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Economic design of multivariate Bayesian control chart with two sampling intervals

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  • Naderkhani, Farnoosh
  • Makis, Viliam

Abstract

In this paper, the economic design of a multivariate Bayesian control chart with two sampling intervals for monitoring the process mean is developed. The economic design of traditional control charts such as T2, SPRT, EWMA, and CUSUM charts with variable sampling interval (VSI) has been developed in recent years to control the industrial processes by minimizing the average cost of the process, however, it has been proved that these traditional, non-Bayesian process control charts are not optimal. There has been some limited development of the economic design of Bayesian control charts with a single sampling interval (SSI), but no results regarding the economic design of the Bayesian control chart with two sampling intervals have been reported in the literature. The objective is to find the optimal control chart parameters, namely the sample size, sampling intervals and the control limits, minimizing the total long-run expected average cost per unit time. The proposed problem is formulated and solved in the semi-Markov decision process (SMDP) framework. A numerical example is provided to illustrate the whole procedure. The effectiveness of the newly proposed control chart is demonstrated through a comparison with the multivariate Bayesian control chart with a single sampling interval as well as with a recently developed MEWMA control chart. The results indicate that the proposed control chart has a considerably better performance.

Suggested Citation

  • Naderkhani, Farnoosh & Makis, Viliam, 2016. "Economic design of multivariate Bayesian control chart with two sampling intervals," International Journal of Production Economics, Elsevier, vol. 174(C), pages 29-42.
    • Handle: RePEc:eee:proeco:v:174:y:2016:i:c:p:29-42
    DOI: 10.1016/j.ijpe.2016.01.007
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    References listed on IDEAS

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    Cited by:

    1. Xiao, Xiao & Jiang, Wei & Luo, Jianwen, 2019. "Combining process and product information for quality improvement," International Journal of Production Economics, Elsevier, vol. 207(C), pages 130-143.
    2. Duan, Chaoqun & Li, Yifan & Pu, Huayan & Luo, Jun, 2022. "Adaptive monitoring scheme of stochastically failing systems under hidden degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Duan, Chaoqun & Makis, Viliam & Deng, Chao, 2020. "A two-level Bayesian early fault detection for mechanical equipment subject to dependent failure modes," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    5. Kampitsis, Dimitris & Panagiotidou, Sofia, 2022. "A Bayesian condition-based maintenance and monitoring policy with variable sampling intervals," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. Tomohiro, Ryosuke & Arizono, Ikuo & Takemoto, Yasuhiko, 2020. "Economic design of double sampling Cpm control chart for monitoring process capability," International Journal of Production Economics, Elsevier, vol. 221(C).

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