Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to prevent production delays and reduce risks in the supply chains. However, information about supply chains, especially at the deeper levels, is often intransparent and incomplete, making it difficult to obtain precise predictions about prospective risks. By connecting different data sources, we model the supply network as a knowledge graph and achieve transparency up to tier-3 suppliers. To predict missing information in the graph, we apply state-of-the-art knowledge graph completion methods and attain a mean reciprocal rank of 0.4377 with the best model. Further, we apply graph analysis algorithms to identify critical entities in the supply network, supporting supply chain managers in automated risk identification.

翻译：全球危机和监管发展要求提高供应链的透明度和弹性。企业不仅需要应对动态环境，还必须主动采取行动，实施措施以防止生产延误并降低供应链中的风险。然而，关于供应链的信息，尤其是在更深层次上，往往不透明且不完整，这使得难以对潜在风险获取精确预测。通过连接不同的数据源，我们将供应链网络建模为知识图谱，并实现了对三级供应商的透明度。为预测图谱中的缺失信息，我们应用了最先进的知识图谱补全方法，并通过最佳模型达到了0.4377的平均倒数排名。此外，我们运用图分析算法来识别供应链网络中的关键实体，从而支持供应链管理人员实现自动化风险识别。