JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Optimal Design of a EWMA Chart to Monitor the Normal Process Mean
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Optimal Design of a EWMA Chart to Monitor the Normal Process Mean
Lee, Jae-Heon;
  PDF(new window)
 Abstract
EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.
 Keywords
Design of a control chart;average run length;statistical process control;EWMA chart;
 Language
Korean
 Cited by
 References
1.
Capizzi, G. and Masarotto, G. (2003). An adaptive exponentially weighted moving average control chart, Technometrics, 45, 199-207. crossref(new window)

2.
Crowder, S. V. (1987a). A simple method for studying run length distributions of exponentially weighted moving average charts, Technometrics, 29, 401-407.

3.
Crowder, S. V. (1987b). Average run lengths of exponentially weighted moving average control charts, Journal of Quality Technology, 19, 161-164.

4.
Jiang, W., Shu, L. and Apley, D. W. (2008). Adaptive CUSUM procedures with EWMA-based shift estimators, IIE Transactions, 40, 992-1003. crossref(new window)

5.
Lucas, J. M. (1982). Combined Shewhart-CUSUM quality control schemes, Journal of Quality Technology, 14, 52-59.

6.
Page, E. (1954). Continuous inspection schemes, Biometrika, 41, 100-115. crossref(new window)

7.
Reynolds, JR., M. R. and Lou, J. (2010). An evaluation of a GLR control chart for monitoring the process mean, Journal of Quality Technology, 42, 287-310.

8.
Reynolds, JR., M. R. and Stoumbos, Z. G. (2004a). Control charts and the efficient allocation of sampling resources, Technometrics, 46, 200-214. crossref(new window)

9.
Reynolds, JR., M. R. and Stoumbos, Z. G. (2004b). Should observations be grouped for effective process monitoring?, Journal of Quality Technology, 36, 343-366.

10.
Reynolds, JR., M. R. and Stoumbos, Z. G. (2006). Comparisons of some exponentially weighted moving average control charts for monitoring the process mean and variance, Technometrics, 48, 550-567. crossref(new window)

11.
Roberts, S. W. (1959). Control chart tests based on geometric moving averages, Technometrics, 1, 239-250. crossref(new window)

12.
Ryu, J.-H., Wan, H. and Kim, S. (2010). Optimal design of a CUSUM chart for a mean shift of unknown size, Journal of Quality Technology, 42, 311-326.

13.
Shu, L. and Jiang, W. (2006). A Markov chain model for the adaptive CUSUM control chart, Journal of Quality Technology, 38, 135-147.

14.
Sparks, R. S. (2000). CUSUM charts for signaling varying location shifts, Journal of Quality Technology, 32, 157-171.

15.
Zhao, Y., Tsung, F. and Wang, Z. (2005). Dual CUSUM control schemes for detecting a range of mean shifts, IIE Transactions, 37, 1047-1057. crossref(new window)