A Product Feature Evolution Validation Model for Engineering Change Management
Bibliographic record
Abstract
Product design integrates several disciplines in a concurrent engineering (CE) environment. Each one of these disciplines has a specific point of view on the product being developed. While each discipline exerts its own expertise and methods on the definition of the product and its related processes, information must remain consistent for all disciplines and through the evolution of the product definition. This paper proposes a product feature evolution validation (PFEV) model that aims at controlling the information flow needed to support a product definition evolution (PDE) while insuring its validation by all disciplines involved. The model applies both to the product design and modification phases, i.e., before and after releasing its definition. The PFEV model thus supports CE and enables managing the product feature evolution throughout the product life cycle. The PFEV model defines an exchange protocol between the disciplines in order to preserve the consistency of the numerical model, which includes the complete numerical information characterizing the product. The model addresses two qualities of an information system: dispatching relevant PDE information to appropriate disciplines and providing this information according to specific views. This is achieved by centralizing the product numerical model and by exploiting the product’s features rather than managing product model as black boxes. Links between features are formalized in a shared product features table that is used to dynamically identify all disciplines impacted by a product feature evolution (PFE). A PFE is also characterized by its potential impact, detrimental or beneficial, on every discipline previously identified as impacted. In the case of a detrimental impact, the discipline is asked to validate the evolution. If the impact is beneficial, the discipline is simply notified about the evolution. Specific views are generated for the impacted disciplines based on feature filtering and adaptation mechanisms.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".