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The application of complex network theory for resilience improvement of knowledge-intensive supply chains
With frequent political conflicts and public health emergencies, global supply chains are constantly under risk interference, significantly reducing supply chain resilience (SCR), especially for the knowledge-intensive supply chains (KISCs). To assess and improve the resilience of KISC, this paper u...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088763/ http://dx.doi.org/10.1007/s12063-023-00365-0 |
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author | Chen, Jiakuan Wen, Haoyu |
author_facet | Chen, Jiakuan Wen, Haoyu |
author_sort | Chen, Jiakuan |
collection | PubMed |
description | With frequent political conflicts and public health emergencies, global supply chains are constantly under risk interference, significantly reducing supply chain resilience (SCR), especially for the knowledge-intensive supply chains (KISCs). To assess and improve the resilience of KISC, this paper uses complex network theory to construct a directed weighted network model suitable for KISC and expresses the SCR as a comprehensive capability that can resist risk and recover from it. Using quantitative indicators plus qualitative assessment to quantify the resilience index and identify the network key nodes. Two resilience improvement paths are proposed for KISCs, improving firms’ development capacity and industrial backup. In the case study, the resilience of the integrated circuit (IC) supply chain is assessed and improved according to real data from the global IC industry. The findings show that (i) The resilience assessment based on the directed weighted network aligns with industrial reality. (ii) Improving firms’ development capability and industrial backup can improve SCR. (iii) Effective improvement of resilience requires targeting key nodes in the supply chain network (SCN). Moreover, the degree of firms’ development capability improvement and industrial backup intensity should be within a specific range. |
format | Online Article Text |
id | pubmed-10088763 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-100887632023-04-12 The application of complex network theory for resilience improvement of knowledge-intensive supply chains Chen, Jiakuan Wen, Haoyu Oper Manag Res Article With frequent political conflicts and public health emergencies, global supply chains are constantly under risk interference, significantly reducing supply chain resilience (SCR), especially for the knowledge-intensive supply chains (KISCs). To assess and improve the resilience of KISC, this paper uses complex network theory to construct a directed weighted network model suitable for KISC and expresses the SCR as a comprehensive capability that can resist risk and recover from it. Using quantitative indicators plus qualitative assessment to quantify the resilience index and identify the network key nodes. Two resilience improvement paths are proposed for KISCs, improving firms’ development capacity and industrial backup. In the case study, the resilience of the integrated circuit (IC) supply chain is assessed and improved according to real data from the global IC industry. The findings show that (i) The resilience assessment based on the directed weighted network aligns with industrial reality. (ii) Improving firms’ development capability and industrial backup can improve SCR. (iii) Effective improvement of resilience requires targeting key nodes in the supply chain network (SCN). Moreover, the degree of firms’ development capability improvement and industrial backup intensity should be within a specific range. Springer US 2023-04-10 /pmc/articles/PMC10088763/ http://dx.doi.org/10.1007/s12063-023-00365-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chen, Jiakuan Wen, Haoyu The application of complex network theory for resilience improvement of knowledge-intensive supply chains |
title | The application of complex network theory for resilience improvement of knowledge-intensive supply chains |
title_full | The application of complex network theory for resilience improvement of knowledge-intensive supply chains |
title_fullStr | The application of complex network theory for resilience improvement of knowledge-intensive supply chains |
title_full_unstemmed | The application of complex network theory for resilience improvement of knowledge-intensive supply chains |
title_short | The application of complex network theory for resilience improvement of knowledge-intensive supply chains |
title_sort | application of complex network theory for resilience improvement of knowledge-intensive supply chains |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088763/ http://dx.doi.org/10.1007/s12063-023-00365-0 |
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