Simulation modeling with memory-type control charts for monitoring the process variability

https://doi.org/10.48185/jaai.v4i2.875

Authors

  • Kashinath Chatterjee Augusta University
  • Mustapha Hached University of Lille
  • Christos Koukouvinos National Technical University of Athens
  • Angeliki Lappa National Technical University of Athens

Keywords:

average run-length, exponentially weighted moving average, dispersion control chart, Monte-Carlo simulations, standard deviation of run-length

Abstract

Memory-type control charts, renowned for their effectiveness in identifying small deviations in the process variance, are commonly used to monitor the process variability. In this article, we introduce a new tool, the Quadruple Exponentially Weighted Moving Average (QEWMA) chart, which is designed for the specific purpose of monitoring changes in the process variability. We refer to this chart as the -QEWMA chart. The performance of the -QEWMA chart is assessed through an extensive series of Monte-Carlo simulations, carefully considering the run-length distribution. Comparing it with other well-known memory-type charts, it becomes evident that the -QEWMA chart excels in its ability to effectively detect small shifts in the process dispersion. To illustrate the practical application of this chart, we provide an example.

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Published

2023-12-28

How to Cite

Chatterjee, K., Hached, M., Koukouvinos, . C., & Lappa, A. (2023). Simulation modeling with memory-type control charts for monitoring the process variability . Journal of Applied Artificial Intelligence, 4(2), 47–64. https://doi.org/10.48185/jaai.v4i2.875

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Articles