The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible.

Despite recent progresses in smart manufacturing, existing architectures often fail to meet the comprehensive demands of modern manufacturing environments, such as flexibility, scalability, and seamless human-machine interaction. The COGNitive Industries for smart MANufacturing (COGNIMAN) architecture addresses these gaps by integrating advanced technologies with a focus on human-centric design. This article introduces COGNIMAN architecture as an essential pillar for Industry 5.0, enhancing operational efficiency through continuous monitoring, real-time data analysis, and predictive insights powered by machine learning. Its modular design optimizes resource utilization, reduces waste, and improves energy efficiency, aligning with eco-conscious manufacturing trends. By integrating sensors, robotics, digital twins, simulation, and AI, COGNIMAN offers a flexible, resilient, and sustainable platform adaptable to various manufacturing processes. The novelty of COGNIMAN architecture lies in its comprehensive approach, addressing key requirements such as flexibility, interoperability, maintainability, and adaptability. It fosters inclusive and safer working environments while supporting sustainable manufacturing practices. A case study in glass fiber manufacturing demonstrates COGNIMAN’s architecture applicability across different fields, enhancing the global competitiveness of European technology and manufacturing sectors.