Hybrid Machine Learning and Process Mining for Predictive Business Process Automation

Abstract

This research explores a hybrid approach that combines machine learning (ML) and process mining techniques to predict and address bottlenecks in business processes, thereby optimizing business process automation. By integrating these two powerful methodologies, organizations can achieve more accurate process predictions and enhance operational efficiency. Process mining provides insights into the actual execution of business processes, uncovering inefficiencies, while machine learning algorithms, particularly predictive models, enable the forecasting of future process behaviors. This synergy allows for real-time identification of potential delays and disruptions in workflows, facilitating proactive process optimization. The paper investigates use cases in three critical industries—retail, supply chain, and telecommunications—demonstrating how this hybrid approach can be applied to various business scenarios. In retail, it is shown how predictive analytics can optimize inventory management and customer interactions. In supply chain management, it highlights how bottlenecks in procurement and distribution can be forecasted. Finally, in telecommunications, the paper explores how predictive models can enhance service delivery by preempting network issues. The findings indicate that integrating machine learning with process mining significantly improves process automation, enabling businesses to reduce costs, improve throughput, and enhance customer satisfaction.