Enhancing supply chain visibility with generative AI: an exploratory case study on relationship prediction in knowledge graphs
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| Title: | Enhancing supply chain visibility with generative AI: an exploratory case study on relationship prediction in knowledge graphs |
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| Authors: | Ge Zheng, Alexandra Brintrup |
| Contributors: | Apollo - University of Cambridge Repository |
| Source: | International Journal of Production Research. :1-23 |
| Publication Status: | Preprint |
| Publisher Information: | Informa UK Limited, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Generative artificial intelligence (GenAI), Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, machine learning, Artificial Intelligence (cs.AI), supply chain visibility, Computer Science - Artificial Intelligence, pretrained language models (pretrained LMs), Computer Science - Computational Engineering, Finance, and Science, knowledge graph (KG), link prediction |
| Description: | A key stumbling block in effective supply chain risk management for companies and policymakers is a lack of visibility on interdependent supply network relationships. Relationship prediction, also called link prediction is an emergent area of supply chain surveillance research that aims to increase the visibility of supply chains using data-driven techniques. Existing methods have been successful for predicting relationships but struggle to extract the context in which these relationships are embedded - such as the products being supplied or locations they are supplied from. Lack of context prevents practitioners from distinguishing transactional relations from established supply chain relations, hindering accurate estimations of risk. In this work, we develop a new Generative Artificial Intelligence (Gen AI) enhanced machine learning framework that leverages pre-trained language models as embedding models combined with machine learning models to predict supply chain relationships within knowledge graphs. By integrating Generative AI techniques, our approach captures the nuanced semantic relationships between entities, thereby improving supply chain visibility and facilitating more precise risk management. Using data from a real case study, we show that GenAI-enhanced link prediction surpasses all benchmarks, and demonstrate how GenAI models can be explored and effectively used in supply chain risk management. 18 pages, 5 figures |
| Document Type: | Article |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 1366-588X 0020-7543 |
| DOI: | 10.1080/00207543.2025.2543964 |
| DOI: | 10.48550/arxiv.2412.03390 |
| Access URL: | http://arxiv.org/abs/2412.03390 |
| Rights: | CC BY arXiv Non-Exclusive Distribution |
| Accession Number: | edsair.doi.dedup.....84bb5ee30e8c5aaea3397c815318e7e9 |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | A key stumbling block in effective supply chain risk management for companies and policymakers is a lack of visibility on interdependent supply network relationships. Relationship prediction, also called link prediction is an emergent area of supply chain surveillance research that aims to increase the visibility of supply chains using data-driven techniques. Existing methods have been successful for predicting relationships but struggle to extract the context in which these relationships are embedded - such as the products being supplied or locations they are supplied from. Lack of context prevents practitioners from distinguishing transactional relations from established supply chain relations, hindering accurate estimations of risk. In this work, we develop a new Generative Artificial Intelligence (Gen AI) enhanced machine learning framework that leverages pre-trained language models as embedding models combined with machine learning models to predict supply chain relationships within knowledge graphs. By integrating Generative AI techniques, our approach captures the nuanced semantic relationships between entities, thereby improving supply chain visibility and facilitating more precise risk management. Using data from a real case study, we show that GenAI-enhanced link prediction surpasses all benchmarks, and demonstrate how GenAI models can be explored and effectively used in supply chain risk management.<br />18 pages, 5 figures |
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| ISSN: | 1366588X 00207543 |
| DOI: | 10.1080/00207543.2025.2543964 |