A Decision Framwork for Prioritizing Industrial Materials Research and Development
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Word Count
10,000 words, Guess
Page Count
40 pages
Physical Format
Paperback
Identifiers
- Open LibraryOL9482539M
- ISBN-139780833032614
- ISBN-100833032615
- OCLC Control Number50503214
- Library of Congress Control Number2002031767
and 1 more
- Goodreads2019752
Classifications
- LCCTA402.5.U6 S55 2002
Description
Federal research and development (R & D) sponsors, managers, and other decisionmakers are faced with increasingly difficult choices on how to allocate shrinking resources among various R & D programs. The authors explore this issue from the perspective of the Department of Energy's Office of Industrial Technologies (OIT). In particular, they examine the challenges the OIT's Industrial Materials for the Future program faces in allocating resources to maximize the reduction of energy use and waste production in nine energy- and waste-intensive U.S. industries. In response to these challenges, RAND developed a decision framework to prioritize multiple R & D activities. The framework uses quantitative estimates of anticipated benefits, the potential of industrial materials to achieve certain properties, and the probability of success of an R & D project to compute an expected value for R & D in a manner that is logical, auditable, and straightforward. The authors recommend using the decision framework to evaluate proposed R & D and to identify and evaluate data that are needed to address various research needs. They also recommend extending the framework's application to other OIT programs and using it to reevaluate programs at a later date as R & D activities progress.
First Sentence
As government research and development (R&D) budgets continue to decrease in real dollars, and marketplace forces require industry to focus its R&D on incremental improvement of existing technologies and processes, federal R&D sponsors, managers, and other decisionmakers are faced with increasingly difficult choices on how to most effectively use shrinking R&D budgets.
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