To Improve Sensitivity and Resilience to Change Tracking for Novel Distribution-free Extended EWMA Control Chart

Saowanit  Sukparungsee; Anyamanee  Kaewphengkro; Chuthapat  Chantarapap; Thanakrit  Maneerat

Authors

Saowanit Sukparungsee Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
Anyamanee Kaewphengkro Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
Chuthapat Chantarapap Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
Thanakrit Maneerat Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

Keywords:

Average run length, distribution-free control chart, performance, exponentially weighted moving average, change

Abstract

A quality control technique called statistical process control (SPC) makes it possible to use statistical approaches for process monitoring. Since the true distribution of the quality characteristic in question is unknown, nonparametric control charts—such as the Tukey’s control chart (TCC) are a reliable and efficient tool for evaluating a method. Because it can quickly identify changes, the new extended exponentially weighted moving average (NEEWMA) control chart was used to track the mean process. In order to optimize the advantages of both control charts, we created a technique called NEEWMA-TCC, which blends NEEWMA and TCC. Using a variety of individual and aggregate performance metrics based on average run length (ARL), the effectiveness of the suggested chart was assessed under both symmetrical and asymmetrical distributions. According to our results, the recommended chart performs better in rapidly identifying shifts than control charts such as the traditional Shewhart, EWMA, and Extended EWMA control charts, however, the new extended EWMA (NEEWMA) chart outperformed to detect the small and moderate magnitudes of shift when the process observations are from normal, Laplace and Gamma distribution. Otherwise, the mixed NEEWMA-TCC perform better than other control chart for the case of exponential distribution, moderate shifts and short production run process. This research presents a case study from real data on urinary tract infection (UTI) in hospital. According to the study’s findings, the NEEWMA chart is more successful than other similar control charts at identifying changes, while the NEWMA-TCC control chart performs the second-best in terms of detection.

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