The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Supply network disruption and resilience: A network structural perspective
Open accessAbstract
Increasingly, scholars recognize the importance of understanding supply network disruptions. However, the literature still lacks a clear conceptualization of a network‐level understanding of supply disruptions. Not having a network level understanding of supply disruptions prevents firms from fully mitigating the negative effects of a supply disruption. Graph theory helps to conceptualize a supply network and differentiate between disruptions at the node/arc level vs. network level. The structure of a supply network consists of a collection of nodes (facilities) and the connecting arcs (transportation). From this perspective, small events that disrupt a node or arc in the network can have major consequences for the network. A failure in a node or arc can potentially stop the flow of material across network. This study conceptualizes supply network disruption and resilience by examining the structural relationships among entities in the network. We compare four fundamental supply network structures to help understand supply network disruption and resilience. The analysis shows that node/arc‐level disruptions do not necessarily lead to network‐level disruptions, and demonstrates the importance of differentiating a node/arc disruption vs. a network disruption. The results also indicate that network structure significantly determines the likelihood of disruption. In general, different structural relationships among network entities have different levels of resilience. More specifically, resilience improves when the structural relationships in a network follow the power‐law. This paper not only offers a new perspective of supply network disruption, but also suggests a useful analytical approach to assessing supply network structures for resilience.
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Cites
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Cited by
Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021) · International Journal of Production Research · doi:10.1080/00207543.2019.1582820 · Cited by 958
Ripple effect in the supply chain: an analysis and recent literature
Dolgui, A., Ivanov, D., & Sokolov, B. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1387680 · Cited by 895
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
Chowdhury, M. T., Sarkar, A., Paul, S. K., & Moktadir, M. A. (2022) · Operations Management Research · doi:10.1007/s12063-020-00166-9 · Cited by 266
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
- Kim, Y., Chen, Y., & Linderman, K.
- Venue
- Journal of Operations Management
- Publisher
- Wiley
- Published
- 2014-10-27
- Volume
- 33-34
- Issue
- 1
- Pages
- 43-59
- ISSN
- 0272-6963 (ISSN-L)
- 1873-1317
- OpenAlex
- W2007834894
- Cited by
- 776
- References
- 7
- FWCI
- 69.05
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.