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" Conceptual Framework for adapting Technical Performance Measurement Methodology for Early Stage Research and Development Projects "
Ross E. Agee
Eggstaff, Justin; Islam, Muhammed
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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804789
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| Doc. No
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TL49625
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| Call number
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1969120233; 10636922
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| Main Entry
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Siddiqui, Niloufer Aamina
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| Title & Author
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Conceptual Framework for adapting Technical Performance Measurement Methodology for Early Stage Research and Development Projects\ Ross E. AgeeEggstaff, Justin; Islam, Muhammed
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| College
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The George Washington University
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| Date
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2018
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| Degree
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D.Engr.
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| field of study
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Engineering Management
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2018
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| Page No
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101
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| Note
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Committee members: Eggstaff, Justin; Islam, Muhammed; Mazzuchi, Thomas A.; Sarkani, Shahram; Strack, Otto E.
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| Note
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Place of publication: United States, Ann Arbor; ISBN=978-0-355-43040-0
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| Abstract
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Early stage research and development (R&D) projects, programs, or efforts are characterized by a high degree of uncertainty. In the public sector, these efforts are defined as those with technology readiness levels up to 4. Both project management and systems engineering cultures proliferate throughout the public sector. Project management traditionally uses earned value management system to track the progress of its projects. Systems engineers apply Technical Performance Measurement methodology (TPM) to R&D efforts to monitor progress of quality factors with respect to those efforts. Both of these methods have numerous alternative implementations, and both strive to provide risk management indicators and insight into future performance. This paper discusses a conceptual framework for monitoring early stage R&D by altering a TPM method. By quantifying and monitoring the “learning” or technical uncertainty toward the project goal, early stage R&D is given a measurement for technical progress. Learning values are determined using pairwise comparisons of R&D activities that contribute to learning. How the effort increases learning, or degrades uncertainty, over time serves as the baseline for performance management. In order to predict future performance, the learning baseline is regressed in linear segments. Using simulated project status, those predictions are shown to have acceptable uncertainty by validating against prediction intervals.
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| Subject
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Management; Engineering
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| Descriptor
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Applied sciences;Social sciences;Innovation management;Performance management;Public sector;Research and development;Technology maturation
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| Added Entry
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Eggstaff, Justin; Islam, Muhammed
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Engineering ManagementThe George Washington University
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