Predictive analytics and AI in sustainable logistics: A review of applications and impact on SMEs

Abstract

This paper provides a comprehensive review of the applications of predictive analytics and artificial intelligence (AI) in sustainable logistics, with a particular focus on the impact on small and medium-sized enterprises (SMEs). The objective is to explore how these advanced technologies are transforming logistics operations by enhancing efficiency, reducing environmental impact, and promoting sustainability in supply chains. Through an extensive literature review, the study analyzes various use cases where predictive analytics and AI are employed to optimize routing, demand forecasting, inventory management, and energy consumption. The research methodology is based on a systematic review of existing academic and industry publications, supplemented by case studies highlighting the practical implementation of AI-driven tools in SME logistics operations. The findings demonstrate that SMEs, despite their limited resources, are increasingly adopting these technologies to gain competitive advantages, improve decision-making processes, and meet sustainability goals. Furthermore, the study identifies key challenges SMEs face, including the high cost of implementation, lack of technical expertise, and data privacy concerns. The paper concludes that the integration of predictive analytics and AI in sustainable logistics presents significant opportunities for SMEs to enhance operational efficiency, lower costs, and reduce their carbon footprint. However, to fully realize these benefits, SMEs must overcome technological and resource barriers through targeted investments, partnerships, and policy support aimed at fostering technological adoption and sustainability in the logistics sector. The implications of these findings for future research and SME practices are also discussed.