Efficient production planning, machine optimization, and bottleneck analysis are critical for reducing cycle times in manufacturing processes. This paper introduces an automated process mining (PM) approach designed to streamline these tasks by generating and optimizing finite state machines (FSMs) from event logs derived from signal changes in industrial plants. Unlike conventional methods, this approach requires no prior knowledge of the configuration or behavior of the target programmable logic controllers (PLCs). The synthesized FSMs facilitate the extraction of process-oriented insights, enabling the identification of underperforming manufacturing processes and detailed analysis of their durations. The proposed method is validated through a case study on a real industrial plant, demonstrating its efficacy in uncovering process inefficiencies and supporting decision-making. This work provides a novel, generalizable framework for process analysis in manufacturing environments, contributing to the broader field of automated process optimization.