Mixed Extended Exponentially Weighted Moving Average–Cumulative Sum Charts for Process Mean Monitoring

Abstract

Statistical quality control (SPC) is common main technique used for examining the process’s output during production. One of its tools which is extensively used worldwide is Control charts. It helps to point out the variations in production process such as common cause and assignable cause and differentiate between them to take further action. Assignable cause occurs in the form of shifts which are the values of parameter process(s). Small, moderate and high shifts are the three categories of shifts. Two different type of charts work for different shifts. Memory-less control charts are available for large shifts monitoring whereas Memory-based control charts are useful small-moderate type shifts. However, some great researchers have formed some mixed control charts which have all types of shift detection ability by one chart such as Mixed Shewhart-CUSUM control chart. Meanwhile many studies have shown an improvement in ability of monitoring the shift in a process parameter when control charts of EWMA and CUSUM are mixed-up. Therefore, this study is finding a space to mix-up EEWMA by CUSUM control chart in order to get more efficient control chart to monitor the process mean than EEWMA control chart and CUSUM control chart. Furthermore, this study finds a space to make the practice of auxiliary variable to make the mixed-EEWMA-CUSUM control chart more competent to monitor the shift in the mean of process. Thus, in this study, a mixed chart based on classical CUSUM and EEWMA named as EEWMA-CUSUM is proposed. Evaluation is conducted through average run length (ARL) criterion. The solo purpose of this research is to make the chart more sensitive to smaller shifts than existing charts. The comparisons of proposed chart are done with different literature control chart naming CUSUM, EWMA, EEWMA. The comparison purpose is to show how the proposed Mixed EEWMA CUSUM surpasses other charts in the form of small to moderate shifts in the value of process mean. Eventually, to prove the superiority of proposed chart numerically and practically, simulation study is used. Furthermore, as auxiliary information gives more power to detection ability of control chart. So, Mixed EEWMA-CUSUM using auxiliary information is proposed as well to check if its results outcast the Mixed proposed EEWMA-CUSUM control chart’s performance. ARLs method is adopted to evaluate this chart.