Statistical process control (SPC) tools are used for the investigation and identification of unnatural variations in the manufacturing, industrial, and service processes. The control chart, the basic and the most famous tool of SPC, is used for process monitoring. Generally, control charts are constructed under normality assumption of the quality characteristic of interest, but in practice, it is quite hard to hold the normality assumption. In such situations, parametric charts tend to offer more frequent false alarms and invalid out-of-control performance. To rectify these problems, non-parametric control charts are used, as these have the same in-control run length properties for all the continuous distributions and are known as in-control robust. This study intends to develop a new non-parametric exponentially weighted moving average (NPEWMA) chart based on sign statistics under a ranked set sampling scheme that is hereafter named (NPREWMA-SN). The run-length profiles of the NPREWMA-SN chart are computed using the Monte Carlo simulation method. The proposed scheme is compared with NPEWMA-SN and classical EWMA-$\overline{X}$ charts, using different run length measures. The comparison reveals the in-control robustness and superiority of the proposed scheme over its competitors in detecting all kinds of shifts in the process location. A practical application related to the substrate manufacturing process is included to show the demonstration of the proposed chart.

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.

Control charts are used to improve the quality of outputs in manufacturing, industrial, and service processes. The parametric control charts produce more false alarms and unacceptable out-of-control signals when the underlying distribution of the process is not normal. Nonparametric/distribution-free control charts are efficient alternatives to overcome the said situation. In this article, the performance of a distribution-free double exponentially weighted moving average (EWMA) chart has been investigated based on the sign test statistic under simple and ranked set sampling schemes. The run-length properties of the proposed charts have been evaluated and compared with nonparametric EWMA sign, parametric EWMA, and parametric double EWMA control charts, using different run-length measures. The comparison reveals the efficiency of the proposed chart over its alternatives in detecting small and medium shifts in the process location. A real-data application using the substrate manufacturing process has been provided to show the implementation of the proposed chart.