. Would anyone want to drive the same car for 50 years even if he or she lived long enough to do so? Obviously, styles and tastes change. This is particularly true for high technology products that might be obsolete after a year or two. How long should a personal computer be built to last?

In statistical terms, quality is largely determined by the amount of variability in what is being measured. Assume that the target for producing certain invoices is 15 days, with anything less than, say, 10 days being almost physically impossible. If records for a 6-month period showed that all invoices of this type were processed within 17 days, this invoice-processing operation would seem to be of high quality.

In general, the objective should be to reduce variability and to “hit the target” if target values exist for process characteristics. The latter objective has been influenced by Genichi Taguchi (see Chapter 14) who has defined quality as the “cost to society.”

1.1 QUALITY AND PRODUCTIVITY

One impediment to achieving high quality has been the misconception of some managers that there is an inverse relationship between productivity and quality. Specifically, it has been believed (by some) that steps taken to improve quality will simultaneously cause a reduction in productivity.

This issue has been addressed by a number of authors including Fuller (1986) who related that managers at Hewlett–Packard began to realize many years ago that productivity rose measurably when nonconformities (i.e., product defects) were reduced. This increase was partly attributable to a reduction in rework that resulted from the reduction of nonconformities. Other significant gains resulted from the elimination of problems such as the late delivery of materials. These various problems contribute to what the author terms “complexity” in the workplace, and he discusses ways to eliminate complexity so as to free the worker for productive tasks. Other examples of increased productivity resulting from improved quality can be found in Chapter 1 of Deming (1982).

1.2 QUALITY COSTS (OR DOES IT?)

It is often stated that “quality doesn't cost, it pays.” Although Crosby (1979) said that quality is free (the title of his book) and reiterated this in Crosby (1996), companies such as Motorola and General Electric, which launched massive training programs a few decades ago, would undoubtedly disagree. The large amount of money that GE committed to a particular training program, Six Sigma, was discussed in, for example, the January 13, 1997 issue of the Wall Street Journal. Wall Street has recognized Six Sigma companies as companies that operate efficiently, have greater customer satisfaction, and so on. Six Sigma is discussed in detail in Chapter 17.

What is the real cost of a quality improvement program? That cost...

Table of contents

• Cover
• Series
• Title Page
• Copyright
• Preface
• Preface to the Second Edition
• Preface to the First Edition
• Part I: Fundamental Quality Improvement and Statistical Concepts
• Part II: Control Charts and Process Capability
• Part III: Beyond Control Charts: Graphical and Statistical Methods
• Answers to Selected Exercises
• Appendix: Statistical Tables
• Author Index
• Subject Index