In the modern era of digitalization, manufacturing industries needed monitoring methods to timely detect an abrupt change in the process. Control charts are widely used online monitoring method and used in several sectors for the surveillance of the process. Usually, control charts are developed for a single study variable, but there exists auxiliary information along with the study variable. Because of the linear relation between the study variable and auxiliary variable, several control chart studies are designed based on the simple linear regression model, but they are restricted to the normally distributed response variable. When the response variable follows an exponential family distribution, then the generalized linear modeling (GLM) approach provides better estimates. Hence, this study is designed to propose GLM‐based control charts when the response variable follows the inverse Gaussian (IG) distribution. In GLM‐based control charts, deviance and Pearson residuals of the IG regression are considered as plotting statistics. For the evaluation purpose, a simulation study is designed, and the performance of the proposed methods is compared with existing counterparts in terms of the run length properties. Moreover, run‐rules are also implemented to gain the efficiency of the Shewhart type GLM‐based control charts under small‐to‐moderate shifts. Finally, an example related to the yarn manufacturing industry is also used to highlight the importance of the stated proposal.

Control charts are commonly applied for monitoring and controlling the performance of the manufacturing process. Usually, control charts are designed based on the main quality characteristics variable. However, there exist numerous other variables which are highly associated with the main variable. Therefore, generalized linear model (GLM)-based control charts were used, which are capable of maintaining the relationship between variables and of monitoring an abrupt change in the process mean. This study is an effort to develop the Phase II GLM-based memory type control charts using the deviance residuals (DR) and Pearson residuals (PR) of inverse Gaussian (IG) regression model. For evaluation, a simulation study is designed, and the performance of the proposed control charts is compared with the counterpart memory less control charts and data-based control charts (excluding the effect of covariate) in terms of the run length properties. Based on the simulation study, it is concluded that the exponential weighted moving average (EWMA) type control charts have better detection ability as compared with Shewhart and cumulative sum (CUSUM) type control charts under the small or/and moderate shift sizes. Moreover, it is shown that utilizing covariate may lead to useful conclusions. Finally, the proposed monitoring methods is implemented on the dataset related to the yarn manufacturing industry to highlight the importance of the proposed control chart.

Usage of mobile phone is growing rapidly in all over the world and nowadays, mobile phones have advanced features. The concept of ringtone selection in the coming generation is increasing day by day. This study is conducted about the musical elements (tone color, rhythm, melody and tempo) of ringtone. The objective is to examine the elements that are mostly preferred during ringtone selection. For this purpose, a questionnaire was designed and sample size of 380 respondents (students) was selected from the University of Sargodha, Punjab, Pakistan by using simple random sampling technique. Bivariate analysis was used and the results conclude that the most frequently used elements during the ringtone selection are rhythm and tempo. Moreover; chi-square test for association reveals that age and tone color have a significant relationship.