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Identification of supply chain disruptions with economic performance of firms using multi-category support vector machines

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  • ShiJie Ye
  • Zhi Xiao
  • Guangfu Zhu

Abstract

From a practical perspective, a novel method using multi-category support vector machines (MC-SVM) is proposed to identify supply chain disruptions (SCD). With the data related to economic performance from quarter statements and individual announcements published by the listed firms, the variables of MC-SVM are constructed firstly. Secondly, MC-SVM is used for matching the portfolios of firms, which helps the map from economic performance to SCD by applying MC-SVM again. Finally, a case study is given to testify the ability of proposed method with the data from the listed firms in China.

Suggested Citation

  • ShiJie Ye & Zhi Xiao & Guangfu Zhu, 2015. "Identification of supply chain disruptions with economic performance of firms using multi-category support vector machines," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 3086-3103, May.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:10:p:3086-3103
    DOI: 10.1080/00207543.2014.974838
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    Cited by:

    1. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    2. Lechtenberg, Sandra & Hellingrath, Bernd, 2021. "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers 37, University of Münster, European Research Center for Information Systems (ERCIS).
    3. A. V. Thomas & Biswajit Mahanty, 2021. "Dynamic assessment of control system designs of information shared supply chain network experiencing supplier disruption," Operational Research, Springer, vol. 21(1), pages 425-451, March.
    4. Oh, YeongGwang & Ransikarbum, Kasin & Busogi, Moise & Kwon, Daeil & Kim, Namhun, 2019. "Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 202-212.

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