Abstract

Big Data Analytics (BDA) and Artificial Intelligence (AI) are transforming supply chain sustainability by enhancing efficiency and minimizing environmental impact. The integration of these technologies in logistics and transportation enables real-time monitoring, predictive modeling, and optimized decision-making, thereby reducing greenhouse gas (GHG) emissions. BDA facilitates data-driven insights by aggregating information from diverse sources such as IoT sensors, GPS tracking, and enterprise resource planning (ERP) systems. These insights support route optimization, demand forecasting, and dynamic inventory management, minimizing fuel consumption and waste. AI-powered solutions, including machine learning algorithms and reinforcement learning models, improve fleet management and predictive maintenance, reducing energy usage and emissions. Moreover, AI-driven automation enhances warehouse operations through smart robotics and efficient resource allocation, leading to lower carbon footprints. Advanced AI applications, such as digital twins and blockchain-based transparency, further improve sustainability by enabling end-to-end visibility and accountability in supply chains. Furthermore, BDA and AI facilitate compliance with environmental regulations and corporate sustainability goals by providing accurate carbon footprint assessments and scenario analysis for emission reduction strategies. Challenges such as data privacy, infrastructure costs, and integration complexities remain, but advancements in cloud computing and AI-driven analytics continue to mitigate these barriers. This explores the role of BDA and AI in optimizing supply chain sustainability and reducing GHG emissions in logistics and transportation. It examines case studies and emerging trends, highlighting the potential for these technologies to drive sustainability in global supply chains. By leveraging AI and big data, businesses can achieve operational efficiency while contributing to global climate change mitigation efforts.