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

Saumyaranjan Sahoo Orcid Logo, Satish Kumar Orcid Logo, Mohammad Abedin Orcid Logo, Weng Marc Lim Orcid Logo, Suresh Kumar Jakhar Orcid Logo

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

Swansea University Author: Mohammad Abedin Orcid Logo

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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa64251
first_indexed 2023-09-19T10:54:14Z
last_indexed 2024-11-25T14:13:43Z
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spelling 2023-09-20T10:38:43.9673264 v2 64251 2023-08-31 Deep learning applications in manufacturing operations: a review of trends and ways forward 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE 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 Management School COLLEGE CODE CBAE 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 Mohammad Abedin 0000-0002-4688-0619 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
Mohammad 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_***_Mohammad Abedin
author Mohammad Abedin
author2 Saumyaranjan Sahoo
Satish Kumar
Mohammad 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
active_str 0
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 2023-01-27T02:41:54Z
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score 11.04748

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