The above posts dealt with the measurement of variation, specifically with Cp and CpK parameters. This section will concentrate on more powerful statistical techniques, generally called Design of Experiments or DOE. They are particularly important in two areas: (1) for resolving chronic quality problems in production, and (2) at the design stage of both products and processes. A chronic problem can be described as a product with an acceptable defect rate, but also one that has measurably higher dollar waste and that has defied traditional engineering solutions for a long time. DOE techniques are especially important on all new designs, so the chronic quality problems in production can be prevented before firefighting becomes necessary. The objectives in both these areas are to: (a) identify the important variables, whether they are product or process parameters; materials or components from suppliers; environmental or measuring equipment factors. (b) separate these important variables out, as generally there are no more than 1 to 4 important ones. (c) reduce variation of the important variables (including the tight control of interaction effects) to close tolerances through redesign, supplier process improvement, etc. (e) open up the tolerances on the unimportant variables to reduce costs substantially.
There are three approaches to the design of experiments, the classical, Taguchi, and Shainin. The classical approach is based on the pioneering work of Sir Ronald Fisher, who applied design of experiment techniques to the field of agriculture as early as the 1930s. Dr. Taguchi of Japan adopted the classical approach with the development of orthogonal arrays. The third DOE approach is a collection of the techniques taught by Shainin. The “Three Approaches to the Design of Experiments” figure shows the principal methods used by each approach. The classical tools start with fraction factorials and end with evolutionary optimization (EVOP). The Taguchi methods use orthogonal arrays (inner and outer) in “tolerance design”, employing analysis of variance and signal-to-noise for statistical evaluation. All three approaches are far superior to the conventional SPC which attempts to solve chronic problems by means of control charts. All three approaches are also far superior to experiments in which one variable at a time is varied, with the other variables kept rigidly constant. Besides an inordinate amount of time needed for such experimentation, the central statistical weakness of this approach is a chronic inability to separate the main from interaction affects. These weaknesses create frustration and high costs.
Of the three DOE methodologies the Shainin methodology is the best use. This is because there are several fundamental improvements over the Taguchi methods. These include lack of randomization, interactions, and use of orthogonal arrays. In short the Taguchi results are suboptimal and time is better spent using the Shainin DOE tools that can diagnose and greatly reduce variation, leading to zero defects, and to near zero their ability. In short these tools are: (1) simple, understood by engineers and line workers alike. The mathematics involved are unbelievably, and almost embarrassingly, elementary. (2) logical, based on common sense. (3) practical, easy to implement, in production, in design, and with suppliers. (4) universal and scope, applicable in a wide variety of industries, big and small, process intensive as well as assembly intensive. (5) statistically powerful, in terms of accuracy, with no violations of statistical principles. (6) excellent in terms of results, with quality gains not in the inconsequential range of 10 to 50% improvement but in the 100 to 500% range.
The “Variation Reduction Roadmap” figure represents a time-tested romance to variation reduction. He consists of seven DOE tools invented or perfected by Shainin. They are based on his philosophy of “Don’t let the engineers do the guessing, let the parts to the talking.” The analogy of a detective story is appropriate to use in this diagnostic journey. Clues can be gathered with each DOE tool, each progressively more positive, until the culprit cause, the Red X in that Shainin lexicon, is captured, reduced, and controlled. The second most important cause is called the Pink X, and the third most important the Pale Pink X. Generally by the time the top one, two, or three causes, the Red X, the Pink X, and the Pale Pink X, are captured, well over 80% of the variation allowed within the specification of limits is eliminated. In short a minimum CpK of 5.0 is achieved with just one, two, or three DOE experiments.
The “Seven DOE Tools” figure presents a capsule summary of each of the seven DOE tools used, their objectives, and where and when each is applicable. The figure also gives a sample size needed, depicting the unbelievable economy of experimentation. It is strongly recommended that new product and process developments utilize DOE experiments as they move through the scale-up phase of their development. It is only when the diagnostic journey using the seven DOE tools has ended, with a substantial reduction variation, that the focus can shift from DOE to SPC. The true role of SPC therefore is maintenance to insure that the variation, now captured and reduced, is held to set levels.