A comparative study on the nonparametric memory-type charts for monitoring process location

Control charts may help keep industrial and manufacturing processes running smoothly and optimize their performance. In situations when it is uncertain how the data will be distributed, nonparametric control charts are more reliable and useful than parametric charts. The sign test statistic and arcsine transformation are rapidly used in designing the nonparametric control charts. This article has designed Exponentially weighted moving average and Cumulative sum control charts using sign test statistic and arcsine transformation under zero-state and steady-state at head-to-head optimal design parameter choices. The proposed study quantifies the effect of both tests for the in-control (IC) and out-of-control (OOC) processes. The run-length (RL) properties have been calculated by using the Monte Carlo simulation method to assess the performance of the designed schemes. The RL profiles show that all the nonparametric schemes have IC robust behavior against the non-normality of the distributions. The results of the study reveal that the sign test statistic reveals almost equal but quite better results at small shifts, if the actual shifts are medium to large then arcsine transformation may respond quite better. Application of the charts under sign and arcsine transformation has been executed using an artificial dataset.