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Conceptual Framework for adapting Technical Performance Measurement Methodology for Early Stage Research and Development Projects.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Conceptual Framework for adapting Technical Performance Measurement Methodology for Early Stage Research and Development Projects./
作者:	Agee, Ross E.
面頁冊數:	1 online resource (101 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
標題:	Engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355430400

Conceptual Framework for adapting Technical Performance Measurement Methodology for Early Stage Research and Development Projects.
Agee, Ross E.

Conceptual Framework for adapting Technical Performance Measurement Methodology for Early Stage Research and Development Projects. - 1 online resource (101 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (D.Engr.)

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355430400Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Conceptual Framework for adapting Technical Performance Measurement Methodology for Early Stage Research and Development Projects.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Advisers: Justin Eggstaff; Muhammed Islam.
502 $a Thesis (D.Engr.) $c The George Washington University $d 2018.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Management. $3 558618
655 7 $a Electronic books. $2 local $3 554714
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