The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Forecasting for a Greener Future: Predictive Analytics in Food Waste Management
|
|
Author(s): Amandeep Kaur (CGC University, Mohali, India), Gaganpreet Kaur (CGC University, Mohali, India), Ramandeep Sandhu (Lovely Professional University, Jalandhar, India)and Deepika Ghai (Lovely Professional University, India)
Copyright: 2026
Pages: 24
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.ch010
Purchase
|
Abstract
The global challenge of food waste—nearly one-third of all food produced—is a critical issue with environmental, economic, and social impacts. In alignment with UN SDG 12.3 to halve food waste by 2030, this chapter explores how predictive analytics can transform the food supply chain. Leveraging AI, ML, IoT, and real-time data, predictive models are used to forecast demand, optimize inventory, and anticipate spoilage. Advanced solutions like digital twins, blockchain traceability, computer vision for freshness detection, and context-aware recommendation systems are discussed in this chapter. These tools adapt to variables such as seasonality, consumer behavior, supply chain disruptions, and climate change. The chapter also addresses ethical concerns, data management, and the scalability of predictive technologies in both developed and emerging economies. It emphasizes the need for interdisciplinary research and collaboration among governments, tech companies, and stakeholders to institutionalize predictive analytics for a sustainable and resilient global food ecosystem.
Related Content
|
Humera Shaziya, Saif Ali Alsaidi.
© 2026.
30 pages.
|
|
Nizirwan Anwar, Titik Khawa Abdul Rahman, Husna Sarirah Husin.
© 2026.
26 pages.
|
|
S. Anand.
© 2026.
34 pages.
|
|
Rajeev Kumar, Meetu Malhotra, C. Kishor Kumar Reddy.
© 2026.
36 pages.
|
|
M. Srivarshini, R. Vanithamani.
© 2026.
36 pages.
|
|
Shashank Solanki, Rituraj Sinha.
© 2026.
26 pages.
|
|
Ushaa Eswaran, Vishal Eswaran.
© 2026.
40 pages.
|
|
|