The data revolution is having a remarkable impact over the management and design of manufacturing systems. While data are fruitfully used for diagnosis and monitoring of complex manufacturing processes, the optimization and control of manufacturing systems still rely on heuristics rules when a large set of cooperating machines is considered. In this paper, we explore an alternative way to extend the intelligence of local control to the system level, while explicitly accounting for the inherent system complexity. In particular, we propose the simulation-predictive control framework and we discuss how, according to the proposed approach, simulation has to be performed to allow the control of complex systems. In this direction, we show how simulation should be thought for control purposes and how the optimization/control routines should be seamlessly integrated within the simulation procedure. In order to show the advantage of the proposed approach, we test our control framework against a traditional simulation optimization algorithm and against a retrospective optimizer, which chooses the optimal actions with awareness of the future events. The promising results motivate us to further investigate the family of the approaches proposed herein.