Abstract
To enable the management of project-related risk on a portfolio level in an owner organisation, project contingency estimation should be performed consistently and objectively. This article discusses the development of a contingency estimation tool for a large portfolio that contains similar construction projects. The purpose of developing this tool is to decrease the influence of subjectivity on contingency estimation methods throughout the project life cycle, thereby enabling consistent reflection on project risk at the portfolio level. Our research contribution is the delivery of a hybrid tool that incorporates both neural network modelling of systemic risks and expected value analysis of project-specific risks. The neural network is trained using historical project data, supported by data obtained from interviews with project managers. Expected value analysis is achieved in a risk register format employing a binomial distribution to estimate the number of risks expected. By following this approach, the contingency estimation tool can be used without expert knowledge of project risk management. In addition, this approach can provide contingency cost and duration output on a project level, and it contains both systemic and project-specific risks in a single tool.
Highlights
The effective management of project-related risk on a portfolio level is often restricted by contingency estimation methods that are not consistent and objective throughout the portfolio
This paper aims to address this gap by discussing the method employed to develop such a tool in the study environment
Chen and Hartman [11] report improved results with regard to contingency estimation using a neural network, when project data is grouped into two or more disjoint sets based on, for example, project cost range, and each set is used with a separate neural network. This difference in results could be attributed to the fact that while Lhee et al [16] and Chen and Hartman [11] were searching for networks to model all project risks and estimate total contingency, the artificial neural network (ANN) model in this study aims to approximate only the impact of systemic risks, which are more likely to follow a pattern throughout all types of projects, as they apply to all projects within the system
Summary
The effective management of project-related risk on a portfolio level is often restricted by contingency estimation methods that are not consistent and objective throughout the portfolio. This is especially detrimental in a large portfolio of projects, where a knowledgeable portfolio manager would need to maintain a portfolio-level risk analysis of all ongoing projects, so as to be able to monitor the risks and vulnerabilities of the entire portfolio. Project risks include risks that could materialise due to project execution, and risk conditions inherent to the project or the environment of the organisation. Project risks can be broadly classified into two categories: systemic and project-specific risks [1]:
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