Implementation of AI Transportation Routing in Reverse Logistics to Reduce CO2 Footprint - Mandal, Joydeb; Mohammed, Irshadullah (International Journal of Supply Chain Management, 2024)
Purpose: This study explores the implementation of artificial intelligence (AI) in transportation routing within reverse logistics to reduce CO2 emissions. By optimizing routing processes, we aim to enhance the efficiency of logistics operations while minimizing environmental impact. Methodology: we analyze the implications of improved transportation strategies. Furthermore, we discuss the intersection of technological innovation and environmental economics in shaping sustainable practices. Findings: The findings underscore the importance of AI-driven decision-making frameworks (D81) and their role in advancing IT management in logistics. The study on AI-driven transportation routing in reverse logistics highlights significant implications for both businesses and the environment. By optimizing routes, AI reduces fuel consumption and CO₂ emissions, helping companies meet sustainability goals. This leads to cost savings, as AI improves route efficiency and fleet utilization, while also enhancing operational efficiency. AI’s ability to predict and adjust to real-time conditions ensures scalability and adaptability, making it easier to handle increasing returns volumes in e-commerce. Moreover, AI enables better inventory management and resource planning, reducing the need for urgent shipments. It also paves the way for innovations like autonomous vehicles, further transforming reverse logistics. Unique Contribution to Theory, Practice and Policy: The study shows how AI can help businesses comply with environmental regulations, providing a competitive edge. Ultimately, AI integration in reverse logistics contributes to both operational improvements and environmental sustainability, reinforcing corporate social responsibility.