Development of Efficient Control Charts for Monitoring Mean of Paired Differences of Quality Characteristics

In the manufacturing process, numerous quality characteristics are pair-correlated, which influence the output of quality of products. Examining these characteristics and their interactions can help identify the root cause of quality defects and maintain product quality consistency. The natural correlation between the paired quality characteristics provides the basis for using their differences as a potential measure for assessment and comparison. Control charts are a vital tool in statistical process control (SPC), enabling the oversight of the manufacturing process output and helping to identify variations, thereby ensuring product quality. The Shewhart and exponentially weighted moving average (EWMA) schemes are good for noticing the large and small changes in understudy quality characteristics. The control charts for monitoring paired quality characteristics are uncommon and rare in the literature. In this study, we develop two new EWMA and combined Shewhart-EWMA (CSE) schemes to observe the location of paired differences in quality characteristics. The Monte Carlo simulation method is used to evaluate the run-length properties of the proposed schemes. The performance of the developed schemes is compared with that of their existing classical counterparts. The numerical results show that the developed schemes are more powerful than their counterparts in detecting the changes in the understudy process parameter. A practical and two hypothetical examples are also given to implement the proposed structures for the practitioners.