17 Facts About Taguchi methods

Taguchi methods are statistical methods, sometimes called robust design methods, developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently applied to engineering, biotechnology, marketing and advertising.

Traditionally, statistical methods have relied on mean-unbiased estimators of treatment effects: Under the conditions of the Gauss–Markov theorem, least squares estimators have minimum variance among all mean-unbiased linear estimators.

Reacting to Fisher's methods in the design of experiments, Taguchi interpreted Fisher's methods as being adapted for seeking to improve the mean outcome of a process.

However, Taguchi methods realised that in much industrial production, there is a need to produce an outcome on target, for example, to machine a hole to a specified diameter, or to manufacture a cell to produce a given voltage.

Taguchi methods realised, as had Walter A Shewhart and others before him, that excessive variation lay at the root of poor manufactured quality and that reacting to individual items inside and outside specification was counterproductive.

Taguchi methods therefore argued that quality engineering should start with an understanding of quality costs in various situations.

However, Taguchi methods insisted that manufacturers broaden their horizons to consider cost to society.

Taguchi methods argued that such losses would inevitably find their way back to the originating corporation, and that by working to minimise them, manufacturers would enhance brand reputation, win markets and generate profits.

All these losses are, as W Edwards Deming would describe them, unknown and unknowable, but Taguchi wanted to find a useful way of representing them statistically.

Taguchi methods realized that the best opportunity to eliminate variation of the final product quality is during the design of a product and its manufacturing process.

Taguchi methods's designs aimed to allow greater understanding of variation than did many of the traditional designs from the analysis of variance.

Taguchi methods contended that conventional sampling is inadequate here as there is no way of obtaining a random sample of future conditions.

Taguchi methods proposed extending each experiment with an "outer array" ; the "outer array" should simulate the random environment in which the product would function.

Many of the orthogonal arrays that Taguchi methods has advocated are saturated arrays, allowing no scope for estimation of interactions.

Taguchi methods argues that such interactions have the greatest importance in achieving a design that is robust to noise factor variation.

The Taguchi methods approach provides more complete interaction information than typical fractional factorial designs, its adherents claim.

Genichi Taguchi methods has made valuable contributions to statistics and engineering.