Recent large-scale disruptions to global supply chains - such as the COVID-19 pandemic, the blockage of the Suez Canal, and economic sanctions against Russia - have severely impacted production, causing delays, shortages, and substantial financial losses. These disruptions often originate from specific events but propagate across entire supply networks, amplifying their consequences. This paper identifies gaps in existing literature and highlights structural deficiencies in current resilience approaches for supply chains. It emphasizes the need for precise, quantitative metrics to define resilience and assess disruption severity. To address these challenges, a stochastic model for aggregate production planning is introduced, designed to mitigate large-scale disruptions. The model is then tested through a case study involving a real-world supply chain exposed to high disruption risks derived from a historical data set, providing an assessment of its effectiveness.