Leveraging Machine Learning for Carbon Footprint Reduction and Sustainability Optimization in US Supply Chains

Аннотация

Climate change and environmental sustainability lie at the heart of the growing urgency that has driven innovative technologies to optimize and decarbonize supply chains, including the adoption of machine learning. With increased awareness of climate change in the world, there comes a greater demand for making practices greener, especially in supply chains. The report investigates how Machine Learning could reduce carbon footprints and further the sustainability of U.S. supply chains. Machine learning analyzes data across procurement, transportation, inventory, and production for actionable insights on how to minimize waste, optimize resource allocation, and support sustainable practices. The present study provides an outline of practical applications, and challenges faced, and provides future directions involved in the usage of Machine Learning for green supply chain goals. Key Words: Carbon Footprint; Sustainability; Greenhouse Gas Emissions; Machine Learning; Supply Chain; Optimization; Environmental Impact