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"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."
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 ...
"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."
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:
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.
Preface to the Second Edition xxi
Preface to the First Edition xxiii
PART I FUNDAMENTAL QUALITY IMPROVEMENT AND STATISTICAL CONCEPTS
1 Introduction 3
1.1 Quality and Productivity, 4
1.2 Quality Costs (or Does It?), 5
1.3 The Need for Statistical Methods, 5
1.4 Early Use of Statistical Methods for Improving Quality, 6
1.5 Influential Quality Experts, 7
1.6 Summary, 9
2 Basic Tools for Improving Quality 13
2.1 Histogram, 13
2.2 Pareto Charts, 17
2.3 Scatter Plots, 21
2.4 Control Chart, 24
2.5 Check Sheet, 26
2.6 Cause-and-Effect Diagram, 26
2.7 Defect Concentration Diagram, 28
2.8 The Seven Newer Tools, 28
2.9 Software, 30
2.10 Summary, 31
3 Basic Concepts in Statistics and Probability 33
3.1 Probability, 33
3.2 Sample Versus Population, 35
3.3 Location, 36
3.4 Variation, 38
3.5 Discrete Distributions, 41
3.6 Continuous Distributions, 55
3.7 Choice of Statistical Distribution, 69
3.8 Statistical Inference, 69
3.9 Enumerative Studies Versus Analytic Studies, 81
PARTII CONTROL CHARTS AND PROCESS CAPABILITY
4 Control Charts for Measurements With Subgrouping (for One Variable) 89
4.1 Basic Control Chart Principles, 89
4.2 Real-Time Control Charting Versus Analysis of Past Data, 92
4.3 Control Charts: When to Use, Where to Use, How Many to Use, 94
4.4 Benefits from the Use of Control Charts, 94
4.5 Rational Subgroups, 95
4.6 Basic Statistical Aspects of Control Charts, 95
4.7 Illustrative Example, 96
4.8 Illustrative Example with Real Data, 114
4.9 Determining the Point of a Parameter Change, 116
4.10 Acceptance Sampling and Acceptance Control Chart, 117
4.11 Modified Limits, 124
4.12 Difference Control Charts, 124
4.13 Other Charts, 126
4.14 Average Run Length (ARL), 127
4.15 Determining the Subgroup Size, 129
4.16 Out-of-Control Action Plans, 131
4.17 Assumptions for the Charts in This Chapter, 132
4.18 Measurement Error, 140
4.19 Software, 142
4.20 Summary, 143
5 Control Charts for Measurements Without Subgrouping (for One Variable) 157
5.2 Transform the Data or Fit a Distribution?, 170
5.3 Moving Average Chart, 171
5.4 Controlling Variability with Individual Observations, 173
5.5 Summary, 175
6 Control Charts for Attributes 181
6.1 Charts for Nonconforming Units, 182
6.2 Charts for Nonconformities, 202
6.3 Summary, 218
7 Process Capability 225
7.1 Data Acquisition for Capability Indices, 225
7.2 Process Capability Indices, 227
7.3 Estimating the Parameters in Process Capability Indices, 232
7.4 Distributional Assumption for Capability Indices, 235
7.5 Confidence Intervals for Process Capability Indices, 236
7.6 Asymmetric Bilateral Tolerances, 243
7.7 Capability Indices That Are a Function of Percent Nonconforming, 245
7.8 Modified k Index, 250
7.9 Other Approaches, 251
7.10 Process Capability Plots, 251
7.11 Process Capability Indices Versus Process Performance Indices, 252
7.12 Process Capability Indices with Autocorrelated Data, 253
7.13 Software for Process Capability Indices, 253
7.14 Summary, 253
8 Alternatives to Shewhart Charts 261
8.1 Introduction, 261
8.2 Cumulative Sum Procedures: Principles and Historical Development, 263
8.3 CUSUM Procedures for Controlling Process Variability, 283
8.4 Applications of CUSUM Procedures, 286
8.5 Generalized Likelihood Ratio Charts: Competitive Alternative to CUSUM Charts, 286
8.6 CUSUM Procedures for Nonconforming Units, 286
8.7 CUSUM Procedures for Nonconformity Data, 290
8.8 Exponentially Weighted Moving Average Charts, 294
8.9 Software, 301
8.10 Summary, 301
9 Multivariate Control Charts for Measurement and Attribute Data 309
9.1 Hotelling's T2 Distribution, 312
9.2 A T2 Control Chart, 313
9.3 Multivariate Chart Versus Individual X-Charts, 326
9.4 Charts for Detecting Variability and Correlation Shifts, 327
9.5 Charts Constructed Using Individual Observations, 330
9.6 When to Use Each Chart, 335
