Manufacturing Enterprise Control and Management System Engineering: paradigms and open issues
Bibliographic record
Abstract
Over several decades, control theory has developed its own set of more or less formal modelling techniques designed to automatically control the dynamic behaviour of complicated manufacturing systems and processes. The emerging Internet society is addressing new enterprise control and management integration (ECMI) challenges for agile business to manufacturing (B2M) purposes which enlarge the traditional setting of Automation Engineering to the systems engineering (SE) approach. In order to cope with the increasing complexity of integrating intelligence/information-intensive manufacturing automation within the networked manufacturing enterprise, Automation Engineering should be integrated into the systems engineering approach to achieve a holistic approach that treats in fine the technical operational manufacturing system emerging from the deployment of an ad hoc combination of formal and informal partial models. This paper emphasises that a Holonic Manufacturing Execution System Engineering (HMESE) approach should be a relevant B2M SE approach along with other relevant scientific, industrial and educational areas dealing with information and intelligence control and management issues in agile automation.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".