Autonomicity in Cyber-Physical Systems: Proposing a Theoretical Concept and a Scale Divided into Levels of Automation

The rapid advancement of Industry 4.0 has positioned Cyber-Physical Systems (CPS) as key enablers of intelligent and autonomous manufacturing. While automation and autonomy are widely discussed, the concept of autonomicity—the system’s ability to self-manage, adapt, and make decisions independently—remains theoretically underdeveloped and lacks a structured framework for assessment. This study addresses this gap by defining the concept of autonomicity in CPS and proposing a five-level evaluation scale. A theory development approach was adopted, supported by a systematic literature review and a bibliometric analysis conducted using the Scopus database and VOSviewer software. The resulting scale classifies CPS autonomicity from total human dependence to full self-management, incorporating criteria such as AI capabilities, self-learning, fault tolerance, and autonomous decision-making. The findings contribute to both theory and practice by refining the conceptual understanding of CPS autonomicity and offering a structured tool for its assessment. This work provides a foundation for future empirical research and supports strategic planning in autonomous industrial environments.