Towards Automatically Generating Supply Chain Maps from Natural Language Text
Overview
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. This project investigated automated methods to generate supply chain maps from openly available text sources for the purpose of improving resilience-related decision-making.
PhD Student
Pascal Wichmann
Supervisor
Prof. Duncan McFarlane