Enhanced DEWMA-type control chart for process mean monitoring utilizing auxiliary information

Abstract

Abstract Statistical Process Control (SPC) improves product quality by monitoring process performance, with control charts being the primary tool to detect and manage variability. The effectiveness of a control chart can be enhanced by incorporating additional pertinent information regarding the study variable. This study revisits the DEWMA chart, which is designed to monitor variations in the process mean under the assumption that the underlying process follows a normal distribution. We propose a Regression-based DEWMA (M R DEWMA) control chart that utilizes an auxiliary variable through a regression estimation method to determine the process mean. The control limits for the proposed chart are established based on both time-varying and asymptotic conditions. The features of run length (RL), including Average Run Length (ARL), Standard Deviation of Run Length (SDRL), and Median Run Length (MRL), are evaluated using Monte Carlo simulations. A comparative analysis reveals that the proposed M R DEWMA chart outperforms the traditional DEWMA chart in detecting small to moderate shifts in the process mean. The efficacy of the proposed approach is demonstrated using both Monte Carlo simulated data and a real-life industrial case study.