An Optimal Adaptive Variable Sample Size Scheme for the Multivariate Coefficient of Variation

  • KHAI WAH KHAW School of Management, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
  • XINYING CHEW Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia.
  • MING HA LEE Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, 93350 Kuching, Sarawak, Malaysia
  • WAI CHUNG YEONG School of Mathematical Sciences, Sunway University, 47500 Petaling Jaya, Malaysia
DOI: https://doi.org/10.19139/soic-2310-5070-996
Keywords: Control charting technique, efficient process monitoring, Markov-chain, multivariate coefficient of variation, variable sample size

Abstract

Development of an efficient process monitoring system has always received great attention. Previous studies revealed that the coefficient of variation (CV) is important in ensuring process quality, especially for monitoring a process where its process mean and variance are highly correlated. The fact that almost all industrial process monitoring involves a minimum of two or more related quality characteristics being monitored simultaneously, this paper incorporates the salient feature of the adaptive sample size VSS scheme into the standard multivariate CV (MCV) chart, called the VSS MCV chart. A Markov chain model is developed for the derivation of the chart’s performance measures, i.e the average run length (ARL), the standard deviation of the run length (SDRL), the average sample size (ASS), the average number of observations to signal (ANOS) and the expected average run length (EARL). The numerical comparison shows that the proposed chart prevails over the existing standard MCV chart for detecting small and moderate upward and downward MCV shifts.

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Published
2021-07-11
How to Cite
KHAW, K. W., CHEW, X., LEE, M. H., & YEONG, W. C. (2021). An Optimal Adaptive Variable Sample Size Scheme for the Multivariate Coefficient of Variation. Statistics, Optimization & Information Computing, 9(3), 681-693. https://doi.org/10.19139/soic-2310-5070-996
Issue
Vol 9 No 3 (2021)
Section
Research Articles
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