On the Steady-State Performance of the Poisson Double GWMA Control Chart

Abstract

This article presents a control chart called “Poisson double GWMA” (PDGWMA) chart for monitoring Poisson-distributed processes. Using simulation, the steady-state average run length (SSARL) of PDGWMA chart is evaluated and compared with those of other control charts including Poisson EWMA, Poisson GWMA and Poisson double EWMA (PDEWMA). It is shown that this chart is always more sensitive than other control charts in detecting an out-of-control signal. The PDGWMA chart is a generalized model for which the PDEWMA chart is a special case and also is an effective control chart in situations where the quality deterioration (upward shift) and the quality improvement (downward shift) can be detected agilely, which is important to a modern advanced system.

Key Words:

• Double EWMA
• EWMA
• GWMA
• Poisson observations
• steady-state average run length (SSARL).

Additional information

Notes on contributors

Wen-Chih Chiu

Wen-Chih Chiu is an Associate Professor of Industrial Management at Aletheia University, Taiwan. He received his MSc and Ph.D. degrees in Industrial Management from National Taiwan University of Science & Technology in 1984 and 2007, respectively. His research interests are in the areas of quality management and design of quality control charts. He has published in journals such as Communications in Statistics — Simulation and Computation, Journal of Applied Statistics, Quality and Reliability Engineering International.

Shin-Li Lu

Shin-Li Lu is an Assistant Professor in the Department of Industrial Management and Enterprise Information at Aletheia University, Taipei, Taiwan. He received his Ph.D. in Industrial Management at the National Taiwan University of Science and Technology, Taiwan. His research interests are in the area of quality management and production management. Related papers have appeared in journals such as Journal of Statistical Computation and Simulation, Asia-Pacific Journal of Operational Research, Quality and Reliability Engineering International, Communications in Statistics — Theory and Methods.