Supply chain business interruption has been identified as a key risk factor in recent years, with high impact disruptions due to disease outbreaks, logistic issues such as the recent Suez Canal blockage showing examples of how disruptions could propagate across complex emergent networks. Researchers have highlighted the importance of gaining visibility into procurement interdependencies between suppliers to develop more informed business contingency plans. However extant methods such as supplier surveys rely on the willingness or ability of suppliers to share data and are not easily verifiable. In this article, we pose the supply chain visibility problem as a link prediction problem from the field of Machine Learning, and propose the use of an automated method to detect potential links that are unknown to the buyer with Graph Neural Network (GNN). Using a real automotive network as a test case, we show that our method performs better than existing algorithms and provides a complimentary, AI based approach to improve supply chain visibility. Additionally, we use Integrated Gradient, a widely used technique in machine learning to improve the explainability of our approach by highlighting input features that influence GNN's decisions. We also discuss the advantages and limitations of using GNN for link prediction, outlining future research directions that can help improve supply chain visibility through the use of machine learning.