Warehouses play a central role within supply chains and often form a network of closely interacting exchange points. Stocks can have a limited shelf live or even be part of a higher-level material cycle. In order to find the most cost-effective stock level across an entire network, millions of demands must be predicted with spatial resolution. Countless additional restrictions have to be met too. A combination of classical optimization methods and artificial intelligence identifies our best options.