A Standardized Framework for the Discovery of Candidate Tasks for Robotic Process Automation: A Process Mining Approach

In this paper explores the integration of Process Mining techniques with Robotic Process Automation (RPA) to enhance the selection and prioritization of tasks suitable for automation. Traditional task selection for automation is manual and subjective, relying on employee input rather than data-driven methods. By utilizing Process Mining, this research proposes a framework for automatically discovering automation potential based on event logs, offering an objective evaluation of process behaviors. The study employs Design Science Research Methodology (DSRM) and a Systematic Literature Review (SLR) to develop a standardized framework for identifying potential tasks for automation. The framework is evaluated through a case study using ABAP systems, with event logs visualized in ProM and decision trees and sequential pattern analysis applied via Rapid Miner to establish automation rules. The results demonstrate the framework's effectiveness in accurately identifying tasks for automation, providing valuable insights for organizations looking to optimize their RPA initiatives.