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A multivariate exponentially weighted moving average control chart for monitoring process variability

Author

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  • Arthur Yeh
  • Dennis Lin
  • Honghong Zhou
  • Chandramouliswaran Venkataramani

Abstract

This paper introduces a new multivariate exponentially weighted moving average (EWMA) control chart. The proposed control chart, called an EWMA V-chart, is designed to detect small changes in the variability of correlated multivariate quality characteristics. Through examples and simulations, it is demonstrated that the EWMA V-chart is superior to the &7CS&7C-chart in detecting small changes in process variability. Furthermore, a counterpart of the EWMA V-chart for monitoring process mean, called the EWMA M-chart is proposed. In detecting small changes in process variability, the combination of EWMA M-chart and EWMA V-chart is a better alternative to the combination of MEWMA control chart (Lowry et al. , 1992) and &7CS&7C-chart. Furthermore, the EWMA M- chart and V-chart can be plotted in one single figure. As for monitoring both process mean and process variability, the combined MEWMA and EWMA V-charts provide the best control procedure.

Suggested Citation

  • Arthur Yeh & Dennis Lin & Honghong Zhou & Chandramouliswaran Venkataramani, 2003. "A multivariate exponentially weighted moving average control chart for monitoring process variability," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(5), pages 507-536.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:507-536
    DOI: 10.1080/0266476032000053655
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    Cited by:

    1. Zhang, Jiujun & Li, Zhonghua & Wang, Zhaojun, 2010. "A multivariate control chart for simultaneously monitoring process mean and variability," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2244-2252, October.
    2. Frisén, Marianne, 2011. "Inference Principles For Multivariate Surveillance," Research Reports 2011:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    3. Bock, David, 2007. "Consequences of using the probability of a false alarm as the false alarm measure," Research Reports 2007:3, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    4. Machado, Marcela A.G. & Costa, Antonio F.B., 2008. "The double sampling and the EWMA charts based on the sample variances," International Journal of Production Economics, Elsevier, vol. 114(1), pages 134-148, July.
    5. Ahmad, Shabbir & Riaz, Muhammad & Abbasi, Saddam Akber & Lin, Zhengyan, 2013. "On monitoring process variability under double sampling scheme," International Journal of Production Economics, Elsevier, vol. 142(2), pages 388-400.
    6. Frisén, Marianne, 2011. "On multivariate control charts," Research Reports 2011:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    7. David Bock, 2008. "Aspects on the control of false alarms in statistical surveillance and the impact on the return of financial decision systems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(2), pages 213-227.
    8. Zekâi Şen, 2024. "Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4415-4429, September.
    9. Dariush Najarzadeh, 2019. "Testing equality of standardized generalized variances of k multivariate normal populations with arbitrary dimensions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 593-623, December.
    10. de Oliveira Ventura, Lucas & Melo, Joel D. & Padilha-Feltrin, Antonio & Fernández-Gutiérrez, Juan Pablo & Sánchez Zuleta, Carmen C. & Piedrahita Escobar, Carlos César, 2020. "A new way for comparing solutions to non-technical electricity losses in South America," Utilities Policy, Elsevier, vol. 67(C).

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