Life Cycle Cost (LCC) assessments are of interest during the design phase for new systems. These often involve costs that must be estimated from a variety of different sub-models including cost models constructed from historical data, forecast models that attempt to predict future economic conditions, and economy-of-scale models that impact production schedules and more. When these disparate models are put together to obtain an overall cost model many of these individual sources of uncertainty end up being aggregated or ignored. Consequently, the cost estimates may not provide program managers with appropriate assessments of the risk and overall variability of the new systems. We propose a structured approach for obtaining robust LCC estimates by taking into account a broad set of environmental noise conditions. This will enable program managers to better understand the uncertainty in their overall estimates, and to identify any decision factor combinations that result in both low costs and low cost variability. This may provide guidance on which of the many potential uncertainty sources require close monitoring, and which can safely be disregarded. We illustrate this approach with a model that the USMC is evaluating for use in cost/benefit analysis of alternative energy systems.