Resilience is a critical attribute of modern manufacturing systems, enabling them to maintain operational continuity and adapt effectively to unforeseen disruptions in today’s competitive industrial landscape. Resilience refers to a system’s ability to withstand disruptions, adjust to shocks, and quickly resume regular operations with minimum impact on output. This study focuses on manufacturing system resilience through the integration of dynamic scheduling within a data-driven modeling framework. Leveraging Simio simulation software, production output and risk in a small manufacturing cell are analyzed to evaluate system performance and resilience against due dates under three scenarios. Those scenarios are: nominal production, disrupted production due to resource failure, and recovered production after the failure. For the system recovery we use dynamic resource re-assignment and scheduling. The simulation results are analyzed to provide critical insights into system performance over time, emphasizing the effectiveness of dynamic scheduling in mitigating risks and maintaining operational efficiency during adverse events. This research demonstrates the necessity of adopting proactive strategies to enhance the resilience of manufacturing systems under uncertainties.