Increasing the efficiency of double moving average chart using auxiliary variable

The control chart is the most important tool in the statistical process control to monitor the industrial process. The additional supporting information related to the underlying quality characteristic increases the sensitivity of the control chart. In this article, auxiliary information-based double moving average control chart is proposed for effective monitoring of the process mean. The regression estimate in the form of auxiliary and supporting variables presents an unbiased and efficient statistic of the mean of the process variable. The run-length profiles of the control chart are calculated using the Monto Carlo simulation. The performance of the chart is compared with its memory-type counterparts. The numerical simulation study shows that the proposed charting structure performs uniformly better than the competitors in the terms of average run length and other run length characteristics. Two illustrative examples related to real datasets and a third from simulated datasets are also provided to implement the proposal.

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