Simulation optimization is increasingly popular for solving complicated and mathematically intractable business problems. Focusing on academic articles published between 1998 and 2013, the present survey aims to unveil the extent to which simulation optimization has been used for solving practical inventory problems (as opposed to small, theoretical \toy problems"), and to detect any trends that might have arisen (e.g., popular topics, effective simulation optimization methods, frequently studied inventory system structures). We find that metaheuristics (especially genetic algorithms) and methods that combine several simulation optimization techniques are the most popular. The resulting categorizations provide a useful overview for researchers studying complex inventory management problems, by providing detailed information on the inventory system characteristics and the employed simulation optimization techniques, highlighting articles that involve stochastic constraints (e.g., expected fill rate constraints) or that employ a robust simulation optimization approach. Finally, in highlighting both trends and gaps in the research field, this review suggests avenues for further research.