Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart

In recent years, the coefficient of variation (CV) chart is receiving increasing attention in quality control. A number of studies demonstrated that adaptive charts could detect process shifts faster than traditional charts. This paper proposes an EWMA chart with variable sampling interval (VSI) to...

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Bibliographic Details
Published in:Journal of quality technology 2017-10, Vol.49 (4), p.380-401
Main Authors: Yeong, W. C., Khoo, Michael B. C., Tham, L. K., Teoh, W. L., Rahim, M. A.
Format: Article
Language:English
Subjects:
Adaptive control
Average Time to Signal
Coefficient of variation
Control charts
Expected Average Time to Signal
Exponentially Weighted Moving Average Chart
Markov Chain
Markov chains
Process planning
Quality control
Sampling
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Summary:In recent years, the coefficient of variation (CV) chart is receiving increasing attention in quality control. A number of studies demonstrated that adaptive charts could detect process shifts faster than traditional charts. This paper proposes an EWMA chart with variable sampling interval (VSI) to monitor the CV. Formulas for computing the performance measures of the VSI EWMA-γ 2 chart are derived using Markov chain, where γ 2 denotes the CV squared. Comparative studies show that the VSI EWMA-γ 2 chart significantly outperforms other competing charts. An example using real manufacturing data shows that the VSI EWMA-γ 2 chart performs well in applications.
ISSN:0022-4065
2575-6230
DOI:10.1080/00224065.2017.11918004