An Improved Poisson EWMA Control Chart for Monitoring Nonconformities Per Unit

Abstract

Poisson control charts are most frequently used to track the number of nonconformities per unit in industrial processes during the inspection. As the quality characteristic under examination is based on a nominal scale rather than a quantitative or measured scale, these charting structures are known as attribute control charts. To detect small changes quickly, a Poisson extended exponentially weighted moving average (PEEWMA) control chart is developed in this study and its performance in zero-state and steady-state conditions has been examined. Run-length (RL) profiles, such as the average RL, the standard deviation of RL, and several percentile points of the RL distribution, have been evaluated using Monte Carlo simulation. The RL profiles of the proposed PEEWMA chart have been compared with existing methods. The comparison shows that the suggested PEEWMA chart outperforms its competitors. The application of the proposed design from an artificial dataset has also been included.