With globalization and outsourcing trends, modern industrial companies often rely on an extensive network of suppliers to construct sophisticated products. To maintain effective production planning and scheduling, integrating and managing the extensive information from supply chain has become increasingly critical. In particular, industrial companies, particularly those aiming to achieve Industry 4.0, enable to collect and analyze data related to their supply chains. Due to the vast amount of collected data, there is a continuous challenge in integrating and analyzing dependencies within the supplier network. While the development of Artificial Intelligence (AI) offers a promising solution for extracting and analyzing features from data, the inherently opaque and training-intensive nature of AI-enabled methods still present obstacles to effectively and efficiently analyzing information. To cope with this issue, this paper presents a knowledge-enhanced framework to support supply chain information integration and analysis by combining Knowledge Base (KB) and Graph Neural Networks (GNN). Specifically, constructing a KB enables the integration of extensive collected data with domain knowledge to generate structured and relational information. These knowledge-enhanced data support the training of GNN to encode information about supply chains. The resulting embeddings enable multiple inference tasks for analyzing graph-based data, supporting supply chain management. The case studies cover the usage of encoded embeddings for node classification, link prediction, and scenario classification. The proposed GNN outperforms baseline methods, demonstrating a promising solution for analyzing graph-based data in the context of supply chain management.