Artificial intelligence-enabled sustainability in sme supply chains: a systematic and bibliometric literature review
TL;DR
The findings indicate that machine learning is the dominant AI technology in SME supply chains, primarily used for forecasting, inventory management, process monitoring, logistics optimization, anomaly detection, and operational decision support, and economic and environmental sustainability dimensions receive substantially greater attention than social sustainability.
Abstract
The growing convergence of sustainability pressures, digital transformation, and supply chain complexity lead to a rising interest in how artificial intelligence (AI) can support sustainable supply chain management, particularly in small and medium-sized enterprises (SMEs). Despite the expanding literature on AI and sustainability, important gaps remain regarding how AI-enabled technologies contribute to economic, environmental, and social performance under the resource constraints typically faced by SMEs. Existing studies frequently address AI adoption through broad discussions of digital transformation, forecasting, or automation, while providing comparatively limited integration of sustainability and SME-specific organizational conditions. This research investigates the application of AI technologies in SME supply chains and their contribution to sustainability performance. The research adopts a systematic and bibliometric literature review approach based on the PRISMA methodology. Peer-reviewed journal articles indexed in Scopus and published between 2022 and 2026 were analyzed. Following a structured screening and eligibility assessment process, 49 articles were included in the final sample. The selected studies were examined through descriptive profiling, thematic coding, and bibliometric analyses covering AI technologies, sustainability dimensions, supply chain applications, implementation barriers, and emerging research trends. The findings indicate that machine learning is the dominant AI technology in SME supply chains, primarily used for forecasting, inventory management, process monitoring, logistics optimization, anomaly detection, and operational decision support. Natural language processing, large language models, and computer vision appear less frequently but are increasingly relevant for communication, information management, and intelligent operational analysis. The review also shows that economic and environmental sustainability dimensions receive substantially greater attention than social sustainability. Recurring barriers to AI adoption include financial constraints, weak digital infrastructures, fragmented data environments, limited analytical capabilities, and organizational readiness challenges. At the same time, managerial commitment, strategic alignment, technological partnerships, and policy support emerge as important enabling factors. This investigation contributes to the literature by integrating research on AI, sustainability, and SME supply chains through a combined systematic and bibliometric perspective. The findings highlight that the sustainability potential of AI depends not only on technological capabilities but also on organizational conditions, data governance, and SMEs’ ability to integrate AI-enabled decision support into operational processes. The study also identifies important theoretical, managerial, and methodological gaps and proposes directions for future research on AI-enabled sustainability in SME supply chains.