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Model predictive control (MPC) is presented as a tactical decision module for supply chain management in semiconductor manufacturing. A representative problem which includes distinguishing features of semiconductor manufacturing supply chains, such as material reconfiguration and stochastic product splits, is examined. Fluid analogies are used to model the supply chain dynamics, with stochasticity and nonlinearity occurring on the throughput time, yield and customer demand. Given inventory targets and capacity limits, MPC using linear time invariant models can make the system outputs track the targets and improve customer service levels. The flexibility provided by the choice of tuning parameters in MPC to achieve better performance and robustness in semiconductor manufacturing supply chain management is demonstrated.