AI for Sustainable Supply Chain Optimization and Carbon Footprint Reduction
Abstract
Traditional supply chains contribute significantly to carbon emissions, requiring AI-driven optimizations for sustainability. This paper explores machine learning applications in sustainable logistics, focusing on demand forecasting, route optimization, and inventory management. AI models such as deep reinforcement learning, genetic algorithms, and predictive analytics are used to enhance supply chain efficiency while minimizing environmental impact. Case studies highlight AI-powered supply chain initiatives that reduce emissions, improve resource utilization, and promote eco-friendly transportation solutions.
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