Steel Supply Chain (SSC) is a interpretative segment in Steel's value-creation process for ensuring on-time delivery of the right quality of raw materials, Work In Process (WIP), other goods and services to manufacturing sites, and finished products to the ultimate consumers. Graph theory provides a real-time, end-to-end view of the Steel supply chain, from suppliers to customers. By representing the supply chain as a network of interconnected nodes, graph theory provides a visual representation of the relationships between nodes including: suppliers, manufacturers, distributors, and customers. The main purpose of this study is to focus on Iran’s Steel Supply Chain (ISSC) based on complex adaptive network analysis and graph theory. Data collection performed by steel industry monitoring reports produced by relevant organizations. By mapping the Iranian SSC to the network in the Gephi environment (0.9.2), the graph theory applied to analyze node-level and network-level indices. This hypothesis tested that the corresponding network of the target SSC contained a Complex Adaptive System (CAS) which is an inception to combining social science insights to develop systems-level models and insights that allow for phase transitions and emergent behavior.