A Survey on the Resilience of Supply Chain Networks

Globalization and technological advancements have evolved supply chains from a linear connection of components into intricate, interconnected networks, rendering them vulnerable to disruptions from events like natural disasters, pandemics as well as targeted Cyber attacks. This systematic review investigates resilience of supply chain networks from the perspective of network topology characteristics, modeling them as complex adaptive systems (CAS). The study seeks to address the following research questions; (1) how topological characteristics influence resilience to cascading failures and shocks, (2) what methodologies are used to model resilience of supply chain networks as a property of complex adaptive system and (3) how do multi-stage or multilayer graph-based approaches contribute to enhancing supply chain network resilience models. Findings revealed that supply chain networks were implemented with topologies such as scale-free, random topolgy, small-world, multilayer or multiplex topology as well as specialized structures like multipolar or community, non-planar, core periphery and block diagonal. Structural metrics, such as connectivity, centrality, and clustering, underscore vulnerabilities, while redundancy and alternative paths mitigate cascades. CAS modeling utilized agent-based simulations, Nodes-Edges (NK) frameworks, and evolutionary models to capture adaptive behaviors such as rewiring and trust-building, transforming static robustness into dynamic recovery. Multilayer techniques considered interdependencies across layers, enabling precise risk tracing and optimization, albeit often limited by static assumptions.