  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.
