TY - GEN A1 - Herrmann, Frank A1 - Schoen, Maximilian A1 - Claus, Thorsten A1 - Dai, Chenghao T1 - Aggregate Production Planning under Risk of Disruption T2 - OR 2024, International Conference on Operations Research 2024, September 3-6, 2024, Munich N2 - In recent years, large scale disruptions to global supply chains, like the Covid pandemic, a ship blocking the Suez canal or sanctions against Russia, have caused production to slow down or even to stand still, causing shortages and massive losses for affected businesses. Even if only specific companies were originally affected, shortages and delays rippled along the supply network. The established approach to deal with disruptions is to utilize safety stock and capacity to compensate for fluctuations in uncertain quantities like customer demand. This approach is tried and tested for small fluctuations. To address larger disruptions, like the above given, very high safety stock and capacity would be needed, which would lead to unnecessarily high costs. Resilience has often been viewed as an expensive capability that drives costs. Recent studies however advocate for the development of lean resilience concepts, creating new capabilities, which enable resilience and can deal with large fluctuations, reimagining resilience from the perspectives of efficiency and value creation. This contribution identifies gaps in current research and establishes structural deficits of approaches discussed in the literature regarding supply chains. Firstly, the need for rigorous, quantitative definitions of resilience and relevant disruptions is justified. Then, a stochastic model for aggregate production planning that includes capabilities to compensate for such large fluctuations along several dimensions is proposed. Lastly, this model is then applied to a case study pertaining to a realistic supply chain under the risk of large scale disruptions and the results of this approach are evaluated. KW - Supply Chain Resiliance KW - Aggregate Production Planning KW - Supply Chain Disruption Y1 - 2024 ER -