The Role of Industry 5.0 in Reducing the Risk of Human Error in Manufacturing- A Critical Literature Review
Bibliographic record
Abstract
With technological advances in the modern workplace, no illustration would be complete without mentioning \nthose related to IoTs and especially wearable devices. Industry 5.0 is expected to enhance the relationship between \nmachines and humans as part of the fifth industrial revolution by making it easier for humans to use intelligent machines. \nOperators can use IoTs to reduce human errors; however, the use of this technology can also add new risks to the \nproduction system. Human reliability analysis must therefore be used to attempt to estimate the extent to which human \nerror contributes to both qualitative and quantitative risks. In this study, a critical review of the existing literature is \npresented based on PRISMA. Based on the inclusion and exclusion criteria, 22 articles were considered relevant for \nreview. Several keyword combinations in English were used, including human error, Industry 5.0, IoT, wearables, \ncomplex systems, and manufacturing. Scopus and Web of Science were used to find such keywords from 2013 to 2023. The \nresults demonstrate the need for a reliable and comprehensive model to assess the human error risks related to using IoTs \nin manufacturing. A basis for future research will be provided by the results of this study.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".