Abstract

In the complex landscape of Industry 5.0, traditional management systems for smart manufacturing struggle to harmonize high-speed production with the rapid integration of AI and digital technologies. Crucially, these legacy frameworks often fail to capture tacit human knowledge or ensure trustworthy AI and trusted sharing of sensitive industrial data. This paper proposes a novel Decision Support Framework (DSF) that addresses these challenges through a multi-layered approach. At its core, the framework utilizes Data Spaces to enable secure, sovereign data sharing, ensuring that organizations maintain control over their assets. To handle the inherent ambiguity of industrial data, the system employs fuzzy logic and DAG-based root-cause-oriented investigation to provide robust recommendations, helping users distinguish descriptive correlations from plausible structural dependencies that require expert validation. Furthermore, the framework integrates eXplainable AI (XAI) services and AI-driven visual analytics, transforming complex algorithmic outputs into transparent, intuitive insights. By synthesizing data sovereignty with interpretable machine intelligence, this framework empowers trusted data sharing and human-centric decision-making, providing an advanced platform for achieving operational excellence within the Industry 5.0 vision. The proposed DSF is validated in three different pilot cases with end-users to be a milk industry, an automotive supplier and a machine manufacturer.

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