Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118106
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Type: Journal article
Title: A decision support methodology for a disaster-caused business continuity management
Author: Schätter, F.
Hansen, O.
Wiens, M.
Schultmann, F.
Citation: Decision Support Systems, 2019; 118:10-20
Publisher: Elsevier
Issue Date: 2019
ISSN: 0167-9236
1873-5797
Statement of
Responsibility: 
Frank Schätter, Ole Hansen, Marcus Wiens, Frank Schultmann
Abstract: Supply chain risk management typically deals with the systematic identification, analysis and mitigation of risks which affect the whole supply chain network of a company. Business continuity management (BCM) forms part of supply chain risk management and is an important competitive factor for companies by ensuring the smooth functioning of critical business processes in the case of failures. If business operations are severely disrupted, the companies' decision maker is confronted with a situation which is characterized by a high degree of uncertainty, complexity and time pressure. In such a context, decision support can be of significant value. This article pre- sents a novel decision support methodology which leads to an improved and more robust BCM for severe dis- ruptions caused by disasters. The methodology is part of the Reactive Disaster and supply chain Risk decision Support System (ReDRiSS) to deal with different levels of information availability and to provide decision makers with a robust decision recommendation regarding resource allocation problems. It combines scenario techniques, optimization models and approaches from decision theory to operate in an environment char- acterized by sparse or lacking information and dynamic changes over time. A simulation case study is presented where the methodology is applied within the BCM of a food retail company in Berlin that is affected by a pandemic disaster.
Keywords: Business continuity management; Decision support system; robust decision-making; risk management; disaster managemen
Rights: © 2018 Elsevier B.V. All rights reserved.
RMID: 0030106164
DOI: 10.1016/j.dss.2018.12.006
Appears in Collections:Medicine publications

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