A Product Lifecycle Management Framework to Support the Exchange of Prototyping and Testing Information
Bibliographic record
Abstract
The modern perspective on product life cycle and the rapid evolution of Information and Communication Technologies in general have opened a new era in product representation and product information sharing between participants, both inside and outside the enterprise and throughout the product life. In particular, the Product Development Process relies on cross-functional activities involving different domains of expertise that each have their own dedicated tools. This has generated new challenges in terms of collaboration and dissemination of information at large between companies or even within the same organization. Within this context, the work reported in this paper focuses on a specific stakeholder within product development activities — the testing department. Its business is typically related to the planning and building of prototypes in order to perform specific tests on the future product or one of its sub-assemblies. This research project aims at investigating how results from testing activities can efficiently interface with other departments, in particular to offer relevant information feedback to designers. As a first step, a study based on an open source initiative is described to show how two natively disparate PLM tools can dialogue to merge information in a central environment. The principles applied in the study are then transposed to introduce a framework where information from prototyping and testing activities could be mapped and merged with design activities to match needs in a cross-functional setting.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".