The funnel experiment: The Markov‐based SPC approach

作者: Gonen Singer , Irad Ben-Gal

DOI: 10.1002/QRE.852

关键词:

摘要: The classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). popular example illustrates that implementation simple feedback rules stationary processes violates independence assumption and prevents conventional SPC. However, did not indicate how implement SPC in presence such rules. This pedagogical gap is addressed here introducing a rule results nonlinear which traditional methods cannot be applied. proposed method Markov-based SPC, simplified version context-based method, shown monitor modified well. Copyright © 2007 John Wiley & Sons, Ltd.

参考文章(45)
Irad Ben-Gal, Gail Morag, Armin Shmilovici, CSPC: A Monitoring Procedure for State Dependent Processes ,(2003)
Douglas C Montgomery, J Bert Keats, George C Runger, William S Messina, None, Integrating Statistical Process Control and Engineering Process Control Journal of Quality Technology. ,vol. 26, pp. 79- 87 ,(1994) , 10.1080/00224065.1994.11979508
Author’s Publications Brill Nijhoff. pp. 129- 130 ,(2007) , 10.1163/EJ.9789004158351.I-136.40
Chao-Wen Lu, Marion R. Reynolds, EWMA CONTROL CHARTS FOR MONITORING THE MEAN OF AUTOCORRELATED PROCESSES Journal of Quality Technology. ,vol. 31, pp. 166- 188 ,(1999) , 10.1080/00224065.1999.11979913
Irad Ben-Gal, Gail Morag, Armin Shmilovici, Context-Based Statistical Process Control Technometrics. ,vol. 45, pp. 293- 311 ,(2003) , 10.1198/004017003000000122
John F. Macgregor, A different view of the funnel experiment Journal of Quality Technology. ,vol. 22, pp. 255- 259 ,(1990) , 10.1080/00224065.1990.11979256
W. Edwards Deming, Out of the Crisis ,(1982)
Douglas C Montgomery, Christina M Mastrangelo, None, Some Statistical Process Control Methods for Autocorrelated Data Journal of Quality Technology. ,vol. 23, pp. 179- 193 ,(1991) , 10.1080/00224065.1991.11979321
Michael A Benjamin, Robert A Rigby, D Mikis Stasinopoulos, Generalized Autoregressive Moving Average Models Journal of the American Statistical Association. ,vol. 98, pp. 214- 223 ,(2003) , 10.1198/016214503388619238