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After describing the general supply chain management problem with examples from the semiconductor industry, attention is restricted to the core manufacturing problem. Using a control-oriented approach for this nonlinear stochastic combinatorial optimization problem, an outer loop for addressing the planning parts of the problem and an inner loop to manage the execution aspects are proposed. The outer loop provides a material release plan generated by a linear programming formulation (LP) and inventory safety stock targets generated by a dynamic programming formulation (DP) to the inner loop to guide execution. Portions of the nonlinearity and stochasticity inherent in the problem are addressed by the outer loop that requires iterative convergence between the LP and the DP. The inner loop is formulated from the perspective of model predictive control (MPC) and integrates optimal control and stochastic control. Initial results are presented to demonstrate the ability of the inner loop to track material release and safety stock targets while improving delivery performance in the face of both supply and demand stochasticity. A simulation module is also described that supports the other components of the system by validating their efficacy before application in the real world. This component has to address the integrated flows of materials, information, and decisions through the supply chain, and employs innovative approaches combining a number of specialized models to do so quickly and accurately.