Green supplier development programmes selection: a hybrid fuzzy multi-criteria decision-making approach
Bibliographic record
Abstract
This study aims to evaluate the Green Supplier Development Programs (GSDPs) for greening a supply chain. However, this problem is threatened by restricted quantitative information, the specific context of the organisation, lack of prior experience and varying supplier backgrounds. In this paper, we propose a fuzzy integrated Multi-Criteria Decision-Making approach for investigating and prioritising GSDPs. The approach is developed by integrating fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), fuzzy Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. First, fuzzy DEMATEL is applied to determine the main green factors, then the fuzzy AHP method is used to acquire the local weights of criteria, and finally, the GSDPs are prioritised based on the green factors by fuzzy TOPSIS. The proposed approach is employed to estimate GSDPs of the painting companies. The outcomes indicate that ‘requiring ISO 14,000 certification for suppliers? and ‘building top management commitment for suppliers for green supply practices’ have the highest and lowest impact on improving the environmental performance of suppliers, respectively. It is also concluded that ‘green procurement’ measure has the highest effect on prioritising the GSDPs.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".