The text discusses the importance of building agile and resilient supply chains to meet customer expectations. It highlights the challenges of detecting bottlenecks in supply chain forecasting, which can be caused by various factors such as part shortages, manufacturing process breakdowns, labor strikes, port traffic jams, and weather disasters. The article suggests that graph technology and predictive analytics can help overcome these challenges by uncovering hidden relationships in data and providing a strategic framework for making predictions about the most efficient path forward. It also emphasizes the need for end-to-end visibility into supply chain data to detect bottlenecks before they happen. Graph databases are designed to support this visibility, allowing companies to map out their supply chains as interconnected data points with clear relationships. By applying graph algorithms such as pathfinding and search, centrality, community detection, and similarity algorithms, companies can conduct predictive analytics and extract insights from their supply chain data. The article concludes by highlighting a real-world example of the U.S. Army's adoption of graph technology to transform its parts procurement and logistics system, resulting in improved forecasting, better ordering, and reduced total cost of ownership.