A multivariate adaptive control chart for simultaneously monitoring of the process parameters
Sabahno, Hamed LU
and Khoo, Michael B.C.
(2024)
In Communications in Statistics: Simulation and Computation
53(4).
p.2031-2049
- Abstract
There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: “covariance matrix” and “multivariate coefficient of variation (MCV)”. This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain... (More)
There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: “covariance matrix” and “multivariate coefficient of variation (MCV)”. This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice.
(Less)
- author
- Sabahno, Hamed
LU
and Khoo, Michael B.C.
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Markov chains, Multivariate coefficient of variation, Multivariate normal process parameters, Simultaneous monitoring, Single-chart monitoring, Variable parameters control charts
- in
- Communications in Statistics: Simulation and Computation
- volume
- 53
- issue
- 4
- pages
- 19 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85148335898
- ISSN
- 0361-0918
- DOI
- 10.1080/03610918.2022.2066695
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
- id
- 3f42bced-1f3a-439e-8448-3f505da0556c
- date added to LUP
- 2025-03-20 12:15:45
- date last changed
- 2025-04-04 14:34:17
@article{3f42bced-1f3a-439e-8448-3f505da0556c,
abstract = {{<p>There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: “covariance matrix” and “multivariate coefficient of variation (MCV)”. This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice.</p>}},
author = {{Sabahno, Hamed and Khoo, Michael B.C.}},
issn = {{0361-0918}},
keywords = {{Markov chains; Multivariate coefficient of variation; Multivariate normal process parameters; Simultaneous monitoring; Single-chart monitoring; Variable parameters control charts}},
language = {{eng}},
number = {{4}},
pages = {{2031--2049}},
publisher = {{Taylor & Francis}},
series = {{Communications in Statistics: Simulation and Computation}},
title = {{A multivariate adaptive control chart for simultaneously monitoring of the process parameters}},
url = {{http://dx.doi.org/10.1080/03610918.2022.2066695}},
doi = {{10.1080/03610918.2022.2066695}},
volume = {{53}},
year = {{2024}},
}