Machine Learning in Sustainable Supply Chain Management: Reducing Waste and Carbon Footprint
Abstract
Global supply chains are essential to economic growth but are often associated with high levels of waste and greenhouse gas emissions. This paper explores how machine learning can transform supply chain management for sustainability by optimizing inventory levels, reducing transport emissions, and minimizing resource consumption. Our ML model uses demand forecasting and route optimization to reduce overproduction, waste, and emissions. Results from industry case studies show that ML-powered supply chains can achieve greater efficiency and reduced environmental impact, supporting the shift towards sustainable production and logistics practices.
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