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Cited by 1,336

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Robust strategies for mitigating supply chain disruptions

Open access
Tang, Christopher S.·2006·International Journal of Logistics Research and Applications

Abstract

When major disruptions occur, many supply chains tend to break down and take a long time to recover. However, not only can some supply chains continue to function smoothly, they also continue to satisfy their customers before and after a major disruption. Some key differentiators of these supply chains are cost-effective and time-efficient strategies. In this paper, certain “robust” strategies are presented that possess two properties. First, these strategies will enable a supply chain to manage the inherent fluctuations efficiently regardless of the occurrence of major disruptions. Second, these strategies will make a supply chain become more resilient in the face of major disruptions. While there are costs for implementing these strategies, they provide additional selling points for acquiring and retaining apprehensive customers before and after a major disruption.

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Metadata

Authors
Tang, C. S.
Venue
International Journal of Logistics Research and Applications
Published
2006-03
Volume
9
Issue
1
Pages
33-45
ISSN
  • 1367-5567 (ISSN-L)
  • 1469-848X
OpenAlex
W2031384163
Cited by
1,336
References
1
FWCI
53.56
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Robust strategies for mitigating supply chain disruptions. https://app.cochat.ai/papers/10.1080/13675560500405584
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Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675560500405584

Data sources

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.