Side-sensitive group runs chart for detecting mean shifts using auxiliary information

In recent studies, auxiliary information technique in control charts have shown superior run length performances to control charts developed without using it. This paper proposes a side-sensitive group runs chart for detecting mean shifts using auxiliary information (SSGR-AI), where its charting characteristics require information from the study and auxiliary variables to efficiently monitor the process shifts. The control chart statistic, optimal design of parameters and implementation of the proposed SSGR-AI chart are explained. The performance of the SSGR-AI chart is evaluated using the average run length (ARL) and expected average run length (EARL) criteria. The ARL and EARL performances of the SSGR-AI chart are compared with that of the synthetic chart with auxiliary information (Syn-AI) and exponentially weighted moving average chart with auxiliary information (EWMA-AI). The results show that the SSGR-AI chart outperforms the (i) Syn-AI chart for all shift sizes and values of correlation coefficient, and (ii) EWMA-AI chart, except for small shifts and small values of correlation coefficient.