Project portfolio resource risk assessment considering project interdependency by the fuzzy Bayesian network

Bai, L; Zhang, K; Shi, H; An, M; Han, X

Abstract

Resource risk caused by specific resource sharing or competition among projects due to resource constraints is a major issue in project portfolio management, which challenges the application of risk analysis methods effectively. This paper presents a methodology by using a fuzzy Bayesian network to assess the project portfolio resource risk, determine critical resource risk factors, and propose risk-reduction strategies. In this method, the project portfolio resource risk factors are first identified by taking project interdependency into consideration, and then the Bayesian network model is developed to analyze the risk level of the identified risk factors in which expert judgments and fuzzy set theory are integrated to determine the probabilities of all risk factors to deal with incomplete risk data and information. To reduce the subjectivity of expert judgments, the expert weights are determined by combining experts’ background and reliability degree of expert judgments. A numerical analysis is used to demonstrate the application of the proposed methodology. The results show that project portfolio resource risks can be analyzed effectively and efficiently. Furthermore, “poor communication and cooperation among projects,” “capital difficulty,” and “lack of sharing technology among projects” are considered the leading factors of the project portfolio resource risk. Risk-reduction strategic decisions based on the results of risk assessment can be made, which provide project managers with a useful method or tool to manage project risks.

Citation

Bai, L., Zhang, K., Shi, H., An, M., & Han, X. (2020). Project portfolio resource risk assessment considering project interdependency by the fuzzy Bayesian network. Complexity, 2020, 5410978. https://doi.org/10.1155/2020/5410978

• Journal Article Type: Article
• Acceptance Date: Sep 30, 2020
• Online Publication Date: Nov 5, 2020
• Publication Date: Nov 5, 2020
• Deposit Date: Nov 6, 2020
• Publicly Available Date: Nov 6, 2020
• Journal: Complexity
• Print ISSN: 1076-2787
• Electronic ISSN: 1099-0526
• Publisher: Wiley
• Volume: 2020
• Pages: 5410978
• DOI: https://doi.org/10.1155/2020/5410978
• Publisher URL: https://doi.org/10.1155/2020/5410978
• Related Public URLs: https://www.hindawi.com/journals/complexity/
• Additional Information: Additional Information : ** From Hindawi via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1099-0526; pissn 1076-2787 **Article IDs: publisher-id: 5410978 **History: published_online 05-11-2020; published 05-11-2020; accepted 30-09-2020; rev-recd 16-09-2020; submitted 09-07-2020; Funders : National Natural Science Foundation of China;Ministry of Education Humanities and Social Sciences Fund;Innovation Capacity Support Plan of Shaanxi Province;Major Projects of Shaanxi Social Science Federation;Social Science Planning Fund of Xi’an;Soft Science Foundation of Xi’an;Fundamental Research Funds for the Central Universities;Social Science Planning Fund of Shaanxi Province; Projects : 72002018;300102238620;300102230613; Grant Number: 72002018; Grant Number: 17XJC630001; Grant Number: 2020KJXX-054; Grant Number: 2020Z361; Grant Number: JG207; Grant Number: 2019111813RKX002SF006-5; Grant Number: 300102238620; Grant Number: 300102230613; Grant Number: 2020R028