A new npCEV chart for monitoring process mean shifts based on an attribute inspection

Due to the simplicity and low cost of attribute inspection, several control charts have been proposed for employing attribute inspection to monitor the mean shifts. But these charts usually require a larger sample size to perform well as the X¯ chart. In this paper, a new npCEV chart is proposed to monitor process mean shifts based on attribute inspection in which the conditional expected value (CEV) weight method is adopted to take full advantage of the censored observations obtained from attribute inspection. The optimal design of the proposed chart is derived from optimizing average run length (ARL) properties. Several numerical experiments are conducted to investigate the performance of the proposed chart. The results demonstrate that the proposed chart is always superior to npx chart by reducing the sample size by 70% to 97% while achieving the same ARL as npx chart. In addition, the proposed chart always achieves a smaller ARL1 than X¯rec chart. Furthermore, the proposed chart outperforms X¯ chart as it achieves the same ARL as that of X¯ chart with a lower inspection cost. Finally, an industry example is given to illustrate how to simply apply the proposed chart to the manufacturing process.