Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
권호 표지

Impact of Special Causes on EWMA Feedback Process Adjustment

EWMA 피드백 공정 조정에서 이상원인의 영향

  • Published : 2003.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

A special cause producing temporary deviation in the underlying process can influence on process adjustment in responsive feedback control system. In this paper, the impact of special causes on the EWMA(Exponentially Weighted Moving Average) forecasts and the process adjustment that is based on the EWMA forecasts are derived. For some special causes with patterned type of contamination, the influence of the causes on the output process are explicitly investigated. A data set, contaminated by a special cause of level shift, is analyzed to evaluate the impact numerically.

Keywords

References

  1. Alwan, L. C. and Roberts, H. V.(1988), 'Time-Series Modeling forStatistical Process Control', Journal of Business & Economic Statistics, Vol.6, No.1, pp. 87-95 https://doi.org/10.2307/1391421
  2. Atienza, O. O., Tang, L. C., andAng, B. W. (1998), 'A SPCProcedure for Detecting Level Shiftsof Autocorrelated Processes', Journalof Quality Technology, Vol. 30, No. 4,pp. 340-351
  3. Baxley, R.V., Jr.(1994), 'Applicationof the EWMA for AlgorithmicStatistical Process Control,' Quality Engineering, Vol. 7, pp. 397-418 https://doi.org/10.1080/08982119408918792
  4. Box, G. E. P., Coleman, D. E., and Baxley, R., Jr. (1997), 'A Comparison of Statistical Process Control and Engineering Process Control,' Journal of Quality Technology, Vol. 29, No. 2, pp. 128-130
  5. Box, G. E. P., Jenkins, G. M., andReinsel, G. C. (1994), Time SeriesAnalysis, Forecasting, and Control, 3rded., Prentice Hall, Englewood Cliffs,NJ
  6. Box, G. E. P. and Kramer, T.(1992), 'Statistical Process Monitoringand Feedback Adjustment-A Discussion,' Technometrics, Vol. 34,pp. 251-285 https://doi.org/10.2307/1270028
  7. Box, G. E. P. and Lucceno, A.(1997). Statistical Control : By Monitoring and Feedback Adjustment,John wiley & Sons, New York
  8. Bruce, A. G. and Martin, R. D.(1989), 'Leave-k-Out Diagnostic forTime Series (with Discussion),' Journalof the Royal Statistical Society, SeriesB, Vol. 51, pp. 363-424
  9. Chen, C. and Liu, L.-M. (1993),'Forecasting Time Series withOutliers', Journal of Forecasting, Vol.12, pp. 12-35
  10. Cho, SinsuP, Ryu, Gui Yeol, Park, B.U., and Lee, J. J. (1993), 'OutlierDetection Diagnostic based onInterpolation Method in AutoregressiveModels' Journal of the Korean Statistical Society. Vol. 22, No. 2, 283-306
  11. Crowder, S. V., Hawkins, D. M.,Reynold M. R. JR., and Yashchin, E(1997), 'Process Control andStatistical Inference', Journal of Quality Technology, Vol. 29, No. 2,pp. 134-139
  12. Jiang, W., Tsui, K-L, and Woodall,W. H. (2000), A New SPCMonitoring Method: The ARMAChart, Technometrics, Vol. 42, No. 4,399-410 https://doi.org/10.2307/1270950
  13. Ledolter, J. (1989), 'The Effect ofAdditive Outliers on the Forecastsfrom ARIMA models' International Journal of Forecasting, Vol. 5, pp.231-240 https://doi.org/10.1016/0169-2070(89)90090-3
  14. Lee, Jae June (1994), 'Detection ofLocal Structural Changes in TimeSeries,' The Korean Journal of Applied Statistics, Vol. 7, No. 2, pp.299-311
  15. Tsay, R. S. (1986), 'Time SeriesModel Specification in the Presence ofOutliers,' Journal of Amencan Statistical Association, Vol. 81, pp.131-141
  16. Tucker, W. T., Faltin, F. W., andVander Wiel, S. A. (1993), 'Algorithmic Statistical Process Control: AnElaboration,' Technometrics, Vol. 35,pp. 363-375 https://doi.org/10.2307/1270270
  17. Zhang, N. F. (1998), A StatisticalControl Chart for Stationary ProcessData, Technometrics, Vol. 40, No. 1,24-38 https://doi.org/10.2307/1271390