Efficient homogeneously weighted moving average chart for monitoring process mean using an auxiliary variable

In this paper, we propose an efficient control chart for monitoring small shifts in a process mean for scenarios where the process variable is observed with a correlated auxiliary variable. The proposed chart, called an auxiliary homogeneously weighted moving average (AHWMA) chart, is a homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable. We provide the design structure of the chart and examine its performance in terms of its run length properties. Using a simulation study, we compare its run length performance with several existing methods for detecting a small shift in the process mean. Our simulation results show that the proposed chart is more efficient in detecting a small shift in the process mean than its competitors. We provide a detailed study of the chart's robustness to non-normal distributions and show that the chart may also be designed to be less sensitive to non-normality. We give some recommendations on the application of the chart when the process parameters are unknown and provide an example to show the implementation of the proposed new technique.