Lean 4.0: typology of scenarios and case studies to characterize Industry 4.0 autonomy model
Bibliographic record
Abstract
Industry 4.0 is leading to rethink how operational decisions are made within companies. In particular, it raises the question of the evolution of employee involvement and autonomy in operational decision-making in a Lean 4.0 context. Dealing with such issues within companies presents high stakes but also involves many risks and difficulties. Therefore, it is necessary to test these new Industry 4.0 autonomy models within our Evolutive Learning Factories by developing a suitable experimental protocol. This article proposes a typology of scenarios and case studies that will serve as a basis for future experiments to study these issues in a standardized work context. This first study framework confirmed that the decisions induced by all the problems and opportunities encountered at the operational level are numerous and varied. This research work is a first step and opens up much broader research perspectives on the contribution of Industry 4.0 technologies in implementing new models of autonomy at work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".