Abstract
Multistage statistical process control (SPC) is an effective procedure for ensuring quality of products in multistage manufacturing processes. Effective SPC approaches for monitoring and controlling quality in multistage processes are limited. Beta distributed data explained by process input variables are generally prevalent in multistage process industries. In this paper, we propose a generalized linear model for beta-distributed data in multistage processes in order to estimate the distribution parameters and develop two approaches for process monitoring and control: they are model-based SPC charts and a beta regression model. The proposed control charts outperform the existing control charts in terms of the out-of-control average run length for the detection of process mean shifts.
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