# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 2 Overview of Supply Chain Resilience Development
### Current State of Research on Supply Chain Resilience
Early studies primarily focused on models and methods using supply chain resilience as a context, such as system dynamics and directed graph matrix methods [30; 31], as well as conceptual explorations related to supply chain resilience [32]. Subsequently, studies addressed organizational resilience at the individual enterprise level within supply chains [33]. However, these efforts did not coalesce into well-defined and focused research directions. The sudden outbreak of the COVID-19 pandemic, which caused widespread disruptions to global supply chains, has highlighted that supply chain resilience, once considered a concern at the enterprise level, has now escalated to an industry-wide, regional, and even national strategic issue. Supply chain resilience is closely tied to national security and economic development [34]. At the same time, the increasing complexity of supply chains and the rise of artificial intelligence (AI) have introduced new pathways for enhancing supply chain resilience. [PERSON] et al. noted that AI can help supply chains respond to unexpected events by enhancing transparency, providing personalized services, reducing the impact of disruptions, and promoting flexible procurement strategies. Furthermore, AI can identify risks, analyze potential issues, reconfigure networks, and activate response measures to maintain supply chain resilience under extreme disruptions [35; 36]. [PERSON] et al., through empirical research methods, concluded that the integration of AI technologies not only enhances short-term supply chain performance but also strengthens long-term resilience, thereby ensuring stable operations in uncertain environments [37]. Integrating AI to strengthen supply chain resilience has thus become a critical direction for future research and practice. The ripple effect describes a phenomenon in which throwing stones into a calm lake creates waves that gradually spread to distant areas [38]. In risk management, the ripple effect refers to the negative impact that a risk event can have on its surroundings, akin to the ripples caused by a stone thrown into the water. If left uncontrolled, the ripple effect of an initial risk can often trigger more significant crises [39]. Although the concept of the ripple effect was introduced long ago, it is evident from Figure 3 that research related to this effect in the context of supply chain resilience surged following the outbreak of the COVID-19 pandemic. The pandemic's impact on global supply chains exposed numerous vulnerabilities in modern supply chains, making the ripple effect--referring to the propagation and impact of risks within the supply chain--one of the core issues in current supply chain resilience research [40]. In recent years, supply chain risk management has garnered significant attention in academic research, with methodologies spanning mathematical optimization, simulation, and game theory. When studying risk propagation within supply chains, [PERSON] et al. identified network characteristics as a critical factor influencing supply chain resilience. They argued that specific combinations of network features could enhance the overall risk-resistance capacity of the network [41]. [PERSON] et al. found that the topological structure of supply chains directly affects the speed and extent of risk propagation under different types of disruptions. Adjusting the network structure can effectively mitigate the impact of the ripple effect [3]. By strategically selecting network structures and characteristics, it is possible to impede risk propagation within the supply chain network, thereby improving resilience. Furthermore, existing assessment frameworks of supply chain resilience often overlook the ripple effect, leading to biased evaluations. To address this, [PERSON] et al. introduced resilience-related indicators, such as absorptive capacity, adaptive capacity, and recovery capacity, to comprehensively assess the supply chain's ability to respond to risk propagation [42].