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A pragmatic approach to experimental design in industry

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  • T. N. Goh

Abstract

The importance of statistically designed experiments in industry has been well recognized. However, the use of 'design of experiments' is still not pervasive, owing in part to the inefficient learning process experienced by many non-statisticians. In this paper, the nature of design of experiments, in contrast to the usual statistical process control techniques, is discussed. It is then pointed out that for design of experiments to be appreciated and applied, appropriate approaches should be taken in training, learning and application. Perspectives based on the concepts of objective setting and design under constraints can be used to facilitate the experimenters' formulation of plans for collection, analysis and interpretation of empirical information. A review is made of the expanding role of design of experiments in the past several decades, with comparisons made of the various formats and contexts of experimental design applications, such as Taguchi methods and Six Sigma. The trend of development shows that, from the realm of scientific research to business improvement, the competitive advantage offered by design of experiments is being increasingly felt.

Suggested Citation

  • T. N. Goh, 2001. "A pragmatic approach to experimental design in industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 391-398.
  • Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:391-398
    DOI: 10.1080/02664760120034126
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    Cited by:

    1. K. Hafeez & H. Rowlands & G. Kanji & S. Iqbal, 2002. "Design optimization using ANOVA," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(6), pages 895-906.
    2. Ediz Atmaca & S. Girenes, 2013. "Lean Six Sigma methodology and application," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2107-2127, June.

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