The resilience of European rail freight corridors is increasingly challenged by disruptions affecting the continuity of critical goods flows, including raw materials, semiconductors, energy equipment, and essential medical and chemical products. Although digital platforms and real-time monitoring systems have improved corridor observability, disruption management remains limited by the insufficient integration of operational data into formal decision processes. This paper argues that corridor performance monitoring should be conceived as a decision infrastructure rather than as a stand-alone technological layer. Adopting an industrial engineering and socio-technical systems perspective, we propose an AI-enabled decision-architecture framework linking operational events, corridor-level performance indicators, predictive analytics, and governance mechanisms. The framework formalizes decision rights, escalation thresholds, and accountability requirements, enabling the transformation of analytical outputs into decision-ready information for coordinated interventions under disruption. Particular attention is devoted to critical freight scenarios involving reserve-capacity activation and exceptional priority regimes, where legitimacy, traceability, and consistency are essential. Artificial intelligence is positioned as a human-in-the-loop decision-support component that enhances anticipation and transparency while preserving governance authority. The architecture supports the integration of dual-use innovations within the rail value chain and contributes to capacity optimization through structured monitoring and predictive disruption management. The contribution lies in reframing corridor resilience as a property of decision design and governance. Implementation implications and research directions are outlined with reference to strengthening rail freight corridors as a backbone of European industrial continuity and strategic mobility.Keywords — Decision architecture, Corridor governance, Critical freight, Rail freight resilience, Industrial resilience, Performance monitoring, Predictive disruption management, Artificial intelligence as decision support, Military Mobility
Maciariello et al. (Thu,) studied this question.