Abstract
The paper presents the model resilience measurement based on the complex network theory and analyzes the resilience of project portfolio network considering risk propagation. The model can be used to evaluate the resilience and improve its success probability of projects in the portfolio network. Firstly, the research analyzes the dynamic changes of the portfolio network derived from the construction of the project portfolio matrix, as well as the main factors to be taken into consideration in measuring the resilience of the project portfolio. Further, the research measures the resilience of the project portfolio network according to the node attributes (project) and the relationship attributes (the relationship that one project will impact another in the portfolio network) respectively. Then, to integrate the resilience of the projects and influence relationship between projects, the research proposes the dynamic PageRank algorithm to analyze the resilience of the project portfolio based on the analysis of traditional PageRank algorithm. In addition, resilience is not only affected by the projects and its influence relationship between them, but is also affected by risk propagation. Therefore, the research presents a model for analyzing the portfolio network resilience considering multiple risk propagation. Finally, a research and development project portfolio are taken as an example to demonstrate the effectiveness of the method presented in this research. Our approach can be used by managers to identify the scores of project resilience capacity in portfolio network. Our method explicitly allows to uncover the most resilient projects considering the resilience of project (node) and their influence relationship (network structure), and the risk propagation. Utilizing the outcomes of this research can enhance the capacity of the whole project portfolio to manage risks and improve the success probability of the whole project portfolio by enhancing network resilience.
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References
Agryzkov, T., Oliver, J. L., Tortosa, L., & Vicent, J. F. (2012). An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector. Applied Mathematics and Computation, 219(4), 2186–2193.
Atsiz, E., Balcik, B., Gunnec, D., et al. (2021). A coordinated repair routing problem for post-disaster recovery of interdependent infrastructure networks. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03909-w
Bilgin, G., Eken, G., Ozyurt, B., Dikmen, I., Birgonul, M. T., & Ozorhon, B. (2017). Handling project dependencies in portfolio management. Procedia Computer Science, 121, 356–363.
Bozorgi-Amiri, A., Jalilibal, Z., & Hahi Yakhchali, S. (2020). Balancing construction projects by considering resilience factors in crisis. Journal of Industrial and Systems Engineering, 12(Special issue on Project Management and Control), 100–109.
Dolgui, A., & Ivanov, D. (2021). Ripple effect and supply chain disruption management: new trends and research directions. International Journal of Production Research, 59(1), 102–109.
Field, R. D., & Parrott, L. (2017). Multi-ecosystem services networks: a new perspective for assessing landscape connectivity and resilience. Ecological Complexity, 32, 31–41.
Ghasemi, F., Sari, M. H. M., Yousefi, V., et al. (2018). Project portfolio risk identification and analysis, considering project risk interactions and using Bayesian networks. Sustainability, 10(5), 1609.
Gondia, A., Ezzeldin, M., & El-Dakhakhni, W. (2022). Dynamic networks for resilience-driven management of infrastructure projects. Automation in Construction, 136, 104149.
Goodarzian, F., Ghasemi, P., Gunasekaren, A., et al. (2021). A sustainable-resilience healthcare network for handling COVID-19 pandemic. Annals of operations research. https://doi.org/10.1007/s10479-021-04238-2
Hosseini, S., & Ivanov, D. (2019). A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03350-8
Kaiser-Bunbury, C. N., Mougal, J., Whittington, A. E., et al. (2017). Ecosystem restoration strengthens pollination network resilience and function. Nature, 542(7640), 223–227.
Karlsen, J. T., & Berg, M. E. (2020). A study of the influence of project managers’ signature strengths on project team resilience. Team Performance Management: An International Journal, 26(3), 247–262.
Koh, E. C. Y., Caldwell, N. H. M., & Clarkson, P. J. (2012). A method to assess the effects of engineering change propagation. Research in Engineering Design, 23(4), 329–351.
Li, R., Yang, N., Zhang, Y., Liu, H., & Zhang, M. (2020). Impacts of module-module aligned patterns on risk cascading propagation in complex product development (CPD) interdependent networks. Physica A: Statistical Mechanics and its Applications, 564, 125531.
Li, Y., & Zobel, C. W. (2020). Exploring supply chain network resilience in the presence of the ripple effect. International Journal of Production Economics, 228, 107693.
Li, Y., Zobel, C. W., Seref, O., et al. (2020). Network characteristics and supply chain resilience under conditions of risk propagation. International Journal of Production Economics, 223, 107529.
Mahmoudi, A., Abbasi, M., & Deng, X. (2022). A novel project portfolio selection framework towards organizational resilience: robust ordinal priority approach. Expert Systems with Applications, 188, 116067.
Nabati, M., & Ashrafi, M. (2021). Modeling projects interdependencies to measure their synergic impacts on a project portfolio. Journal of Project Management, 6(3), 143–156.
Naderpajouh, N., Matinheikki, J., Keeys, L. A., et al. (2020). Resilience and projects: an interdisciplinary crossroad. Project Leadership and Society, 1, 100001.
Neumeier, A., Radszuwill, S., & Garizy, T. Z. (2018). Modeling project criticality in IT project portfolios. International Journal of Project Management, 36(6), 833–844.
Ojha, R., Ghadge, A., Tiwari, M. K., et al. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research, 56(17), 5795–5819.
Ouyang, M., Dueñas-Osorio, L., & Min, X. (2012). A three-stage resilience analysis framework for urban infrastructure systems. Structural Safety, 36, 23–31.
Pavez, I., Gómez, H., Laulié, L., et al. (2021). Project team resilience: the effect of group potency and interpersonal trust. International Journal of Project Management, 39(6), 697–708.
Pavlov, A., Ivanov, D., Pavlov, D., et al. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03182-6
Plaza, M., & Rohlf, K. (2008). Learning and performance in ERP implementation projects: a learning-curve model for analyzing and managing consulting costs. International Journal of Production Economics, 115(1), 72–85.
Project management Institute. (2017). A Guide to the Project Management Body of Knowledge (PMBOK® Guide)—Sixth Edition[M]. Newtown Square (PA): Project Management Institute.
Rahi, K. (2019). Project resilience: a conceptual framework. International Journal of Information Systems and Project Management, 7(1), 69–83.
Tian, Y., Shi, Y., Shi, X., et al. (2021). Research on supply chain network resilience considering the exit and reselection of enterprises. IEEE Access, 9, 91265–91281.
Yang, Q., Zou, X., Ye, Y., et al. (2022). Evaluating the criticality of the product development project portfolio network from the perspective of risk propagation. Physica A: Statistical Mechanics and its Applications, 593, 126901.
Zou, X., & Yang, Q. (2019). R&D Project Portfolio Selection based on Domination and Diffusion Relationship in the Project Network [J]. Chinese Management Science (in Chinese), 27(4), 198–209.
Acknowledgements
This study was supported by the Yunnan Provincial Department of Education Science Research Fund Project (2022J0085) and the National Natural Science Foundation of China (No.72271022, No.71872011 and No.71929101).
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Zou, X., Yang, Q., Wang, Q. et al. Measuring the system resilience of project portfolio network considering risk propagation. Ann Oper Res (2022). https://doi.org/10.1007/s10479-022-05100-9
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DOI: https://doi.org/10.1007/s10479-022-05100-9