Causal Learning to Discover Supply Chain Vulnerability

Ying Zhao, Jacob Jones, Douglas MacKinnon


This paper illustrates a methodology of causal learning using pair-wise associations discovered from data. Taking advantage of a U.S. Department of Defense supply chain use case, this causal learning approach was substantiated and demonstrated in the application of discovering supply chain vulnerabilities. By integrating lexical link analysis, a data mining tool used to discover relationships in specific vocabularies or lexical terms with pair-wise causal learning, supply chain vulnerabilities were recognized. Evaluation of results from this methodology reveals supply chain opportunities, while exposing weaknesses to develop a more responsive and efficient supply chain system.


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