Toward a Decision Tool for Eco-Design Strategy Selection of Aircraft Manufacturers Considering Stakeholders Value Network
Bibliographic record
Abstract
This paper introduces a novel approach in order to compare different eco design practices considering the value network of stakeholders. The proposed decision tool framework in this study helps manufactures in early stage of design to select a portfolio of eco design techniques to maximize the value perceived by all stakeholders in a dual life cycle approach including the business product life cycle as well as physical life cycle. A portfolio selection approach has been used to maximize the network value of stakeholders considering the life cycle cost and risk of techniques while satisfying the diversity of allocation resources on eco design practices based on strategic objectives of manufacturers. The dynamic characteristic of stakeholder's network as the result of implementing the different eco design techniques has also been considered in order to evaluate the synergy in stakeholder's network. Therefore the developed framework in this study is an effective way to assist the decision makers in prioritizing eco-design techniques and managing an optimal portfolio of them. As an application of developed model, we proposed a guideline for Aircraft manufacturers in order to select the best set of design for end of life techniques considering all stakeholder's needs and expectations. This guideline can help aircraft manufacturer to compare the variety of eco practices in three categories including material usage, providing information and manual and design for 3R (reducing, re-using and recycling) considering pertinent impacts to all stakeholders in addition to business priority in life cycle perspective.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".