Robust Multivariate Simultaneous Control Chart Based on Minimum Regularized Covariance Determinant (MRCD)

Control charts are widely used in the industrial world to monitor the average and variability of production processes. Max-Half-Mchart is a multivariate control chart that is less effective in handling many outliers. This research aims to develop a control chart that is more resistant to outliers by using Minimum Regularized Covariance Determinant (MRCD). MRCD is a development of the MCD method which is better at dealing with 'fat data', namely situations where the number of variables is greater than the number of observations. The performance evaluation of the robust Max-Half-Mchart control chart based on MRCD using Average Run Length (ARL) against shifts in process mean, process variance, and simultaneous shifts. In addition, a comparison is made of the outlier detection accuracy between the robust Max-Half-Mchart based on MRCD and the standard Max-Half-Mchart. The research results show that the MRCD-based Robust Max-Half-Mchart provides better accuracy and Area Under Curve (AUC) in detecting outliers compared to the traditional Max-Half-Mchart, especially at outlier levels of 10%, 20%, 30%, and 40%. Application of this method to cement quality data also shows superiority in detecting outliers.