We concern ourselves with the process of making optimized production planning decisions in the face of low frequency, high impact uncertainty, which takes the form of a small number of discrete scenarios. Computational results provide evidence that the computational effort for the full stochastic mixed integer problem can be reduced by first solving scenario sub-problems and then blending them to find values for some of the binary variables.