The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Bayesian network modelling for supply chain risk propagation
Open accessAbstract
Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.
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Cites
Performance measurement system design: A literature review and research agenda
Neely, A., Gregory, M., & Platts, K. (1995) · International Journal of Operations & Production Management · doi:10.1108/01443579510083622 · Cited by 3,259
A review of definitions and measures of system resilience
Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2015.08.006 · Cited by 1,892
Cited by
Reconfigurable supply chain: the X-network
Dolgui, A., Ivanov, D., & Sokolov, B. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1774679 · Cited by 501
Supply chain resilience: new challenges and opportunities
Shi, X., Liu, W., & Lim, M. K. (2024) · International Journal of Logistics Research and Applications · doi:10.1080/13675567.2023.2262396 · Cited by 39
Metadata
- Authors
- Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2018-05-04
- Volume
- 56
- Issue
- 17
- Pages
- 5795-5819
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2802200907
- Cited by
- 313
- References
- 2
- FWCI
- 46.93
- Type
- journal article
- Language
- en
- Open access
- green
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- Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1467059
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
