Artificial Intelligence in Reverse Logistics for E-Commerce: Streamlining Returns Processing and Ensuring Supply Chain Efficiency
Farizul Zainal
Universiti Malaysia Kelantan, Department of Computer Science, Jalan Kota Bharu-Machang, 18500 Machang, Kelantan, Malaysia
Hakim Baharudin
Universiti Tun Hussein Onn Malaysia, Department of Computer Science, Jalan Masjid Tanah, Parit Raja, 86400 Batu Pahat, Johor, Malaysia.
Aina Khalid
Universiti Teknologi MARA, Faculty of Computer and Mathematical Sciences, 40450 Shah Alam, Selangor, Malaysia.
Abstract
Reverse logistics in e-commerce has emerged as a critical focus area, driven by increasing consumer expectations for seamless returns and growing e-commerce adoption. Managing returned products efficiently requires robust strategies to minimize costs, improve customer satisfaction, and reduce environmental impacts. Artificial Intelligence (AI) offers transformative potential by enabling intelligent decision-making and automation in reverse logistics processes. This paper examines the integration of AI in reverse logistics within e-commerce, focusing on its role in streamlining returns management, optimizing resource allocation, and ensuring overall supply chain efficiency. Key AI applications such as predictive analytics, automated inspection systems, and dynamic routing are explored. Furthermore, the study highlights challenges such as data integration, algorithmic transparency, and system scalability that must be addressed for effective AI deployment. By leveraging advanced machine learning techniques, natural language processing, and computer vision, AI not only accelerates returns processing but also contributes to a circular economy by enhancing the reuse and recycling of returned goods. This paper synthesizes existing research, industry practices, and case studies to propose a comprehensive framework for AI-driven reverse logistics. The findings emphasize the need for collaborative efforts between technology providers, e-commerce companies, and policymakers to achieve sustainable and efficient reverse logistics operations.
