The Automated Galley” Quantifying the Impact of Smart-Kitchen Interventions on Food Waste Mitigation in Hospitality Catering

Abstract

The Global Food Waste in Hospitality is contextualised and showcases how the Global Hospitality sector faces a dual crisis of escalating operational costs and intensifying pressure to align with international sustainability standards, specifically United Nations Sustainable Development Goal (SDG) 12.3, which aims to halve per capita food waste by 2030. Within hospitality operations, commercial catering—particularly buffet-style service—represents the most significant institutional contributor to food waste. Buffets require high-volume production, constant replenishment, and wide menu variety, leading to significant waste from both overproduction and post-consumer plate waste i.e. uneaten food. Traditional waste mitigation strategies in commercial galleries relies on manual records and staff intuition, offering limited accuracy in managing fluctuating guest attendance and consumption patterns. To address this, smart-kitchen interventions, including IoT devices, predictive analytics, and computerized waste-tracking solutions, have been introduced. Despite their promising potential, empirical evidence on their specific impact on waste reduction in large-scale buffet operations remains limited. This study quantifies the impact of an AI-driven smart kitchen system on total food waste, production and plate waste streams, and operational ROI in a high-volume buffet catering environment. This study employed quantitative six-month quantitative quasi-experimental pre-test/post-test design, at a major star-category hotel buffet facility serving an average of 800 covers daily comparing phase wise 12 weeks of traditional waste tracking with 12 weeks of AI-enabled smart-kitchen waste analytics in a high-volume hotel buffet operation.

Statistical analysis will be performed using paired-samples t-tests and multivariate analysis of variance (MANOVA) to compare the mean weight of food waste across both phases, controlling for variable guest counts and seasonal menu alterations. Pilot projections indicated that smart-kitchen interventions may reduce total food waste by 28% - 35%, cut production waste by 40% - 45% and plate waste by 10% - 15%, while lowering food procurement costs by 4% - 6%. This research moves beyond qualitative "green kitchen" aspects and provides empirical evidence that AI-driven smart kitchens enhance sustainability, reduce costs, and improve operational efficiency by enabling data-driven food production and waste management without compromising service quality or guest satisfaction