Abstract
Abstract Control charts are statistical methods used to detect shifts in the location parameter of a process that is monitored over time. Here, we propose an improvement to the performance of the classical exponentially weighted moving average (EWMA) control charts, by making use of auxiliary information that is correlated with the process variable. We present an EWMA P and its modifications: EWMA-type control charts based on a product estimator where the location parameter of the process is monitored using an auxiliary variable. The charts are developed using different sampling schemes: simple random sampling, ranked set sampling (RSS) and median ranked set sampling (MRSS), and we evaluate their performance using average run length, and other performance measures such as extra quadratic loss and relative average run length. It is observed that the proposed control charts are performing better than the classical EWMA control chart in monitoring shifts in the location parameter of a process, especially when a strong negative correlation exists between the process and the auxiliary variables. They are particularly efficient in detecting small to moderate shifts in the process.
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