A Catalyst Method for an Innovative Eco-Design Strategy Using TRIZ Approach
Bibliographic record
Abstract
Today, in the context of product innovation, the environmental dimension takes a new dimension and cannot be separated from other product requirements which make it competitive. Tools are available but unfortunately proficiency levels required for their use are at the expert’s level. Those tools must also be able to clearly identify their benefits, strengths and weaknesses for a given multi-criteria environmental evaluation. In the present work we have adopted an approach using a qualitative evaluation matrix including parameters related to the ease of use of the product related to the organizational preparation for the appropriation of an eco-design approach, in addition to the standard factors of eco-efficiency. In order to help the designer to make a decision, an adapted TRIZ method is proposed (Téoria Rechénia Izobrétatelskikh Zadatch or theory of inventive problem solving). The applicability of this method is justified by the many contradictions in the choices in a study of the life cycle and can help designers and companies to choose an approach to attain a satisfying level of eco-design for the resources invested in it. An application, based on inventive principles, will be adapted and completed. This matrix can help the designer to reduce the scope of his creative investigations. The results of our methodological approach have been tested on cases of creative projects and a certain number of recently patented projects.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".