In open-loop optimal control, inputs to a dynamic system are computed that optimize a specified performance criterion. A novel approach is proposed that quantifies the impact of parameter and control implementation inaccuracies on the performance of open-loop control policies. Such information can be used to decide whether more laboratory experiments are needed to produce parameter estimates of higher accuracy, or to define performance objectives for lower-level control loops that implement the optimal control trajectory. The novel features of the approach include (1) computational efficiency; (2) the parametric uncertainty description, which is produced by most process identification algorithms; and (3) addressing of control implementation inaccuracies. The merits of the approach are illustrated on a batch crystallization process.