Systematic node fortification for enhanced supply network resilience: a real-world network approach

Purpose This study questions the need for more visibility to improve supply chain network resilience (SCNR). It investigates how disruptions propagate through real-world supply chain networks and evaluates the effectiveness of different strategies for fortifying key nodes against such disruptions. The aim is to identify practical, data-driven methods that enhance SCNR by prioritising critical nodes for protection using social network analysis (SNA) metrics. Design/methodology/approach Agent-based modelling combined with the susceptible–infected–recovered (SIR) model from epidemiology literature is applied to simulate disruption propagation in ten real-world supply chain networks. Fortification strategies are based on five SNA metrics and evaluated against random node selection. Fortification is implemented by increasing a node's resistance to disruption and accelerating its recovery, an abstract representation of real-world resilience measures such as redundancy, information sharing or collaborative strategies. Each scenario is tested under single-node and multi-node disruption conditions, with 100 repetitions per configuration to ensure robustness. Findings Targeted node fortification based on SNA metrics significantly outperforms random fortification in reducing performance loss. While page rank yields best resilience benefits on average, simpler metrics like node degree deliver nearly equivalent improvements, demonstrating that effective resilience strategies can be implemented without requiring full network visibility. Originality/value This research closes a relevant gap in SCNR literature by validating fortification strategies on realistic, large-scale supply chain networks, moving beyond idealised or synthetic structures. Findings provide actionable, scalable guidance for supply chain practitioners, demonstrating that even basic network metrics enable meaningful resilience improvements in complex supply chains.