Abstract
In statistical process monitoring, violating the assumption of independent data results in a control chart that exhibits increased false alarms and trends on both sides of the centerline. Autocorrelation requires modification to traditional control chart techniques. This paper explores the shift detection capability of the moving centerline exponentially weighted moving average (MCEWMA) chart and recommends enhancements for quicker detection of small process upsets.
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Notes on contributors
Christina M. Mastrangelo
Dr. Mastrangelo is an Assistant Professor in the Department of Systems Engineering. She is a Member of ASQ. Her email address is [email protected].
Evelyn C. Brown
Dr. Brown is an Assistant Professor in the Department of Management Science and Information Technology.