Application of AI-Driven Clustering to Address Territorial Heterogeneity and Enhance Socio-Economic Resilience in Ecuadorian Agri-Food Supply Chains

Ecuadorian agri-food supply chains are characterized by strong territorial heterogeneity, crop perishability, and unequal production concentration, creating significant socio-economic and logistical challenges. To address these territorial disparities, this study employs an AI-driven clustering approach to classify Ecuador’s 23 continental provinces by their agri-food production and supply chain integration profiles. Using official 2024 provincial tabulations from the National Institute of Statistics and Censuses of Ecuador, seven variables were analyzed: harvested banana area, banana production, cocoa production, rice production, corn production, potato production, and banana yield. The methodology combined descriptive statistics and Pearson correlations with unsupervised machine learning techniques, specifically hierarchical agglomerative clustering and k-means algorithms after z-score standardization. The descriptive results showed extreme dispersion across provinces, with coefficients of variation exceeding 100% for all variables and marked right skew. The final four-cluster solution (k = 4, BSS/TSS = 78.65%, average silhouette = 0.43) differentiated: (cluster 1) a potato-specialized Andean profile represented by Carchi; (cluster 2) a broad group of low-intensity provinces requiring inclusive logistical integration; (cluster 3) medium-scale banana provinces with high yields; and (cluster 4) the dominant tropical production core formed by Guayas and Los Rios. The typology explains 78.65% of the standardized variability and provides the territorial evidence base required for designing differentiated supply-chain planning, resilience strategies, and public policies tailored to each provincial typology.