ABSTRACT Effectively tracking process variability is crucial for maintaining control and improving quality of processes across various fields, including industrial, business, and healthcare sectors. Examining the paired differences using control schemes facilitates the early detection of changes in the process dispersion parameter, ensuring consistent process improvement. This work introduces various novel control strategies, including the Shewhart control schemes and the exponentially weighted moving average (EWMA) schemes. These schemes are specifically designed to monitor the increase in the dispersion parameter of paired differences in quality characteristics. For the assessment of the suggested control schemes, the run‐length metric is used, which is generated by creating an algorithm in the R language. The performance of the developed control schemes is compared with some well‐known existing schemes. From the numerical results, it is concluded that the proposed schemes are more powerful in detecting the changes in the understudy process dispersion parameter than their counterparts. Two practical applications related to the healthcare sector and chemical engineering are also provided to implement the proposed scheme in a real‐world system.
Ali et al. (2026) studied this question.