Investigating the state-of-the-art and challenges of artificial intelligence for production management in Italian manufacturing SMEs

Abstract

This study aims to examine how manufacturing SMEs assess and prioritise AI applications for production management and investigates the technological, organisational, and environmental challenges hindering their adoption. A mixed-methods approach was used. First, semi-structured interviews with 12 experts, conducted via the Delphi method, identified nine conceptual AI application areas. These were tested through a survey of 229 Italian manufacturing SMEs, with quantitative ratings and qualitative comments analysed to reveal adoption patterns and challenges. SMEs show strong interest in AI for resource optimisation, energy efficiency, maintenance, and simulation. However, scepticism surrounds scheduling, root-cause analysis, and predictive quality control, due to data limitations, reliance on human expertise, and perceived complexity. Organisational barriers, like a lack of planning, skills, and trust also hinder adoption. The study focuses on Italian manufacturing SMEs, which may limit generalisability. The initial identification of AI applications was expert-driven, possibly introducing bias. Future research should include cross-national comparisons and longitudinal studies to track the evolving adoption of this approach. The study provides a practical framework to guide AI adoption in SMEs, highlighting core applications that offer immediate operational benefits, as well as addressing technological, organisational, and environmental challenges. This research offers one of the first application-specific analyses of AI in SME production management, revealing nuanced adoption priorities and challenges. It bridges digital transformation and operations management literature, providing a structured roadmap for SMEs to assess AI readiness and pursue dynamic, context-aware integration.