| Titre : | How LARG is a supply chain? Application of adaptive neuro fuzzy inference system in agriculture sector (2026) |
| Auteurs : | M. Homayounfar ; H. Gheibdoust |
| Type de document : | Article |
| Dans : | Journal of Cleaner Production (vol. 563, May 2026) |
| Article en page(s) : | p. 148496 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 11 - COMMERCE ; 11.2 - Commercialisation. DistributionThésaurus IAMM CHAINE D'APPROVISIONNEMENT ; AGRICULTURE ; DURABILITE ; EFFICACITE ; PERFORMANCE ENVIRONNEMENTALE ; MODELE |
| Résumé : | The increasing complexity of the agricultural supply chain necessitates integrating the principles of Leanness, Agility, Resilience, and Greenness (LARG) to enhance efficiency, adaptability, and sustainability. This study assesses the adoption of LARG in the agricultural sector using an adaptive neuro-fuzzy inference system (ANFIS) and provides a data-driven assessment of key supply chain (SC) indicators. The results reveal that resilience (0.55) is the strongest dimension, indicating a well-developed capacity to withstand disruptions, primarily driven by technological advancements. Leanness (0.50) demonstrates moderate efficiency, with room for improvement in lead time and cost optimization. However, agility (0.48) and greenness (0.47) are weaker, highlighting the need for enhanced delivery efficiency, employee satisfaction, and sustainable practices. The study suggests that integrating real-time tracking, AI-driven forecasting, and circular economy principles can significantly improve SC responsiveness and environmental sustainability. Future research should explore sector-specific LARG applications, cross-country comparative analyses, and dynamic ANFIS models for real-time decision-making. By strengthening LARG dimensions, the agricultural sector can develop a more competitive, adaptive, and sustainable SC, ensuring long-term efficiency and environmental responsibility. |
| Cote : | Réservé lecteur CIHEAM |
| URL / DOI : | https://doi.org/10.1016/j.jclepro.2026.148496 |
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