Process simulation tools used for batch process design, cycle time analysis, and cost estimation, typically rely on deterministic models. However most industries, and especially the pharmaceutical and specialty chemical industries, are characterized by uncertainty and variability in crucial process and market parameters that greatly influence the profitability of a project. This paper presents a framework for integrating reliable state of the art process simulation tools, which rely on deterministic models, with risk analysis tools, which allow for stochastic modeling of uncertain variables. A methodology is presented for performing a parametric study of batch process design and analysis under uncertainty, providing precise forecast statistics for the decision variables. The results of such analyses are useful for quantifying risks and facilitating decision making under uncertainty. The risk analysis methodology is illustrated with two case study, the involving the synthesis of a small pharmaceutical molecule, and the second dealing with production of pharmaceutical tablets.

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