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Evaluating the criticality of the product development project portfolio network from the perspective of risk propagation

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  • Yang, Qing
  • Zou, Xingqi
  • Ye, Yunting
  • Yao, Tao

Abstract

There are numerous projects with many interdependencies in the new product development (PD) portfolio, which can be viewed as a complex network. Due to the dependency among projects, the risks in one project may transmit to others, which will ultimately affect the success of the entire project portfolio. Thus, evaluating the priority and criticality of projects under the influence of risk propagation is a fundamental challenge for the PD project portfolio. This paper presents a risk propagation model using the Bayesian network and brittle risks. The criticality analysis presented is based on both the local and the global effects of risk in the portfolio. Firstly, we build a Bayesian network model of the portfolio based on the analysis of relationships between projects. Further, we measure the criticality of the project based on the local impact of risk propagation using Bayesian and complex networks. Then, we measure the criticality of the project based on the global impact of risk propagation using brittleness. Finally, we synthesize the calculation results of criticality to measure the integrated criticality of the portfolio, and examine an exemplar industrial portfolio to verify the effectiveness of the proposed model and method.

Suggested Citation

  • Yang, Qing & Zou, Xingqi & Ye, Yunting & Yao, Tao, 2022. "Evaluating the criticality of the product development project portfolio network from the perspective of risk propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    • Handle: RePEc:eee:phsmap:v:593:y:2022:i:c:s0378437122000309
    DOI: 10.1016/j.physa.2022.126901
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    1. Foroogh Ghasemi & Mohammad Hossein Mahmoudi Sari & Vahidreza Yousefi & Reza Falsafi & Jolanta Tamošaitienė, 2018. "Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    2. Kwag, Shinyoung & Gupta, Abhinav & Dinh, Nam, 2018. "Probabilistic risk assessment based model validation method using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 380-393.
    3. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    4. Li, Ruimeng & Yang, Naiding & Zhang, Yanlu & Liu, Hui & Zhang, Mingzhen, 2021. "Impacts of module–module aligned patterns on risk cascading propagation in complex product development (CPD) interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    5. Guo, Jingni & Xu, Junxiang & He, Zhenggang & Liao, Wei, 2021. "Research on risk propagation method of multimodal transport network under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    6. Fu, Guiyuan & Zhang, Weidong & Li, Zhijun, 2015. "Opinion dynamics of modified Hegselmann–Krause model in a group-based population with heterogeneous bounded confidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 558-565.
    7. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Gao, Xiangyun, 2016. "Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 331-344.
    8. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    9. Tseng, Chiu-Che & Gmytrasiewicz, Piotr J., 2006. "Real-time decision support and information gathering system for financial domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 417-436.
    10. Liu, Hui & Yang, Naiding & Yang, Zhao & Lin, Jianhong & Zhang, Yanlu, 2020. "The impact of firm heterogeneity and awareness in modeling risk propagation on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    11. Chen, Wei & Qu, Shuai & Jiang, Manrui & Jiang, Cheng, 2021. "The construction of multilayer stock network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    12. Opper, Manfred & Winther, Ole, 2001. "Probabilistic data modelling with adaptive TAP mean-field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 302(1), pages 119-125.
    13. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    14. Huo, Liang’an & Guo, Hongyuan & Cheng, Yingying & Xie, Xiaoxiao, 2020. "A new model for supply chain risk propagation considering herd mentality and risk preference under warning information on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    15. Ying Xiang & Rong Mo & Hu Qiao, 2018. "An Evaluation Method for Brittle Source of the Key Procedure in Complex Parts’ Manufacturing," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, January.
    16. Yan, Ying & Zhang, Shen & Tang, Jinjun & Wang, Xiaofei, 2017. "Understanding characteristics in multivariate traffic flow time series from complex network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 149-160.
    17. Wang, Jingbei & Yang, Naiding & Zhang, Yanlu & Song, Yue, 2018. "Development of the mitigation strategy against the schedule risks of the R&D project through controlling the cascading failure of the R&D network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 390-401.
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    1. Camilo Micán & Gabriela Fernandes & Madalena Araújo, 2022. "Disclosing the Tacit Links between Risk and Success in Organizational Development Project Portfolios," Sustainability, MDPI, vol. 14(9), pages 1-19, April.

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