With many industries changing to a just-in-time manufacturing environment, the ability to reliably meet due dates and/or determine delivery/promise dates is gaining importance. In order to achieve this, the ability to obtain realistic production plans is paramount. For these plans to be reliable, uncertainty needs to be incorporated. These uncertainties include, but are not limited to, processing time uncertainties, equipment reliability/availability, process yields, demands, and manpower fluctuations. In this paper we present a framework for including uncertainty by means of Monte Carlo sampling. This framework is not limited to a specific model, but results obtained show that the need of including sufficient detail is best satisfied by a scheduling model. A number of stopping criteria are derived and the framework utilized to obtain operating policies for an industrially based example.