Auxiliary Information Based Exponentially Weighted Moving Coefficient of Variation Control Chart using Regression Estimator (AIB-EWMCV_Reg)

Control charts are essential tools in Statistical Process Control (SPC) to monitor mean and variability process parameters. However, some manufacturing industries face the conditions that process variability changes with the mean and the mean shift from time to time but are considered in control. The coefficient of variation (CV) based control chart effectively monitors process variability in these cases. Some studies have been evaluating an auxiliary variable that correlated with a study variable to accelerate the detection ability of the CV control chart. This study proposed an auxiliary information-based exponentially weighted moving coefficient of variation control chart using a regression estimator (AIB-EWMCVReg) to detect shifts of process variability. The average run length (ARL) is computed using Monte Carlo simulation to evaluate the performance of the control chart. Simulation results show that the increase in levels of correlation coefficient gives better performance in detecting shift of process. An actual occurrence in monitoring NPK fertilizer process production is demonstrated to illustrate the implementation of the proposed control chart.