Process simulation tools used for batch process design, throughput 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 design 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 implementation was accomplished through the integration of SuperPro Designer, a batch process simulator from Intelligen Inc. (Scotch Plains, NJ, USA), with Crystal Ball from Decissioneering (Denver, CO, USA), an Excel based Risk Analysis and Monte Carlo simulation tool. The risk analysis methodology is illustrated with an example involving a synthetic pharmaceutical process. The base case is designed and modeled with SuperPro Designer using the mean value for the uncertain variables. The SuperPro model is used as the basis for the Monte Carlo simulation, which is performed with Crystal Ball. Important technical and market parameters that are inputs to the SuperPro model and are uncertain or exhibit variance are identified and ranked in terms of the effect of their perturbations on the model decision variables using a static sensitivity analysis; their probability distributions are best estimated based on historical data and fitted through the Crystal Ball gallery. The crucial uncertain input parameters include process times of selected operations and prices of key raw materials. Delays in process steps that are likely time bottlenecks reduce the number of batches that can be processed per campaign and result in higher production cost. Similarly, the variance in the prices of key raw materials directly affects production cost and project profitability. Monte Carlo simulations are used for examining how the uncertainties propagate through the model altering its outputs. The uncertainty / variance of these decision variables are quantified in terms of their probability distribution, expected value, standard deviation, median, and mode. In addition the contribution of each uncertain parameter to the variance of the decision variables is quantified with a dynamic sensitivity study. The process simulation tool is indispensable in this study, as it provides the only reliable correlation between the input-uncertain variables and the output objective functions on which decisions are made. Equivalently, Monte-Carlo simulation is essential as it provides the framework for considering uncertainty in input variables and generating the scenarios that need to be evaluated. In conclusion, the combination of deterministic process simulators (or process models in general) with stochastic analysis tools provides a solid and a conclusive framework for analyzing and evaluating the profitability and viability of industrial processes under uncertainty.