Sensitivity analysis for base-stock levels in multiechelon production-inventory systems

Effective management of inventories in large-scale production and distribution systems requires methods for bringing model solutions closer to the complexities of real systems. Motivated by this need, the authors develop simulation-based methods for estimating sensitivities of inventory costs with respect to policy parameters. These sensitivity estimates are useful in adjusting optimal parameters predicted by a simplified model to complexities that can be incorporated in a simulation. The authors consider capacitated, multiechelon systems operating under base-stock policies and develop estimators of derivatives with respect to base-stock levels. They show that these estimates converge to the correct value for finite-horizon and infinite-horizon discounted and average cost criteria. The present methods are easy to implement and experiments suggest that they converge quickly. The authors illustrate their use by optimizing base-stock levels for a subsystem of the PC assembly and distribution system of a major computer manufacturer.