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Large engineering project risk management using a Bayesian belief network

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dc.contributor.authorLee, Eunchang-
dc.contributor.authorPark, Yongtae-
dc.contributor.authorShin, Jong Gye-
dc.date.accessioned2012-03-05T02:29:35Z-
dc.date.available2012-03-05T02:29:35Z-
dc.date.issued2009-04-01-
dc.identifier.citationEXPERT SYSTEMS WITH APPLICATIONS; Vol.36 3; 5880-5887-
dc.identifier.issn0957-4174-
dc.identifier.urihttps://hdl.handle.net/10371/75322-
dc.description.abstractThis paper presents a scheme for large engineering project risk management using a Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six different risks were deduced from expert interviews and a literature review. A survey analysis was conducted on 252 experts from 11 major Korean shipbuilding companies in April 2007. The overall major risks were design change, design manpower, and raw material supply as internal risks, and exchange rate as external risk in both large-scale and medium-sized shipbuilding companies. Differences of project performance risks between large-scale and medium-sized shipbuilding companies were identified. Exceeding time schedule and specification discontent were more important to large-scale shipbuilding companies, while exceeding budget and exceeding time schedule were more important to medium-sized shipbuilding companies. The change of project performance risks was measured by risk reduction activities of quality management, and strikes at headquarters and subcontractors, in both large-scale and medium-sized shipbuilding companies. The research results should be valuable in enabling industrial participants to manage their large engineering project risks and in extending our understanding of Korean shipbuilding risks. (C) 2008 Elsevier Ltd. All rights reserved.-
dc.language.isoen-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectRisk management in large engineering projects-
dc.subjectShipbuilding industry-
dc.subjectBayesian belief network-
dc.titleLarge engineering project risk management using a Bayesian belief network-
dc.typeArticle-
dc.contributor.AlternativeAuthor이은창-
dc.contributor.AlternativeAuthor박용태-
dc.contributor.AlternativeAuthor신종계-
dc.identifier.doi10.1016/j.eswa.2008.07.057-
dc.citation.journaltitleEXPERT SYSTEMS WITH APPLICATIONS-
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