Steven A. Yourstone is Assistant Professor of Production and Operations Management at the Anderson Schools of Management, University of New Mexico. He received his Ph.D. in operations management from the University of Washington. He is a member of the American Society for Quality Control, Decision Sciences Institute, and the Institute of Management Science, and serves on the board of directors for the Albuquerque, New Mexico chapter of the American Production and Inventory Control Society. His primary research interests are in the areas of statistical process control methods, project management, and manufacturing strategy. Recent publications have appeared in Quality and Reliability Engineering International.
Non-Normality and the Design of Control Charts for Averages*
Version of Record online: 7 JUN 2007
Volume 23, Issue 5, pages 1099–1113, September 1992
How to Cite
Yourstone, S. A. and Zimmer, W. J. (1992), Non-Normality and the Design of Control Charts for Averages. Decision Sciences, 23: 1099–1113. doi: 10.1111/j.1540-5915.1992.tb00437.x
The authors wish to thank the referees and the Associate Editor for many helpful suggestions and proposed changes. Their input has resulted in a much improved paper.
- Issue online: 7 JUN 2007
- Version of Record online: 7 JUN 2007
- Received: June 7, 1991. Accepted: March 24, 1992.
- Quality Control and Statistical Techniques
Non-normality has a significant effect on the performance of control charts for averages. The design considerations for a control chart for averages must include recognition of the degree of non-normality of the underlying data. The performance of a control chart may be judged on its ability to correctly identify the probabilities of assignable causes of variation and chance causes of variation in a process. This paper examines the effects of non-normality, as measured by skewness and kurtosis, on the performance, and hence the design, of control charts for averages and provides an alternative method of designing charts for averages of data with non-normal distributions.