Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo, Saumyaranjan; Kumar, Satish; Abedin, Abedin; Lim, Weng Marc; Jakhar, Suresh Kumar

doi:10.1108/jeim-01-2022-0025

Journal article 337 views

Deep learning applications in manufacturing operations: a review of trends and ways forward

Saumyaranjan Sahoo

, Satish Kumar

, Abedin Abedin, Weng Marc Lim

, Suresh Kumar Jakhar

Journal of Enterprise Information Management, Volume: 36, Issue: 1, Pages: 221 - 251

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.1108/jeim-01-2022-0025

Abstract

Purpose: Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations. Design/methodology/approach: Using bibliometric analysis and the...

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Published in:	Journal of Enterprise Information Management
ISSN:	1741-0398
Published:	Emerald 2023
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa64251
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first_indexed	2023-09-19T10:54:14Z
last_indexed	2023-09-19T10:54:14Z
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spelling	v2 64251 2023-08-31 Deep learning applications in manufacturing operations: a review of trends and ways forward 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF Purpose: Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations. Design/methodology/approach: Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations. Findings: This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling. Research limitations/implications: This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published. Originality/value: This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0. Journal Article Journal of Enterprise Information Management 36 1 221 251 Emerald 1741-0398 Deep learning, Industry 4.0, Manufacturing, Operations, Maintenance, Quality, Logistics, Sustainability, Supply chain 27 1 2023 2023-01-27 10.1108/jeim-01-2022-0025 http://dx.doi.org/10.1108/jeim-01-2022-0025 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-20T10:38:43.9673264 2023-08-31T17:52:54.6939645 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Saumyaranjan Sahoo 0000-0003-3609-2448 1 Satish Kumar 0000-0001-5200-1476 2 Abedin Abedin 3 Weng Marc Lim 0000-0001-7196-1923 4 Suresh Kumar Jakhar 0000-0003-4733-6129 5
title	Deep learning applications in manufacturing operations: a review of trends and ways forward
spellingShingle	Deep learning applications in manufacturing operations: a review of trends and ways forward Abedin Abedin
title_short	Deep learning applications in manufacturing operations: a review of trends and ways forward
title_full	Deep learning applications in manufacturing operations: a review of trends and ways forward
title_fullStr	Deep learning applications in manufacturing operations: a review of trends and ways forward
title_full_unstemmed	Deep learning applications in manufacturing operations: a review of trends and ways forward
title_sort	Deep learning applications in manufacturing operations: a review of trends and ways forward
author_id_str_mv	4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv	4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author	Abedin Abedin
author2	Saumyaranjan Sahoo Satish Kumar Abedin Abedin Weng Marc Lim Suresh Kumar Jakhar
format	Journal article
container_title	Journal of Enterprise Information Management
container_volume	36
container_issue	1
container_start_page	221
publishDate	2023
institution	Swansea University
issn	1741-0398
doi_str_mv	10.1108/jeim-01-2022-0025
publisher	Emerald
college_str	Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id	facultyofhumanitiesandsocialsciences
hierarchy_top_title	Faculty of Humanities and Social Sciences
hierarchy_parent_id	facultyofhumanitiesandsocialsciences
hierarchy_parent_title	Faculty of Humanities and Social Sciences
department_str	School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url	http://dx.doi.org/10.1108/jeim-01-2022-0025
document_store_str	0
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description	Purpose: Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations. Design/methodology/approach: Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations. Findings: This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling. Research limitations/implications: This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published. Originality/value: This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.
published_date	2023-01-27T10:38:41Z
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Deep learning applications in manufacturing operations: a review of trends and ways forward

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