Skip to main content

Research Repository

Advanced Search

Machine learning applications for sustainable manufacturing: A bibliometric-based review for future research

Jamwal, Anbesh; Agrawal, Rajeev; Sharma, Monica; Kumar, Anil; Kumar, Vikas; Garza-Reyes, Jose Arturo Arturo

Machine learning applications for sustainable manufacturing: A bibliometric-based review for future research Thumbnail


Authors

Anbesh Jamwal

Rajeev Agrawal

Monica Sharma

Anil Kumar

Jose Arturo Arturo Garza-Reyes



Abstract

Purpose: The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

Design/Methodology/Approach: In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

Findings: The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

Originality: A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.

Citation

Jamwal, A., Agrawal, R., Sharma, M., Kumar, A., Kumar, V., & Garza-Reyes, J. A. A. (in press). Machine learning applications for sustainable manufacturing: A bibliometric-based review for future research. Journal of Enterprise Information Management, https://doi.org/10.1108/JEIM-09-2020-0361

Journal Article Type Article
Acceptance Date Apr 11, 2021
Online Publication Date May 6, 2021
Deposit Date Apr 11, 2021
Publicly Available Date Jun 7, 2021
Journal Journal of Enterprise Information Management
Print ISSN 1741-0398
Publisher Emerald
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1108/JEIM-09-2020-0361
Keywords Sustainable manufacturing; Data analytics; machine learning; manufacturing systems; Industry 4.0; bibliometric review
Public URL https://uwe-repository.worktribe.com/output/7257618

Files

Machine learning applications for sustainable manufacturing: A bibliometric-based review for future research (2.7 Mb)
Document

Licence
http://creativecommons.org/licenses/by-nc/4.0/

Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com


Machine learning applications for sustainable manufacturing: A bibliometric-based review for future research (1.2 Mb)
PDF

Licence
http://creativecommons.org/licenses/by-nc/4.0/

Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com







You might also like



Downloadable Citations