Product evolution analysis for design adaptation using big sales data
Bibliographic record
Abstract
Products are constantly evolving through design adaptations to satisfy market requirements. Efficient design adaptation via maximal reusing of existing design can improve product quality, reduce manufacturing cost, and time to market. Understanding of product evolution mechanism is required for design adaptations through rationalized modularity and interfaces. Emerging big sales data provide rich resources for product evolution analysis. To support efficient design adaptations, a framework and associated methods are developed in this work using big sales data for product evolution analysis. Reproduction, mutation, and crossover of product specifications are introduced as specification adaptation operations. Methods are proposed for identification of specification adaptations and estimation of the adaptation probability. Based on modeling and analyzing relationships among product specifications and components, different types of modules and interfaces are proposed through components clustering and their potential operation (mutation, reproduction, and crossover) probabilities. Design recommendations of adaptable product architecture with modules and interfaces are made for facilitating design adaptations. A case study of consumer Unmanned Aerial Vehicles (UVAs) illustrates the proposed method. Limitations and potential extensions of the newly developed method are discussed. Further investigations of the product evolution mechanism using game theory to support competitive product development are included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".