A Resetting Scheme for Process Parameters using the Mahalanobis-Taguchi System

Park, Chang-Soon

  • Received : 2012.05.10
  • Accepted : 2012.07.19
  • Published : 2012.08.31


Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.


Classification analysis;reset procedure;contribution;multivariate data;normal group;threshold


  1. Alt, F. B. (1985). Multivariate quality control, Encyclopedia of Statistical Sciences, 6, N.L. Johnson and S. Kotz, (eds.) Wiley, New York.
  2. Jensen, W. A., Jones-Farmer, L. A., Champ, C. W. and Woodall, W. H. (2006). Effects of parameter estimation on control chart properties: A literature review, Journal of Quality Technology, 38, 95-108.
  3. Kanetaka, T. (1988). Application of Mahalanobis distance, standardization and quality control, Japanese Standards Association, 41(5 and 6).
  4. Lowry, C. A. and Montgomery, D. C. (1995). A review of multivariate control charts, IIE Transactions, 27, 800-810.
  5. Park, C. (2012). Economic design of charts when signals may be misclassified and the bounded reset chart, IIE Transactions, To be published.
  6. Park, C. (2013). An economic design of the bounded reset chart using the integral controller for batchoriented processes, Communications in Statistics-Theory and Methods, DOI: 10.1080/0740817X.2012.695101.
  7. Runger, G. C., Alt, F. B. and Montgomery, D. C. (1996). Contributors to a multivariate statistical process control signal, Communications in Statistcs - Theory and Method, 25, 2203-2213.
  8. Taguchi, G., Chowdhury, S. and Wu, Y. (2005). Taguchi's Quality Engineering Handbook, John Wiley & Sons, Inc., ASI Consulting Group, LLC, Livonia, Michigan.
  9. Taguchi, G. and Jugulum, R. (2002). The Mahalanobis-Taguchi Strategy, John Wiley & Sons, Inc., New York.
  10. Tracy, N. D., Young, J. C. and Mason, R. L. (1992). Multivariate control charts for individual observation, Journal of Quality Technology, 24, 88-95.
  11. Williams, J. D., Woodall, W. H., Birch, J. B. and Sullivan, J. H. (2006). Distribution of Hotelling's T2 statistic based on the successive difference estimator, Journal of Quality Technology, 38, 217-229.
  12. Woodall, W., Koudelik, R., Tsui, K.-L., Kim, S. B., Stoumbos, Z. and Carvounis, C. (2003). A review and analysis of the Mahalanobis-Taguchi system, Technometrics, 45.

Cited by

  1. DD-plot for Detecting the Out-of-Control State in Multivariate Process vol.26, pp.2, 2013,
  2. Implementation of Mahalanobis-Taguchi System for the Election of Major League Baseball Hitters to the Hall of Fame vol.26, pp.2, 2013,


Supported by : National Research Foundation of Korea(NRF)