Research Issues in Robust QFD

  • Kim, Kwang-Jae (Department of Industrial and Management Engineering Pohang University of Science and Technology) ;
  • Kim, Deok-Hwan (Department of Industrial and Management Engineering Pohang University of Science and Technology)
  • 발행 : 2008.09.30

초록

Quality function deployment (QFD) provides a specific approach for ensuring quality throughout each stage of the product development and production process. Since the focus of QFD is placed on the early stage of product development, the uncertainty in the input information of QFD is inevitable. If the uncertainty is neglected, the QFD analysis results are likely to be misleading. It is necessary to equip practitioners with a new QFD methodology that can model, analyze, and dampen the effects of the uncertainty and variability in a systematic manner. Robust QFD is an extended version of QFD methodology, which is robust to the uncertainty of the input information and the resulting variability of the QFD output. This paper discusses recent research issues in Robust QFD. The major issues are related with the determination of overall priority, robustness evaluation, robust prioritization, and web-based Robust QFD optimizer. Our recent research results on the issues are presented, and some of future research topics are suggested.

키워드

참고문헌

  1. Brunk, H. (1960), Mathematical models for ranking from comparisons, Journal of the American Statistical Association, 55(291), 503-520. https://doi.org/10.2307/2281911
  2. Cook, W. and Kress, M. (1988), Deriving weights from pairwise comparisons ratio matrices: an axiomatic approach, European Journal of Operational Research, 37, 355-362. https://doi.org/10.1016/0377-2217(88)90198-1
  3. Hadar, J. and Russell, W. (1969), Rules for ordering uncertain prospects, American Economic Review, 59, 25-34.
  4. Kendall, M. (1955), Further contributions to the theory of paired comparisons, Biometrics, 11, 43-62. https://doi.org/10.2307/3001479
  5. Kim, D. and Kim, K. (2006), Determining robust ranking from pairwise comparisons, Working paper, POSTECH.
  6. Kim, D. and Kim, K. (2008), Robustness Indices and Robust Prioritization in QFD, Expert Systems With Applications, Accepted for Publication.
  7. Kim, K., Kim, D., and Min, D. (2007), Robust QFD: framework and a case Study, Quality and Reliability Engineering International, 23(1), 31-44. https://doi.org/10.1002/qre.821
  8. Kim, K., Moskowitz, H., Dhingra, A., and Evans, G. (2000), Fuzzy multicriteria models for quality function deployment, European Journal of Operational Research, 121, 504-518. https://doi.org/10.1016/S0377-2217(99)00048-X
  9. Kmietowicz, Z. and Pearman, A. (1984), Decision theory, linear partial information and statistical dominance, OMEGA, 12(4), 391-399. https://doi.org/10.1016/0305-0483(84)90075-6
  10. Law, A. and Kelton, W. (2000), Simulation Modeling and Analysis, MaGraw-Hill.
  11. Montgomery, D. (1985), Introduction to Statistical Quality Control (2nd ed.), Wiley, Inc., New York.
  12. Montgomery, D. and Runger, G. (2003), Applied Statistical and Probability for Engineers, Wiley.
  13. Rardin, R. (1998), Optimization in Operations Research, Prentice Hall Inc., New Jersey.
  14. Saaty, T. (1977), A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology, 15, 234-281. https://doi.org/10.1016/0022-2496(77)90033-5
  15. Wasserman, G. (1993), On how to prioritize design requirements during the QFD planning process, IIE Transactions, 25 (3), 59-65. https://doi.org/10.1080/07408179308964291
  16. Xie, M., Tan, K., and Goh, T. (2003), Advanced QFD Applications, Milwaukee, ASQ Press.