Project complexity and risk management (ProCRiM) : towards modelling project complexity driven risk paths in construction projects

Qazi, Abroon and Quigley, John and Dickson, Alex and Kirytopoulos, Konstantinos (2016) Project complexity and risk management (ProCRiM) : towards modelling project complexity driven risk paths in construction projects. International Journal of Project Management, 34 (7). pp. 1183-1198. ISSN 0263-7863 (https://doi.org/10.1016/j.ijproman.2016.05.008)

[thumbnail of Qazi-etal-IJPM-2016-towards-modelling-project-complexity-driven-risk-paths-in-construction-projects]
Preview
Text. Filename: Qazi_etal_IJPM_2016_towards_modelling_project_complexity_driven_risk_paths_in_construction_projects.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (1MB)| Preview

Abstract

Project complexity has been extensively explored in the literature because of its contribution towards the failure of major projects in terms of cost and time overruns. Focusing on the interface of Project Complexity and Interdependency Model ling of Project Risks, we propose a new process that aids capturing interdependency between project complexity, complexity induced risks and project objectives. The proposed modelling approach is grounded in the theoretical framework of Expected Utility Theory and Bayesian Belief Networks. We consider the decision problem of identifying critical risks and selecting optimal risk mitigation strategies at the commencement stage of a project, taking into account the utility function of the decision maker with regard to the importance of project objectives and holistic interaction between project complexity and risk. The proposed process is supported by empirical research that was conducted in the construction industry in order to explore the current practices of managing project compl exity and the associated risks. The experts interviewed acknowledged the contribution of the proposed process to the understanding of complex dynamics between project complexity attributes and risks. Application of the proposed process is illustrated through a simulation study.