# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
## 7 Conclusions  
In this study, an SCN model with several real-life SCN characteristics was constructed. And the parameters of multiple network characteristics of the model can be adjusted. We combined it with the SIS model to construct an SCN risk propagation model. We proposed using the average of the maximum full-type subgraph sizes over a period of time after the SCN risk propagation reached a steady state as a new SCNR metric. We studied the effects of \(q,e\), and \(S/R/M/D\) on SCNR when risk propagation reaches a steady state. The simulation results mainly indicated that the higher the value of \(e\), the larger the value of \(Y\); the larger the value of \(q\), the smaller the value of \(Y\); and the more uniform the distribution of \(S/R/M/D\), the larger the value of \(Y\). In addition, the two methods of intentional infection and random infection for the initial risky node types have no effect on SCNR.  
When risk propagation occurs, managers can adjust the structure of the SCN to improve SCNR. Nodal companies with large degrees represent a low percentage of the SCN, and managers should focus on managing and protecting these nodes so that the impact of risk can be reduced. Managers can enhance inter-company communication and cooperation, as appropriate, to weather crises by increasing the transparency of an SCN. In addition, managers could reduce business transactions and avoid business expansion; in particular, they should reduce transactions with businesses of large size to reduce the impact caused by risks. Different types of node enterprises have different functions in an SCN, but all of them play equally important functions. Managers should choose a variety of different types of companies to work with in order to avoid the lack of a certain type of company causing the SCN not to function properly.  
In our model, we assume that all nodes have the same probability of being infected and self-healing, but this is not always the case. The individual differences of enterprises in actual SCNs are very obvious, and the probability of being affected by risks may be different. In addition, we found that network characteristics have a significant impact on SCNR, but we only considered three network characteristics here. We believe that studying more factors affecting SCNR from the perspective of network characteristics is a challenging and meaningful direction for future research.  
Figure 17: The resilience of a real-life SCN at each time step when using the resilience metrics \(s\) and \(y\).  
**Author Contributions:** Conceptualization, S.X. and X.S.; methodology, S.X. and M.L.; software, J.L. and M.L.; validation, S.X. and J.Y.; writing--original draft preparation, S.X.; writing--review and editing, S.X. and X.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially supported by the Natural Science Foundation of Sichuan Province (Grant No. 2023 NSFS0507), the Program for the HUST State Key Laboratory of Intelligent Manufacturing Equipment and Technology (Grant No. IMETKF2023026), and the Key Laboratory of Icing and Anti/De-icing of CARDC (Grant No. IADL 20220405).  
**Insituual Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.