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 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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 221-251 |
| Number of pages | 31 |
| Journal | Journal of Enterprise Information Management |
| Volume | 36 |
| Issue number | 1 |
| Early online date | 16 Aug 2022 |
| DOIs | |
| Publication status | Published - 27 Jan 2023 |