9.7 Actual Alpha Levels for Multiple Points, 336
9.8 Requisite Assumptions, 336
9.9 Effects of Parameter Estimation on ARLs, 337
9.10 Dimension-Reduction and Variable Selection Techniques, 337
9.11 Multivariate CUSUM Charts, 338
9.12 Multivariate EWMA Charts, 339
9.13 Effect of Measurement Error, 343
9.14 Applications of Multivariate Charts, 344
9.15 Multivariate Process Capability Indices, 344
9.16 Summary, 344
10 Miscellaneous Control Chart Topics 353
10.1 Pre-control, 353
10.2 Short-Run SPC, 356
10.3 Charts for Autocorrelated Data, 359
10.4 Charts for Batch Processes, 364
10.5 Charts for Multiple-Stream Processes, 364
10.6 Nonparametric Control Charts, 365
10.7 Bayesian Control Chart Methods, 366
10.8 Control Charts for Variance Components, 367
10.9 Control Charts for Highly Censored Data, 367
10.10 Neural Networks, 367
10.11 Economic Design of Control Charts, 368
10.12 Charts with Variable Sample Size and/or Variable Sampling Interval, 370
10.13 Users of Control Charts, 371
10.14 Software for Control Charting, 374
PART III BEYOND CONTROL CHARTS: GRAPHICAL AND STATISTICAL METHODS
11 Graphical Methods 387
11.1 Histogram, 388
11.2 Stem-and-Leaf Display, 389
11.3 Dot Diagrams, 390
11.4 Boxplot, 392
11.5 Normal Probability Plot, 396
11.6 Plotting Three Variables, 398
11.7 Displaying More Than Three Variables, 399
11.8 Plots to Aid in Transforming Data, 399
11.9 Summary, 401
12 Linear Regression 407
12.1 Simple Linear Regression, 407
12.2 Worth of the Prediction Equation, 411
12.3 Assumptions, 413
12.4 Checking Assumptions Through Residual Plots, 414
12.5 Confidence Intervals and Hypothesis Test, 415
12.6 Prediction Interval for Y, 416
12.7 Regression Control Chart, 417
12.8 Cause-Selecting Control Charts, 419
12.9 Linear, Nonlinear, and Nonparametric Profiles, 421
12.10 Inverse Regression, 423
12.11 Multiple Linear Regression, 426
12.12 Issues in Multiple Regression, 426
12.13 Software For Regression, 429
12.14 Summary, 429
13 Design of Experiments 435
13.1 A Simple Example of Experimental Design Principles, 435
13.2 Principles of Experimental Design, 437
13.3 Statistical Concepts in Experimental Design, 439
13.4 t-Tests, 441
13.5 Analysis of Variance for One Factor, 445
13.6 Regression Analysis of Data from Designed Experiments, 455
13.7 ANOVA for Two Factors, 460
13.8 The 23 Design, 469
13.9 Assessment of Effects Without a Residual Term, 474
13.10 Residual Plot, 477
13.11 Separate Analyses Using Design Units and Uncoded Units, 479
13.12 Two-Level Designs with More Than Three Factors, 480
13.13 Three-Level Factorial Designs, 482
13.14 Mixed Factorials, 483
13.15 Fractional Factorials, 483
13.16 Other Topics in Experimental Design and Their Applications, 493
13.17 Summary, 500
14 Contributions of Genichi Taguchi and Alternative Approaches 513
14.1 "Taguchi Methods", 513
14.2 Quality Engineering, 514
14.3 Loss Functions, 514
14.4 Distribution Not Centered at the Target, 518
14.5 Loss Functions and Specification Limits, 518
14.6 Asymmetric Loss Functions, 518
14.7 Signal-to-Noise Ratios and Alternatives, 522
14.8 Experimental Designs for Stage One, 524
14.9 Taguchi Methods of Design, 525
14.10 Determining Optimum Conditions, 553
14.11 Summary, 558
15 Evolutionary Operation 565
15.1 EVOP Illustrations, 566
15.2 Three Variables, 576
15.3 Simplex EVOP, 578
15.4 Other EVOP Procedures, 581
15.5 Miscellaneous Uses of EVOP, 581
15.6 Summary, 582
16 Analysis of Means 587
16.1 ANOM for One-Way Classifications, 588
16.2 ANOM for Attribute Data, 591
16.3 ANOM When Standards Are Given, 594
16.4 ANOM for Factorial Designs, 596
16.5 ANOM When at Least One Factor Has More Than Two Levels, 601
16.6 Use of ANOM with Other Designs, 610
16.7 Nonparametric ANOM, 610
16.8 Summary, 611
17 Using Combinations of Quality Improvement Tools 615
17.1 Control Charts and Design of Experiments, 616
17.2 Control Charts and Calibration Experiments, 616
17.3 Six Sigma Programs, 616
17.4 Statistical Process Control and Engineering Process Control, 624
Answers to Selected Exercises 629
Appendix: Statistical Tables 633
Author Index 645
Subject Index 657
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.
