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Reducing the Time and Cost Overruns ...

Button, Scott D.

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Reducing the Time and Cost Overruns for Aerospace Development Programs Using Precedence Networks Patterns.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reducing the Time and Cost Overruns for Aerospace Development Programs Using Precedence Networks Patterns./
作者:	Button, Scott D.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	134 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-06, Section: A.
Contained By:	Dissertations Abstracts International80-06A.
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10981303
ISBN:	9780438701861

Reducing the Time and Cost Overruns for Aerospace Development Programs Using Precedence Networks Patterns.
Button, Scott D.

Reducing the Time and Cost Overruns for Aerospace Development Programs Using Precedence Networks Patterns. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 134 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: A.

Thesis (D.Engr.)--The George Washington University, 2019.

This item must not be added to any third party search indexes.

Many commercial and defense product development programs are large-scale, complex, and contain significant innovation. Such programs often suffer cost and schedule over-runs. The goal of this work is to increase the production rate of well-ordered precedence networks for complex, large-scale aerospace product development. Earlier work in correspondence pattern automation enabled the automatic selection of inputs based on name matching. This praxis pursued broadening the scope to evaluate patterns of nodes and inputs. This research will accelerate the identification and reuse of patterns resident in project precedence networks, enabling a reduction in time and cost overruns for aerospace Product Development. This work has applied link prediction techniques developed for social network analysis to a recommendation system which augments human selection of inputs during precedence network development. This work also introduces the Triad Significance Profile for a project management precedence network. Using the link prediction techniques developed in social network analysis, and using a triadic closure algorithm developed in this praxis, it is shown that inputs may be predicted for the studied product development network, with reasonable prediction accuracy.

ISBN: 9780438701861Subjects--Topical Terms:

1030799
Information Technology.
Subjects--Index Terms:

Motif

Reducing the Time and Cost Overruns for Aerospace Development Programs Using Precedence Networks Patterns.
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300 $a 134 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-06, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Etemadi, Amir H.;Malalla, Ebrahim A.
502 $a Thesis (D.Engr.)--The George Washington University, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Many commercial and defense product development programs are large-scale, complex, and contain significant innovation. Such programs often suffer cost and schedule over-runs. The goal of this work is to increase the production rate of well-ordered precedence networks for complex, large-scale aerospace product development. Earlier work in correspondence pattern automation enabled the automatic selection of inputs based on name matching. This praxis pursued broadening the scope to evaluate patterns of nodes and inputs. This research will accelerate the identification and reuse of patterns resident in project precedence networks, enabling a reduction in time and cost overruns for aerospace Product Development. This work has applied link prediction techniques developed for social network analysis to a recommendation system which augments human selection of inputs during precedence network development. This work also introduces the Triad Significance Profile for a project management precedence network. Using the link prediction techniques developed in social network analysis, and using a triadic closure algorithm developed in this praxis, it is shown that inputs may be predicted for the studied product development network, with reasonable prediction accuracy.
590 $a School code: 0075.
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650 4 $a Engineering. $3 586835
650 4 $a Information science. $3 554358
653 $a Motif
653 $a Pattern
653 $a Precedence network
653 $a Program management
653 $a Project management
653 $a Recommendation system
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710 2 $a The George Washington University. $b Engineering Management. $3 1262973
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