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Quantitative method for analyzing en...

Chachere, John Marvin.

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Quantitative method for analyzing engineering defect risks in novel projects.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Quantitative method for analyzing engineering defect risks in novel projects./
Author:	Chachere, John Marvin.
Description:	222 p.
Notes:	Adviser: M. Elisabeth Pate-Cornell.
Contained By:	Dissertation Abstracts International68-11B.
Subject:	Business Administration, Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3292327
ISBN:	9780549353607

Quantitative method for analyzing engineering defect risks in novel projects.
Chachere, John Marvin.

Quantitative method for analyzing engineering defect risks in novel projects. - 222 p.

Adviser: M. Elisabeth Pate-Cornell.

Thesis (Ph.D.)--Stanford University, 2008.

Novel projects, such as those producing manned space missions, new cancer drugs, and unique civil facilities, require difficult decisions that tradeoff the costs of development reliability against the risks of operations failure. Yet, no social science or engineering method is both precise and holistic enough to estimate quantitatively the risks of engineering defects for specific projects where product, organization, process, and context strongly interact. To address this gap, the thesis provides a model of engineering defects as a source of critical dependencies between novel projects' upstream development and downstream operations stages. The thesis method elicits quantitative judgments from project experts regarding different engineering defects' causes during knowledge-based development and those defects' consequences during physical operations. With those data, the thesis models development-stage shortcomings as a function of failures to complete necessary rework, interprets those shortcomings to assess distributions of engineering defect severities, and estimates those defects' potential to reduce the developed product's capacities during operations. The thesis uses a project analysis framework, PRA-VDT, which integrates the model of engineering defects with the existing Virtual Design Team (VDT) simulation of development organizations and processes, the Probabilistic Risk Analysis (PRA) model of product functions and operating contexts, and the Decision Analysis (DA) method of rational decision support. In PRA-VDT, the Defect Model translates VDT output (defects' causes) into PRA input (defects' consequences), thus enabling the framework to formally explain relationships between diverse project features (such as component redundancy, engineering defects, and developer backlog) typically addressed by separate theories. The thesis finally presents PRA-VDT analyses of a hypothetical satellite project and of the Stanford Green Dorm Project as evidence that, compared with standalone models, the new framework can more holistically evaluate a broad range of alternative plans for novel projects.

ISBN: 9780549353607Subjects--Topical Terms:

626628
Business Administration, Management.

Quantitative method for analyzing engineering defect risks in novel projects.
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020 $a 9780549353607
035 $a (UMI)AAI3292327
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040 $a UMI $c UMI
100 1 $a Chachere, John Marvin. $3 1280848
245 1 0 $a Quantitative method for analyzing engineering defect risks in novel projects.
300 $a 222 p.
500 $a Adviser: M. Elisabeth Pate-Cornell.
500 $a Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7639.
502 $a Thesis (Ph.D.)--Stanford University, 2008.
520 $a Novel projects, such as those producing manned space missions, new cancer drugs, and unique civil facilities, require difficult decisions that tradeoff the costs of development reliability against the risks of operations failure. Yet, no social science or engineering method is both precise and holistic enough to estimate quantitatively the risks of engineering defects for specific projects where product, organization, process, and context strongly interact. To address this gap, the thesis provides a model of engineering defects as a source of critical dependencies between novel projects' upstream development and downstream operations stages. The thesis method elicits quantitative judgments from project experts regarding different engineering defects' causes during knowledge-based development and those defects' consequences during physical operations. With those data, the thesis models development-stage shortcomings as a function of failures to complete necessary rework, interprets those shortcomings to assess distributions of engineering defect severities, and estimates those defects' potential to reduce the developed product's capacities during operations. The thesis uses a project analysis framework, PRA-VDT, which integrates the model of engineering defects with the existing Virtual Design Team (VDT) simulation of development organizations and processes, the Probabilistic Risk Analysis (PRA) model of product functions and operating contexts, and the Decision Analysis (DA) method of rational decision support. In PRA-VDT, the Defect Model translates VDT output (defects' causes) into PRA input (defects' consequences), thus enabling the framework to formally explain relationships between diverse project features (such as component redundancy, engineering defects, and developer backlog) typically addressed by separate theories. The thesis finally presents PRA-VDT analyses of a hypothetical satellite project and of the Stanford Green Dorm Project as evidence that, compared with standalone models, the new framework can more holistically evaluate a broad range of alternative plans for novel projects.
590 $a School code: 0212.
650 4 $a Business Administration, Management. $3 626628
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Engineering, System Science. $3 1018128
690 $a 0454
690 $a 0546
690 $a 0790
710 2 $a Stanford University. $3 754827
773 0 $t Dissertation Abstracts International $g 68-11B.
790 $a 0212
790 1 0 $a Pate-Cornell, M. Elisabeth, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3292327