Discovering simulation models from labor-intensive manufacturing systems

Simulation modeling has become essential in industries for enhancing processes, improving efficiency, and mitigating risks within manufacturing systems. However, the automatic discovery of these models remains challenging, particularly in labor-intensive manufacturing systems (LIMSs), which are widespread in industries like food or apparel manufacturing. LIMSs are characterized by the central and direct involvement of human operators throughout the value chain. In this paper, we investigate state-of-the-art modeling approaches for capturing behaviors of human operators in LIMSs and examine their implications for extracting simulation models. Specifically, we use these insights to automatically extract a simulation model of LIMSs as a stochastic Petri net (SPN): this SPN explicitly models operators' fatigue and its impact on task
durations. Our research contributes to laying the groundwork for developing Digital Twins for LIMSs. By automating model creation and ensuring continuous updates, our approach facilitates the automatic adaptation of simulation models to reflect changes in the system.