Alfonso Pedro Fernández del Hoyo

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence.

This paper introduces cognitive adaptivity as a novel framework for addressing human factors in cybersecurity during the Industry 5.0–6.0 transition, with a focus on hard-to-abate industries where digital transformation intersects sustainability constraints. While the integration of IoT, automation, digital twins, and artificial intelligence expands industrial efficiency, it simultaneously exposes organizations to increasingly sophisticated social engineering and AI-powered attack vectors. Traditional resilience-based models, centered on recovery to baseline, prove insufficient in these dynamic socio-technical ecosystems. We propose cognitive adaptivity as an advancement beyond resilience and antifragility, defined by three interrelated dimensions: learning, anticipation, and human–AI co-evolution. Through an in-depth case study of the ceramic value chain, this research develops a conceptual model demonstrating how organizations can embed trust calibration, behavioral evolution, sustainability integration, and systemic antifragility into their cybersecurity strategies. The findings highlight that effective protection in Industry 6.0 environments requires continuous behavioral adaptation and collaborative intelligence rather than static controls. This study contributes to cybersecurity literature by positioning cognitive adaptivity as a socio-technical capability that redefines the human–AI interface in industrial security. Practically, it shows how organizations in hard-to-abate sectors can align cybersecurity governance with sustainability imperatives and regulatory frameworks such as the CSRD, turning security from a compliance burden into a strategic enabler of resilience, competitiveness, and responsible digital transformation.

This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm.

This study investigates how AI-driven innovations are reshaping manufacturing value chains through the transition from Industry 4.0 to Industry 6.0, particularly in resource-intensive sectors such as ceramics. Addressing a gap in the literature, the research situates the evolution of manufacturing within the broader context of digital transformation, sustainability, and regulatory demands. A mixed-methods approach was employed, combining semi-structured interviews with key industry stakeholders and an extensive review of secondary data, to develop an Industry 6.0 model tailored to the ceramics industry. The findings demonstrate that artificial intelligence, digital twins, and cognitive automation significantly enhance predictive maintenance, real-time supply chain optimization, and regulatory compliance, notably with the Corporate Sustainability Reporting Directive (CSRD). These technological advancements also facilitate circular economy practices and cognitive logistics, thereby fostering greater transparency and sustainability in B2B manufacturing networks. The study concludes that integrating AI-driven automation and cognitive logistics into digital ecosystems and supply chain management serves as a strategic enabler of operational resilience, regulatory alignment, and long-term competitiveness. While the industry-specific focus may limit generalizability, the study underscores the need for further research in diverse manufacturing sectors and longitudinal analyses to fully assess the long-term impact of AI-enabled Industry 6.0 frameworks.

The world is currently undergoing an unprecedented period of global disruption, marked by the COVID-19 pandemic, the ongoing war in Ukraine, and escalating geopolitical tensions

Disruption in many supply chains has shown the vulnerability of global supply networks, especially resource-intensive ones, due to the simultaneous effects of pandemics and geopolitical crises. Reshoring and nearshoring strategies are the possible responses of manufacturing companies to disruptions in order to adapt to unforeseen events. The supply chain for the Italian ceramic sector, which is characterized by a high intensity of natural and energy resource consumption and a sourcing system with a high geopolitical risk, is examined in this study. The functional decomposition technique was used to analyze the supply chain; then, three scenarios were developed as potential remedies for the unexpected termination of Ukrainian plastic clay supply. The study also showed that complex issues require multifaceted analysis approaches, which is why a transdisciplinary approach was chosen. In addition, the analysis of the ceramic industry showed that nearshoring and reshoring strategies can reduce supply risk and have a positive impact on the environment. The study also showed how bringing extraction sources closer to factories significantly reduces CO₂ emissions to the atmosphere from transportation. The main contribution of this paper is the analysis of the complexity of supply chains in times of disruption, configuring reshoring and nearshoring options through transdisciplinarity.

Purpose: The purpose of this paper is to explore the application of a hermeneutic-based approach as innovative way to study the Cultural Heritage management in a mesoeconomic space. Design/methodology/approach: The paper builds a theoretical framework based on the analysis of relevant literature in the field of cultural economics, heritage economics and conservation and restoration techniques. Then, after having defined the conceptual hypothesis, a hermeneutical interpretative model is designed for the analysis of the processes of Cultural Heritage management with particular regard to the strategies of stakeholder engagement. Findings: The research shows how the mesoeconomic space is that border area where it is possible to solve more easily the conflicts that arise as a result of the different expectations of stakeholders. Hermeneutical analysis, applied in iterative form, allows us to find common connections, points of contact and convergences between the interpretative horizons of the various stakeholders. Practical implications: The application of the interpretative model allows the identification of the expectations of stakeholders, improving the knowledge of the tangible and intangible attributes of works of art, in order to design appropriate interventions of restoration, conservation and valorization. Social implications: The new model of analysis, based on hermeneutic methodology, is designed to understand and describe the social and economic relations between the different stakeholders involved in the management of Cultural Heritage. Originality/value: This paper examines for the first time the Cultural Heritage sector within the mesoeconomic area between the micro and the macroeconomy. In addition to this mesoeconomic analysis and conceptual approach, the authors introduce as methodology the economic hermeneutics that represents an innovative tool in the field of economic and business disciplines.