An EWMA control chart for the multivariate coefficient of variation

This is the peer reviewed version of the following article: Giner-Bosch, V, Tran, KP, Castagliola, P, Khoo, MBC. An EWMA control chart for the multivariate coefficient of variation. Qual Reliab Engng Int. 2019; 35: 1515-1541, which has been published in final form at https://doi.org/10.1002/qre.2459. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Monitoring the multivariate coefficient of variation over time is a natural choice when the focus is on stabilising the relative variability of a multivariate process, as is the case in a significant number of real situations in engineering, health sciences, and finance, to name but a few areas. However, not many tools are available to practitioners with this aim. This paper introduces a new control chart to monitor the multivariate coefficient of variation through an exponentially weighted moving average (EWMA) scheme. Concrete methodologies to calculate the limits and evaluate the performance of the chart proposed and determine the optimal values of the chart's parameters are derived based on a theoretical study of the statistic being monitored. Computational experiments reveal that our proposal clearly outperforms existing alternatives, in terms of the average run length to detect an out-of-control state. A numerical example is included to show the efficiency of our chart when operating in practice.Generalitat Valenciana, Grant/Award Number: BEST/2017/033 and GV/2016/004; Ministerio de Economia y Competitividad, Grant/Award Number: MTM2013-45381-PGiner-Bosch, V.; Tran, KP.; Castagliola, P.; Khoo, MBC. (2019). An EWMA control chart for the multivariate coefficient of variation. Quality and Reliability Engineering International. 35(6):1515-1541. https://doi.org/10.1002/qre.2459S15151541356Kang, C. W., Lee, M. S., Seong, Y. J., & Hawkins, D. M. (2007). 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