Large engineering project risk management using a Bayesian belief network

Authors:
Eunchang Lee;Yongtae Park;Jong Gye Shin
Affiliations:
Graduate Program in Technology and Management, Seoul National University, San 56-1, Shillim-Dong, Gwanak-Gu, Seoul 151-742, Republic of Korea;Department of Industrial Engineering, Seoul National University, San 56-1, Shillim-Dong, Gwanak-Gu, Seoul 151-742, Republic of Korea;Department of Naval Architecture and Ocean Engineering, Seoul National University, San 56-1, Shillim-Dong, Gwanak-Gu, Seoul 151-742, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 6
Cited 13

Real-world applications of Bayesian networks

Communications of the ACM
A fuzzy approach to constuction project risk assessment and analysis: construction project risk management system

Advances in Engineering Software
Learning Bayesian networks from data: an information-theory based approach

Artificial Intelligence
Bayesian Networks for Data Mining

Data Mining and Knowledge Discovery
Software Risk Management: Principles and Practices

IEEE Software
BBN-based software project risk management

Journal of Systems and Software - Special issue: Applications of statistics in software engineering

A multilingual ontology framework for R&D project management systems

Expert Systems with Applications: An International Journal
Risk assessment for highway projects using jackknife technique

Expert Systems with Applications: An International Journal
A probabilistic expert system for predicting the risk of Legionella in evaporative installations

Expert Systems with Applications: An International Journal
Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey

Expert Systems with Applications: An International Journal
Feature fatigue analysis in product development using Bayesian networks

Expert Systems with Applications: An International Journal
An information systems security risk assessment model under uncertain environment

Applied Soft Computing
Risk analysis of changing and evolving systems using CORAS

Foundations of security analysis and design VI
Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Expert Systems with Applications: An International Journal
A simulation-based risk network model for decision support in project risk management

Decision Support Systems
Development of risk based dynamic backorder replenishment planning framework using Bayesian Belief Network

Computers and Industrial Engineering
Decision support analysis for safety control in complex project environments based on Bayesian Networks

Expert Systems with Applications: An International Journal
Software project risk analysis using Bayesian networks with causality constraints

Decision Support Systems
A fuzzy based assessment method for comparison of ship launching methods

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	12.06

Visualization

Abstract

This 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.