Quantitative Assessment Framework for Product Value and Change Risk Analysis in Early Design Process
Why this work is in the frame
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Bibliographic record
Abstract
The ever changing trends in current markets along with customers’ rising demands for quality require many companies to make frequent changes in their products. In this paper, a framework for a comprehensive Decision Support System (DSS) is described and illustrated with a simple example of a thermo-flask. The DSS aims to obtain an optimal balance between customer and enterprise satisfaction by taking into account different design decision attributes: customer requirements, cost and design risk. The system allows the recalculation of cost, value, effort and risk when engineering change occurs during the creation of new design solutions. The proposed DSS integrates House of Quality (HOQ), Functional Analysis System Technique (FAST), risk assessment and change propagation analysis to provide a view of the design process from product attributes and design risk to cost and effort. The goal is to increase product knowledge in the early stages of design, to calculate the effects of engineering change, and to support design engineers in decision making.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it