The exponentiated exponentially weighted moving average control chart

Vasileios Alevizakos, Arpita Chatterjee, Kashinath Chatterjee and Christos Koukouvinos ()
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Vasileios Alevizakos: National Technical University of Athens
Arpita Chatterjee: Georgia Southern University
Kashinath Chatterjee: Augusta University
Christos Koukouvinos: National Technical University of Athens

Statistical Papers, 2024, vol. 65, issue 6, No 19, 3853-3891

Abstract: Abstract Memory-type control charts are widely used for monitoring small to moderate shifts in the process parameter(s). In the present article, we present an exponentiated exponentially weighted moving average (Exp-EWMA) control chart that weights the past observations of a process using an exponentiated function. We evaluated the run-length characteristics of the Exp-EWMA chart via Monte Carlo simulations. A comparison study versus the CUSUM, EWMA and extended EWMA (EEWMA) charts under similar in-control (IC) run-length properties demonstrates that the Exp-EWMA chart is more effective for detecting small and, under certain circumstances, moderate shifts for both the zero-state and steady-state cases. Moreover, the Exp-EWMA chart has better zero-state out-of-control (OOC) performance than an EWMA chart with smoothing parameter equal to the limit to the infinity of the exponentiated function, while the two charts perform similarly for the steady-state case. Finally, it is shown that the Exp-EWMA chart is more IC robust than its competitors under several non-normal distributions. Two examples are provided to explain the implementation of the proposed chart

Keywords: Exp-EWMA chart; Monte Carlo simulation; Run-length distribution; Steady-state; Zero-state (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-024-01544-2

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