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.

翻译：供应链网络数据是希望了解其道德状况、供应安全和效率的企业的宝贵资产。然而,单凭拥有数据集并不能充分促成可采取行动的决定,因为信息不完整。在本文件中,我们提出了一个图表说明学习方法,以发现核心公司可能不知道的隐性依赖关系。据我们所知,我们的工作首先代表供应链,作为具有可学习嵌入内容的多样化知识图。我们证明,我们的代表性有助于利用关系图相联网络预测全球汽车供应链网络的链接最新业绩。我们预计,我们的方法将直接适用于希望断绝与邪恶实体的联系并减少供应失败风险的企业。更抽象地说,预计我们的方法将有助于为供应链网络的代表性学习提供信息,以完成超越链接预测的下游任务。