Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry.
Aslam, M , Rao, G , AL-Marshadi, A & Jun, C (2021). A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes. Afribary. Retrieved from https://afribary.com/works/a-nonparametric-hewma-p-control-chart-for-variance-in-monitoring-processes
Aslam, Muhammad et. al. "A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes" Afribary. Afribary, 22 May. 2021, https://afribary.com/works/a-nonparametric-hewma-p-control-chart-for-variance-in-monitoring-processes. Accessed 11 Dec. 2023.
Aslam, Muhammad, G. Rao , Ali AL-Marshadi and Chi-Hyuck Jun . "A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes". Afribary, Afribary, 22 May. 2021. Web. 11 Dec. 2023. < https://afribary.com/works/a-nonparametric-hewma-p-control-chart-for-variance-in-monitoring-processes >.
Aslam, Muhammad , Rao, G. , AL-Marshadi, Ali and Jun, Chi-Hyuck . "A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes" Afribary (2021). Accessed December 11, 2023. https://afribary.com/works/a-nonparametric-hewma-p-control-chart-for-variance-in-monitoring-processes