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Resiliency of Mutualistic Supplier-Manufacturer Networks
Current Supplier-Manufacturer (SM) networks are highly complex and susceptible to local and global disruptions, due to connectivity and interdependency among suppliers and manufacturers. Resiliency of supply chains is critical for organizations to remain operational in the face of disruptive events....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6753084/ https://www.ncbi.nlm.nih.gov/pubmed/31537847 http://dx.doi.org/10.1038/s41598-019-49932-1 |
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author | Xu, Mengkai Radhakrishnan, Srinivasan Kamarthi, Sagar Jin, Xiaoning |
author_facet | Xu, Mengkai Radhakrishnan, Srinivasan Kamarthi, Sagar Jin, Xiaoning |
author_sort | Xu, Mengkai |
collection | PubMed |
description | Current Supplier-Manufacturer (SM) networks are highly complex and susceptible to local and global disruptions, due to connectivity and interdependency among suppliers and manufacturers. Resiliency of supply chains is critical for organizations to remain operational in the face of disruptive events. Existing quantitative analyses oversimplify the mutualistic nature of SM networks, in which failure of individual entities affects not only the directly connected entities but also those connected indirectly. In this work we investigate resiliency of SM networks using the quantitative methods employed to study mutualistic ecological systems. Much like in ecological systems, catastrophic failures of SM networks are difficult to predict due to high dimensionality of their interactive space. To address this, first we create a bipartite representation and generate a multidimensional nonlinear model that captures the dynamics of a SM network. We transform the multidimensional model into a two-dimensional model without sacrificing the model’s ability to predict the point of collapse. We extensively validate the model using real-world global automotive SM networks. We observe that the resiliency of a SM network depends on both the network structure and parameters. The current work offers a means for designing resilient supply chains that can remain robust to local and global perturbations. |
format | Online Article Text |
id | pubmed-6753084 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67530842019-10-01 Resiliency of Mutualistic Supplier-Manufacturer Networks Xu, Mengkai Radhakrishnan, Srinivasan Kamarthi, Sagar Jin, Xiaoning Sci Rep Article Current Supplier-Manufacturer (SM) networks are highly complex and susceptible to local and global disruptions, due to connectivity and interdependency among suppliers and manufacturers. Resiliency of supply chains is critical for organizations to remain operational in the face of disruptive events. Existing quantitative analyses oversimplify the mutualistic nature of SM networks, in which failure of individual entities affects not only the directly connected entities but also those connected indirectly. In this work we investigate resiliency of SM networks using the quantitative methods employed to study mutualistic ecological systems. Much like in ecological systems, catastrophic failures of SM networks are difficult to predict due to high dimensionality of their interactive space. To address this, first we create a bipartite representation and generate a multidimensional nonlinear model that captures the dynamics of a SM network. We transform the multidimensional model into a two-dimensional model without sacrificing the model’s ability to predict the point of collapse. We extensively validate the model using real-world global automotive SM networks. We observe that the resiliency of a SM network depends on both the network structure and parameters. The current work offers a means for designing resilient supply chains that can remain robust to local and global perturbations. Nature Publishing Group UK 2019-09-19 /pmc/articles/PMC6753084/ /pubmed/31537847 http://dx.doi.org/10.1038/s41598-019-49932-1 Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Xu, Mengkai Radhakrishnan, Srinivasan Kamarthi, Sagar Jin, Xiaoning Resiliency of Mutualistic Supplier-Manufacturer Networks |
title | Resiliency of Mutualistic Supplier-Manufacturer Networks |
title_full | Resiliency of Mutualistic Supplier-Manufacturer Networks |
title_fullStr | Resiliency of Mutualistic Supplier-Manufacturer Networks |
title_full_unstemmed | Resiliency of Mutualistic Supplier-Manufacturer Networks |
title_short | Resiliency of Mutualistic Supplier-Manufacturer Networks |
title_sort | resiliency of mutualistic supplier-manufacturer networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6753084/ https://www.ncbi.nlm.nih.gov/pubmed/31537847 http://dx.doi.org/10.1038/s41598-019-49932-1 |
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