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An adaptive exponentially weighted moving average-type control chart to monitor the process mean

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  • Mitra, Amitava
  • Lee, Kang Bok
  • Chakraborti, Subhabrata

Abstract

Exponentially weighted moving average (EWMA) control charts are typically used for faster detection of shifts in the process mean, relative to a Shewhart control chart, when the degree of shift is small. Normal guidelines suggest using a small (large) value of the weighting constant, λ, for detecting smaller (larger) shifts in the process mean. Prior research has suggested that the choice of λ should depend on the observed data and have considered the use of a weighting constant, that varies and adapts as monitoring continues and new data are collected. One such adaptive control chart, called the AEWMA chart, utilizes a rather computationally complex scheme to determine the weighting constant λ and it requires knowledge of the size of the shift, to specify whether it is “small” or “large”. A complex two-phase optimization scheme is then solved to yield “good solutions”. As an alternative, we propose an adaptive EWMA-type control chart that does not require knowledge of the degree of the shift. Further, the computational scheme is easier and completed in one stage. The performance of the proposed chart is studied using simulations, where the degree of the shift in the process mean is varied over a wide range of values. Based on the average run length (ARL), as a performance measure, the proposed chart is demonstrated to perform uniformly better than the traditional EWMA chart with a constant weight.

Suggested Citation

  • Mitra, Amitava & Lee, Kang Bok & Chakraborti, Subhabrata, 2019. "An adaptive exponentially weighted moving average-type control chart to monitor the process mean," European Journal of Operational Research, Elsevier, vol. 279(3), pages 902-911.
  • Handle: RePEc:eee:ejores:v:279:y:2019:i:3:p:902-911
    DOI: 10.1016/j.ejor.2019.07.002
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    References listed on IDEAS

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    1. Wenpo Huang & Lianjie Shu & Yan Su, 2014. "An accurate evaluation of adaptive exponentially weighted moving average schemes," IISE Transactions, Taylor & Francis Journals, vol. 46(5), pages 457-469.
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    Cited by:

    1. Zhang, Jinchun & Hou, Jinxiu & Zhang, Zichuan, 2022. "Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter," Energy, Elsevier, vol. 238(PB).
    2. Johannssen, Arne & Chukhrova, Nataliya & Castagliola, Philippe, 2022. "The performance of the hypergeometric np chart with estimated parameter," European Journal of Operational Research, Elsevier, vol. 296(3), pages 873-899.
    3. Nguyen, H.D. & Tran, K.P. & Tran, K.D., 2021. "The effect of measurement errors on the performance of the Exponentially Weighted Moving Average control charts for the Ratio of Two Normally Distributed Variables," European Journal of Operational Research, Elsevier, vol. 293(1), pages 203-218.

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