Abstract: In this research, we propose to extend the robust optimization technique and target it for problems encountered in supply chain management. Our method represents uncertainty as polyhedral uncertainty sets made of simple linear constraints derivable from macroscopic economic data. We avoid the probability distribution estimation of stochastic programming. The constraints in our approach are intuitive and meaningful. This representation of uncertainty is applied to capacity planning and inventory optimization problems in supply chains. The representation of uncertainty is the unique feature that drives this research. It has led us to explore different problems in capacity/inventory planning under this new paradigm. A decision support system package has been developed, which can conveniently interface to manufacturing/firm data warehouses, inferring and analyzing constraints from historical data, analyzing performance (worst case/best case), and optimizing plans.