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 in uenced 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 have launched massive training programs, would undoubtedly disagree. The large amount of money that GE has committed to a particular training program, Six Sigma, is discussed in, for example, the January 13, 1997 issue of the Wall Street Journal. Wall Street is beginning to recognize Six Sigma companies as companies that, for example, operate efficiently and have greater customer satisfaction. Six Sigma is discussed in detail in Chapter 17.
What is the real cost of a quality improvement program? That cost is impossible to determine precisely, since it would depend in part on the quality costs for a given time period without such a program as well as the costs of the program for the same time period. Obviously we cannot both have a program and not have a program at the same point in time, so the quality costs that would be present if the program were not in effect would have to be estimated from past data.
Such a comparison would not give the complete picture, however. Any view of quality costs that does not include the effect that a quality improvement program will have on sales and customers' perceptions is a myopic view of the subject. Should a supplier consider the cost of a statistical quality control program before deciding whether or not to institute such a program? The supplier may not have much choice if it is to remain a supplier. As a less extreme example, consider an industry that consists of 10 companies. If two of these companies implement a statistical quality improvement program and, as a result, the public soon perceives their products to be of higher quality than their competitors' products, should their competitors consider the cost of such a program before following suit? Definitely not, unless they can adequately predict the amount of lost sales and weigh that against the cost of the program.
1.3 THE NEED FOR STATISTICAL METHODS
Generally, statistical techniques are needed to determine if abnormal variation has occurred in whatever is being monitored, to determine changes in the values of process parameters, and to identify factors that are in uencing process characteristics. Methods for achieving each of these objectives are discussed in subsequent chapters. Statistics is generally comparable to medicine in the sense that there are many subareas in statistics, just as there are many medical specialties. Quality "illnesses" generally can be cured and quality optimized only through the sagacious use of combinations of statistical techniques, as discussed in Chapter 17.
1.4 EARLY USE OF STATISTICAL METHODS FOR IMPROVING QUALITY
Although statistical methods have been underutilized and underappreciated in quality control/improvement programs for decades, such methods are extremely important. Occasionally their importance may even be overstated. In discussing the potential impact of statistical methods, Hoerl (1994) points out that Ishikawa (1985, pp. 14-15) stated the following: "One might even speculate that the second world war was won by quality control and by the utilization of modern statistics. Certain statistical methods researched and utilized by the Allied powers were so effective that they were classified as military secrets until the surrender of Nazi Germany." Although such a conclusion is clearly arguable, statistical methods did clearly play a role in World War II.
Shortly after the war, the American Society for Quality Control was formed in 1946; it published the journal Industrial Quality Control, the first issue of which had appeared in July 1944. In 1969 the journal was essentially split into two publications-- the Journal of Quality Technology and Quality Progress. The former contains technical articles whereas the latter contains less technical articles and also hasnewsitems. The early issues of Industrial Quality Control contained many interesting articles on how statistical procedures were being used in firms in various industries, whereas articles in the Journal of Quality Technology are oriented more toward the proper use of existing procedures as well as the introduction of new procedures. Publication of Quality Engineering began in 1988, with case studies featured in addition to statistical methodology. The Annual Quality Congress has been held every year since the inception of the ASQC, and the proceedings of the meeting are published as the ASQ Annual Quality Transactions.
