Optimal one? and two?sided adaptive EWMA scheme for monitoring Poisson count data

The Poisson distribution assumption often arises in several industrial applications for modeling defects or nonconformities. In this work, we investigate the one- and two-sided performance of a new adaptive EWMA (exponentially weighted moving average)-type chart monitoring count data. An appropriate discrete-state Markov chain technique is provided to compute exact ARL (average run length) properties. Moreover, comparative studies are conducted demonstrate higher sensitivity proposed detection shifts with various magnitudes. Advices on how select parameters an illustrative numerical example proposed.