Generalized linear model based monitoring methods for high-yield processes

Emerge in technology brought well‐organized manufacturing systems to produce high‐quality items. Therefore, monitoring and control of products have become a challenging task for quality inspectors. From these highly efficient processes, produced items are mostly zero‐defect and modeled based on zero‐inflated distributions. The zero‐inflated Poisson (ZIP) and zero‐inflated Negative Binomial (ZINB) distributions are the most common distributions, used to model the high‐yield and rare health‐related processes. Therefore, data‐based control charts under ZIP and ZINB distributions (i.e., Y‐ZIP and Y‐ZINB) are proposed for the monitoring of high‐quality processes. Usually, with the defect counts, few covariates are also measured in the process, and the generalized linear model based on the ZIP and ZINB distributions are used to estimate their parameters. In this study, we have designed monitoring structures (i.e., PR‐ZIP and PR‐ZINB) based on the ZIP and ZINB regression models which will provide the monitoring of defect counts by accounting the single covariate. Further, proposed model‐based charts are compared with the existing data‐based charts. The simulation study is designed to access the performance of monitoring methods in terms of run length properties and a case study on the number of flight delays between Atlanta and Orlando during 2012–2014 is also provided to highlight the importance of the stated research.