Hybrid AHP-DEMATEL Model for Prioritizing Key Resilience and Sustainability Drivers and Controllers in Agri-Food Supply Chains
Bibliographic record
Abstract
Agri-food systems are fundamental at the intersection of agriculture and industrialization, playing a crucial role in global food production and distribution.However, each agri-food supply chain (AFSC) has unique characteristics, posing the challenge of identifying tools and strategies that can be adapted to diverse contexts.Sustainability has now become a priority, driven by increasing consumer awareness of the environmental impact of agricultural and industrial practices.This shift has transformed sustainability from a trend into a necessity to ensure efficient and resilient operations.Achieving this requires effective cooperation across all supply chain links, promoting the integration of economic, social, political, and environmental aspects.This study aims to identify and prioritize the key drivers and enablers of resilience and sustainability in AFSCs.Through a systematic literature review (SLR), drivers were grouped with their respective enablers, creating a framework to assess their impact on the performance of each supply chain component.The Analytical Hierarchy Process (AHP) was used to assign weights to the drivers, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was applied to evaluate interactions between variables and establish a ranking of their relative influence.The results revealed that the driver "Evolution of Agricultural Systems" had the highest weighting, followed by "Water Footprint."Regarding enablers, the highest-scoring factors were "Redesign and Coordination of Operations" and "Collaboration Across Supply Chain Links."These findings provide a solid foundation for strategic decision-making to enhance the sustainability and resilience of AFSCs.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".