Supporting implementation and use of process mining

Abstract

In competitive and continuously changing business environments, companies have a constant need for business process improvement. Simultaneously, digitalization of business processes has allowed better availability of data about the processes. Consequently, there is a growing interest in data-driven analysis and improvement of business processes. Process mining is a discipline for creating transparency and discovering fact-based insights about business processes based on event logs that are available from different enterprise information systems. These insights can support process transformation towards process excellence and bring organizations both economic and ecologic value. Even though process mining has been researched by the academic community from the late 1990’s, there is still limited amount of information available on implementation and use of process mining in business context and in large organizations. To harness the full potential of process mining, it is important for organizations to understand how to implement it successfully. This research has been commissioned by an international industrial company that is interested in the possibilities and requirements of utilizing process mining for process improvement, and for supporting automation. The purpose of the research is to study different factors that affect the implementation and use of process mining and provide the target company useful information about process mining, how to support it, and how to tackle the major challenges related to it. In addition, the research aims to shed light on the role of process mining as part of hyperautomation. The thesis functions as an introduction to process mining and provides information and practical guidelines about its implementation and use. A literature review was conducted to examine the theoretical background of what process mining is and how can it be implemented in practice. Both technological and organizational aspects of the implementation were discussed. In the empirical part of the research, expert interviews were conducted with process mining professionals to gather practical information about the interviewees’ experiences in implementation and use of process mining, and to recognize typical challenges and most important success factors which the interviewees had faced. The role of process mining as part of hyperautomation was also examined both in the literature review and in the interviews. The results of the empirical research were further evaluated against literature. As a result, lists of typical challenges and most important success factors for process mining are introduced. The practical implications of the study are presented as a set of recommendations for an organization to address the identified challenges and to successfully implement and use process mining. In general, implementation and use of process mining at large scale requires that each individual process mining project within the organization is carried out properly, but also that organizational and technological support activities are in place to facilitate wider adoption of the technology.