The Application of Machine Learning in E-commerce Supply Chains and Guiding Consumer Green Behavior

As the digital economy and green development concepts advance in parallel, e-commerce supply chains are facing the dual challenges of improving efficiency and transitioning to a low-carbon economy. A Gartner survey shows that 70% of supply chain leaders report experiencing more frequent business disruptions. Traditional supply chain models, relying on manual decision-making, are finding it increasingly difficult to keep up with complex and volatile market demands and meet environmental requirements. At the same time, consumer demand for sustainable products continues to grow. McKinsey research indicates that over 70% of consumers are willing to pay a 5% premium for green products, providing a solid market foundation for e-commerce platforms to encourage green consumption. The maturity of machine learning technology offers a new avenue for resolving this conflict. By integrating data from across the entire supply chain, machine learning enables intelligent decision-making in demand forecasting, inventory optimization, and logistics scheduling, improving operational efficiency while reducing resource waste. For example, Amazon leveraged cloud technology and machine learning to restructure its forecasting system, helping it to smoothly navigate demand fluctuations during the pandemic. JD Logistics launched the "Jingtanhui" platform, which visually displays the carbon footprint of its entire logistics chain. Cainiao Network's intelligent algorithms have helped reduce packaging material consumption by 15%. These real-world examples demonstrate that machine learning is not just a tool for improving supply chain efficiency but also a core force driving its green transformation. Based on real-world application cases from e-commerce companies, this article systematically analyzes the application of machine learning in areas such as supply chain inventory management, logistics optimization, and carbon footprint tracking. It focuses on how it can guide consumers' green behavior through data empowerment. The goal is to clarify the path to sustainable development in the e-commerce supply chain driven by technology, and to provide valuable references for industry practice and academic research.