Design of a new adaptive EWMA control chart

Abstract

Abstract The memory‐based control charts (CCs) such as exponentially weighted moving average (EWMA) and Cumulative sum (CUSUM) are good for quick detection of small or moderate shifts in the process mean. This study proposes a memory‐based CC to monitor the mean of a normally distributed process. The idea is to estimate the mean shift using an estimator based on an EWMA statistic and after that determine the smoothing constant of plotting EWMA statistics through the proposed continuous function. The performance of the proposed adaptive EWMA (AEWMA) chart is focused on utilizing extensive Monte Carlo simulations method with various shifts in the process mean. Taking into account the run‐length (RL) profiles, as an evaluation tool, the proposed chart is efficient as compared to the competitor charts. The demonstration of the proposed chart is presented by using a real dataset from the manufacturing process of substrates where the quality characteristic is the resistance of flow width.

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