A BSC method for supplier selection strategy using TOPSIS and VIKOR: A case study of part maker industry
Why this work is in the frame
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Bibliographic record
Abstract
In recent decades, provision-chain management has been one of the major concepts. The main reason that attracts attention to the concept is the increase in competition and struggle for the survival. There are different ways to increase the competition in organizations such as increasing productivity by acquiring information technology. In this paper, we present an integrated model with the balanced score card framework for supplier selection strategy. The proposed model of this paper gathers 161 important factors suggested in the literature and selects the six most important ones using different multi criteria techniques. We also propose a goal programming techniques with some hard constraints and implement the mathematical model for real-world case study of auto industry. The proposed model is solved in four different forms using TOPSIS, VIKOR and the combination of these 2 factors with factor analysis. The preliminary results indicate that a combination of VIKOR and factor analysis presented better results with 9% reduction in costs, 38% increase of quality, and 3.2% increase in acceptability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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