Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
The aim of this study is to investigate the impact of network characteristics on supply chain network resilience (SCNR) when risk propagation occurs in supply chain networks (SCNs). The network characteristics we study here (e.g., clustering coefficient, scalar index, node type, etc.) are exceptiona...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/7/2675 |
| _version_ | 1797212902398099456 |
|---|---|
| author | Shuaihao Xue Jia Li Jiaxin Yu Minghui Li Xiaoqiu Shi |
| author_facet | Shuaihao Xue Jia Li Jiaxin Yu Minghui Li Xiaoqiu Shi |
| author_sort | Shuaihao Xue |
| collection | DOAJ |
| description | The aim of this study is to investigate the impact of network characteristics on supply chain network resilience (SCNR) when risk propagation occurs in supply chain networks (SCNs). The network characteristics we study here (e.g., clustering coefficient, scalar index, node type, etc.) are exceptionally important for real-life SCNs, but are not sufficiently considered in the previous research on SCNR. To this end, we first construct an SCN model with adjustable parameters for multiple network characteristics. Second, this SCN model is combined with a susceptible–infectious–susceptible model to construct an SCN risk propagation model. Third, we propose using the average (i.e., a novel SCNR metric considering node type) of the sizes of the maximum connected subgraphs (which contain all node types) over a period of time after risk propagation reaches a steady state. Fourth, the parameters of the SCN model are adjusted to generate SCNs with different network characteristics, and then the resilience of these SCNs is addressed accordingly. The simulation results mainly show the following: the larger the scalar index of an SCN is, the higher its resilience; the larger the clustering coefficient of an SCN is, the smaller its resilience; and the more uniform the distribution of node types is in an SCN, the higher its resilience. Our research work will help optimize SCNs’ structure, which has important implications for society and practice. |
| first_indexed | 2024-04-24T10:49:46Z |
| format | Article |
| id | doaj.art-452df51083d041cbaa12ace9b177250c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:49:46Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-452df51083d041cbaa12ace9b177250c2024-04-12T13:14:30ZengMDPI AGApplied Sciences2076-34172024-03-01147267510.3390/app14072675Research on Supply Chain Network Resilience: Considering Risk Propagation and Node TypeShuaihao Xue0Jia Li1Jiaxin Yu2Minghui Li3Xiaoqiu Shi4College of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaCollege of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaCollege of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaCollege of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaCollege of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaThe aim of this study is to investigate the impact of network characteristics on supply chain network resilience (SCNR) when risk propagation occurs in supply chain networks (SCNs). The network characteristics we study here (e.g., clustering coefficient, scalar index, node type, etc.) are exceptionally important for real-life SCNs, but are not sufficiently considered in the previous research on SCNR. To this end, we first construct an SCN model with adjustable parameters for multiple network characteristics. Second, this SCN model is combined with a susceptible–infectious–susceptible model to construct an SCN risk propagation model. Third, we propose using the average (i.e., a novel SCNR metric considering node type) of the sizes of the maximum connected subgraphs (which contain all node types) over a period of time after risk propagation reaches a steady state. Fourth, the parameters of the SCN model are adjusted to generate SCNs with different network characteristics, and then the resilience of these SCNs is addressed accordingly. The simulation results mainly show the following: the larger the scalar index of an SCN is, the higher its resilience; the larger the clustering coefficient of an SCN is, the smaller its resilience; and the more uniform the distribution of node types is in an SCN, the higher its resilience. Our research work will help optimize SCNs’ structure, which has important implications for society and practice.https://www.mdpi.com/2076-3417/14/7/2675supply chain network resiliencerisk propagationnetwork characteristicnode type |
| spellingShingle | Shuaihao Xue Jia Li Jiaxin Yu Minghui Li Xiaoqiu Shi Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type Applied Sciences supply chain network resilience risk propagation network characteristic node type |
| title | Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type |
| title_full | Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type |
| title_fullStr | Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type |
| title_full_unstemmed | Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type |
| title_short | Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type |
| title_sort | research on supply chain network resilience considering risk propagation and node type |
| topic | supply chain network resilience risk propagation network characteristic node type |
| url | https://www.mdpi.com/2076-3417/14/7/2675 |
| work_keys_str_mv | AT shuaihaoxue researchonsupplychainnetworkresilienceconsideringriskpropagationandnodetype AT jiali researchonsupplychainnetworkresilienceconsideringriskpropagationandnodetype AT jiaxinyu researchonsupplychainnetworkresilienceconsideringriskpropagationandnodetype AT minghuili researchonsupplychainnetworkresilienceconsideringriskpropagationandnodetype AT xiaoqiushi researchonsupplychainnetworkresilienceconsideringriskpropagationandnodetype |