The Legacies of Genichi Taguchi | Quality Digest

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The Legacies of Genichi Taguchi

Genichi Taguchi passed away in Tokyo on June 2, 2012, at the age of 88. He started his career by studying textile engineering with the expectation of entering his family's kimono business, but was drafted into Japan's Imperial Navy during World War II. He became interested in statistics after the war and worked with such well-known figures in statistics as C. R. Rao, Walter A. Shewhart, and Ronald A. Fisher. He also worked at the Institute of Statistical Mathematics and made many contributions to industrial experimentation.

After years of consulting in industrial experimentation, Taguchi joined a Japanese research organization called the Electrical Communication Laboratory in 1950. His work contributed to the development of phone system components that were so successful they beat out the well-established Bell Labs for a contract. During this time Taguchi developed what would become robust engineering. This was followed by several decades of statistics-related work. In 1982 Taguchi became involved in training executives at Ford Motor Co. By 1983 he was the executive director of the Ford Supplier Institute that later became the American Supplier Institute, which continues to encourage the use of Taguchi's methodology today.

Introduction to the Taguchi Methods

According to Taguchi's Quality Engineering Handbook (Wiley-Interscience, 2004), Taguchi's method differs from what the United States terms "quality engineering." His method, variously known as "the Taguchi method," "the Taguchi paradigm," or "Taguchi quality engineering" in both Europe and the United States, is "robust engineering based on the following three procedures: (1) orthogonal array, (2) SN ratio, and (3) loss function."

Taguchi's DOE methodology has inspired many DOE practitioners who use his methodology, and much discussion and controversy regarding it. Some writers, such as Ranjit K. Roy in Design of Experiments Using the Taguchi Approach (Wiley-Interscience, 2001) and the American Supplier Institute's James O. Wilkins Jr. report successes using Taguchi's DOE methodology; others, such as George E. P. Box,Søren Bisgaard, and Dorian Shainin and Peter Shainin, take a more critical view.

Taguchi's design of experiments (DOE) uses orthogonal arrays. In DOE, "orthogonal" means the columns of arrays are balanced, and "balanced" means the number of levels in the columns are equal. Balancing ensures that there are an equal number of all possible combinations of factors.

The signal-to-noise (SN) ratio is a key element of analyzing DOE conducted using Taguchi's orthogonal arrays. The SN ratio is the reciprocal of the variance of the measurement error, and it uses the logarithm of the standard deviation to separate dispersion and location effects in DOE. This is in contrast to other methods of DOE that use the more conventional analysis of variance (ANOVA) for analyzing experimental results.

Taguchi's loss function can be illustrated with the example of a low-quality automotive component (see figure 1). The component may be produced within specification, but with a large standard deviation that will result in some customers receiving good parts, and other customers receiving parts that will present a problem. Those customers with a problem will need to take the time to bring their vehicles to a dealer. The dealer will need to have a large staff of mechanics if many vehicles are returned due to problematic components. The dealer loses money by employing a staff dedicated to fixing problems that could have been prevented, and society as a whole experiences a loss when many people miss work to take their vehicles to a dealer for repairs. Taguchi's solution is to design quality into a product as early as possible during the engineering phase.

Figure 1: The Taguchi loss function

According to Genichi Taguchi, Subir Chowdhury, and Shin Taguchi in Robust Engineering (McGraw-Hill Professional, 1999), robustness is "the state where the technology, product, or process performance is minimally sensitive to factors causing variability (either in the manufacturing or user's environment) and aging at the lowest unit manufacturing cost." Robust components minimize the loss in the loss function because the process is at the center of the curve in the Taguchi loss function.

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