New dependency model and biological analogy for integrating product design for variety with market requirements
Bibliographic record
Abstract
Variety in product design is a result of diversity of needs in different domains and market segments. The two-way interaction and dependency between product design features and customer requirements is analogous to co-evolution in nature, where two groups of different species evolve to co-exist. A new method for designing products, families and platforms by recognising commonalities and core features, using the concept of co-evolution, is introduced in this paper. Cladistics is used to identify product component modules which correspond to common regional market requirements. Algorithms for functional and structural analysis as well as product variants generation have been developed. Complex dependency interactions and modularity relationships are modelled using liaison graphs and cladograms. A case study of washing machines is detailed and used to validate this novel application of the co-evolution dependency model in product families and platform design, demonstrating its use in the world of artefacts co-development. The proposed model is capable of satisfying different market segments’ requirements, while minimising the cost associated with product variety, by promoting modular product family design. It selects the best product variant(s) for each market segment and minimises component redundancy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".