A Data-Driven Predictive Approach to Achieve Waste Management at the Local Scale: A Case Study in a University Cafeteria

University cafeterias generate solid waste as a result of high user turnover and routine food service operations. While waste characterization studies are common in higher education institutions, data-driven predictive modeling remains limited, particularly in Latin American contexts. This study addresses this gap by integrating physical waste generation with behavioral surveys to develop predictive tools for operational decision-making. The findings should be interpreted as a single-site operational demonstration; broader generalization requires replication and local recalibration in cafeterias with different operational and social characteristics. Waste generation was characterized in a Panamanian university cafeteria by shift over 20 consecutive working days, separating organic and inorganic fractions, and collecting 705 user surveys on consumption habits. Two complementary predictive approaches were developed: a rule-based classification model and a Monte Carlo simulation framework. Organic waste exhibited a stable pattern throughout the study period, with clear concentration during lunch hours and a strong dependence on user volume. In contrast, inorganic waste showed higher day-to-day variability and increased during evening service, reflecting changes in service practices rather than attendance alone. Statistical analysis indicated that waste generation was more closely associated with food type purchased and faculty affiliation than with self-reported environmental awareness. Overall, the results demonstrate that straightforward predictive approaches can support shift-level planning and operational waste management decisions in university cafeterias.