Advancing sustainable manufacturing: a systematic exploration of Industry 5.0 supply chains for sustainability, human-centricity, and resilience
Bibliographic record
Abstract
The emergence of Industry 5.0 provides new perspectives for the manufacturing sector, aiming to create sustainable, human-centric, and resilient approaches. Supply chains perform a vital role in realising these objectives by connecting suppliers to customers and providing value-added products and services. However, despite growing interest, the consideration for this paradigm shift in the manufacturing industry remains amorphous. In order to address this gap, this paper presents a systematic literature review of 103 research articles from an initial corpus of 8,079 and proposes a conceptual framework for Supply Chain 5.0 within the manufacturing sector. The framework is scaffolded on a thematic analysis of the literature, including drivers to transition, impacts on manufacturing supply chains, challenges, and outcomes. This study provides valuable insights for researchers, practitioners, and policymakers seeking to examine the implications of Industry 5.0 supply chains, highlighting its potential to enhance sustainability, social well-being, and economic growth. Furthermore, the proposed conceptual framework and research opportunities serve to guide future research and practical applications around this emerging topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".