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

Author

Listed:
  • Vasileios Alevizakos
  • Kashinath Chatterjee
  • Christos Koukouvinos
  • Angeliki Lappa

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 $ S2-DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the $ S^2 $ S2-DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the $ S^2 $ S2-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 $ S2-DGWMA chart.

Suggested Citation

  • Vasileios Alevizakos & Kashinath Chatterjee & Christos Koukouvinos & Angeliki Lappa, 2023. "A double generally weighted moving average control chart for monitoring the process variability," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(10), pages 2079-2107, July.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2079-2107
    DOI: 10.1080/02664763.2022.2064977
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