New adaptive EWMA control charts for monitoring univariate and multivariate coefficient of variation

Abstract

The coefficient of variation (CV), a measure of relative variability, is an important quality control issue worthy of consideration in some manufacturing and service-oriented companies when the process mean is not constant and/or the process variance is a function of the process mean. In this paper, we propose two adaptive EWMA (AEWMA) charts for monitoring the infrequent changes in the CV and multivariate CV (MCV) when sampling from univariate and multivariate normally distributed processes, named the AEWMA CV and AEWMA MCV charts, respectively. With extensive Monte Carlo simulations, the run length characteristics of the proposed control charts are computed. It is found that the AEWMA CV chart performs substantially and uniformly better than the existing optimal EWMA and CUSUM CV charts when detecting moderate-to-large shifts in the process CV. Moreover, the AEWMA MCV chart also performs substantially and uniformly better than the existing Shewhart MCV chart. The proposed control charts are implemented on real datasets to support the theory.