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Data-driven business process improvement

An illustrative case study about the impacts and success factors of business process mining

Written by Sebastian Decker

Paper category

Master Thesis


Business Administration>Management




Master Thesis: The process of extracting relevant literature A preliminary literature review on process mining has yielded few and unsatisfactory results. The search process mining on the trusted academic database of "Web of Science" brought 1,555 results. When applying filtering options by research area, I found that a large part of it comes from computer science and engineering technology sources. Random inspections of the most cited articles confirm the technical focus. From a few non-technical articles, such as the process mining manifesto by Van der Aalst et al. (2011), I applied citation search and further snowballing (Easterby-Smith, Thorpe, and Jackson, 2015), which helped me determine the theoretical background of process mining. During the analysis, I admitted to mentioning the contribution of the scholar Wil van der Aalst many times. According to the results of Google Scholar, Wil van der Aalst's contributions are related to disciplines such as BPM, data science, and process mining, and have been cited more than 90,000 times. The author's book "Process Mining: Data Science in Action" published in 2016 provides the most comprehensive and up-to-date literary works in the field of process mining. The second finding from reviewing the literature is that process mining seems to be related to the field of business process management (BPM). The results of a combined keyword search on Web of Science and Google Scholar confirmed this hypothesis. While expanding the search scope to Google Scholar, I used Emerald Insight’s business and management database to determine that the Business Process Management Journal is the most relevant and important journal on the subject of process mining and BPM. According to ABS Academic Journal Guide 2015, the impact factor of this journal is 2. Here, the relevant articles are further confirmed. Regarding the theory of BPM, I admit that the literature is very extensive and covers a variety of viewpoints. Since the theory of process mining is relatively young, I further applied keyword and citation search to identify the pioneering but recent contributions of most relevant scientists in the field to BPM research. These include Thomas Davenport (1993), Michael Rosemann and Jan vom Brocke (2005), Mathias Weske (2012), Marlon Dumas (Dumas et al., 2013) and Jan Mendling (Manova & Meidling, 2018). 2.2 Business Process Every organization must manage business processes independently of its type or size (Dumas, La Rosa, Mendling & Reijers, 2013). The literature provides various definitions of business processes (Davenport, 1993; Hammer & Champy, 1993; Weske, 2012; Dumas et al.; 2013; Burratin, 2015). 2.3.1 Data mining and process mining From the perspective of data science, the field of process mining is usually related to data mining (Van der Aalst et al., 2011; Van der Aalst, 2016; Pourmasoumi & Bagheri, 2017). The purpose of data mining is to use data mining techniques, such as cluster classification or regression (Linoff & Berry, 2011; Pourmasoumi & Bagheri, 2017) to find hidden relationships between variables in usually large and unstructured data sets. In process mining, data mining technology is used to “extract knowledge from a set of data generated by systems and processes” (Thiede et al., 2018, p. 902). Compared with data mining that is data-centric and mainly relies on historical data, process mining technology is process-centric and can discover end-to-end processes based on historical and real-time event data (Van der Aalst, 2016; Pourmasoumi & Bagheri, 2017 Years). In addition to data mining, process mining tools also utilize other areas of data science, such as machine learning (Burratin, 2015), statistics and visualization, and visual analysis (Van der Aalst, 2016). According to van der Aalst (2011b, 2016), data mining tools and literature do not involve process mining techniques. On the other hand, process mining is related to process science disciplines, such as business process management. BPM and process mining Van der Aalst, etc. (2011) pointed out that process mining “has become one of the “hot topics” in business process management (BPM) research” (page 1). The identified relevant literature on process mining mainly comes from business process management (BPM) and confirms the statement of van der Aalst et al. (2011). Van der Aalst (2016), Lederer and others. (2017) and Thiede et al. (2018) Agree that process mining is part of the broader research field of BPM. Refer to van der Aalst (2011), BPM is a discipline that combines information technology knowledge and management science knowledge and applies it to operational business processes" (van der Aalst, 2011, p. 3). van der Aalst, Hofstede and Weske (2013) The definition of BPM was followed by the understanding of BPM as "supporting business processes using methods, technologies and software to design, formulate, control and analyze operational processes involving personnel, organizations, applications, documents, and other information sources" (Page 4). The definition of Dumas et al. (2013) and Lederer et al. (2017) are corresponding. In addition, BPM aims to comprehensively and continuously improve end-to-end processes to “add value to the organization and its customers "(Dumas et al., 2013, p. 1). Researchers in this field (Rosemann et al., 2008); van der Aalst, 2011a) agree that BPM has received great attention from the scientific community and practitioners because it " It has the potential to significantly increase productivity and save costs". Read Less