AI-Augmented Decision-Making in Business Process Mining: Data Fusion Techniques and Real-World Applications

Abstract

The increasing complexity of modern business processes necessitates the use of advanced methodologies to enhance decision-making and operational efficiency. In this context, Artificial Intelligence (AI) has emerged as a transformative tool for business process mining, particularly through its ability to augment decision-making in real-time. This research explores the intersection of AI-augmented decision-making and business process mining, focusing specifically on the role of data fusion techniques in enhancing decision support and enabling dynamic, real-time process adjustments. Business process mining, which involves the extraction of process-related knowledge from event logs, typically provides insights into operational workflows, performance bottlenecks, and inefficiencies. However, the integration of AI methodologies, particularly those centered around data fusion, offers the opportunity to take these insights a step further by enabling real-time, predictive decision-making and continuous process optimization.

Data fusion, as applied in the context of AI-augmented business process mining, involves the integration of multiple data sources—ranging from traditional process logs to more complex sensor data, enterprise resource planning (ERP) system outputs, and social media or customer feedback. These heterogeneous data sources are combined using AI-driven techniques to create comprehensive, actionable insights that are both temporally and contextually relevant for decision-makers. The primary focus of this paper is to examine how AI, through data fusion, enhances decision-making capabilities by improving the accuracy, timeliness, and adaptability of business process analyses.

The research first establishes a theoretical framework for understanding the critical role of data fusion in business process mining. It outlines various types of data fusion techniques, such as sensor fusion, belief fusion, and probabilistic fusion, and discusses their relevance and application to process mining tasks. Sensor fusion, for instance, refers to the combination of data from multiple sensors or monitoring systems to provide a more reliable and accurate picture of business operations. In contrast, belief fusion involves the aggregation of subjective data and expert opinions, which is increasingly valuable in environments where quantitative data is sparse or incomplete. Probabilistic fusion techniques, which apply probabilistic models to combine uncertain or incomplete data, are particularly relevant for decision support in dynamic business environments where processes are subject to variability.

This paper further delves into the application of AI and data fusion in real-time process adjustments, focusing on how AI systems can leverage fused data to automatically suggest or implement changes in business operations. For example, in manufacturing, AI-augmented decision support systems can monitor production lines in real-time and adjust parameters such as machine speed or resource allocation based on fused data from multiple sources. Similarly, in customer service operations, AI can dynamically adjust workflows, such as call routing and case prioritization, based on real-time data fusion, ensuring optimal resource utilization and enhanced customer satisfaction.

A central element of this study is the integration of machine learning (ML) algorithms into the business process mining framework. ML techniques, including supervised learning, unsupervised learning, and reinforcement learning, are used to train models that can predict outcomes based on historical and real-time data. These models are then utilized in decision-making processes to anticipate process performance issues or identify opportunities for optimization. For instance, by analyzing past process data, ML algorithms can predict potential bottlenecks or inefficiencies in the business workflow, allowing organizations to make proactive adjustments. This capability is particularly valuable in highly dynamic environments where traditional, reactive decision-making methods are insufficient.