Systematic Lifecycle Design for Sustainable Product Development
Bibliographic record
Abstract
Sustainable product development (SPD) requires that product designs achieve minimal or zero environmental impacts, in addition to satisfying the traditional design criteria, such as product functionality, quality, features, costs, and time to market. Environmental evaluations must, therefore, be incorporated into the design stage. In this research, a product design process model is proposed that includes three design requirements, two design tasks, and three comprehensive assessment streams. The functional requirement is derived from the customers’ needs, reflecting the product's functional purpose; the environmental requirement reflects society's need for protecting natural resources and the environment; and, the economic requirement ensures the company's basic business goals. Accordingly, SPD aims to simultaneously carry out the two tasks of designing products’ physical and lifecycle structures. In the assessment phase of product design, three assessment streams, including lifecycle quality (LCQ) analysis, lifecycle assessment (LCA) and lifecycle cost (LCC), are conducted with respect to the functional, environmental, and economic evaluations. A process-based analysis concept is proposed for the analysis of LCQ, LCA, and LCC evaluations. A simplified LCA is used for the environmental evaluations. Detailed assessment techniques are also developed for effective design evaluations. A case study example is provided to illustrate the methods and models.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".