Using natural language processing to map defence industry supply chains
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Global supply chains have become increasingly complex, making it harder for organisations to identify where critical dependencies and vulnerabilities sit. For the defence sector, this challenge is particularly significant.
As NATO defence industries increase production in response to the war in Ukraine, existing supply chain constraints and vulnerabilities have become more visible. Single points of failure, reliance on suppliers in potentially hostile third countries and foreign ownership of critical suppliers can all create risks for the resilience and security of defence supply chains.
This research explores how Natural Language Processing (NLP) could help address these challenges.
The research reports on a pilot project that uses NLP to map defence industry supply chains, focusing on major weapons systems supporting NATO’s response to the war in Ukraine. By extracting information about supply chain relationships from publicly available news articles, the study investigates how technology can help build a clearer picture of complex supply networks.
The pilot demonstrates the feasibility of producing supply chain maps with a relatively limited amount of training data, offering a potential new approach to identifying critical dependencies and supply chain vulnerabilities. It also identifies limitations that require further refinement and validation before the approach can support operational decision-making.
What does the research explore?
The research examines:
Why does this matter?
A better understanding of defence supply chains can help organisations identify vulnerabilities before they become critical issues.
By using emerging technologies to map complex supplier relationships, defence organisations and procurement teams could gain greater visibility of their supply chains and make more informed decisions about resilience, risk and procurement.

Using natural language processing to map defence industry supply chains
File type: pdf
File size: 4.22Mb
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