Enhanced adaptive multivariate EWMA and CUSUM charts for process mean

Abstract

The multivariate charts are mostly used to simultaneously monitor several quality characteristics in manufacturing processes. In this study, we enhance the sensitivities of the recently proposed adaptive multivariate EWMA (AME) and weighted adaptive multivariate CUSUM (WAMC) charts with an auxiliary-information-based (AIB) estimator, namely the AIB-AME and AIB-WAMC charts, for monitoring different kinds of shifts in the mean of a multivariate normally distributed process. In addition, the variable sampling interval (VSI) feature is also incorporated into the proposed charts. The run length properties of these control charts are computed using Monte Carlo simulations. It is found that the AIB-AME and AIB-WAMC charts are uniformly and substantially more sensitive than the AME and WAMC charts, respectively. The same trend is observed when these control charts have the VSI feature incorporated into them. Real datasets are used to demonstrate the implementation of the proposed charts. 2021 Informa UK Limited, trading as Taylor & Francis Group.