Causal Learning to Discover Supply Chain Vulnerability

Ying Zhao, Jacob Jones, Douglas MacKinnon

Abstract

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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Paper Citation


in Harvard Style

Zhao Y., Jones J. and MacKinnon D. (2019). Causal Learning to Discover Supply Chain Vulnerability.In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, pages 305-309. DOI: 10.5220/0008070503050309


in Bibtex Style

@conference{kdir19,
author={Ying Zhao and Jacob Jones and Douglas MacKinnon},
title={Causal Learning to Discover Supply Chain Vulnerability},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2019},
pages={305-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008070503050309},
isbn={978-989-758-382-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Causal Learning to Discover Supply Chain Vulnerability
SN - 978-989-758-382-7
AU - Zhao Y.
AU - Jones J.
AU - MacKinnon D.
PY - 2019
SP - 305
EP - 309
DO - 10.5220/0008070503050309