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Designing robust automotive supply chain networks under disruption risk: A sensitivity-based optimization approach
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School of Business |
Master's thesis
Authors
Neuhierl, Lia
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Mcode
Language
en
Pages
108
Series
Abstract
Global supply chain networks are increasingly subject to disruptions that can severely impact operations. The causes are various and can be both man-made and natural. The automotive industry, in particular, which is characterized by extensive global networks, is reaching its limits when it comes to managing disruptions. Examples of such disruptions that have also greatly affected the automotive industry include earthquakes in Japan or the blockage of the Suez Canal. The challenge in dealing with such disruptions is their unpredictability. This circumstance means that established supply chain network optimization methods often only allow for a reaction to disruptions that have already occurred.
This thesis shifts the perspective by conducting a sensitivity analysis for an optimization model tailored to the automotive industry. The aim is to gain insights into overall cost development and network performance even before a real disruption occurs. Specifically, this involves developing a multi-period mixed-integer linear programming model that is applied to a four-tier automotive supply chain network. Various artificial disruption scenarios are analyzed for the sensitivity analysis. The results reveal that the automotive network holds particular potential in inventory management policy to mitigate disruption risks. Furthermore, flexible capacities that can be activated on an ad hoc basis offer a way to compensate for otherwise lost capacities. Lastly, the results support the development of contingency plans that take effect in the event of a disruption. This helps affected companies remain capable of acting when a disruption occurs and continue operations without interruption. Overall, the type of analysis presented in this thesis helps practitioners create greater transparency and use the insights gained to prepare for disruptions in their supply chain networks accordingly.