To Enhance Municipal Revenue Department Processes Using Process Mining Techniques (A Case Study: Hamedan Province)

Over the past decade, process mining has emerged as a research field analyzing processes using event data. Unlike traditional data mining techniques, which focus on specific steps, process mining looks at end_to_end business processes. This is made possible by the increasing availability of event data and advancements in process discovery and conformance checking techniques. Process models, which were previously created manually without using event data, are now used for analysis, simulation, verification, and enactment by Business Process Management and Workflow Management systems (BPM/WFM). Process mining techniques utilize event logs to discover, analyze, and enhance business processes by examining the activities performed by people, machines, and software. In this paper, four process mining miners (Alpha Miner, Alpha ++ Miner, Genetic Miner and Heuristics Miner) were utilized to enhance the processes of revenue department in the municipality of Hamadan based on the event logs. The contextualization of this research lies in its importance in improving municipal revenue practices, enhancing transparency, and optimizing resource allocation, which is a critical aspect of efficient governance. Process mining offers a data-driven approach to identify bottlenecks, delays, and deviations in the revenue process, ultimately leading to more effective decision_making and improved public service delivery. The results demonstrated that the Genetic Miner achieved the best result with an impressive 98% overall score, which is an average across key performance metrics including fitness, simplicity, precision, and generalization. The implications of this finding highlight the potential for municipalities and similar organizations to leverage process mining as a means of enhancing their revenue processes, thereby fostering more efficient resource allocation and governance.