An integrated fuzzy MCDM approach for risk evaluation of new product in a pipe industry
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
New Product Development (NPD) process is recognised as one important competitive advantage for most companies which include high risk and uncertainty. Hence, this study aims at accelerating new product introduction and improving the quality of decision-making in NPD process under risks and uncertain conditions. This paper suggests an integrated framework based on the Fuzzy Analytic Hierarchy Process (AHP) and the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), to evaluate new products in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterised by triangular fuzzy numbers. The Fuzzy AHP is implemented to calculate weights of the risk criteria, and the Fuzzy TOPSIS method is applied to obtain a final ranking. Finally, the proposed approach is implemented for NPD evaluation of a pipe and fitting industry. The results reveal the supply risks have the highest effect on risk evaluation of new products.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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