An adaptive EWMA mean chart in the presence of outliers

Abstract

ABSTRACT Occasional outliers that might be a natural part of a process may distort the properties of a control chart. In this paper, we show that a recently proposed adaptive EWMA (AE) mean chart is highly sensitive to the outliers. The false alarm rate of the AE chart increases when the proportion and/or magnitude of the outliers increase and vice versa. In order to circumvent this demerit of the AE chart, we propose a truncated normal distribution-based AE (TAE) chart for monitoring the mean of a normal process in the presence of outliers. The zero-state and steady-state average run-length profiles of the proposed chart are estimated using Monte Carlo simulations. Based on detailed run-length comparisons, it is found that the TAE chart may outperform the existing EWMA chart (based on a truncated normal distribution) when detecting various mean shift sizes of an outlier-prone normal process. Illustrative examples are also included in this study to demonstrate the implementation of the existing and proposed charts.