Main challenges and best practices to be adopted in management training for Industry 4.0
Bibliographic record
Abstract
Purpose The objectives of this study are twofold: identify the main challenges in performing training on Industry 4.0 concepts to managers operating in the manufacturing sector who are not familiar with them but aspire for an Industry 4.0 broad view and validate training practices that can be adopted to reduce managerial knowledge differences. Design/methodology/approach A Delphi method was carried out in two rounds to identify the Industry 4.0 training challenges and a Fuzzy Delphi method was applied in one round to validate the training practices. Both methods used the same set of participants composed of experts in training for Industry 4.0. Results were discussed considering literature statements. Findings In total, 11 challenges in Industry 4.0 training were identified and grouped into: challenges associated with the necessary knowledge, challenges of breaking paradigm, challenges associated with training characteristics and challenges associated with expected results. In total, 11 training practices were directly validated, including actions to be adopted before, during, and after the training process. Originality/value The findings are relevant for professionals, academics, or consultants as the findings enable better training planning and execution. No similar papers were found in scientific databases, reinforcing this present study's originality and contribution.
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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 | 0.050 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".