Ranking factors involved in product design using a hybrid model of Quality Function Deployment, Data Envelopment Analysis and TOPSIS technique
Bibliographic record
Abstract
Quality function deployment (QFD) is one such extremely important quality management tool, which is useful in product design and development.Traditionally, QFD rates the design requirements (DRs) with respect to customer requirements, and aggregates the rating to get relative importance score of DRs.An increasing number of studies emphasize on the need to incorporate additional factors, such as cost and environmental impact, while calculating the relative importance of DRs.However, there are different methodologies for driving the relative importance of DRs, when several additional factors are considered.TOPSIS (technique for order preferences by similarity to ideal solution) is suggested for the purpose of the research.This research proposes new approach of TOPSIS for considering the rating of DRs with respect to CRs, and several additional factors, simultaneously.Proposed method is illustrated using by step-by-step procedure.The proposed methodology was applied for the Sanam Electronic Company in Iran.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".