Decision-Making Assistance in Engineering-Change Management Process
Bibliographic record
Abstract
Effective engineering-change management (ECM) is a real challenge in mechanical engineering industry and manufacturing companies. Computer-aided design systems are usually connected to other systems such as ERP or product data management, but currently this integration does not provide effective means to manage engineering change (EC). While communication between multidisciplinary teams working on a project is known to have a significantly positive impact on the ECM, the communication between disciplines is generally performed solely through message exchange. Experts could feel the need to meet to agree on the requested changes, which in turn translates into longer design and manufacturing processes. There is a need for a system that assists human experts in making decisions about ECs. Such a system will considerably reduce the processing time following a change-request procedure. This paper proposes a collaborative tool named EchoMag, which assists designers and experts during the change-management process. The proposed system ensures the coherence of data between the various disciplines involved in the change process. EchoMag also assists experts in making decisions by proposing alternative solutions when change requests are not agreed upon. Software agents were used to implement EchoMag for which a prototype was developed. Results of the implementation are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".