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Cited by 168

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An insurance risk management framework for disaster relief and supply chain disruption inventory planning

Lodree Jr, E J, Taskin, S·2008·Journal of the Operational Research Society

Abstract

Government agencies, not-for-profit organizations, and private corporations often assume leading roles in the delivery of supplies, equipment, and manpower to support initial response operations after a disaster strikes. These organizations are faced with challenging logistics decisions to ensure that the right supplies (including equipment and personnel) are in the right places, at the right times, and in the right quantities. Such logistics planning decisions are further complicated by the uncertainties associated with predicting whether or not a potential threat will materialize into an emergency situation. This paper introduces newsvendor variants that account for demand uncertainty as well as the uncertainty surrounding the occurrence of an extreme event. The optimal inventory level is determined and compared to the classic newsvendor solution and the difference is interpreted as the insurance premium associated with proactive disaster-relief planning. The insurance policy framework represents a practical approach for decision makers to quantify the risks and benefits associated with stocking decisions related to preparing for disaster relief efforts or supply chain disruptions.

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Metadata

Authors
Lodree Jr, E. J., & Taskin, S.
Venue
Journal of the Operational Research Society
Published
2008-05
Volume
59
Issue
5
Pages
674-684
ISSN
  • 0160-5682 (ISSN-L)
  • 1476-9360
OpenAlex
W1995695340
Cited by
168
References
0
FWCI
10.46
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An insurance risk management framework for disaster relief and supply chain disruption inventory planning. https://app.cochat.ai/papers/10.1057/palgrave.jors.2602377
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Lodree Jr, E. J., & Taskin, S. (2008). An insurance risk management framework for disaster relief and supply chain disruption inventory planning. Journal of the Operational Research Society. https://doi.org/10.1057/palgrave.jors.2602377

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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.