New adaptive control charts for monitoring the multivariate coefficient of variation. (December 2018) - Khaw, Khai Wah; Khoo, Michael B.C.; Castagliola, Philippe; Rahim, M.A.
Highlights: Adaptive charts for the multivariate coefficient of variation are proposed. Average time to signal and expected average time to signal criteria are used. The proposed charts surpass existing multivariate coefficient of variation chart. The best proposed chart has variable sample size and sampling interval features. The implementation of the proposed charts is demonstrated via real and simulated datasets. Abstract: An adaptive control chart is one of the most effective techniques in Statistical Process Control (SPC). The coefficient of variation (CV) is common in many real life applications, especially in manufacturing and materials engineering, finance, medical and biological sciences. This paper proposes three adaptive charts to monitor the multivariate coefficient of variation (MCV), in order to improve the sensitivity of the standard MCV chart, in detecting small and moderate MCV shifts. The proposed charts are designed using the Markov chain approach and they are compared with the existing standard MCV chart using the average time to signal (ATS), standard deviation of the time to signal (SDTS) and expected average time to signal (EATS) criteria. The performance comparison shows that the proposed adaptive MCV charts, particularly the variable sample size and sampling interval (VSSI) MCV chart, outperform the existing MCV charts, in terms of the ATS and EATS criteria. By allowing the sample size and sampling interval to be varied in the VSSI MCV chart, process engineers will have better control in process monitoring and at the same time are able to detect an out-of-control signal quicker. Illustrative examples are presented by considering the VSSI MCV chart to show the chart's implementation.