Process Mining for the multi-faceted analysis of business processes—A case study in a financial services organization
Highlights
► We demonstrate the applicability of process mining techniques to profoundly expose organizational inefficiencies. ► A methodological framework for applying process mining techniques in practice is proposed and validated in a real-life case study. ► The analysis shows that Process Mining is an ideal means for steering process improvement and creating a company-wide process awareness. ► Intelligent approaches to diagnose processes from highly flexible environments are required.
Introduction
These days it is impossible for an organization to operate without some sort of enterprise information system. During the last decades, information systems (IS) have transformed from simple systems with limited functionality to complex, integrated architectures. As a result, it becomes harder to understand and monitor how these systems impact the execution of every-day processes in organizations. Process Mining [17] offers a solution based on the extraction, analysis, diagnosis and visualization of the data recorded by an IS during process execution. Although in the past, major contributions to the process mining literature were predominantly technical in nature, techniques have proved their usefulness in practice as well. Nevertheless, application-oriented studies have only received modest attention and therefore, this study demonstrates the benefits and challenges of applying process mining techniques in practice by a multi-faceted analysis of business processes within the back office of a Belgian insurance company.
Process Mining goes beyond the capabilities of traditional business intelligence tools [6] with respect to process analysis. Accordingly, it can be considered as a proficient means for helping organizations understanding their actual way of working and thereby serving as a foundation for process improvement. This is mainly due to the fact that the cornerstone of Process Mining is real data that comprises how business operations are actually carried out in an organization. This is significantly different from other approaches to process improvement, for instance relying on interviews with key stakeholders.
Based on existing literature and our own experiences, a methodology framework is described, which structures the process mining study in a financial services organization. This framework is similar to earlier works [3], [13], however it puts an emphasis on data extraction and exploration as well as on the multi-faceted nature of analyzing process execution data. Furthermore, this study clarifies benefits as well as challenges of conducting a real-life process mining study. For instance, the study points out the importance of intensive two-way communication between process analysts and organization's experts and management.
Accordingly, this paper is structured as follows. First, Section 2 outlines how the field has emerged in the last decades and why the application of process mining techniques in services organizations faces distinctive challenges. We continue with a discussion of former real-life case studies in Section 3. Then Section 4 elaborates on the followed research methodology which is applied within a financial services organization in Section 5. Finally, we formulate conclusions in Section 6.
Section snippets
State-of-the-art of Process Mining
In [4], Dumas et al. bring out one of the most influential trends of the past decades: the shift from a data-orientation to a process-orientation. In the seventies and eighties, most information systems were built on top of the operating system with the single goal of storing, retrieving and presenting information. Data was at the center of design, such that process modeling was limited to the boundaries of the information system. This could result in inefficiencies, low responsiveness and a
Related work
In the process mining literature, attention has been largely bestowed on the development of novel techniques and algorithms with a strong focus on the control-flow discovery perspective [21]. Only a limited number of articles describe practical applications. As such, this work complements former rather technical contributions by proposing a distinctive methodology for the analysis of process inefficiencies within services organizations. Our findings are validated with a case study in which
A methodological framework for applying Process Mining in practice
The objective of this paper is to conduct a case study that proves the utility of Process Mining in practice and to provide a suggestion on how Process Mining can be applied in real-life. It has been found that many of the proposed algorithms have difficulties in dealing with real-life event logs since these logs typically exhibit much less structured process behavior [5], [8], [22]. Today, this remains one of the most important challenges for process mining research. As such, so as to apply
Case study
With the purpose of this paper being the demonstration of the usefulness of process mining analyses in practice, we describe a case study in the financial services industry. This industry is of main interest for Process Mining since there exist plenty of human-centric business processes for which the analysis of event logs proves especially worthwhile. The case at hand involves a large Belgian insurance company. Products include life and non-life insurances and retirement savings, which are
Conclusion
In this paper we addressed the issue of the applicability of Process Mining in real-life environments. We acknowledge the importance of applied process mining studies, especially since their conclusions often differ from the contributions advanced in studies that introduce novel analysis techniques. However, the related work section indicates that the issue of applying process mining techniques in practice should receive more attention, also in academic literature. Accordingly, we provide a
Jochen De Weerdt received a Master's degree in Business Economics - Information Systems Engineering from KU Leuven, Belgium. He is currently employed as a scientific researcher at the Department of Decision Sciences and Information Management at the KU Leuven. His research interests include data mining, process mining, and web intelligence..
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Jochen De Weerdt received a Master's degree in Business Economics - Information Systems Engineering from KU Leuven, Belgium. He is currently employed as a scientific researcher at the Department of Decision Sciences and Information Management at the KU Leuven. His research interests include data mining, process mining, and web intelligence..
Annelies Schupp received a Master's degree in Business Economics - Information Systems Engineering from the KU Leuven, Belgium. She is currently working as a functional/business analyst at AE nv.
An Vanderloock received a Master's degree in Business Economics - Information Systems Engineering from the KU Leuven, Belgium. She is currently working as a functional/business analyst at AE nv.
Bart Baesens holds a Master's degree in Business Engineering (option: Management Informatics) and a PhD in Applied Economic Sciences from KU Leuven, Belgium. He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications, for instance in the fields of CRM, credit risk management, fraud detection, software engineering, business process intelligence, web analytics and mining, and social networks.