# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 5 Summary and Future Outlook  
Current research on supply chain resilience often overlooks its dynamic evolution over time, focusing mainly on static analyses. Future studies should develop dynamic models to capture resilience fluctuations throughout the supply chain's life cycle. Additionally, resilience assessments should include broader factors like policies, market conditions, and technological advancements. There is also a need for cross-industry applications that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Metric** & **Definition** \\ \hline \multirow{3}{*}{Centrality} & Closeness Centrality & Measures how close a given node is to all other nodes in the network. \\  & Degree Centrality & A metric of the positional significance of a given node based on its degree. \\  & Betweenness Centrality & How often does a node act as a bridge on the shortest paths between other nodes. \\ \hline \multirow{2}{*}{Clustering} & \multirow{2}{*}{Clustering Coefficient} & A measure of the tendency of nodes to form tightly knit groups, calculated as the \\  & & ratio of the number of closed triplets (triangles) to the total triplets. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._explore resilience in specific scenarios, such as political conflicts or natural disasters, in order to enhance coping strategies.  
We propose an integrated approach that combines complex network theory with agent-based modeling, offering a more comprehensive and dynamic framework for supply chain network modeling. Through this integrated approach, the model is capable of analyzing the supply chain's structure, information flow, and node connectivity at a macro level while also simulating the autonomous behavior and decision-making processes of individual business entities at a micro level. This combination allows for a more accurate representation of the interactions and coordination during task execution within the supply chain, especially in uncertain and complex environments. Specifically, the agent-based modeling approach helps reveal the behavioral characteristics and decision logic of individual nodes in the supply chain. In contrast, complex network modeling effectively describes the efficiency, paths, and topology of information transmission. By combining these two methods, researchers can more precisely simulate task allocation, resource configuration, and information-sharing processes within the supply chain, thereby improving the supply chain's emergency response capability, resource optimization, and overall resilience. This innovative modeling approach (Figure 13) provides a new perspective for enhancing the task completion capability of supply chain networks.  
In addition, by systematically comparing and distinguishing concepts such as resilience, flexibility, robustness, vulnerability, and reliability, the intrinsic relationships and interactions between them in response to external shocks and internal failures are revealed, providing a new perspective for understanding the adaptability and recovery capabilities of complex systems.  
The multiplier effect of supply chain resilience is a key takeaway from this research. Strengthening resilience within supply chains generates far-reaching benefits that extend beyond the immediate operational stability of firms. These effects cascade across industries, regions, and economies, creating a ripple effect that drives broader economic growth, mitigates risks, and fosters national and global stability. By enhancing supply chain resilience, this research illustrates how firms, industries, and even nations can experience compounded positive outcomes, reinforcing the interconnectedness of global economies.