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March 13, 2026The International Journal of Social Sustainability in Economic Social and Cultural Context0 citationsOpen Access

From Data to Sustainability

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AHAkash HalderPCParul ChouhanSJSatish Jha

Key Points

  • Assess the effectiveness of a smart buffet management system in reducing food waste in university kitchens.
  • Implemented a smart buffet management system in three university kitchens.
  • Utilized Bluetooth-enabled digital scales to monitor food weight in real-time.
  • Analyzed food waste before and after the system implementation.
  • Achieved a 44.8% reduction in daily food waste, from 30.0 kg to 15.9 kg.
  • Student satisfaction with portion sizes increased significantly, with 65%–88% approval.
  • Identified a 40% decline in high-waste consumer behavior.

Abstract

Food waste is a persistent challenge in the hospitality sector, particularly in buffet-style dining, where overproduction and leftovers are common. This study assessed the effectiveness of a smart buffet management system (SBMS) implemented across three university kitchens to improve portion planning and reduce waste. The system incorporated an ultra-thin Bluetooth-enabled digital scale (~20–40 g, 3.6–4.2 mm thickness, 10–15 kg capacity, ±2–5 g accuracy) placed beneath buffet containers, transmitting real-time food weight data to a mobile application for continuous monitoring of availability, replenishment, and leftovers. At baseline, the sites generated an average of 30.0 kg of food waste per day (8.8% of food prepared). After implementation, daily waste declined to 15.9 kg (4.6%), representing a 44.8% reduction (p < 0.05). Meal-wise analysis showed reductions of 44% to 48% for breakfast, lunch, and dinner, while category-wise analysis identified vegetables and starchy foods as the major contributors to waste, both achieving reductions of ~45%. Overall, total leftovers fell by 75%, the proportion of food wasted decreased by 52.3%, and procurement costs dropped by 18%, without reducing menu diversity. Student satisfaction with portion sizes also improved significantly (65%–88%). K-means clustering further revealed a 40% decline in high-waste consumer behavior, with a shift toward moderate- and low-waste groups. This multi-site experiment indicates that a combination of real-time, under-container food monitoring with analytics-driven decision support can substantially reduce food waste and procurement at the kitchen level in institutional buffet operations in India.

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Cite This Study

Halder et al. (2026) studied this question.

synapsesocial.com/papers/69b3acf302a1e69014ccf1c8https://doi.org/10.18848/2325-1115/cgp/a255
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