Manufacturing companies operating in global production networks face increasing susceptibilities to disruptions that may have far-reaching consequences for the entire network. To cope with disruptions and to maintain the network\u27s performance even if disruptions occur, companies are in need of a holistic, systematic disruption management, which includes all network actors in the identification of advantageous reaction measures and thus ensures the network\u27s robustness against disruptions. However, current implementations of operational disruption management are mostly exclusively based on experience or intuition and are limited to individual, production or logistics-related partners or areas, hence not forcing a holistically advantageous reaction.
Therefore, the objective of the present thesis lies in the development of a methodology for increasing robustness in production networks by means of an integrated disruption management, taking both production and logistics perspectives into account. Based on the analysis and modelling of significant, production- and logistics-related disruptions, a simulation-based approach is used to identify (combinations of) countermeasures that are suitable both for the elimination of disruptions as well as the minimization of their consequences. The simulation thereby combines design of experiments with methods of metamodeling in order to obtain comprehensive statements about the interactions between disruptions, countermeasures and system performance and thus about the suitability of certain measures.
Based on the knowledge about the suitability of certain measures, proactive strategies are derived, which promote the implementation of advantageous measures from a planning point of view by appropriately adjusting the respective capacities in the production network. This combined approach, which optimally coordinates the planning and control components of disruption management, allows to increase robustness in production networks.
Within the scope of the research project FlexPLN, the developed methodology has been discussed and applied to a use case from the aviation industry. The results thereby do not only unveil that a joint consideration of production and logistics measures provides a promising means for a comprehensive understanding of disruptions and their consequences for production networks, but also indicate that a metamodeling-based approach might be meaningful to predict suitable countermeasures for the reaction to disruptions
