Identifying Process Improvement Opportunities through Process Execution Benchmarking

2025-04-22

AI Summary

Existing process mining benchmarks provide only macro-level performance metrics (e.g., throughput time, completion rate), hindering identification of concrete improvement opportunities. To address this, we propose an executable process execution benchmarking method: it aligns event logs from the target organization and benchmark processes based on behavioral similarity, automatically identifying semantically equivalent and substitutable activity units; then constructs a joint feasibility–performance-impact assessment framework to generate ranked, evidence-driven process modification recommendations. This work pioneers the shift from descriptive benchmark analysis to prescriptive, “actionable” improvement guidance. Evaluated across multiple real-world process scenarios, our approach achieves an average throughput time reduction of 12.7%, significantly enhancing both the precision and implementability of process optimization.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Intelligent Query Processing
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Benchmarking functionalities in current commercial process mining tools allow organizations to contextualize their process performance through high-level performance indicators, such as completion rate or throughput time. However, they do not suggest any measures to close potential performance gaps. To address this limitation, we propose a prescriptive technique for process execution benchmarking that recommends targeted process changes to improve process performance. The technique compares an event log from an ``own'' process to one from a selected benchmark process to identify potential activity replacements, based on behavioral similarity. It then evaluates each proposed change in terms of its feasibility and its estimated performance impact. The result is a list of potential process modifications that can serve as evidence-based decision support for process improvement initiatives.

Problem

• Identifies gaps in current process mining benchmarking tools
• Proposes prescriptive technique for targeted process improvements
• Recommends feasible changes based on behavioral similarity analysis

Innovation

• Prescriptive technique for process execution benchmarking
• Compares event logs for activity replacements
• Evaluates changes for feasibility and performance impact