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EWMA control charts for monitoring high-yield processes based on non-transformed observations

Author

Listed:
  • Arthur B. Yeh
  • Richard N. Mcgrath
  • Mark A. Sembower
  • Qi Shen

Abstract

We propose and study exponentially weighted moving average (EWMA) control charts for monitoring high-yield processes. The EWMA control charts are developed based on non-transformed geometric, binomial and Bernoulli counts. The proposed charts are evaluated based on the average number of items sampled before the first out-of-control signal is detected. By selecting small smoothing constants, the proposed EWMA control charts outperform in numerous cases the recently developed CUSUM control charts [Chang, T.C. and Gan, F.F., Cumulative sum charts for high yield processes. Statist. Sin., 2001, 11, 791–805], which are considered the most efficient control charting mechanisms in the existing literature for monitoring fraction non-conforming as small as 0.0001. Numerous simulations are included for performance comparisons. An example is also given to demonstrate the applicability of the proposed EWMA control charts.

Suggested Citation

  • Arthur B. Yeh & Richard N. Mcgrath & Mark A. Sembower & Qi Shen, 2008. "EWMA control charts for monitoring high-yield processes based on non-transformed observations," International Journal of Production Research, Taylor & Francis Journals, vol. 46(20), pages 5679-5699, January.
    • Handle: RePEc:taf:tprsxx:v:46:y:2008:i:20:p:5679-5699
    DOI: 10.1080/00207540601182252
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    Cited by:

    1. Bersimis, Sotiris & Koutras, Markos V. & Maravelakis, Petros E., 2014. "A compound control chart for monitoring and controlling high quality processes," European Journal of Operational Research, Elsevier, vol. 233(3), pages 595-603.
    2. Gordon Ross & Dimitris Tasoulis & Niall Adams, 2013. "Sequential monitoring of a Bernoulli sequence when the pre-change parameter is unknown," Computational Statistics, Springer, vol. 28(2), pages 463-479, April.

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