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Exponentially weighted moving average control charts for monitoring increases in Poisson rate

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  • Lianjie Shu
  • Wei Jiang
  • Zhang Wu

Abstract

The Exponentially Weighted Moving Average (EWMA) control chart has been widely studied as a tool for monitoring normal processes due to its simplicity and efficiency. However, relatively little attention has been paid to EWMA charts for monitoring Poisson processes. This article extends EWMA charts to Poisson processes with an emphasis on quick detection of increases in Poisson rate. Both cases with and without normalizing transformation for Poisson data are considered. A Markov chain model is established to analyze and design the proposed chart. A comparison of the results obtained indicates that the EWMA chart based on normalized data is nearly optimal.

Suggested Citation

  • Lianjie Shu & Wei Jiang & Zhang Wu, 2012. "Exponentially weighted moving average control charts for monitoring increases in Poisson rate," IISE Transactions, Taylor & Francis Journals, vol. 44(9), pages 711-723.
    • Handle: RePEc:taf:uiiexx:v:44:y:2012:i:9:p:711-723
    DOI: 10.1080/0740817X.2011.578609
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    Cited by:

    1. Honghao Zhao & Huajun Tang & Chuan Pang & Huimin Jiang, 2022. "A Markov Chain Model for Approximating the Run Length Distributions of Poisson EWMA Charts under Linear Drifts," Mathematics, MDPI, vol. 10(24), pages 1-12, December.

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