A double progressive mean control chart for monitoring Poisson observations

Abstract The control charts are widely used for monitoring quality characteristics of a process. In many real world applications, the number of defects or nonconformities in a production unit are used to denote the quality of a product. Such data can be modeled using a Poisson distribution. In the present article, a new attribute control chart based on the double progressive mean statistic is proposed for monitoring Poisson data (regarded as PDPM chart). Through a simulation study and using the average run-length (ARL) measure, the performance of the proposed control chart is compared with the c -chart, ARL-unbiased c -chart, PCUSUM, PEWMA, PDEWMA, PGWMA and PPM charts. The performance comparison study indicates that the proposed control chart outperforms the other charts at almost all levels of shifts. Finally, the practical application of the PDPM chat is given through an illustrative example.

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