Statistical Quality Control, Strategies and Tools for Continual Improvement

This book serves as an excellent exposure to basic quality assessment techniques. It is extremely broad-based in its efforts to offer a plethora of useful methods for identifying, analyzing, monitoring, and fixing problems. It contains sensible business tips outlining steps needed to conduct brainstorming sessions, and it renders statistical equations for control charts. Consider it a toolbox for beginning quality practitioners. A considerably distracting element in the discourse is the lack of integration of these two types of tools. This book suffers from the Janus syndromeconjure up the image of two authors standing back-to-back looking in opposite directions. On amazon.com (accessed i/12/00, 1:22 p.m.), the editorial review from the publisher, John Wiley and Sons, describes this book as one that “addresses the managerial aspects . ” At the wiley.com Web site (accessed 3/16/00, 1O:lO a.m.), the book description purports it to be “written for technique oriented courses. ” These need not be dichotomous statements. In the preface, the authors lament that quality courses are often taught from only one of two viewpoints, either with statistical tools or managerial tools. It appears that they had hoped to bridge that chasm. Yet, the beginning half is a book for a first course in business management with applications in quality control (QC) and the second half is a first course in statistics with applications in QC. The two separate treatises are glued together at Chapter 7. Who is the intended audience? This is an important question because quality is defined by the consumer. According to the authors, the reader should be a “college undergraduate (or) graduate in Industrial Engineering, Statistics Business (or a person in an) in-house company training course . ” Does that student find the relevant topics and an appropriate difficulty level in this book? What are the unique contributions dramatic enough to warrant use? Are the shortcomings inconsequential enough to be tolerated? Although some of the book’s examples are exceptional, the topics are pretty conventional. Chapter 2 contains the usual discussions of problem categorization and mechanisms for developing criteria for rank ordering problem severity via flowcharting and Pareto diagrams. There are ample examples covering a cash sale, a dental appointment, steps to achieve promotion and tenure at a university, product assembly, and problem resolution. In Chapter 3, we see tools like the cause-and-effect diagrams and Deming wheels. Brainstorming techniques are addressed in Chapter 4 and reward structures in Chapter 5. There is a nice discussion in Chapter 6 on measurement. The examples are real-life relevant with the making-themorning-coffee quite noteworthy for exemplifying noise and variability. All of these managerial topics are confined to the first six chapters. Beginning in Chapter 7, the reader sees the traditional statistical quality tools. Chapter 7 deals with graphs. The incorporation of the digidots graphical technique of J. S. Hunter (1988) is novel and useful. The bead and funnel exercises developed by W. E. Deming (1986) in Chapters 8 and 12 aid the understanding of variability and probability distributions. But there is too much coin tossing. The inclusion of bivariate distributions is laudable, even though the section is quite brief, as are all topics. One of the nonpedestrian topics is found in Chapter 9 on constructing surveys, an important area when considering quality of service. Testing and estimation occur in Chapter 10 with acceptance sampling in 11 and control charts in 12, followed by capability of processes. The last section of the book addresses design of experiments and regression techniques and concludes with a Taguchi chapter seemingly more obligatory than informative in which authors repeatedly include several cautionary notes on the various procedures. The authors claim that “only minimal mathematics background” is required to understand the tools outlined in the book. The reader who is successfully mastering brainstorming and Pareto diagrams might balk when arriving at Chapter 7 and attempting exercise 7.15. It asks for a proof. Albeit only an algebraic manipulation of the correlation coefficient is required, but this could exceed the skill set of the student who is enjoying the first six chapters. You can tell that the authors are sympathetic to those who have a distaste for lengthy sterile equations with overwhelming amounts of complicated notation. They must have encountered audiences who have required an explanation of mathematical material from an alternate approach. Chapters 14 and 15 on design of experiments avoid traditional computational formulas and take an exploratory approach to elucidate differences. There is a very clear treatment of multicollinearity and a clean explanation of indicator variables. They are careful to intercede on confusion. For example, on page 504 when discussing Cp for regression, they quickly point out that this is not the Cp of capability indexes. Some of the more challenging details are omitted. For example, only in passing is it mentioned that s is a biased estimator of cr. That adjustment factor is not made available in tables. They certainly skimped on model checking in regression and virtually avoid double and multiple sampling plans. In defense of this position, omissions are needed to cover so many topics. The authors are not afraid to use the phrase “Beyond the scope of this book. ” None of the “many new techniques and procedures (such as) multivariate quality control, autocorrelated data, (and) economic design . ” as discussed by Montgomery and Woodall (1997) are even alluded to in this book. Some care is taken to ease a student into the tougher concepts like operating characteristic curves. These are introduced early during the discussion of the Poisson distribution in Chapter 8 in preparation for Chapter 1 l’s acceptance sampling. Still, the mathematically uninitiated willnot survive beyond Chapter 7. The authors aspired to the following innovations (in their own words): “we believe that this book stands out (because of) team-based problems, (and) projects involv(ing) the analysis of large case-oriented data sets. ” For example, in Chapter 2 about detecting and prioritizing problems, one of the exercises has students query their cohorts about the trouble they are having getting good grades in college. These kinds of questions certainly will enhance learning (and not just about quality per se but about the difficulty in formulating survey questions and in collecting valid information and will give the student exposure to a myriad of experiences related to personal interfacing). A total of 19 projects ranging from the quality of an anodizing process [Project 4 from Kane (1989)] to the evaluation of wine [Project 16 from Ashenfelter, Ashmore, and Lalonde (1995)] are also included at the end of each of the five sections. A projects-and-exercises data disk accompanies the text or, theoretically, the data can be accessed at the Web site www.wiley.com/college/stat/ledolterI83784. Upon download (accessed 3/16/00, lo:15 a.m.) and unzipping, this appeared to be a false conjecture. The authors supply an adequate number of project-oriented resources. A reference to a very informative Web site pertaining to design of experiments is offered at www.macomb. k12.mi. us/math/webl.htm (accessed 3/16/00, IO:40 a.m.). Not surprisingly, upon access the counter changed from 2,937 to a meager 2,938 hits (since August 1995). Does a quality advocate find it an exceptional effrontery when errors and nuisances are detected in a book? Perhaps we can “blame” the worker, or the material, or the machinery, or maybe that fact that this is a first edition of the book. There is an instructor’s manual, which was not reviewed along with the text. It surely must contain answers to selected problems. But not a single answer appears in the text itself. This is horribly frustrating for students and does require remedy to consider this a book for text adoption. The computing issue needs to be resolved prior to teaching from this book. The use (or really nonuse) of automated quality tools is troublesome. Section 7.5. 5 has a note on statistical computing. The authors should slip in their apology here for summarily dismissing any attention to software. Only one package, EXCEL, is referred to in the entire text and disparagingly at that. Although, the phrase “the computer output for our example shows . ” (p. 486) is seen often, there are no printouts included in the text. Supplementary instructional materials dealing with quality software capabilities would need to be distributed to a student. An idiosyncrasy of this book is the fact that the numerous tables required by quality assessors are embedded within the section where they are discussed; that is, the binomial tables are found in Chapter 8 instead of in an appendix. This