Quality Gurus - Genichi Taguchi

Genichi Taguchi (January 1, 1924 - June 2, 2012) was an engineer and statistician. Taguchi studied textile engineering at Kiryu Technical College.³³ After WWII he worked for the Japanese Ministry of Public Health and Welfare and conducted the nation's first study on health and nutrition. He also applied his quality improvement knowledge at Morinaga Pharmaceutical and even worked for a candy maker, Morinaga Sieka, to reduce the melting properties of caramel at room temperature.³⁴

From the 1950s onwards, Taguchi developed a methodology for applying statistics to improve the quality of manufactured goods. Taguchi methods have been controversial among some conventional Western statisticians,³⁵ but others have accepted many of the concepts introduced by him as valid extensions to the body of knowledge. He is the executive director of the American Supplier Institute, the director of the Japan Industrial Technology Institute, and an honorary professor at Nanjing Institute of Technology in China. Genichi Taguchi is well known for developing a methodology to improve quality and reduce costs, which, in the United States, is referred to as the Taguchi Methods.³⁶ He also developed the quality loss function.

Taguchi received the Indigo Ribbon from the Emperor of Japan in 1986 for his outstanding contributions to Japanese economics and industry. That year he also received the International Technology Institute's Willard F. Rockwell Medal for combining engineering and statistical methods to achieve rapid improvements in cost and quality by optimizing product design and manufacturing processes. In 1995, the Japanese Society of Quality Control made him an honorary member.³⁷

Major contributions of Genichi Taguchi are:

• Taguchi loss function
• The philosophy of off-line quality control
• Design of experiments

Taguchi Loss Function

Taguchi devised an equation to quantify the decline of a customer's perceived value of a product as its quality declines. Essentially, it tells managers how much revenue they are losing because of variability in their production process.³⁴ It is a powerful tool for projecting the benefits of a quality improvement program. Taguchi was the first person to equate quality with cost. Taguchi loss function is a diagram of the loss for the company that actual results differ from a target value. Taguchi loss function is intended to capture not only the loss to the customer, but to society and society at large which can be measured by cost.

The loss function estimates loss even if parts are made within specification limits. This is necessary to allow for the fact that a company that makes all parts within specification limits still has warranty and customer complaints. That is, there is some loss associated with a population of parts no matter how well they are produced. As long as any parts differ from the target specifications, there is some loss.

Taguchi defines loss as a quadratic expression in terms of measured quality characteristics of the part that ranges between the target value and the specification limits, that is, upper and lower specification limits. The loss function is defined such that when the part is made on the target, the loss is absent. The loss becomes the same as the cost of production of a single part, which is the cost of rejection, when all parts are made outside specification limits. The loss between the target and the specification limits is of parabolic shape, symmetrical about the target.³⁸ The application of Taguchi loss functions can be an excellent tool when faced with determining the utility of competing scheduling policies or practices. A critical insight of Taguchi’s theory is that the total expected cost can be reduced by moving the mean closer to the target and reducing variance. Thus, even if the average prosthetic component is aligned properly, cost reductions can be obtained by reducing the variance of the alignment.³⁹

Design of Experiments

The time required to complete an experiment is extremely long especially for investigating and evaluating large quantity of factors that are affecting the desired quality characteristics. The difficulties are further encountered when experiment has to be repeated for several modelling and verification purpose until accurate and validated result is obtained. Therefore the Taguchi method for design of experiment has become an alternative in solving these problems and also chosen as the right solution to industrial organization in improving their product and process design. The Taguchi method which was designed to reduce the engineering experimental time and cost stimulate the initiative and effort for product improvement and assisted the continuous improvement in processing capability. Its simplicity in data collection as well as practical in designing the product and process parameters make design of experiment possible in any organization and business operation⁴⁰.

Taguchi constructed a special set of general designs for factorial experiments that covers many applications. They are orthogonal arrays with number of experiment, factors and levels for each special design orthogonal arrays. The use of these arrays helps to determine numbers of experiments needed for a given set of factors. When fixed number of levels for all factors is involved and the interaction are unimportant, standard orthogonal arrays will satisfy most experimental design needs. Taguchi method successfully resolves the difficulties in compacting experimental design by having the orthogonal arrays that represents the possible experimental condition and a standard procedure to analyse the experimental result⁴¹. The Taguchi method is a concept developed base on the optimization through design of experiments, in which, experiment will be carried out and the value of quality is very much significant to discipline the way for developing a product and investigating complex problems⁴². Undoubtedly, this method has provided cost effective ways to examine and find available alternatives in design and processing issues.

Eight-Steps in Taguchi Methodology:

Step-1: Identify the main function, side effects, and failure mode

Step-2: Identify the noise factors, testing conditions, and quality characteristics

Step-3: Identify the objective function to be optimized

Step-4: Identify the control factors and their levels

Step-5: Select the orthogonal array matrix experiment

Step-6: Conduct the matrix experiment

Step-7: Analyze the data, predict the optimum levels and performance

Step-8: Perform the verification experiment and plan the future action

The philosophy of off-line quality control

The Taguchi method considers design to be more important than the manufacturing process in quality control and tries to eliminate variances in production before they can occur.⁴³ Taguchi realized that the best opportunity to eliminate variation is during the design of a product and its manufacturing process. Consequently, he developed a strategy for quality engineering that can be used in both contexts. The process has three stages⁴⁴:

I. System design

II. Parameter design

III. Tolerance design

System design: This is design at the conceptual level, involving creativity and innovation.

Parameter design: Once the concept is established, the nominal values of the various dimensions and design parameters need to be set, the detail design phase of conventional engineering. This is sometimes called robustification.

Tolerance design: With a successfully completed parameter design, and an understanding of the effect that the various parameters have on performance, resources can be focused on reducing and controlling variation in the critical few dimensions.

References

33. Wikipedia, http://en.wikipedia.org/wiki/Genichi_Taguchi
34. Genichi Taguchi and Taguchi Methods - Practical, Rapid Quality,                        http://www.skymark.com/resources/leaders/taguchi.asp
35. Wadsworth, Harrison M., Handbook of statistical methods for engineers and scientists (2nd ed.). New York: McGraw-Hill Professional,1997 Pg :214
36. Smith, Gerald F. ,Quality problem solving, American Society for Quality, New York, 1998, pp. 250-251
37. Genichi Taguchi, http://asq.org/about-asq/who-we-are/bio_taguchi.html
38. Ranjith K Roy, Design of Experiments Using The Taguchi Approach, Wiley Interscience Publications, Canada, 2001, pg:14
39. http://www.academia.edu/4265348/Taguchi_loss_function
40. Jaharah A. Ghani, Haris Jamaluddin, Philosophy of Taguchi Approach and Method in Design of Experiment ,  http://scialert.net/fulltext/?doi=ajsr.2013.27.37&org=11#92489_b
41. Chen, W.C., M.W. Wang, G.I. Fu and C.T. Chen, 2008. Optimization of plastic injection molding process via Tauchi's parameter design method, BPNN and DFP.
42. Roy, R.K., A Primer on The Taguchi Method. Van Nostrand Reinhold, New York, USA.,1990, Pages: 247.
43. Taguchi Method Of Quality Control ,http://www.investopedia.com/terms/t/taguchi-        method-of-quality-control.asp
44. Shyam Kumar Karna & Dr. Rajeshwar Sahai, An Overview on Taguchi Method,      International Journal of Engineering and Mathematical Sciences, Volume 1, Issue – 1, June 2012, pp.1-7