![]()
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 ânot truths, only major steps in a never-ending (and diverging) process that helped predict natural phenomena,â Box (1997b) pointed out that âOrville and Wilber Wright undoubtedly had profound knowledge about the design of flying machinesâ 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 âfitness for useâ 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 âhit the targetâ 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 âcost to society.â
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 HewlettâPackard 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 âcomplexityâ 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 âquality doesn't cost, it pays.â 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...
