Supply Chain Management has gained renewed interest among researchers in recent years. This is primarily due to the availability of timely information across the various stages of the supply chain, and therefore the need to effectively utilize the information for improved performance. Although information plays a major role in effective functioning of supply chains, there is a paucity of studies that deal specifically with the dynamics of supply chains and how data collected in these systems can be used to improve their performance. In this paper we develop a framework, with machine learning, for automated supply chain configuration. Supply chain configuration used to be mostly a one-shot problem. Once a supply chain was configured, researchers and practitioners were more interested in means to improve performance given that initial configuration. However, recent developments in e-Commerce applications and faster communication over the Internet in general necessitate dynamic (re)configuration of supply chains over time to take advantage of better configurations. We model each actor in the supply chain as an agent who makes independent decisions based on information gathered from the next level upstream. Using examples, we show performance improvements of the proposed adaptive supply chain configuration framework over static configurations.