Reducing E-commerce Carbon Footprint through AI-Driven Warehouse and Supply Chain Optimization
Author(s): Gautham Ram Rajendiran, Rajapriya Ayyadurai
Publication #: 2410008
Date of Publication: 07.09.2023
Country: USA
Pages: 1-8
Published In: Volume 9 Issue 5 September-2023
DOI: https://doi.org/10.5281/zenodo.13949398
Abstract
The growth in ECommerce has significantly improved the carbon footprint of supply chain and logistics industries. This paper explores the use of machine learning models like Random Forests, Gradient Boosting Machines (GBMs), Clustering Algorithms and Neural Networks to optimize supply chain operations and reduce emissions. The models help predict and refine emission data, identify nodes that have a high emission and forecast emissions for future. These insights allow businesses to target inefficiencies and implement emission-reducing strategies, such as optimizing routes and improving energy use in warehouses. This paper explores how machine learning and artificial intelligence can be effectively used in order to reduce the carbon footprint in supply chain and logistics.The growth in ECommerce has significantly improved the carbon footprint of supply chain and logistics industries. This paper explores the use of machine learning models like Random Forests, Gradient Boosting Machines (GBMs), Clustering Algorithms and Neural Networks to optimize supply chain operations and reduce emissions. The models help predict and refine emission data, identify nodes that have a high emission and forecast emissions for future. These insights allow businesses to target inefficiencies and implement emission-reducing strategies, such as optimizing routes and improving energy use in warehouses. This paper explores how machine learning and artificial intelligence can be effectively used in order to reduce the carbon footprint in supply chain and logistics.
Keywords: Supply Chain, Ecommerce, Machine Learning, Artificial Intelligence, Sustainability
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