Monitoring the multivariate coefficient of variation in presence of autocorrelation with variable parameters control charts
Sabahno, Hamed LU
and Celano, Giovanni
(2023)
In Quality Technology and Quantitative Management
20(2).
p.184-210
- Abstract
The coefficient of variation is a very important process parameter in many processes. A few control charts have been considered so far for monitoring its multivariate counterpart, i.e., the multivariate coefficient of variation (MCV). In addition, autocorrelation is very likely to occur in processes with high sampling frequency. Hence, designing suitable control charts and investigating the effect of autocorrelation on these charts is necessary. However, no control chart has been developed so far for the coefficient of variation that is capable of accounting for autocorrelation in either univariate or multivariate cases. This paper fills the gap by developing multivariate Shewhart-type control charts to monitor MCV with different... (More)
The coefficient of variation is a very important process parameter in many processes. A few control charts have been considered so far for monitoring its multivariate counterpart, i.e., the multivariate coefficient of variation (MCV). In addition, autocorrelation is very likely to occur in processes with high sampling frequency. Hence, designing suitable control charts and investigating the effect of autocorrelation on these charts is necessary. However, no control chart has been developed so far for the coefficient of variation that is capable of accounting for autocorrelation in either univariate or multivariate cases. This paper fills the gap by developing multivariate Shewhart-type control charts to monitor MCV with different autocorrelation structures for the observations: vector autoregressive, vector moving average, and vector mixed autoregressive and moving average. In addition, we add variable parameters adaptive features to the Shewhart-type scheme, in order to improve its performance. We develop a Markov chain model to get the statistical performance measures; then, we perform extensive numerical analyses to evaluate the effect of autocorrelation on adaptive and non-adaptive charts in the presence of downward and upward MCV shifts. Finally, we present an illustrative example from a healthcare process to show the implementation of this scheme in real practice.
(Less)
- author
- Sabahno, Hamed
LU
and Celano, Giovanni
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adaptive control charts, autocorrelation, Markov chains, multivariate coefficient of variation, vector time series models
- in
- Quality Technology and Quantitative Management
- volume
- 20
- issue
- 2
- pages
- 27 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85131415297
- ISSN
- 1684-3703
- DOI
- 10.1080/16843703.2022.2075193
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
- id
- 84d1d271-2d2a-438f-97b5-a05b9d2841cc
- date added to LUP
- 2025-03-20 12:11:19
- date last changed
- 2025-04-04 14:45:13
@article{84d1d271-2d2a-438f-97b5-a05b9d2841cc,
abstract = {{<p>The coefficient of variation is a very important process parameter in many processes. A few control charts have been considered so far for monitoring its multivariate counterpart, i.e., the multivariate coefficient of variation (MCV). In addition, autocorrelation is very likely to occur in processes with high sampling frequency. Hence, designing suitable control charts and investigating the effect of autocorrelation on these charts is necessary. However, no control chart has been developed so far for the coefficient of variation that is capable of accounting for autocorrelation in either univariate or multivariate cases. This paper fills the gap by developing multivariate Shewhart-type control charts to monitor MCV with different autocorrelation structures for the observations: vector autoregressive, vector moving average, and vector mixed autoregressive and moving average. In addition, we add variable parameters adaptive features to the Shewhart-type scheme, in order to improve its performance. We develop a Markov chain model to get the statistical performance measures; then, we perform extensive numerical analyses to evaluate the effect of autocorrelation on adaptive and non-adaptive charts in the presence of downward and upward MCV shifts. Finally, we present an illustrative example from a healthcare process to show the implementation of this scheme in real practice.</p>}},
author = {{Sabahno, Hamed and Celano, Giovanni}},
issn = {{1684-3703}},
keywords = {{Adaptive control charts; autocorrelation; Markov chains; multivariate coefficient of variation; vector time series models}},
language = {{eng}},
number = {{2}},
pages = {{184--210}},
publisher = {{Taylor & Francis}},
series = {{Quality Technology and Quantitative Management}},
title = {{Monitoring the multivariate coefficient of variation in presence of autocorrelation with variable parameters control charts}},
url = {{http://dx.doi.org/10.1080/16843703.2022.2075193}},
doi = {{10.1080/16843703.2022.2075193}},
volume = {{20}},
year = {{2023}},
}