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.
Andrea Chiarini, A. Grando, Surajit Bag· Journal of Manufacturing Tec...· 0 citations
The rapid adoption of artificial intelligence (AI) in labor-intensive manufacturing raises concerns about how trust between humans and AI develops under production pressure. This study examines the erosion and consequences of human–AI trust in garment factories, where workers must quickly adapt to AI-driven systems in highly monitored environments. Drawing on the Swift Trust Theory and the Job Demands–Resources model, we propose a framework that considers relationships among constructs, such as compressed trust formation, trust fragility, sacrificial compliance, perceived organizational support, and workplace techno-pressure. We employed a two-phase mixed-methods design. An exploratory qualitative study informed construct development, followed by a quantitative study for scale validation and hypothesis testing. Results show that compressed trust formation is positively associated with trust fragility, and both are positively linked to sacrificial compliance. Trust fragility partially mediates the relationship between compressed trust formation and sacrificial compliance. Perceived organizational support weakens the relationship between compressed trust formation and trust fragility, whereas workplace techno-pressure strengthens the relationship between trust fragility and sacrificial compliance. The findings suggest that trust formed rapidly under techno-pressure can enable short-term coordination but remains structurally fragile and may convert into self-sacrificial work behaviors. The study extends Swift Trust Theory to human–AI collaboration and embeds trust dynamics within the Job Demands–Resources model, highlighting how organizational support and techno-pressure management shape whether digital transformation supports sustainable or harmful forms of adaptation.
Surajit Bag, Muhammad Sabbir Rahman, S. Alam· IEEE transactions on enginee...· 0 citations
An integrated conceptual framework is developed with the help of key findings and propositions that emerged from the selected studies to address specific research questions, find gaps and propose future research agendas.
Koustav Dakshit, Sachin Modgil, Rohan Mukherjee et al.· The TQM Journal· 0 citations
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