A BSC method for supplier selection strategy using TOPSIS and VIKOR: A case study of part maker industry
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".