A New EWMA Control Chart for Monitoring Both Location and Dispersion

Abstract

A new control chart, which employs the exponentially weighted moving average (EWMA) technique, is proposed. The statistic for the chart defines the area below a straight line as the control region, which makes the charting procedure easier than the usual approach. This chart can effectively monitor the process mean and the increased process variability simultaneously, and can detect the source and the direction of a change easily.

Key Words:

• ARL
• control chart
• dispersion
• EWMA

Additional information

Notes on contributors

Gemai Chen

Gemai Chen Professor of Statistics in the Department of Mathematics and Statistics, University of Calgary. His research interests include parametric and nonparametric regression, sample survey, goodness-of-fit, survival analysis, industrial statistics, quality control and improvement, nonlinear time series modeling of environmental changes, and statistical consulting.

Smiley W. Cheng

Smiley Cheng Professor and Head of the Department of Statistics at the University of Manitoba. He has served as Associate Head and Acting Head of the Department in the past. He was the President of the International Chinese Statistical Association and the Managing Editor of Statistica Sinica. He is currently the Associate Editor or member of Editorial Board of five international statistics journals. Dr. Cheng is heavily involved in the research in statistical quality control (SQC), statistical inference, order statistics, and lottery. He is a Senior Member of American Society for Quality, a member of Statistical Society of Canada, American Statistical Association, International Chinese Statistical Association, and an Elected Member of the International Statistical Institute.

Hansheng Xie

Hansheng Xie Methodologist at Business Survey Methods Division of Statistics Canada, Ottawa, Ontario, Canada. He is a member of Statistical Society of Canada and the International Chinese Statistical Association. His research interests are in Statistical Quality Control, Survey Methodology and Applied Probability.