The evolution of the current technological landscape has opened an emergent paradigm that enables interoperability between the digital and physical world, leading to a new generation of industrial systems. This new digitalization era marks the beginning of the fourth industrial revolution, usually referred to as "Industry 4.0". By employing recent technologies and concepts such as Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS), Cloud-based technologies, , Service-oriented Architecture (SoA), and Artificial Intelligence (AI), the Industry 4.0 approach aims to address the dynamic evolution of contemporary requirements as well as improve the sustainability and efficiency in industrial production. 

While this new industrial paradigm facilitates the integration and collaboration among industrial components, it also introduces greater complexity to the industrial systems, thereby potentially increasing costs related to system development and maintenance. Specifically, significant engineering effort is dedicated to addressing the heterogeneity, interoperability and scalability of those integrated components. As a result, in order to mitigate those challenges, the self-adaptive solution appears as a potential approach to automate the management and supervision of the systems. Self-adaptation allows the system to adapt in the face of changes in its operating environment and in the system itself without human intervention.

This thesis outlines the progress made towards self-adaptation in industrial production. It proposes an architectural design that enables dynamic adaptation for IIoT systems. Particularly, in order to facilitate the integration of heterogeneous and numerous physical components, the proposed approach shifts from tightly-coupled automation systems to loosely-coupled flexible information and communication infrastructure by employing service-oriented and decentralized technologies. Furthermore, the concept of Autonomic Computing (AC) is exploited to address the interoperability among the systems with the goal to enable autonomous decision-making based on real-time information from the integrated components.   

To illustrate the potential of this design, an Autonomic Adaptation System is proposed to provide dynamic adaptation as a service in order to assist IIoT systems to re-orchestrate the communication among them or re-configure their internal functionality. The prototype of the system has been implemented and tested with a simulated industrial use case.