Artificial intelligence in SME supply chains: a bibliometric and case study analysis

This study investigates the application of artificial intelligence (AI) in supply chain management (SCM) within small and medium-sized enterprises (SMEs). A bibliometric analysis of 135 documents retrieved from the Scopus database identifies four core research clusters: (1) digital transformation and Industry 4.0, (2) decision support and operational efficiency, (3) AI adoption in SMEs, and (4) supply chain resilience and competition. The analysis highlights the growing academic focus on AI as a key enabler of supply chain innovation yet also reveals fragmentation in the literature regarding SME-specific challenges. To complement the quantitative mapping, four case studies are analyzed to examine how SMEs are implementing AI technologies in practice. Findings confirm that AI contributes to improved forecasting, inventory management, process visibility, and responsiveness. However, barriers such as limited resources, data fragmentation, and digital maturity persist. The study demonstrates that effective AI integration in SME supply chains depends on the alignment between technological tools and organizational capabilities. By combining bibliometric and case-based evidence, the paper offers a structured overview of the current research landscape and suggests future research directions. Despite the growing interest in AI-enabled supply chains, the existing literature remains fragmented: most studies focus on large enterprises, while evidence on SME-specific adoption and implementation remains scarce. This study addresses this gap by integrating bibliometric mapping with real-world case studies, thereby providing both a theoretical synthesis and a practice-oriented framework for AI adoption in SMEs.