An Approach Based on Bayesian Network for Improving Project Management Maturity: An Application to Reduce Cost Overrun Risks in Engineering Projects

•Proposal of a method to establish the causal relationship between project management maturity and project overcost•Use of machine learning and human expertise to predict the risk of cost overruns•Qualitative (by experts) and quantitative assessment of Bayesian network performance•User case in a his...

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Bibliographic Details
Published in:Computers in industry 2020-08, Vol.119 (2020), p.103227, Article 103227
Main Authors: Sanchez, Felipe, Bonjour, Eric, Micaelli, Jean-Pierre, Monticolo, Davy
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bayesian Networks
Business administration
Computer Science
Cost Overrun
Humanities and Social Sciences
Knowledge Modeling
Maturity Model
Project Management
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Summary:•Proposal of a method to establish the causal relationship between project management maturity and project overcost•Use of machine learning and human expertise to predict the risk of cost overruns•Qualitative (by experts) and quantitative assessment of Bayesian network performance•User case in a historical database of Oil and Gas Offshore engineering projects.•Simulation of scenarios, and proposition of recommendations to reduce the probability of overcosts. The project management field has the imperative to increase the success probability of projects. Experts have developed several Project Management Maturity (PMM) models to assess project management practices and improve the project outcome. However, the current literature lacks models that allow experts to correlate the measured maturity with the expected probability of success. The present paper develops a general framework and a method to estimate the impact of PMM on project performance. It uses Bayesian networks to formalize project management experts’ knowledge and to extract knowledge from a database of past projects. An industrial case concerning large projects in the oil and gas industry is used to illustrate the application of the method to reduce the risk of project cost (or budget) overruns.
ISSN:0166-3615
1872-6194
DOI:10.1016/j.compind.2020.103227