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The Taguchi method
Author(s) -
Mitra Amitava
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.169
Subject(s) - taguchi methods , robustness (evolution) , orthogonal array , computer science , measure (data warehouse) , function (biology) , quality (philosophy) , process capability , mathematics , data mining , statistics , engineering , work in process , operations management , biochemistry , chemistry , philosophy , epistemology , evolutionary biology , biology , gene
Achieving robustness in product and process designs is of importance to various stakeholders such as manufacturers, suppliers, and consumers. As variability exists in all operations, it is desirable to create products and processes that are not very sensitive to factors that are not controllable. The Taguchi method is an approach to robust design. Inherent in the Taguchi method is the definition of a loss function. This loss function formulation is influenced by the type of quality characteristic under consideration, that is, smaller‐is‐better, larger‐is‐better, or target‐is‐best. Furthermore, based on the selected type of quality characteristic, a performance measure is defined. Such performance measures, usually called signal‐to‐noise (S/N) ratios, are used to determine optimal settings of the controllable factors. Typically, a two‐step procedure is adopted in the Taguchi method. In the first step, the S/N ratio is maximized, whereas in the second step, using an adjustment factor that does not affect the S/N ratio, the mean response is adjusted to meet the target value, where appropriate. Experimental designs make use of orthogonal arrays to determine factor settings for obtaining data for subsequent analysis. The number of experimental runs is very modest in relation to the number of factors being investigated. WIREs Comp Stat 2011 3 472–480 DOI: 10.1002/wics.169 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust Methods Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data