An Automated Data-Driven Simulation Framework for Digital Twin-Enabled Manufacturing Analytics Using Process Mining

Modern manufacturing systems operate in highly dynamic environments, where continuous innovation demands extensive monitoring and adaptation. Bottlenecks in these systems pose challenges for testing and implementing improvements without disrupting ongoing operations. Discrete Event Simulation (DES) offers a forward-looking methodology for assessing alternative scenarios within complex systems. However, the manual design of DES models is typically resource- and time-intensive, making it difficult to accommodate dynamic, rapidly changing conditions. The current approaches are largely conceptual and do not incorporate evidence-based DES model building. This limits their effectiveness and scalability, particularly in modern manufacturing environments characterized by high variability and constant evolution. This study targets the complexity of manually designing DES environments by proposing an automated framework—WEFTSIM—that extracts simulation models directly from manufacturing data. The goal is to enhance the accuracy, efficiency, and adaptability of DES models in representing real-world processes. The evaluation combines two approaches: (i) A real-world case-study-driven assessment of WEFTSIM’s applicability to the automatic, data-driven design of DES models through a systematic comparison of scenario performance. (ii) Validation assessment through a comparative performance analysis of the proposed methods against historical data to quantify their fidelity in representing the system behavior. WEFTSIM effectively derived DES models that closely mirror the actual manufacturing operations, attaining around 80% coverage at the activity level and achieving a high trace similarity of 90% when validated against the observed data. The automated approach reduced the manual and time-intensive conceptualization phase. The evaluation against existing benchmark methods shows WEFTSIM’s capability to automatically design DES models, detect bottlenecks, and rapidly identify an improvement scenario.