A Robust Design Approach to Cost Estimation: Solar Energy for Marine Corps Expeditionary Operations
Morse, Matthew M.
Upton, Stephen C.
McDonald, Mary L.
Nussbaum, Daniel A.
MetadataShow full item record
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 the USMC is evaluating for use in cost/benefit analysis of alternative energy systems.
Approved for public release; distribution is unlimited.Acquisition Research Program Sponsored Report SeriesAnnual Acquisition Research Symposium.
NPS Report NumberNPS-CE-14-177
Showing items related by title, author, creator and subject.
Beattie, Jodi C. (Monterey, California. Naval Postgraduate School, 2003-03);As mesoscale models increase in resolution there is a greater need to understand predictability on smaller scales. The predictability of a model is related to forecast skill. It is possible that the uncertainty of one scale ...
Nunez, Jesse A. (Monterey, California: Naval Postgraduate School, 2017-03);Due to uncertainty in target locations, stochastic models are implemented to provide a representation of location distribution. The reliability of these models has a profound effect on the ability to successfully interdict ...
Batarseh, Ola; Singham, Dashi (2013);Interval-based simulation (IBS) has been proposed to model input uncertainty in discrete-event simulation. The foundation of this new simulation paradigm is imprecise probability, which models systems under both aleatory ...