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A Simulation-based Analysis of Supply Chain Resilience

: Güller, Mustafa; Koc, Emre; Henke, Michael; Noche, Bernd; Hingst, Lennart

Kersten, W. (Hrsg.):
Innovations and strategies for logistics and supply chains : technologies, business models and risk management : Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg, 24.-25.09.2015
Berlin: epubli, 2015
ISBN: 3-7375-4057-8 (print)
ISBN: 978-3-7375-4057-5 (print)
ISBN: 978-3-7375-4059-9 (ePDF)
Hamburg International Conference of Logistics (HICL) <10, 2015, Hamburg>
Fraunhofer IML ()
Supply Chain Resiliencey; Simulation; Flexibility; Supply Contract

The increased interest in supply chain risk management (SCRM) is not only a consequence of recent natural disasters, but moreover the recognition that even samll incidents can have a severe impact on the entire supply chain (SC). Instead of making high investments in eliminating every potential risk, it is much more appreciated to incorporate the concept of resilience in supply chain design and operations that provides the ability to reduce the consequences of disruptions and to reduce the time to recover normal performance. However, as resilience significantly increases the ability to adapt quickly and efficiently to changes in the environment, it comes along with an increase in costs in most cases. Moreover, achieving resilience in supply chains and agile response requires a holistic approach, wich contributes to the complexity of decision making processes in supply chains. Since most of the researches has been discussed the resilience of supply chains from a qualitative point of view in the literature, there is a lack of research concerning the resilience from a quantitative perspective. In this context, the main purpose of this paper is to provide a simulation-based decision support framework for accessing supply chain resilience and evaluating the cost and resilience trade-off with different mitigation strategies in an uncertain environment. The decision framework incorporates the supply chain resilience metrics and argues their relationship to the impacts of those disruptions on the performance and to the time required for recovery.