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Bechtel SAIC & the Yucca mountain project

Bechtel SAIC, LLC is a joint enterprise of Bechtel National and Science Applications International Corporation (SAIC).

BSC is the management and operating contractor for the Yucca Mountain Project of the U.S. Department of Energy (DoE). Yucca Mountain, located about 100 miles northwest of Las Vegas, Nevada, is the proposed site for the long-term geologic repository for spent nuclear fuel and high-level radioactive waste.

These materials, now stored at 131 sites around the USA, are a result of nuclear power generation and national defense programs.

Why Analytica?

Bechtel SAIC chose Analytica on the basis of an independent comparison of 12 competing software packages, commissioned from Robert Kenley, PhD. His 16-page study recommended against spreadsheet add-ins because of the need for computational efficiency in uncertainty analysis as well as the need for simplicity and flexibility in managing multidimensional arrays. The study made Analytica its sole recommendation based on its array-handling, influence diagrams, rich function set, database access, ability to handle time series, and the quality of its documentation.

The solution

Bechtel SAIC is now using Analytica to create the lifecycle analysis model. The Analytica model file size is now just under 1 Megabyte, about one tenth of the size of the two spreadsheets it replaces, even though it has substantially expanded capabilities. The size decrease results from replacing huge numbers of repeated cells with simple array formulas, along with the multidimensional array capabilities. These make the model much easier to manage and verify.

The Analytica Lifecycle Model provides tremendously expanded decision analysis capabilities, which would have been very difficult or impossible in a spreadsheet. It sets the stage for adding statistical representations and Monte Carlo analysis of cost risk as risk data becomes available from a new program risk analysis effort. All of this has been accomplished while improved cost estimating methods are producing greater detail (and quantities of data) for use in the model analysis.

Authors

Robert Kenley, Kenley Consulting

Rob Brown, Decision Strategies, Inc

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