Project portfolio risk analysis with the consideration of project interdependencies

Libiao Bai · Huijing Shi · Shuyun Kang · Bingbing Zhang

30 citations0/0 files storedEngineering Construction & Architectural Management

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Construction Project Management and Performance Multi-Criteria Decision Making Capital Investment and Risk Analysis

Abstract

Purpose Comprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network. Design/methodology/approach In this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks. Findings The interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved. Originality/value The findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.

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References

2007Understanding the key risks in construction projects in China 2010A fuzzy approach to construction project risk assessment 2019Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review 2017Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment 2014An analytical framework for supply network risk propagation: A Bayesian network approach 2016Quantitative risk analysis on leakage failure of submarine oil and gas pipelines using Bayesian network 2019Learning about risk: Machine learning for risk assessment 2017A fuzzy Bayesian network approach for risk analysis in process industries

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