Date of Award


Document Type


Degree Name

Master of Science in Operations Research


Department of Operational Sciences

First Advisor

Marcus B. Perry, PhD


Statistical control charts are often used to detect a change in an otherwise stable process. This process may contain several variables affecting process stability. The goal of any control chart is to detect an out-of-control state quickly and provide insight on when the process actually changed. This reduces the off-line time the quality engineer spends assigning causality. In this research, a multivariate magnitude robust chart (MMRC) was developed using a change point model and a likelihood-ratio approach. Here the process is considered in-control until one or more normally distributed process variables permanently and suddenly shifts to out-of-control, stable value. Using average run length (ARL) performance and the relative mean index (RMI), the MMRC is compared to the multivariate cumulative sum (MC1) and the multivariate exponentially weighted moving average (MEWMA). These results show the MMRC performs favorably to the MC1 and MEWMA when the process is initially in-control before shifting out-of-control. Additionally, the MMRC provides an estimate for the change point and out-of-control mean vector. This change point estimator is shown effective for medium to large sudden mean shifts.

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