Towards automatically generating supply chain maps from natural language text. Issue 11 (2018) - Wichmann, Pascal; Brintrup, Alexandra; Baker, Simon; Woodall, Philip; McFarlane, Duncan
Abstract: Supply chains are increasingly global, complex and multi-tiered. Consequently, companies often struggle to maintain complete visibility of their upstream supply network. This poses a problem as visibility of the network is required in order to effectively manage supply chain risk. In this paper, we discuss supply chain mapping as a means of maintaining (structural) visibility of a company's supply chain, and we derive the requirements for automatically generating supply chain maps from openly available text sources. Early results show that supply chain mapping solutions generated by Natural Language Processing (NLP) 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.