A multi-layer digital twin framework for enhanced production resilience in railcar manufacturing

As manufacturing transitions towards Industry 5.0, resilient and human-centred production systems are increasingly important in complex sectors such as railcar manufacturing. This paper proposes a multi-layer Digital Twin (DT) framework integrating physical assets, information flows, and virtual models. The framework was tested through an AnyLogic-based robotic welding and assembly case study. Verification and internal validation confirmed that the model logic operated as intended. The DT-supported response improved throughput and robot utilisation while reducing lead time and work-in-process compared with the unmanaged disruption scenario. The study also provides a nine-step implementation pathway for supporting production resilience and digital transformation in the rail industry.