Quality and Quantity

, Volume 40, Issue 2, pp 157–174 | Cite as

Quality Improvement by Using Inverse Gaussian Model in Robust Engineering



The concept of robust engineering (RE) which is based on the philosophy of Genichi Taguchi aims at providing industries with a cost effective methodology for enhancing their comptetive position in the global market. Since in most cases it is not possible to model the mathematical relationship between quality characteristic (QC), parameter designs and noise factors of situation under study, a proper statistical model in design of experiments (DOE) is proposed. However, the used statistical procedures in DOE are based on normality assumption of real data of QC or its transformed distribution. In many engineering cases, the data is highly skewed and therefore cannot be always removed by usual transformations; and even if it will be removed to a great extend, it may lead to inaccurate inferences in model parameters. Alternatively, the Inverse Gaussian family of distributions is flexible enough to provide a suitable model for these types of data. In this study, in dealing with such type of data, the concept of RE method is combined with Inverse-Gaussian (IG) model to reduce total deviations from target values of 17 quality characteristics in oil pump housings produced by Iranian diesel engine manufacturing (IDEM) company. As the distridution of data obtained from RE methodology follows the IG, the analysis without any data transformation (uncontrary in traditional RE procedure) is done straight forward through an IG model, and then its analysis is compared with customary analysis of RE method. This paper consists of four sections. The first section provides a brief description of problem. Section two gives a brief introduction to RE methodology. Section three devoted to introducing the proposed DOE model which is base upon inverse-Gaussian distribution. In section four, application of the two approaches to improve quality of produced oil pump housings in IDEM are considered and their relative results are obtained. And finally, in section five, the analysis results of application of the two models are compared.


Optimization Robust Engineering Inverse-Gaussian model 


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  1. Achcar, R. J., Rosales, O. L. A. 1992A Bayesian approach for accelerated life test assuuming an Inverse Gaussian distributionEstadistica22532Google Scholar
  2. American Supplier Institute Inc.1989Taguchi Methods: Implementation ManualASIDearborn, MIGoogle Scholar
  3. Bameni M. M. (2004). Application of Robust Engineering Method in Oil Pump Housing Production Process. International Journal of Engineering Science 15(2).Google Scholar
  4. Banerjee, A. K., Bhattacharyya, G. K. 1979Bayesian results for the Inverse Gaussian distribution with an applicationTechnometrices21247251Google Scholar
  5. Bendel, A. 1988Introduction to Taguchi Methodology. Taguchi Methods: Proceedings of the 1988 European ConferenceElsevier Applied ScienceLondon114Google Scholar
  6. Bisgaard, S., Ankenman, B. 1996Analytic Parameter DesignQuality Engineering87591Google Scholar
  7. Bhattacharyya, G. K., Fries, A. 1983Analysis of two-factor experiments under an Inverse Gaussian modelJournal of American Statistical Association78820826Google Scholar
  8. Chhicara, R. S., Folks, L. 1989Inverse Gaussian distribution, Theory and applicationsMarcel DekkerNew YorkGoogle Scholar
  9. Folks, J. L., Chhicara, R. S. 1978The Inverse Gaussian distribution and its statistical application – a reviewJournal of the Royal Statistic Society of Britain40263275Google Scholar
  10. Fries, A., Bhattacharyya, G. K. 1983Analysis of two-factor experiments under an Inverse Gaussian modelJournal of American Statistical Association78820826Google Scholar
  11. Kackar, R. 1985Off-line Quality Control, Parameter Design, and the Taguchi MethodJournal of Quality Technology17176188Google Scholar
  12. Meshkani, M. R. (2004). One-way and two-way analysis of variance for Inverse Gaussian distribution by empirical Bayes procedure. Journal of Science, to be appeared.Google Scholar
  13. Phadke, S. M. 1989Quality Engineering Using Robust DesignPrentice HallEnglewood Cliffs, NJGoogle Scholar
  14. Roy, K., Ranjit,  2001Design of Experiments Using the Taguchi ApproachJohn Wiley & SonsNYGoogle Scholar
  15. Shuster, J. J., Muira, C. 1972Two-way analysis of reciprocalsBiometrika59478481Google Scholar
  16. Taguchi, G. 1986Introduction to Quality EngineeringAsian Productivity Organization, American Supplier Institute Inc.Dearborn, MIGoogle Scholar
  17. Taguchi, G. (1987). System of Experimental Designs. In: Don Clausing (ed.), Vol. 1 and 2 New York: UNIPUB/Kraus International Publications.Google Scholar
  18. Taguchi, G., Konishi, S. 1987Orthogonal Arrays and Linear GraphsAmerican Supplier Institute Inc.Dearborn, MIGoogle Scholar
  19. Taguchi, G., Jugulum, R. 2002The Mahalanobis-Taguchi Strategy: A Pattern Technology SystemWileyNew YorkGoogle Scholar
  20. Tweedie, M. C. K. 1957Statistical Properties of Inverse Gaussian distributionsAnnals Mathematical Statistics28362377Google Scholar
  21. Unal, R. & Dean, E. B. (1999). Taguchi Approach to Design Optimization for Quality and Cost. Paper presented at the Annual Conference of the International Society of Parametric Analysis.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.Department of Statistics, Faculty of EconomicsAllameh Tabatabaee UniversityTehranIran

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