Supply chain management (SCM) in semiconductor manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level decision policies. Unique features of semiconductor manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a tactical decision tool for semiconductor manufacturing and similar forms of high-volume discrete-parts manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.