Monitoring the multivariate coefficient of variation in presence of autocorrelation with variable parameters control charts

The coefficient of variation is a very important process parameter in many processes. A few control charts have been considered so far for monitoring its multivariate counterpart, i.e., the (MCV). In addition, autocorrelation likely to occur processes with high sampling frequency. Hence, designing suitable and investigating effect on these necessary. However, no chart has developed that capable accounting either univariate or cases. This paper fills gap by developing Shewhart-type monitor MCV different structures observations: vector autoregressive, moving average, mixed autoregressive average. we add variable parameters adaptive features scheme, order improve performance. We develop Markov chain model get statistical performance measures; then, perform extensive numerical analyses evaluate non-adaptive presence downward upward shifts. Finally, present an illustrative example from healthcare show implementation this scheme real practice.