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Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic

The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individua...

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Detalles Bibliográficos
Autores principales: Golan, Maureen S., Jernegan, Laura H., Linkov, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261049/
https://www.ncbi.nlm.nih.gov/pubmed/32837820
http://dx.doi.org/10.1007/s10669-020-09777-w
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author Golan, Maureen S.
Jernegan, Laura H.
Linkov, Igor
author_facet Golan, Maureen S.
Jernegan, Laura H.
Linkov, Igor
author_sort Golan, Maureen S.
collection PubMed
description The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007–2016, while 94 were found in 2017–2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of “unknown unknowns” remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10669-020-09777-w) contains supplementary material, which is available to authorized users.
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spelling pubmed-72610492020-06-01 Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic Golan, Maureen S. Jernegan, Laura H. Linkov, Igor Environ Syst Decis Review The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007–2016, while 94 were found in 2017–2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of “unknown unknowns” remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10669-020-09777-w) contains supplementary material, which is available to authorized users. Springer US 2020-05-30 2020 /pmc/articles/PMC7261049/ /pubmed/32837820 http://dx.doi.org/10.1007/s10669-020-09777-w Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Review
Golan, Maureen S.
Jernegan, Laura H.
Linkov, Igor
Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic
title Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic
title_full Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic
title_fullStr Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic
title_full_unstemmed Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic
title_short Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic
title_sort trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the covid-19 pandemic
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261049/
https://www.ncbi.nlm.nih.gov/pubmed/32837820
http://dx.doi.org/10.1007/s10669-020-09777-w
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