A Qualitative Evaluation of End-to-End Process Optimization using Commercial Process Mining Tools
BSc Thesis Project
Title: A Qualitative Evaluation of End-to-End Process Optimization using Commercial Process Mining Tools
Author: Philipp Alexander Heisenberger
Supervisor: Dr. Sebastiaan J. van Zelst
1st examiner: Prof.Dr. ir. Wil M.P. van der Aalst
2nd examiner: Prof. Dr. Thomas Rose
Summary
Digital Transformation entails the move to a fully transparent and digitally available copy of companies, e.g. internal processes, also called Digital Twin. Business processes, as central part of organizations, are designed to create value by transforming specified inputs into specified outputs in a series of predefined and repeatable steps. Process mining is a databased technology that allows to extract knowledge from structured data sources and analyse business processes by combining analytical methods from Data Science and Business Process Management (BPM). Finally, obtained process knowledge can reveal potential improvements, thereby supporting business practitioners in databased decision making. Over the past years, process mining is widely applied in business practices. Accordingly, the market for commercial process mining tools is growing. Yet, due to their limited features, they fail in accurately measuring process performance and identifying potential improvements. To date, there is no structured overview on the performance improvement capabilities of commercial tools. Therefore, this study provides a qualitative evaluation of end-to-end performance improvement using six commercial tools. To this end, experiments with artificial event logs were conducted, quantifying process performance measurement and improvement capabilities. The results provide an overview of features & capabilities of examined tools, pointing at potential challenges w.r.t. process visualization and information accuracy. More specifically, tools differ in their suitability for performance improvement and operational support. This thesis calls for more research on process performance improvement using process mining techniques to ultimately achieve full process transparency.