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Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Author

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  • Alexandra Brintrup
  • Johnson Pak
  • David Ratiney
  • Tim Pearce
  • Pascal Wichmann
  • Philip Woodall
  • Duncan McFarlane

Abstract

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.

Suggested Citation

  • Alexandra Brintrup & Johnson Pak & David Ratiney & Tim Pearce & Pascal Wichmann & Philip Woodall & Duncan McFarlane, 2020. "Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3330-3341, June.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:11:p:3330-3341
    DOI: 10.1080/00207543.2019.1685705
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    Citations

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    Cited by:

    1. Dmitry Ivanov & Alexandre Dolgui, 2022. "Stress testing supply chains and creating viable ecosystems," Operations Management Research, Springer, vol. 15(1), pages 475-486, June.
    2. Farheen Naz & Anil Kumar & Abhijit Majumdar & Rohit Agrawal, 2022. "Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research," Operations Management Research, Springer, vol. 15(1), pages 378-398, June.
    3. Papanagnou, Christos & Seiler, Andreas & Spanaki, Konstantina & Papadopoulos, Thanos & Bourlakis, Michael, 2022. "Data-driven digital transformation for emergency situations: The case of the UK retail sector," International Journal of Production Economics, Elsevier, vol. 250(C).
    4. Kong, Lingxuan & Zheng, Ge & Brintrup, Alexandra, 2024. "A federated machine learning approach for order-level risk prediction in Supply Chain Financing," International Journal of Production Economics, Elsevier, vol. 268(C).
    5. Balezentis, Tomas & Zickiene, Agne & Volkov, Artiom & Streimikiene, Dalia & Morkunas, Mangirdas & Dabkiene, Vida & Ribasauskiene, Erika, 2023. "Measures for the viable agri-food supply chains: A multi-criteria approach," Journal of Business Research, Elsevier, vol. 155(PA).
    6. Spreitzenbarth, Jan & Stuckenschmidt, Heiner & Bode, Christoph, 2021. "The state of artificial intelligence: Procurement versus sales and marketing," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 223-243, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    8. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    9. Meike Schroeder & Sebastian Lodemann, 2021. "A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management," Logistics, MDPI, vol. 5(3), pages 1-17, September.
    10. Gonçalves, João N.C. & Sameiro Carvalho, M. & Cortez, Paulo, 2020. "Operations research models and methods for safety stock determination: A review," Operations Research Perspectives, Elsevier, vol. 7(C).
    11. Chowdhury, Priyabrata & Paul, Sanjoy Kumar & Kaisar, Shahriar & Moktadir, Md. Abdul, 2021. "COVID-19 pandemic related supply chain studies: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    12. Antoshchenkova, Vitalina & Onegina, Viktoriya & Gutsul, Tetiana & Boblovskyi, Oleksandr & Kravchenko, Yuliia, 2023. "Methodological approach for determining the size of the optimal raw material zone in the logistics system of dairy processing enterprise," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(1), March.
    13. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    14. Burgos, Diana & Ivanov, Dmitry, 2021. "Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).

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