Integrating Value Analysis and Quality Function Deployment for Evaluating Design Alternatives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a hybrid framework that integrates value analysis and decision making for eliciting and evaluating design alternatives. Value analysis approach relies on the integration of the functional analysis through the systematic use of the functional analysis system technique and quality function deployment. This value analysis methodology enables customer requirements to be linked to specific design alternatives during the project design stage. The degree of project complexity will affect the number of design alternatives to be evaluated. As such, the data envelopment analysis (DEA) is incorporated as a decision support tool to evaluate the degree to which each design alternative satisfies the customer requirements. DEA is used to calculate a customer requirement efficiency index for each alternative. This index is a measure of how well a particular alternative achieves the requirements taking into account its overall cost. The framework was successfully used on a high-tech research facility construction project.
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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 | 0.005 | 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 | 0.000 | 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