A model for optimizing the identification of process variants in Robotic Process Automation

This research focuses on enhancing the identification of process variants in robotic process automation (RPA) by using process mining techniques. It introduces a novel approach for identifying RPA automation candidates, combining diverse data and process science techniques not explored previously. The study analyzed event logs from the Figshare database, applying various process mining methods to evaluate processes and identify RPA-suitable activities. The proposed model's efficiency was measured against two baseline models, showing superior performance in multiple criteria.