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Artificial Intelligence for Greener Warehousing: From Predictive Planning to Operational Excellence
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Author(s): R. N. Ravikumar (Marwadi University, Rajkot, India), S. Aarthi (Marwadi University, Rajkot, India)and Samariddin Makhmudov (Termez University of Economics and Service, Termez, Uzbekistan)
Copyright: 2026
Pages: 32
Source title:
Innovations in Green and Energy-Efficient Warehousing
Source Author(s)/Editor(s): Mohamed Amine Frikha (King Faisal University, Saudi Arabia), Mohieddine Rahmouni (King Faisal University, Saudi Arabia)and Ben Othman Soufiane (King Faisal University, Saudi Arabia)
DOI: 10.4018/979-8-3373-3176-8.ch009
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Abstract
Energy efficiency and sustainability are key priorities in modern warehousing and logistics. AI plays a transformative role by enabling smart operations through accurate demand forecasting and inventory management. Traditional models often lead to overstocking, understocking, and energy waste. AI techniques such as time-series models, RNNs, and adaptive systems support just-in-time inventory, reduce resource use, and cut emissions. Integrated with IoT, RFID, and WMS, AI improves storage, picking, and restocking, lowering labor and energy costs. It also manages slow-moving or excess stock to reduce waste. The chapter highlights AI's synergy with digital twins, cloud, and edge computing for adaptive, sustainable warehouses. Real-world case studies show gains in forecasting, carbon reduction, and SKU efficiency. Key challenges like data quality and integration are addressed with practical strategies. AI, aligned with SDGs and carbon neutrality goals, is not just a tech upgrade but a core enabler of sustainable, intelligent warehousing.
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