Arithra
Problem: Despite sufficient global food production, inefficiencies in food consumption and waste management lead to significant food waste in restaurant chains. This not only contributes to environmental issues, such as methane emissions, but also exacerbates global hunger and resource scarcity. With 1.3 billion tons of food wasted annually, it is imperative to develop a system that can address these challenges by reducing surplus and waste in real-time. Implemented Solution: To tackle this issue, I developed an AI-based predictive algorithm as part of the Mercedes-Benz beVisioneers Fellowship. The algorithm, trained on extensive data, predicts with 85% accuracy whether food is likely to go to waste in restaurant chains. It leverages real-time feedback loops to continually improve its predictions, helping restaurants optimize their food production and consumption. This solution has the potential to reduce food waste by 25-30%, minimize environmental impact by lowering methane emissions, and contribute to sustainable resource management. It aligns with SDG 12 (Responsible Consumption and Production) by promoting efficient food utilization and addressing global hunger indirectly by potentially redistributing surplus food to those in need.
Artificial Intelligence And Digital Services
Artificial intelligence
Beta testing has been successfully completed, and full-scale implementation is expected to commence very soon. The transition from beta to full implementation will be carried out in phases to ensure smooth integration and adaptation.
Implemented at Osmania University Cafeteria and a local Hyderabad restaurant, achieving a 20-25% reduction in food waste.
1. High Prediction Accuracy: With an 85% accuracy rate in forecasting food waste, the algorithm provides reliable insights that help restaurants make informed decisions, reducing unnecessary surplus. 2. Real-time Adaptability: The system incorporates real-time feedback loops, allowing it to learn and improve continuously. This dynamic adaptability sets it apart from static models and ensures its predictions remain relevant in fluctuating business environments. 3. Cost Efficiency: By optimizing food consumption, the algorithm helps restaurant chains significantly cut costs associated with overproduction, spoilage, and waste management, leading to improved profit margins. 4. Sustainability Alignment: The solution directly addresses growing consumer demand for eco-conscious businesses by reducing food waste and lowering methane emissions. This alignment with global sustainability goals, such as SDG 12, strengthens its market appeal. 5. Scalability: The algorithm is designed to scale easily across various types of restaurants and food service industries, making it adaptable for large chains and small businesses alike. 6. Enhanced Customer Experience: By minimizing food waste, restaurants can offer fresher, higher-quality meals, improving customer satisfaction and loyalty.
Mercedes Benz beVisioneers Fellowship: Recognized for developing an innovative AI-based solution for food waste management. Bronze and Silver Medals at the Queen’s Commonwealth Essay Competition: Awarded by the Royal Commonwealth Society for exceptional writing achievements. Finalist in National AI Hackathon in Defence Electronics: Acknowledged for innovative contributions in artificial intelligence and technology. Indian Regional Ambassador for the United Nations Environment Program (UNEP): Honored for leadership and advocacy in environmental sustainability.
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