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Reducing Food Waste Through Predictive Analytics
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Author(s): Vikanksha Vikanksha (Lovely Professional University, India), Arun Kumar (Lovely Professional University, India)and Jatinder Singh (Lovely Professional University, India)
Copyright: 2026
Pages: 26
Source title:
Revolutionizing Sustainable Food Production With Quantum Computing
Source Author(s)/Editor(s): Mong Fong Horng (National Kaohsiung University of Science and Technology, Taiwan), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq), Chin-Shiuh Shieh (National Kaohsiung University of Science and Technology, Taiwan)and Vishal Jain (School of Engineering and Technology, Vivekananda Institute of Professional Studies, New Delhi, India)
DOI: 10.4018/979-8-3373-3957-3.ch009
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Abstract
Reducing food waste is a critical challenge that impacts environmental sustainability, economic efficiency, and global food security. Predictive analytics, leveraging big data and machine learning technologies, provides innovative solutions to address this issue by enhancing demand forecasting, optimizing inventory management, and improving supply chain responsiveness. By analyzing historical consumption patterns and employing advanced algorithms, businesses can better align production with consumer demand, thereby minimizing overproduction and food spoilage. This paper discusses various predictive analytics applications in the food industry, including real-time monitoring, demand forecasting, and waste analysis tools. It highlights case studies demonstrating the effectiveness of these technologies in reducing food waste, ultimately contributing to a more sustainable food system.
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