Data Considerations in Graph Representation Learning for Supply Chain Networks
This work addresses the need for businesses to identify and mitigate risks in supply chains, though it is incremental as it applies existing graph learning methods to a new domain.
The paper tackles the problem of incomplete information in supply chain networks by using a graph representation learning approach to uncover hidden dependency links, achieving state-of-the-art performance on link prediction for a global automotive supply chain network.
Supply chain network data is a valuable asset for businesses wishing to understand their ethical profile, security of supply, and efficiency. Possession of a dataset alone however is not a sufficient enabler of actionable decisions due to incomplete information. In this paper, we present a graph representation learning approach to uncover hidden dependency links that focal companies may not be aware of. To the best of our knowledge, our work is the first to represent a supply chain as a heterogeneous knowledge graph with learnable embeddings. We demonstrate that our representation facilitates state-of-the-art performance on link prediction of a global automotive supply chain network using a relational graph convolutional network. It is anticipated that our method will be directly applicable to businesses wishing to sever links with nefarious entities and mitigate risk of supply failure. More abstractly, it is anticipated that our method will be useful to inform representation learning of supply chain networks for downstream tasks beyond link prediction.