Other excellent sources of information include the Fall Technical Conference, which is jointly sponsored by ASQ and the American Statistical Association (ASA), the annual Quality and Productivity Research Conference, and the annual meetings of ASA, which are referred to as the Joint Statistical Meetings (JSM).
There are also various "applied" statistics journals that contain important articles relevant to industry, including Technometrics, published jointly by ASQ and ASA, Quality and Reliability Engineering International, IIE Transactions, Applied Statistics (Journal of The Royal Statistical Society, Series C), andThe Statistician (Journal of the Royal Statistical Society, Series D). The latter two are British publications.
Readers interested in the historical development of statistical quality control in Great Britain are referred to Pearson (1935, 1973). An enlightening look at the early days of quality control practices in the United States, as seen through the eyes of Joseph M. Juran, can be found in Juran (1997). See also Montgomery (1996, pp. 10-11) for a chronology of some important events in the history of quality improvement.
1.5 INFLUENTIAL QUALITY EXPERTS
Walter A. Shewhart (1891-1967) came first. As discussed more fully in Chapter 2, he invented the idea of a control chart, with certain standard charts now commonly referred to as "Shewhart charts." Shewhart (1931) is still cited by many writers as an authoritative source on process control. The book was reprinted in 1980 by the ASQC. Shewhart (1939) was Shewhart's other well-known book.
W. Edwards Deming (1900-1993) was such a prominent statistician and quality and productivity consultant that his passing was noted on the front page of leading newspapers. His "14 points for management" for achieving quality have been frequently cited (and also changed somewhat over the years). It has been claimed that there are as many as eight versions. One version is as follows:
Although these 14 points are typically applied in industrial settings, they can be slightly modified and applied in other settings. For an application that is certainly far removed from manufacturing, Guenther (1997) gives a closely related list of 14 points for parenting.
There is one point of clarification that should be made. When Deming argued against target values, he was arguing against targets for production quotas, not target values for process characteristics. The use of target values for process characteristics is advocated and illustrated in Chapter 14.
Deming was constantly berating American management, believing that about 90% of quality problems were caused by management. Deming's views on the shortcomings of American management can be found in many places, including Chapter 2 of Deming (1986). In general, Deming claimed that management (1) emphasizes short-term thinking and quarterly profits rather than long-term strategies, (2) is inadequately trained and does not possess an in-depth knowledge of the company, and (3) is looking for quick results.
Deming has also been given credit for the PDCA (plan-do-check-act) cycle, although in his later years his preference was that it be called the PDSA cycle, with 'study' replacing 'check.' This has been termed Deming's wheel, but Deming referred to it as Shewhart's cycle. The cycle consists of planning a study, performing the study, checking or studying the results, and acting in accordance with what was learned from the study. See, for example, Cryer and Miller (1994) for additional information on the PDCA cycle.
Several books have been written about Deming; one of the best-known books was written by Mary Walton, a journalist (Walton, 1986). See also Walton (1990), which is a book of case studies, and Voehl (1995). The latter is an edited volume that contains chapters written by some prominent people in the field of quality improvement.
Joseph M. Juran (1904- ) is another prominent quality figure. He is mentioned only brie y here, however, because his contributions have been to quality management rather than to the use of statistical methods for achieving quality improvement. His quality control handbook, which appropriately enough was renamed Juran's Quality Control Handbook when the fourth edition came out in 1988, does contain a few chapters on statistical techniques, however. The first edition was published in 1951 and has been used as a reference book by countless quality practitioners.
Eugene L. Grant (1897-1996) has not been accorded the status of other quality pioneers, but nevertheless deserves to be mentioned with the others in this section. In Struebing (1996), Juran is quoted as saying, "His contribution to statistical methodology was much greater than (W. Edwards) Deming's. Even though his impact on quality was profound and he was much more instrumental in advancing quality than Deming, the media -- which overstated Deming's contribution -- didn't publicize Grant's contributions." Grant has been described as a quiet worker who did not seek to extol his accomplishments. He was an academic who spent over 30 years on the faculty of Stanford University. In the field of quality improvement he was best known for his classic book Statistical Quality Control, first published in 1946. Recent editions of the book have been co-authored by Richard S. Leavenworth. The seventh edition was published in 1996. A very large number of copies of the book were sold through the various editions, but some observers felt that his teaching of statistical quality control during World War II contributed at least as much to the increase in the use of quality techniques as has his well-known book.
