Abstract
Control charts are effective tools for signal detection in both manufacturing processes and service processes. Much of the data in service industries comes from a process having non-normal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, are not appropriately used here. In this paper, we propose a new EWMA-V Chart and EWMA-M Chart based on two simple independent statistics to monitor process mean and variance shifts simultaneously. Further, we explore the sampling properties of the new monitoring statistics, and calculate the average run lengths when using both of the proposed EWMA Charts. A numerical example involving non-normal service times from the service system of a bank branch in Taiwan is used to illustrate the applications of the new EWMA-M and EWMA-V Charts, and to compare them with the existing mean and variance (or standard deviation) charts. The proposed new EWMA-M and EWMA-V Charts show superior detection performance compared to the existing mean and variance charts. The new EWMA-M and EWMA-V Charts are thus recommended.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
