Derivative-free optimization (DFO) addresses the problem of optimizing over simulations where a closed form of the objective function is not available. Developments in the theory of DFO algorithms have made them useful for many practical applications. However, most of the existing DFO algorithms do not show satisfactory performance on high-dimensional problems. One contributor to this difficulty is the accuracy of the surrogate models used to guide the search for the optimum. Space mapping approach exploits a simplified simulation, which is a physical surrogate of the original problem at hand. As the simplified simulation is computationally efficient, a larger number of samples can be collected for guiding the search. This work aims to assess the potential of space mapping for derivative-free optimization, understand the difficulties and display its performance on a supply chain simulation optimization problem for identifying optimal inventory allocation. Computational results illustrate the potential of this approach.