Multivariate control charts that combine the Hotelling T2 and classification algorithms

Sung Ho Park, Seoung Bum Kim

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)1-9
Number of pages9
JournalJournal of the Operational Research Society
DOIs
Publication statusAccepted/In press - 2018 Jun 19

Fingerprint

Control charts
Hotelling
Multivariate control charts
Information control
Simulation
Charts

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

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