Enhancing Sensitivity of Nonparametric Tukey Extended EWMA-MA Charts for Effective Process Mean Monitoring

A control chart is a crucial statistical process control (SPC) instrument for identifying method variances that may undermine product efficacy. The combined control chart has been utilized to enhance recognition capability. When testing a methodology, nonparametric statistics make a strong and compelling case when the distribution of a quality feature is uncertain. The primary focus of monitoring this work is to offer a novel control chart to support the surveillance of mean activities. This chart will incorporate a Tukey method, an extended exponentially weighted moving average control chart, and a moving average control chart called the Nonparametric EEWMA-MA chart. The Monte Carlo simulation facilitates assessments for evaluating system performance using average run lengths (ARL) based on zero-state. The comparison analysis demonstrates that the sensitivity of the suggested chart surpasses that of the conventional control chart (including the moving average (MA) chart, the extended exponentially weighted moving average (EEWMA) chart, and the mixed extended exponentially weighted moving average-moving average (EEWMA-MA) chart) in rapidly detecting changes that fluctuate with varying parameter settings by examining the minimal ARL. A simplified monitoring scenario using data on vinyl chloride can be employed to demonstrate the feasibility of the proposed technique.