Inspired by studies in ecological networks, we look for a nested pattern in a large-scale data set describing the global automotive industry, including more than 18 000 firms, their clients, and products. Two bipartite networks are formed, namely, supplier-product distribution and supplier-manufacturer relations. Both networks are found to be significantly nested. The pattern means that suppliers produce proper subsets of what other suppliers produce and rare products are produced only by those suppliers that already produce high numbers of product types. In addition, the manufacturers that procure from few suppliers procure from those that supply to most other manufacturers in the network. Similarly, suppliers that supply to few supply to those manufacturers that procure from most others. A nested structure is more robust than a nonnested structure as disrupted suppliers can be substituted, but nestedness also means that small suppliers face more competition as their production can be redundant. Our finding is contrary to conventional wisdom that associates large diversified firms with efficiency and small specialist firms with rare products, showing that large-scale complex system analysis can lead to the discovery of important systemic characteristics, which are obscured when viewed from local points of view. We then propose a multiagent model that creates more realistic nested structures to study systemic outcomes influenced by topology
