Supply Chain Management (SCM) is focused on developing, optimizing, and operating efficient supply chains. Efficient supply chains are characterized by cost effective decisions, lean flow and structure, high degree of integration, and well-chosen Key Performance Indicators (KPIs). Although there exists a large body of literature on optimizing individual supply chain elements (transportation, distribution, inventory, location, etc.), the literature does not provide an effective methodology that can address the complexity of the supply chain of a large scale industry such as steel producers. This paper, therefore, builds on existing research methods of supply chain modeling and optimization to propose a framework for optimizing supply chain performance of a steel producer. The framework combines deterministic modeling using Linear Programming (LP) with stochastic simulation modeling and optimization. A holistic LP deterministic optimization model is first used to characterize and optimize the supply chain variables. The model minimizes the annual operating cost of the steel company's supply chain. Simulation-based optimization with Simulated Annealing is then used to determine the operational levels of the supply chain drivers that meet a desired level of customer satisfaction. The proposed approach is applied to the supply chain of a major steel producer in the Arabian Gulf.