A double generally weighted moving average control chart for monitoring the process variability

Vasileios Alevizakos; Kashinath Chatterjee; Christos Koukouvinos; Angeliki Lappa

doi:10.1080/02664763.2022.2064977

A double generally weighted moving average control chart for monitoring the process variability

J Appl Stat. 2022 Apr 22;50(10):2079-2107. doi: 10.1080/02664763.2022.2064977. eCollection 2023.

Authors

Vasileios Alevizakos¹, Kashinath Chatterjee², Christos Koukouvinos¹, Angeliki Lappa¹

Affiliations

¹ Department of Mathematics, National Technical University of Athens, Zografou, Greece.
² Department of Population Health Sciences, Division of Biostatistics and Data Science, Augusta University, Augusta, GA, USA.

Abstract

In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the $S^{2}$ -DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the $S^{2}$ -DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the $S^{2}$ -DGWMA chart with several well-known memory-type control charts in the literature. The comparisons indicate that the proposed one is more efficient in detecting small shifts, while it is more sensitive in identifying upward shifts in the process variability. A real data example is given to present the implementation of the new $S^{2}$ -DGWMA chart.

Keywords: Average run-length; control chart; double generally weighted moving average; logarithmic transformation; process variability.