SPPBRD: A decision framework for scalable product platform based robust design.
Bibliographic record
Abstract
Today's competitive and highly volatile market is redefining the way companies do business. A main competitive advantage for many companies is the ability to bring the products to market faster. An effective method to get the advantage is to develop product platforms. This thesis develops a methodology to assist companies in creating product platforms quickly and efficiently. The thesis focuses on building a model for scalable product platforms and developing a framework for the Scalable Product Platform Based Robust Design since there are many researches on module-based product platform and no systematic framework for scalable product platform. In the methodology, the two-stage approach, multiple-objectives, compromise decision support problem (compromise DSP), and robust design are integrated to build the decision model. The model consists of eight steps that describe how to formulate the problem and how it can be solved. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .W36. Source: Masters Abstracts International, Volume: 45-01, page: 0468. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".