Paper Type
ERF
Abstract
Food waste in institutional dining environments poses a significant financial and environmental challenge. This study explores how the Raccoon Eyes AI dashboard (a computer vision and analytics platform) drives data-informed decisions to reduce food waste at Georgia State University Dining Halls. Drawing on over one million meal records and detailed breakdowns by food type, time, and plate count. By monitoring patterns and implementing targeted interventions (e.g., portion resizing, utensil adjustments, and menu optimization), the research quantifies reductions in food waste. The findings offer replicable actions for other institutions to leverage AI tools like Raccoon Eyes to improve operational performance. The results underscore the economic justification for adopting AI-powered waste tracking, highlighting its dual impact on budgets and environmental outcomes.
Paper Number
1347
Recommended Citation
El-Rayes, Nesreen; Roth, Wendy Ann-Swenson; Wilson, Jennifer; and Zhou, Ivan, "AI-Enabled Tool for Food Waste Reduction in University Dining Halls: Data-Driven Study" (2026). AMCIS 2026 Proceedings. 4.
https://aisel.aisnet.org/amcis2026/sig_green/sig_green/4
AI-Enabled Tool for Food Waste Reduction in University Dining Halls: Data-Driven Study
Food waste in institutional dining environments poses a significant financial and environmental challenge. This study explores how the Raccoon Eyes AI dashboard (a computer vision and analytics platform) drives data-informed decisions to reduce food waste at Georgia State University Dining Halls. Drawing on over one million meal records and detailed breakdowns by food type, time, and plate count. By monitoring patterns and implementing targeted interventions (e.g., portion resizing, utensil adjustments, and menu optimization), the research quantifies reductions in food waste. The findings offer replicable actions for other institutions to leverage AI tools like Raccoon Eyes to improve operational performance. The results underscore the economic justification for adopting AI-powered waste tracking, highlighting its dual impact on budgets and environmental outcomes.
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