Shift Detection Properties of Moving Centerline Control Chart Schemes

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.

• Autocorrelated Data
• Exponentially Weighted Moving Average
• Statistical Process Control

Additional information

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.