Personalized production is widely adopted in discrete manufacturing due to its ability to meet diverse customer requirements through flexible combinations of components. However, it also poses significant challenges to production synchronization, which requires all necessary operators, tools, materials, and industrial robots to be available within the prescribed time and space. In complex multi-stage manufacturing processes, execution deviations can accumulate into operation-level uncertainty, namely the risk that an operation cannot start, proceed, or finish as scheduled because the required resources and execution conditions are no longer aligned. Such uncertainty undermines resource utilization and may trigger cascading delays across the entire process. To address this challenge, this paper proposes a spatial–temporal event compiler (STEC) framework comprising digital, knowledge, and reasoning engines for compiling and analyzing manufacturing events. Specifically, the state information of digital models is compiled into a spatial–temporal event graph, which is continuously updated through a multi-clock alignment scheme that aligns distinct planning, scheduling, and execution time scales. Within the reasoning engine, a look-around reasoning approach is developed to capture historical consistency, the current execution context, and near-future evolution trends by integrating look-backward, look-present, and look-forward perspectives, thereby enabling uncertainty assessment. Furthermore, an LLM-based synchronization mechanism is incorporated into the framework to provide status reports through multi-turn interactions, allowing managers to query the current execution status and assess potential downstream impacts for timely intervention. Finally, a case study demonstrates that STEC improves the accuracy and stability of uncertainty identification.