A Study of the Evolution of Uncertainty in Product Development as a Basis for Overlapping
Bibliographic record
Abstract
Overlapping new product design process is widely applied in industry. However, selections of appropriate overlapping strategies based on the prediction of process performance can be problematic due to insufficient understanding on the dependence between design processes and its effect on the performance. This paper introduces a new model for a product design organization that is based on the evolving nature of the design process, the dependence between up and downstream design specifications, and the design technology being adopted. The model presents an evaluation method for quantifying the downstream evolutionary behavior. Through an industrial case study, it is applied to evaluate how the design performances vary under different overlapping strategies and how to determine an optimal overlapping; the results imply that the performance is contingent on the strategy, and no single strategy outperforms in overall performance measures. Furthermore, the model measures process dependency - a quantification of the downstream work that is indifferent to the change of the upstream. This quantification can be applied to determine the rework probability or rework function proposed by other studies. The model also addresses the rationale of how improving design technology efficiency can lead to an upgrading of design performances.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".