Training for managers not skilled in Industry 4.0 basis: what is the most suitable content to be covered?
Bibliographic record
Abstract
This study aims to validate adequate content for Industry 4.0 training recommended to managers unfamiliar with this theme and intends to get a holistic view. A Delphi Method was conducted with experts in Industry 4.0 and training. These experts evaluated an initial training content structure segmented into seven modules based on academic literature. The consensus was reached in two rounds with the participation of twenty-seven (27) experts in the first round and twenty (20) experts in the second round. The results were discussed considering literature statements. The initial training content structure was reordered and an additional module was appended. The validated training content structure considers a total of eight modules ordered as follows: Business Management Models Impact; Product Personalisation and Smart Products; Smart Factory and Integration; Modularity and Service Orientation; Decentralisation and Interoperability; Virtualisation and Real-Time Capability; Corporate Social Responsibility (CSR) and Sustainability Impact; and Industry 4.0 Implementation. The results presented here are helpful for academics, consultants, and professionals who need to design courses and training about Industry 4.0 theme. It is essential to mention that no similar papers were found in scientific databases, reinforcing the originality and the contribution of this research.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".