Efficient GLM-based control charts for Poisson processes

Abstract

The control charts are essential instruments that can impart crucial insights to quality controllers for maintaining the productivity of manufacturing processes. The conventional charting designs are based on a single study variable without exerting auxiliary information. Control charts derived from simple linear regression, generally with normality assumption, are one straightforward way of overcoming this difficulty. But, for a count distributed process, the generalized linear model (GLM) based strategy yields better outcomes. Hence, this study presents GLM-based progressive mean (PM) and double progressive mean (DPM) charting structures by employing the standardized residuals of the Poisson regression model. The functioning of the suggested and existing schemes (i.e., SR-EWMA) is investigated in the matter of run length distributions. The comparative analysis demonstrated that PM structures based on standardized residuals (i.e., SR-PM and SR-DPM) outperform the existing counterpart (i.e., SR-EWMA). Specifically, the SR-DPM chart is found to be more productive in identifying increasing alterations in the process mean. Finally, a case study about a 3D manufacturing procedure is presented to accentuate the significance of the suggested methods.