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Monitoring the coefficient of variation using a variable sample size EWMA chart

Control charts for monitoring the coefficient of variation (CV) have been receiving a lot of attention in the literature, with numerous more powerful and robust CV charts being proposed. CV charts are attracting attention due to their usefulness in monitoring processes with an inconsistent mean and...

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Main Authors: Muhammad, Anis Nabila, Yeong, Wai Chung, Chong, Zhi Lin, Lim, Sok Li, Khoo, Michael Boon Chong
Format: Article
Published: Elsevier 2018
Subjects:
HD Industries. Land use. Labor
QA Mathematics
Online Access:http://eprints.um.edu.my/21206/
https://doi.org/10.1016/j.cie.2018.09.045
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spelling my.um.eprints.212062019-05-09T08:00:24Z http://eprints.um.edu.my/21206/ Monitoring the coefficient of variation using a variable sample size EWMA chart Muhammad, Anis Nabila Yeong, Wai Chung Chong, Zhi Lin Lim, Sok Li Khoo, Michael Boon Chong HD Industries. Land use. Labor QA Mathematics Control charts for monitoring the coefficient of variation (CV) have been receiving a lot of attention in the literature, with numerous more powerful and robust CV charts being proposed. CV charts are attracting attention due to their usefulness in monitoring processes with an inconsistent mean and a standard deviation which changes with the mean. These processes could not be monitored by conventional mean and/or standard deviation-type charts. One of the strategies to improve the performance of CV charts is by incorporating adaptive features, i.e. by varying the chart's parameters according to past sample information. Hence, this paper proposes a variable sample size (VSS) Exponentially Weighted Moving Average (EWMA) chart to monitor the CV squared (γ2), which is not available in the literature. The proposed chart allows different sample sizes to be adopted in the EWMA chart according to prior sample information. This paper shows the derivation of formulae to compute the average run length (ARL), average sample size (ASS) and expected average run length (EARL). Subsequently, an optimization algorithm to optimize the performance of the proposed chart is developed. Tables of optimal charting parameters are also provided. Next, the performance of the proposed chart is compared with five existing CV charts in the literature. The comparison shows that the proposed chart outperforms the five existing CV charts in almost all scenarios. Finally, this paper shows the implementation of the VSS EWMA-γ2chart on an actual industrial example. Elsevier 2018 Article PeerReviewed Muhammad, Anis Nabila and Yeong, Wai Chung and Chong, Zhi Lin and Lim, Sok Li and Khoo, Michael Boon Chong (2018) Monitoring the coefficient of variation using a variable sample size EWMA chart. Computers & Industrial Engineering, 126. pp. 378-398. ISSN 0360-8352 https://doi.org/10.1016/j.cie.2018.09.045 doi:10.1016/j.cie.2018.09.045
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic HD Industries. Land use. Labor
QA Mathematics
spellingShingle HD Industries. Land use. Labor
QA Mathematics
Muhammad, Anis Nabila
Yeong, Wai Chung
Chong, Zhi Lin
Lim, Sok Li
Khoo, Michael Boon Chong
Monitoring the coefficient of variation using a variable sample size EWMA chart
description Control charts for monitoring the coefficient of variation (CV) have been receiving a lot of attention in the literature, with numerous more powerful and robust CV charts being proposed. CV charts are attracting attention due to their usefulness in monitoring processes with an inconsistent mean and a standard deviation which changes with the mean. These processes could not be monitored by conventional mean and/or standard deviation-type charts. One of the strategies to improve the performance of CV charts is by incorporating adaptive features, i.e. by varying the chart's parameters according to past sample information. Hence, this paper proposes a variable sample size (VSS) Exponentially Weighted Moving Average (EWMA) chart to monitor the CV squared (γ2), which is not available in the literature. The proposed chart allows different sample sizes to be adopted in the EWMA chart according to prior sample information. This paper shows the derivation of formulae to compute the average run length (ARL), average sample size (ASS) and expected average run length (EARL). Subsequently, an optimization algorithm to optimize the performance of the proposed chart is developed. Tables of optimal charting parameters are also provided. Next, the performance of the proposed chart is compared with five existing CV charts in the literature. The comparison shows that the proposed chart outperforms the five existing CV charts in almost all scenarios. Finally, this paper shows the implementation of the VSS EWMA-γ2chart on an actual industrial example.
format Article
author Muhammad, Anis Nabila
Yeong, Wai Chung
Chong, Zhi Lin
Lim, Sok Li
Khoo, Michael Boon Chong
author_facet Muhammad, Anis Nabila
Yeong, Wai Chung
Chong, Zhi Lin
Lim, Sok Li
Khoo, Michael Boon Chong
author_sort Muhammad, Anis Nabila
title Monitoring the coefficient of variation using a variable sample size EWMA chart
title_short Monitoring the coefficient of variation using a variable sample size EWMA chart
title_full Monitoring the coefficient of variation using a variable sample size EWMA chart
title_fullStr Monitoring the coefficient of variation using a variable sample size EWMA chart
title_full_unstemmed Monitoring the coefficient of variation using a variable sample size EWMA chart
title_sort monitoring the coefficient of variation using a variable sample size ewma chart
publisher Elsevier
publishDate 2018
url http://eprints.um.edu.my/21206/
https://doi.org/10.1016/j.cie.2018.09.045
_version_ 1643691497468461056
score 13.211869

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