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A Bayesian Network Tool for Selectin...

James, Steven A.

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A Bayesian Network Tool for Selecting New Technology Investment Projects.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Bayesian Network Tool for Selecting New Technology Investment Projects./
Author:	James, Steven A.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	148 p.
Notes:	Source: Dissertations Abstracts International, Volume: 79-10, Section: A.
Contained By:	Dissertations Abstracts International79-10A.
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10786502
ISBN:	9780355829846

A Bayesian Network Tool for Selecting New Technology Investment Projects.
James, Steven A.

A Bayesian Network Tool for Selecting New Technology Investment Projects. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 148 p.

Source: Dissertations Abstracts International, Volume: 79-10, Section: A.

Thesis (D.Engr.)--The George Washington University, 2018.

This item must not be sold to any third party vendors.

When confronted with more R&D project projects than resources, decision makers struggle to identify which projects to select for investment due to incomplete knowledge and uncertainty in project performance for these types of early-stage, high risk projects. As a result, companies often select poor quality projects with low success rates leading to failed corporate strategic growth initiatives. One promising, but never used before approach for this problem, is to develop and test an innovative project evaluation and selection tool using Bayesian Networks. Project evaluation data from experts is extracted from a large, existing R&D field study and applied to test, validate and compare the decision tool to the most commonly used project selection method-the Weighted Scoring Model. Results achieved illustrate a substantial improvement in modeling experts' evaluation knowledge and provides decision makers with a practical capability for conducting multidimensional "what if" queries for evaluating and selecting R&D investment projects.

ISBN: 9780355829846Subjects--Topical Terms:

515831
Mathematics.
Subjects--Index Terms:

Bayesian Networks

A Bayesian Network Tool for Selecting New Technology Investment Projects.
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300 $a 148 p.
500 $a Source: Dissertations Abstracts International, Volume: 79-10, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Sarkani, Dr. Shahram;Mazzuchi, Dr. Thomas.
502 $a Thesis (D.Engr.)--The George Washington University, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a When confronted with more R&D project projects than resources, decision makers struggle to identify which projects to select for investment due to incomplete knowledge and uncertainty in project performance for these types of early-stage, high risk projects. As a result, companies often select poor quality projects with low success rates leading to failed corporate strategic growth initiatives. One promising, but never used before approach for this problem, is to develop and test an innovative project evaluation and selection tool using Bayesian Networks. Project evaluation data from experts is extracted from a large, existing R&D field study and applied to test, validate and compare the decision tool to the most commonly used project selection method-the Weighted Scoring Model. Results achieved illustrate a substantial improvement in modeling experts' evaluation knowledge and provides decision makers with a practical capability for conducting multidimensional "what if" queries for evaluating and selecting R&D investment projects.
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653 $a Project risk
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