Machine Learning Applications in Process Mining: Predictive Maintenance and Resource Allocation Across Enterprise Systems

Abstract

Process mining has emerged as a critical methodology for analyzing and optimizing business processes through event log data extraction and analysis. The integration of machine learning (ML) techniques with process mining has created unprecedented opportunities for predictive maintenance and resource allocation in enterprise systems. This review examines the current state of ML applications in process mining, focusing specifically on predictive maintenance strategies and resource allocation optimization across various enterprise environments. Deep learning (DL) algorithms, including recurrent neural networks (RNN) and long short-term memory (LSTM) networks, have demonstrated remarkable capabilities in identifying process anomalies and predicting equipment failures before they occur. Reinforcement learning (RL) approaches have shown significant promise in optimizing resource allocation decisions by learning from historical process execution patterns. This paper synthesizes recent advances in ML-driven process mining, evaluates the effectiveness of different algorithmic approaches for predictive maintenance and resource allocation, and identifies key challenges and future research directions. The review reveals that hybrid ML architectures combining supervised and unsupervised learning methods achieve superior performance in complex enterprise environments, while transfer learning techniques enable effective model deployment across different organizational contexts.