Abstract
Multivariate control charts, including Hotelling’s T2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling’s T2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.
| Original language | English |
|---|---|
| Pages (from-to) | 1-9 |
| Number of pages | 9 |
| Journal | Journal of the Operational Research Society |
| DOIs | |
| Publication status | Accepted/In press - 2018 Jun 19 |
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Keywords
- classification algorithm
- control
- multivariate control chart
- Quality
- statistical process control
ASJC Scopus subject areas
- Management Information Systems
- Strategy and Management
- Management Science and Operations Research
- Marketing
Cite this
Multivariate control charts that combine the Hotelling T2 and classification algorithms. / Park, Sung Ho; Kim, Seoung Bum.
In: Journal of the Operational Research Society, 19.06.2018, p. 1-9.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multivariate control charts that combine the Hotelling T2 and classification algorithms
AU - Park, Sung Ho
AU - Kim, Seoung Bum
PY - 2018/6/19
Y1 - 2018/6/19
N2 - Multivariate control charts, including Hotelling’s T2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling’s T2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.
AB - Multivariate control charts, including Hotelling’s T2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling’s T2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.
KW - classification algorithm
KW - control
KW - multivariate control chart
KW - Quality
KW - statistical process control
UR - http://www.scopus.com/inward/record.url?scp=85049040974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049040974&partnerID=8YFLogxK
U2 - 10.1080/01605682.2018.1468859
DO - 10.1080/01605682.2018.1468859
M3 - Article
SP - 1
EP - 9
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
SN - 0160-5682
ER -