Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 4 1 2014 05 22 Development of multivariate variance-covariance matrix monitoring methods in phase Development of multivariate variance-covariance matrix monitoring methods in phase 14 22 239031 FA Samina Kabuli Master's student, Faculty of Industrial Engineering, Iran University of Science and Technology. Rasoul Nourossana Professor, Faculty of Industrial Engineering, Iran University of Science and Technology. Journal Article In the statistical control of multivariate processes, two or more quality characteristics must be controlled simultaneously. In controlling such processes, two main goals must be achieved. The first goal is to detect out-of-control conditions and the second goal is to identify the quality characteristics that cause the deviation when an out-of-control condition occurs. In this research, ways to achieve the first goal are investigated and methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented. The main goal of phase 2 is to quickly detect shifts. In this paper, two methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented and the shift in one of the quality characteristics of case 1 (average trail length) and the detection of ARL are investigated. Simulation results show that the proposed methods reduce the out-of-control condition more quickly. In the statistical control of multivariate processes, two or more quality characteristics must be controlled simultaneously. In controlling such processes, two main goals must be achieved. The first goal is to detect out-of-control conditions and the second goal is to identify the quality characteristics that cause the deviation when an out-of-control condition occurs. In this research, ways to achieve the first goal are investigated and methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented. The main goal of phase 2 is to quickly detect shifts. In this paper, two methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented and the shift in one of the quality characteristics of case 1 (average trail length) and the detection of ARL are investigated. Simulation results show that the proposed methods reduce the out-of-control condition more quickly. https://www.pqprc.ir/article_239031_d7f3f1e532df91af9cb02a0d330d0330.pdf