Decision Making Models for Sustainable Supply Chain in Industry 4.0: Opportunities and Future Research Agenda

Sharma, D; Jamwal, A; Agrawal, R; Jain, JK; Machado, J

Decision Making Models for Sustainable Supply Chain in Industry 4.0: Opportunities and Future Research Agenda

Sharma, D

Jamwal, A Agrawal, R Jain, JK Machado, J

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Innovations in Industrial Engineering II, 2023, pp. 175-185
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Sharma, D https://orcid.org/0000-0003-2653-429X
dc.contributor.author	Jamwal, A
dc.contributor.author	Agrawal, R
dc.contributor.author	Jain, JK
dc.contributor.author	Machado, J
dc.date.accessioned	2023-11-09T02:34:59Z
dc.date.available	2023-11-09T02:34:59Z
dc.date.issued	2023-01-01
dc.identifier.citation	Innovations in Industrial Engineering II, 2023, pp. 175-185
dc.identifier.isbn	9783031093593
dc.identifier.uri	http://hdl.handle.net/10453/173263
dc.description.abstract	Managing supply chain activities have become a critical issue for manufacturing organization due to globalization and challenges faced by sustainability. Performance of supply chain can be evaluated by indicators and other factors. The evaluation of these factors becomes difficult due to lack of benchmarks and evaluation method. The supply chain has wide range of opportunities in the Industry 4.0 which is driven by many key enabling technologies such as: artificial intelligence, machine learning, deep learning and blockchain technology. These technologies have a positive impact on the sustainability performance of the supply chain activities. The decision-making models plays an important role in the evaluation of the supply chain strategies. The present study aims to identify and report the different models used in the sustainable supply chain management. These models are multi-criteria decision-making models, multi-objective-based models, artificial intelligence-based models. Bibliometric analysis is done with the VOSviewer and R studio using the bibliographic data of digital scientific databases. The content analysis is done after the bibliometric analysis based on the cluster analysis done in R studio. The main research areas and future research directions have been reported in the end of study. It is expected that this study can be helpful for the practitioners, policymakers and academia working in the area of sustainable supply chain management.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Innovations in Industrial Engineering II
dc.relation.ispartofseries	Lecture Notes in Mechanical Engineering
dc.relation.isbasedon	10.1007/978-3-031-09360-9_15
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Decision Making Models for Sustainable Supply Chain in Industry 4.0: Opportunities and Future Research Agenda
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-09T02:34:57Z
pubs.publication-status	Published

Abstract:

Managing supply chain activities have become a critical issue for manufacturing organization due to globalization and challenges faced by sustainability. Performance of supply chain can be evaluated by indicators and other factors. The evaluation of these factors becomes difficult due to lack of benchmarks and evaluation method. The supply chain has wide range of opportunities in the Industry 4.0 which is driven by many key enabling technologies such as: artificial intelligence, machine learning, deep learning and blockchain technology. These technologies have a positive impact on the sustainability performance of the supply chain activities. The decision-making models plays an important role in the evaluation of the supply chain strategies. The present study aims to identify and report the different models used in the sustainable supply chain management. These models are multi-criteria decision-making models, multi-objective-based models, artificial intelligence-based models. Bibliometric analysis is done with the VOSviewer and R studio using the bibliographic data of digital scientific databases. The content analysis is done after the bibliometric analysis based on the cluster analysis done in R studio. The main research areas and future research directions have been reported in the end of study. It is expected that this study can be helpful for the practitioners, policymakers and academia working in the area of sustainable supply chain management.

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http://hdl.handle.net/10453/173263