A proposed variable sampling interval median chart for identifying out-of-control signals in process control

The assessment of variables that influence the estimation, control, and regulation of the quality of analytical testing processes is increasingly being done using computer simulation. The quality management of manufacturing firms is introduced as a data mining application. For quality control and production management, quality factor analysis is crucial. Numerous studies have investigated the variable sampling interval (VSI) chart for the process average. Despite being significantly more widely used than the median chart, when faced with extremes or unforeseen data sets that cast doubt on the normality assumption, the mean ($\bar X$ ) chart is less resistant. The median chart, however, is more effective than the process average chart when outliers or extreme values are present in the process data being monitored. Since practitioners may believe that process shifts could have happened in the dataset because of the extreme values, incorrect inferences may be drawn. To solve this challenge, the variable sampling interval (VSI) median chart is proposed in this study. The VSI feature is used to enhance the performance of the median chart. The average time to signal (ATS) and expected average time to signal (EATS) criteria are used to evaluate the performance of the proposed charts. Based on the ATS and EATS criteria, the results show that the proposed VSI median chart outperforms the Shewhart (SH) median chart in detecting all sizes of shifts.