On designing a sequential based EWMA structure for efficient process monitoring

Abstract

Control chart is a popular technique that is widely used in statistical process control to identify any possible deviations from a stable state of a process. Shewhart charts are famous for identifying larger shifts, while cumulative sum and exponentially weighted moving average control charts are well known for identifying smaller shifts in process parameters. This study examines the performance of a sequential-based EWMA (namely ${\rm SEWMA}_{\text{\texthtp}} $) chart for observing the location of a normally distributed process. The performance of ${\rm SEWMA}_{\text{\texthtp}} $ is oberved by using several overall run length properties (like average, median and standard deviation). The comparative study reveals that the overall performance of the proposed design is better than the existing counterparts. Moreover, the superiority depends on the choice of the design parameters of the proposed chart. A real-life data set from a steel rod manufacturing industry were considered to show the real-life illustration of the proposed design.

Keywords

• average run length
• control charts
• exponentially weighted moving average
• sequential approach
• manufacturing processes