The idea of this research is to explore the evolution of a supply chain using an empirical approach. This can be achieved by harnessing the power of Bloomberg data with network visualization software. Such an investigation will help identify supply chain archetypes as well as lead to an understanding of how these supply chains might change over time. Coupled with additional secondary data sources, we could learn more about how these changes might be impacted by, and impact, firm performance. In this paper, we explore a number of supply networks and develop their associated supply chain maps. We use key metrics from social network analysis to quantify the nature of these networks and understand how they evolve. This empirical data is then used to create a paradigm which explains the structure of these supply networks. We use the maps and the metrics developed to describe them to draw preliminary conclusions about how supply network topology impacts its performance.