Discovering Supply Chain Links with Augmented Intelligence

2021/11/02 by Achintya Gopal, Gopal, Achintya, Chunho Chang +1
Computer Science · #Advanced Graph Neural Networks #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining

paper · pdf · doi:10.48550/arxiv.2111.01878

openalex publication_date 2021/11/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

One of the key components in analyzing the risk of a company is understanding a company's supply chain. Supply chains are constantly disrupted, whether by tariffs, pandemics, severe weather, etc. In this paper, we tackle the problem of predicting previously unknown suppliers and customers of companies using graph neural networks (GNNs) and show strong performance in finding previously unknown connections by combining the predictions of our model and the domain expertise of supply chain analysts.

Citations

• Long Short-Term Memory
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Related

• Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
• SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks
• Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs
• An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning
• Applying graph neural network to SupplyGraph for supply chain network