This article proposes and validates a novel methodology for automated simulation model generation of production systems based on monitoring sensors and actuator states controlled by Programmable Logic Controllers during regular operations. Although conventional Discrete Event Simulation is essential for material flow analysis and digital experimentation in Industry 4.0, it remains a resource-intensive and time-consuming endeavor—especially for small and medium-sized enterprises. The approach introduced in this research eliminates the need for prior system knowledge, physical inspection, or modification of existing control logic, thereby reducing human involvement and streamlining the model development process. The results confirm that essential structural and operational parameters—such as process routing, operation durations, and resource allocation logic—can be accurately inferred from runtime data. The proposed approach addresses the challenge of simulation model obsolescence caused by evolving automation and shifting production requirements. It offers a practical and scalable solution for maintaining up-to-date digital representations of manufacturing systems and provides a foundation for further extensions into Digital Shadow and Digital Twin applications.