Achieving resiliency in smart manufacturing through Artificial Intelligence

This thesis explores the integration of Artificial Intelligence to enhance resilience in Smart Manufacturing, a domain where AI adoption remains limited despite its significant potential. It introduces a maturity model, based on Fuzzy Cognitive Maps, to assess manufacturing progress and guide future advancements. Additionally, it proposes a framework that incorporates Smart Manufacturing key enabling technologies to improve adaptability at the operational level. Building on and extending this framework, the thesis further investigates the application of automated reasoning techniques, LLMs, and computer vision to enhance resilience across the supply chain and the company (specifically in quality control), while also increasing agility to address human needs.