ABSTRACT 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.

Abstract Many studies are mainly concerned with the estimation of finite population mean and the well‐known preferences for it are ratio estimators. This article offers a remedy for improvement in the estimation of a study variable, based on supplementary information related to dual auxiliary variables in the context of simple random sampling without replacement scheme. We proposed two new general classes of exponential‐cum‐ratio estimators to estimate a finite population mean utilizing dual auxiliary variables with suitable combinations of the conventional and nonconventional measures. The expression for the mean square error (MSE) and theoretical conditions for proposed classes have been obtained for evaluation purposes. The performance of the proposed classes has been compared with existing estimators in terms of MSE. It is revealed that proposed estimators are more efficient than usual and existing estimators considered in this article. The simulation and robustness studies are also part of this article. Moreover, a variety of real data sets is also considered for an empirical study to support the theoretical results.

Abstract Control charts are widely used tool that provides quality inspectors with sensitive information for maintaining manufacturing process productivity. Numerous model‐based techniques have been presented in the literature to monitor industrial operations that focus on the normal response variable. However, non‐normal response results can occur as a result of quality control operations. In such cases, a new approach based on generalized linear model that provides multiple distribution options for response variables is required to achieve better results. Therefore, this study proposes GLM‐based moving average (MA) and double moving average (DMA) schemes formed on standardized residuals derived from a fitted Poisson regression model. The productivity of suggested methods and the existing exponentially weighted moving average (EWMA) scheme is explored in terms of run length attributes. The simulation outcomes revealed that moving average schemes based on standardized residuals (i.e., SR‐MA and SR‐DMA) outperform their predecessor (i.e., SR‐EWMA). Moreover, the SR‐DMA chart, with small values of span , has proven to be more effective at detecting minor to moderate shifts in the process mean. Finally, a case study of a 3D manufacturing operation is shown to emphasize the importance of the proposed approaches.