In the last half century, the aerospace industry has seen a dramatic paradigm shift from a focus on performance-at-any-cost to product economics and value. The steady increase in product requirements, complexity and global competition has driven aircraft manufacturers to seek broad portfolios of advanced technologies. The development costs and cycle times of these technologies vary widely, and the resulting design environment is one where decisions must be made under substantial uncertainty. Modeling and simulation have recently become the standard practice for addressing these issues; detailed simulations and explorations of candidate future states of these systems help reduce a complex design problem into a comprehensible, manageable form where decision factors are prioritized. While there are still fundamental criticisms about using modeling and simulation, the emerging challenge becomes ``How do you best configure uncertainty analyses and the information they produce to address real world problems?” One such analysis approach was developed in this thesis by structuring the input, models, and output to answer questions about the risk and economic impact of technology decisions in future aircraft programs. Unlike other methods, this method placed emphasis on the uncertainty in the cumulative cashflow space as the integrator of economic viability. From this perspective, it then focused on exploration of the design and technology space to tailor the business case and its associated risk in the cash flow dimension. The methodology is called CASSANDRA and is intended to be executed by a program manager of a manufacturer working of the development of future concepts. The program manager has the ability to control design elements as well as the new technology allocation on that aircraft. She is also responsible for the elicitation of the uncertainty in those dimensions within control as well as the external scenarios (that are out of program control). The methodology was applied on a future single-aisle 150 passenger aircraft design. The overall methodology is compared to existing approaches and is shown to identify more economically robust design decisions under a set of at-risk program scenarios. Additionally, a set of metrics in the uncertain cumulative cashflow space were developed to assist the methodology user in the identification, evaluation, and selection of design and technology. These metrics are compared to alternate approaches and are shown to better identify risk efficient design and technology selections. At the modeling level, an approach is given to estimate the production quantity based on an enhanced Overall Evaluation Criterion method that captures the competitive advantage of the aircraft design. This model was needed as the assumption of production quantity is highly influential to the business case risk. Finally, the research explored the capacity to generate risk mitigation strategies in to two analysis configurations: when available data and simulation capacity are abundant, and when they are sparse or incomplete. The first configuration leverages structured filtration of Monte Carlo simulation results. The allocation of design and technology risk is then identified on the Pareto Frontier. The second configuration identifies the direction of robust risk mitigation based on the available data and limited simulation ability. It leverages a linearized approximation of the cashflow metrics and identifies the direction of allocation using the Jacobian matrix and its inversion.