Agent-based assistance for engineering change management: An implementation prototype
Bibliographic record
Abstract
Computer-aided design organizations worry much about the management of the communication between the various multidisciplinary teams working on the same project. The good management of this communication is very important for the success of the engineering change management process. Each discipline must approve or reject a change request according to its constraints and inform the other disciplines involved. Currently, the communication between the various disciplines is performed using messages. The approval or the rejection of a change request can be made only if human experts representing these disciplines meet and negotiate, which in turn can be very time consuming. Organizations thus, have a great need for a system which could automate the communication between experts and assist them in making sound engineering change decisions. Such a system could also help by reducing the time needed to verify an engineering change request therefore accelerating the production start-up and the time to market. This paper proposes an agent-based system that can manage the engineering change requests efficiently. Actually, the proposed system checks if the engineering change request does not create any inconsistency with the constraints stemming from the various disciplines. It also assists experts in making decisions by proposing alternative solutions. Each discipline is represented by an expert agent. When an inconsistency is discovered, a negotiation process among expert agents is launched. The system proposed could be easily connected to various CAD tools.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".