Designing efficient dispersion control charts under various ranked-set sampling approaches

Abstract

This study presents an in-depth evaluation of fourteen distinct ranked-set sampling methodologies employed to enhance the efficacy of Shewhart-type dispersion charts in identifying persistent shifts in underlying industrial processes. Despite the widespread use of Shewhart-type control charts under simple random sampling for monitoring process dispersion parameters, the efficiency of these dispersion charts under ranked-set sampling procedures has been largely overlooked. Through comprehensive simulation exercises, we obtained the run-length characteristics of the proposed charts, thereby laying a foundation for the comparative analysis of different sampling strategies. Our findings emphasize the superior performance of modified neoteric ranked-set sampling, which consistently outperformed the other techniques in all tested scenarios. Furthermore, we included an example to reinforce the significance and utility of the proposed methodologies in real-world industrial settings. This study provides additional results for optimal statistical control processes, highlighting the potential of ranked-set sampling approaches for enhancing process monitoring mechanisms.