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Extracting supply chain maps from news articles using deep neural networks

Author

Listed:
  • Pascal Wichmann
  • Alexandra Brintrup
  • Simon Baker
  • Philip Woodall
  • Duncan McFarlane

Abstract

Supply chains are increasingly global, complex and multi-tiered. Consequently, companies often struggle to maintain complete visibility of their supply network. This poses a problem as visibility of the network structure is required for tasks like effectively managing supply chain risk. In this paper, we discuss automated supply chain mapping as a means of maintaining structural visibility of a company's supply chain, and we use Deep Learning to automatically extract buyer–supplier relations from natural language text. Early results show that supply chain mapping solutions using Natural Language Processing and Deep Learning could enable companies to (a) automatically generate rudimentary supply chain maps, (b) verify existing supply chain maps, or (c) augment existing maps with additional supplier information.

Suggested Citation

  • Pascal Wichmann & Alexandra Brintrup & Simon Baker & Philip Woodall & Duncan McFarlane, 2020. "Extracting supply chain maps from news articles using deep neural networks," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5320-5336, September.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:17:p:5320-5336
    DOI: 10.1080/00207543.2020.1720925
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    Citations

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    Cited by:

    1. 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.
    2. Kusi-Sarpong, Simonov & Mubarik, Muhammad Shujaat & Khan, Sharfuddin Ahmed & Brown, Steve & Mubarak, Muhammad Faraz, 2022. "Intellectual capital, blockchain-driven supply chain and sustainable production: Role of supply chain mapping," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    4. 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).
    5. 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.
    6. MacCarthy, Bart L. & Ahmed, Wafaa A.H. & Demirel, Guven, 2022. "Mapping the supply chain: Why, what and how?," International Journal of Production Economics, Elsevier, vol. 250(C).
    7. Fessina, Massimiliano & Zaccaria, Andrea & Cimini, Giulio & Squartini, Tiziano, 2024. "Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    8. Brylowski, Martin & Schröder, Meike & Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Machine learning in supply chain management: A scoping review," 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 377-406, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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