Extracting supply chain maps from news articles using deep neural networks

Pascal Wichmann, Alexandra Brintrup, Simon Baker, Philip Woodall and Duncan McFarlane

International Journal of Production Research, 2020, vol. 58, issue 17, 5320-5336

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.

Date: 2020
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DOI: 10.1080/00207543.2020.1720925

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