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September 23, 2025Future Internet15 citationsOpen Access

AI-Driven Transformations in Manufacturing: Bridging Industry 4.0, 5.0, and 6.0 in Sustainable Value Chains

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AFAndrés Fernández‐MiguelFMFernando Enrique García MuiñaSOSusana Ortíz-Marcos

Key Points

  • AI-driven technologies improve predictive maintenance and real-time supply chain optimization.
  • The study identifies significant links between digital transformation and regulatory demands, notably the Corporate Sustainability Reporting Directive.
  • A mixed-methods approach combines interviews with industry stakeholders and reviews of secondary data to develop the Industry 6.0 model.
  • Further research in diverse sectors is crucial to fully understand the long-term impacts of AI-enabled frameworks.

Abstract

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.

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Cite This Study

Fernández‐Miguel et al. (2025) studied this question.

synapsesocial.com/papers/68d4724731b076d99fa6a871https://doi.org/10.3390/fi17090430
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