Ever wondered how your favorite products get from the factory to your doorstep? It's a complex journey involving a web of suppliers, manufacturers, and distributors, often hidden from view. This lack of transparency can make it difficult for businesses to manage risks, ensure ethical sourcing, and maintain efficient operations. Recent research explores an innovative way to shed light on these hidden connections using the power of knowledge graphs and large language models (LLMs). Imagine a digital map of your supply chain, showing every connection from raw materials to finished goods. This is the potential of knowledge graphs, which provide a structured way to represent complex relationships between different entities. But building these maps manually is a daunting task. That's where LLMs come in. These AI powerhouses can analyze vast amounts of unstructured data, like news articles and company websites, to automatically extract key information about suppliers, locations, and materials. They can even identify the relationships between these entities, like who supplies whom and where materials are sourced. The research demonstrates this approach in the electric vehicle industry, mapping the complex journey of critical minerals like lithium from the mine to the battery. The results are impressive, revealing hidden connections and alternative sourcing options that were previously difficult to see. For instance, the study shows how different electric vehicle manufacturers share common suppliers, offering insights into potential risks and opportunities for collaboration. This technology has the potential to revolutionize supply chain management, offering unprecedented visibility into even the most complex networks. While there are still challenges, like keeping up with the dynamic nature of supply chains and ensuring data accuracy, this research opens exciting new possibilities for a more transparent and resilient future.