Enhancing Municipal Revenue Processes: A Case Study on Process Mining Approaches in Hamedan Province
محورهای موضوعی : نشریه بینالمللی هوش تصمیم
Mohammad Amin Jafari
1
,
Mansour Esmaeilpour
2
1 - Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
2 - Associate Professor, Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
کلید واژه: Process Mining, Event Log Analysis, Hamedan Municipality, Process Model Enhancement,
چکیده مقاله :
Process mining has gained significant attention in recent years as a method for analyzing and improving business processes through the examination of event data. Unlike traditional data mining, which focuses on isolated tasks, process mining provides a comprehensive view of entire processes, helping organizations understand how work is actually carried out. With the increasing availability of event data and advances in process discovery techniques, process mining has become an essential tool in Business Process Management (BPM) and Workflow Management (WFM) systems. This study evaluates four process mining algorithms: Alpha Miner, Alpha++ Miner, Genetic Miner, and Inductive Miner, in the context of enhancing the revenue processes of Hamadan’s municipality. The goal is to improve efficiency, reduce delays, and optimize resource allocation in the municipality’s revenue system. The results show that the Genetic Miner delivers the highest overall performance with an impressive score of 98%, followed by Inductive Miner at 96%, Alpha++ Miner at 94%, and Alpha Miner at 83%. The Genetic Miner’s impact on the municipality’s operations was particularly striking, leading to a 40% increase in revenue growth, a 33.3% reduction in revenue leakage, and a 50% improvement in payment processing time. These findings illustrate the power of process mining, particularly Genetic Miner, to reveal inefficiencies and enhance decision-making, ultimately contributing to better public service and more effective resource management. This research underscores the potential of process mining to transform municipal revenue processes, driving improvements in governance and operational efficiency.
Process mining has gained significant attention in recent years as a method for analyzing and improving business processes through the examination of event data. Unlike traditional data mining, which focuses on isolated tasks, process mining provides a comprehensive view of entire processes, helping organizations understand how work is actually carried out. With the increasing availability of event data and advances in process discovery techniques, process mining has become an essential tool in Business Process Management (BPM) and Workflow Management (WFM) systems. This study evaluates four process mining algorithms: Alpha Miner, Alpha++ Miner, Genetic Miner, and Inductive Miner, in the context of enhancing the revenue processes of Hamadan’s municipality. The goal is to improve efficiency, reduce delays, and optimize resource allocation in the municipality’s revenue system. The results show that the Genetic Miner delivers the highest overall performance with an impressive score of 98%, followed by Inductive Miner at 96%, Alpha++ Miner at 94%, and Alpha Miner at 83%. The Genetic Miner’s impact on the municipality’s operations was particularly striking, leading to a 40% increase in revenue growth, a 33.3% reduction in revenue leakage, and a 50% improvement in payment processing time. These findings illustrate the power of process mining, particularly Genetic Miner, to reveal inefficiencies and enhance decision-making, ultimately contributing to better public service and more effective resource management. This research underscores the potential of process mining to transform municipal revenue processes, driving improvements in governance and operational efficiency.
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