Generalized likelihood ratio control charts for high-purity (high-quality) processes

Generalized likelihood ratio (GLR) control charts are useful for tailor-made monitoring strategies, but they are less developed for discrete processes. In this paper, the GLR control chart framework applied to aggregate cumulative quantities data is extended. Inspired by the technical note on GLR control charts from Lee and Woodall (2018), unnecessary artificial bounds in the GLR chart for geometric data proposed in literature are removed and parameter restriction errors, common in GLR designs, are corrected. Finally, the Gamma GLR chart for continuous-time time-between-event data that can be modeled by a Poisson process is proposed and its performance are evaluated and compared to its traditional competitors.