A function based adaptive EWMA mean monitoring control chart

Adaptive control charts are getting remarkable place in statistical process control for monitoring production systems with efficiently simple design schemes. These tools are preferred to get rapid detection of shifts in manufacturing items. In this paper, a new exponentially weighted moving average function-based adaptive control chart is proposed. The idea of design is to determine plotting exponentially weighted moving average (EWMA) statistic smoothing constant value as per system arising shift magnitude through a continuous function. As a result, the process mean shift of any size can be catered in a rapid manner than other counterparts. The Monte Carlo simulations are performed to determine the run-length profiles of the proposed chart and the efficacy of the chart is assessed through average run length, standard deviation of run length, and the percentiles of run length. A comparative discussion with the existing adaptive EWMA (AEWMA) charts proved the efficacy of the concept for all types of small to moderate and large shifts in the favor of the concept. The proposed chart application is explained with the help of an illustrative example and a real-life industrial dataset to get a deep insight into the concept.