Smart supplier selection using N-cubic fuzzy aggregation: a case study in agricultural manufacturing
Bibliographic record
Abstract
The rapid evolution of communication and information technologies—such as cloud computing, the Internet of Things (IoT), big data analytics, and machine learning—has revolutionized traditional manufacturing, giving rise to intelligent and interconnected production ecosystems. These technological advancements not only streamline production processes but also reshape supplier selection strategies by incorporating both conventional and sustainability-oriented evaluation criteria. In light of these developments, this study proposes a novel multi-criteria group decision-making (MCGDM) framework for supplier selection under the N-Cubic Fuzzy Set (NCFS) environment. NCFSs offer a robust mathematical structure for capturing uncertain, vague, and imprecise information, particularly within the interval $$[-1 ,0]$$ , making them highly suitable for complex, real-world decision-making scenarios. To facilitate effective aggregation of expert judgments, three advanced aggregation operators are introduced: the N-Cubic Generalized Fuzzy Weighted Average (NCGFWA), the N-Cubic Generalized Fuzzy Ordered Weighted Average (NCGFOWA), and the N-Cubic Generalized Fuzzy Hybrid Weighted Average (NCGFHWA). These operators are designed to systematically consolidate the preferences of multiple decision-makers and produce a reliable ranking of potential suppliers. The proposed methodology is validated through a real-world case study involving a manufacturer of agricultural machinery and implements. The results demonstrate the practical effectiveness, flexibility, and robustness of the NCFS-based framework in supporting supplier selection within the paradigm of smart manufacturing.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".