George E. P. Box (1919- ) is not generally listed as a quality leader or "guru," but his contributions to statistical methods for improving quality are well known. His recent book, Box and Luceno (1997), extols the authors' ideas and suggested approaches for improving quality. The primary message of the book is that control charts and engineering process control should be used in tandem. This idea is discussed in Chapter 17. He is the author of several other books, the best known of which is Box, Hunter, and Hunter (1978). Box also had a column entitled George's Corner during the early years of the journal Quality Engineering. He was named an Honorary Member of the ASQ by the ASQ Board of Directors in 1997 in recognition of his contributions to quality improvement.
There are, of course, many other quality leaders, but they won't be listed here for fear of leaving someone out.
Statistical methods should be used to identify unusual variation and to pinpoint the causes of such variation, whether it be for a manufacturing process or for general business. The use of statistical methods should produce improvements in quality, which, in turn, should result in increased productivity. The tools for accomplishing this are presented in Parts II and III.
Box, G. E. P. and A. Luce~ no (1997). Statistical Control by Monitoring and Feedback Adjustment. New York: Wiley.
Box, G. E. P., W. G. Hunter, and J. S. Hunter (1978). Statistics for Experimenters. New York: Wiley.
Crosby, P. (1979). Quality Is Free: The Art of Making Quality Certain. NewYork: McGraw-Hill.
Crosby, P. (1996). Quality Is Still Free: Making Quality Certain in Uncertain Times. New York: McGraw-Hill.
Cryer, J. D. and R. B. Miller (1994). Statistics for Business: Data Analysis and Modeling, 2nd ed. Belmont, CA: Duxbury.
Deming, W. E. (1982). Quality, Productivity, and Competitive Position. Cambridge, MA: Massachusetts Institute of Technology, Center for Advanced Engineering Study.
Deming, W. E. (1986). Out of the Crisis. Cambridge, MA: Massachusetts Institute of Technology, Center for Advanced Engineering Study.
Fuller, F. T. (1986). Eliminating complexity from work: Improving productivity by enhancing quality. Report No. 17, Center for Quality and Productivity Improvement, University of Wisconsin, Madison.
Guenther, M. (1997). Letter to the Editor. Quality Progress 30(10): 12-14. Hoerl, R. (1994). Enhancing the bottom line impact of statistical methods. W. J. Youden Memorial Address given at the 38th Annual Fall Technical Conference. Chemical and Process Industries Division Newsletter, American Society for Quality Control, Winter, pp. 1-9.
Ishikawa, K. (1985). What Is Total Quality Control? The Japanese Way. Englewood Cliffs, NJ: Prentice-Hall.
Juran, J. M. (1997). Early SQC: A historical supplement. Quality Progress 30( 9): 73-81. Juran, J. M., editor-in-chief, and F. M. Gryna, associate editor (1988). Juran's Quality Control Handbook, 4th ed. New York: McGraw-Hill.
Montgomery, D. C. (1996). Introduction to Statistical Quality Control, 3rd ed. New York: Wiley.
Pearson, E. S. (1935). The Application of Statistical Methods to Industrial Standardisation and Quality Control. London: British Standards Association.
Pearson, E. S. (1973). Some historical re ections on the introduction of statistical methods in industry. The Statistician 22(3): 165-179.
Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. New York: Van Nostrand. (Reprinted in 1980 by the American Society for Quality Control.)
Shewhart, W. A. (1939). Statistical Method from the Viewpoint of Quality Control. Washington, DC: Graduate School, Department of Agriculture (editorial assistance by W. Edwards Deming).
Struebing, L. (1996). Eugene L. Grant: 1897-1996. Quality Progress 29(11): 81-83.
Voehl, F., ed. (1995). Deming: The Way We Knew Him. Delray Beach, FL: St. Lucie.
Walton, M. (1986). The Deming Management Method. New York: Dodd and Mead.
Walton, M. (1990). Deming Management at Work. New York: Putnam.