Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Jiakuan, Wen, Haoyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088763/
http://dx.doi.org/10.1007/s12063-023-00365-0
_version_ 1785022632685469696
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
work_keys_str_mv AT chenjiakuan theapplicationofcomplexnetworktheoryforresilienceimprovementofknowledgeintensivesupplychains
AT wenhaoyu theapplicationofcomplexnetworktheoryforresilienceimprovementofknowledgeintensivesupplychains
AT chenjiakuan applicationofcomplexnetworktheoryforresilienceimprovementofknowledgeintensivesupplychains
AT wenhaoyu applicationofcomplexnetworktheoryforresilienceimprovementofknowledgeintensivesupplychains