A Hybrid Approach to Statistical Process Control

  • Published : 2004.09.01

Abstract

Successful implementation of statistical process control techniques requires for operational definitions and precise measurements. Nevertheless, very often analysts can dispose of process data available only by linguistic terms, that would be a waste to neglect just because of their intrinsic vagueness. Thus a hybrid approach, which integrates fuzzy set theory and common statistical tools, sounds useful in order to improve effectiveness of statistical process control in such a case. In this work, a fuzzy approach is adopted to manage linguistic information, and the use of a Chi-squared control chart is proposed to monitor process performance.

Keywords

References

  1. Beamon, B. M., Ware, T. M. (1998) 'A process quality model for the analysis, improvement and control of supply chain systems', Logistics Information Management, Vol. 11, No. 2, pp. 105-113 https://doi.org/10.1108/09576059810209991
  2. Ghobadian, A., Speller, S., Jones, M. (1994) 'Service Quality - Concepts and Models' International Journal of Quality & Reliability Management, Vol. 11, No. 9, pp. 43-66 https://doi.org/10.1108/02656719410074297
  3. Hude, H. (1991) 'Quality in the Service Sector', Making Statistics More Effective in Schools of Business, 6th Annual Conference, The Wharton School, University of Pennsylvania, June 7-8
  4. Kotz, S., Johnson, N. L. (1985), Encyclopedia of Statistical Sciences, Vol. 5, John Wiley & Sons, New York, pp. 659-664
  5. Krzanowski, W., Marriott F. H. C. (1994), Multivariate Analysis Part 1, Arnold, $1^{st}$ ed, London
  6. Laviolette, M., Seaman, J.W. Jr., Barrett, J.D., WoodaII, W.H. (1995), 'A probabilistic and statistical view of fuzzy methods' (with discussion), Technometrics, Vol. 37, No. 3, pp. 249-292 https://doi.org/10.2307/1269905
  7. MacCarthy, B.L., Wasusri, T. (2002), 'A review of non-standard appIications of statistical process control (SPC) charts', International Journal of Quality & Reliability Management, Vol. 19 No. 3, pp. 295-320 https://doi.org/10.1108/02656710210415695
  8. Marcucci, M. (1985), 'Monitoring Multinomial Processes', Journal of Quality Technology, Vol. 17, No. 2, pp. 86-91
  9. Montgomery, D.C., Woodall, W.H. (1997), 'A Discussion on Statistically-Based process Monitoring and Control' (with discussion), Journal of Quality Technology, Vol. 29, No.2, pp. 121-211
  10. Montgomery, D.C., (2001), Introduction to Statistical Quality Control, John Wiley & S., 4th ed. New York
  11. Wood, M. (1994), 'Statistical Methods for Monitoring Service Processes', International Journal of Service Industry Management, Vol. 5, No. 4, pp. 53-68 https://doi.org/10.1108/09564239410068706
  12. Jones, L.A., Woodal, W. Conerly, M. D. (1999), 'Exact Properties of Demerit ControI Charts', Journal of Quality Technology, Vol. 31, No. 2, pp. 207-216 https://doi.org/10.1080/00224065.1999.11979915
  13. Johnson, N. L., Kotz, S. (1972), Continuous multivariate distributions, John Wiley & Sons, New York
  14. Zadeh, L.A. (1968), 'Fuzzy algorithms', Information and Control, Vol. 12, pp. 94-102 https://doi.org/10.1016/S0019-9958(68)90211-8
  15. Zadeh, L.A. (1973), 'Outline of a new approach to the analysis of complex systems and decision processes', IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No.1, pp. 28-44