Performance of robust EWMA control chart for variability process using non-normal data

Abstract

The main purpose of quality control is to quickly detect the presence of assignable causes and shifts in the process so that an investigation of the process can be carry out as early as possible. The Shewhart control chart provides good performance when the observation data is normally distributed, whereas when the normality assumption is not met, a Robust control chart is needed. The performance of the control chart depends on the stability of the estimator used to estimate the process parameters and establish control limits in phase I. In this study, a Robust Exponentially Weighted Moving Average (EWMA) control chart will be present to monitor process variability using one of estimator Robust scale to estimate standard deviation. This estimator is used to develop robust control limits. Then evaluate the control chart performance using Average Run Length (ARL) and Standard Deviation Run Length (SDRL) with Monte Carlo simulation. Furthermore, the robust chart was applied to monitor the quality characteristics of the number of bacterial colonies in each aquaculture medicine product. The results obtained in this study are formed a control chart that is resistant to the existence of outliers and sensitive to shifts in the process of variability.