Despite the growing conceptual emphasis on Intertwined Supply Networks (ISNs), there is still a lack of simulation-based studies that quantitatively assess their operational adaptability under disruptions and uncertainty. This study develops a discrete-event dynamic simulation model using Python for an intertwined pharmaceutical-food supply network, to evaluate the effectiveness of adaptive sourcing and dynamic re-routing under three scenarios: baseline, supplier disruption, and transportation disruption. The model tracks operational Key Performance Indicators (KPIs) such as backlog, service level, and transportation cost. Comparative analysis of all three scenarios show that adaptive mechanisms reduce backlog by 61% and transportation costs by 14.2%, demonstrating substantial improvements in resilience and efficiency. The approach offers valuable insights for designing more resilient and adaptable supply networks under uncertainty.