Notwithstanding the usefulness of system dynamics in analysing complex policy problems, policy design is far from straightforward and in many instances trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability, an emerging eld in network science, to facilitate the detection of e ective leverage points in system dynamics models and thus to support the design of in uential policies. We illustrate our approach by analysing a classic system dynamics model: the World Dynamics model. We show that it is enough to control only 53% of the variables to steer the entire system to an arbitrary nal state. We further rank all variables according to their importance in controlling the system and we validate our approach by showing that high ranked variables have a signi cantly larger impact on the system behaviour compared to low ranked variables.