Data-driven Management of Interconnected Business Processes - Contributions to Predictive and Prescriptive Process Mining

Business process management (BPM) is an accepted paradigm of organizational design and a source of corporate performance [1]. Due to substantial progress in process identification, analysis, implementation, and improvement [2, 3], BPM receives constant attention from industry [4]. In times of market consolidation and increasing competition, operational excellence (i.e., continuously optimizing an organization’s processes in terms of effectiveness and efficiency) is key to staying competitive. While traditional research in BPM focused on process models and model-based information systems (e.g., workflow management systems), recently, the focus has shifted to datadriven methods such as process mining [5]. In contrast to model-driven BPM, process mining uses execution data in the form of events arising during process enactment, which may be exploited in several ways [6]. Process mining strives to discover, monitor, and improve processes by extracting knowledge from event logs available in information systems [7]. The most commonly applied use case in process mining is discovering as-is process models that also serve as a starting point for more detailed analysis [8]. Based on the mined as-is-process, the use case of conformance checking helps to point out deviations from normative, predefined process models and actual process enactments (e.g., unintended handover of tasks, skipped activities, missed performance goals). As process mining analyzes information on an event-level, it also helps evaluate the actual process performance (e.g., measuring cycle times, interruptions, exceptions). In sum, process mining can help ensure process hygiene, constituting a fundamental requirement to achieve operational excellence [8]. As process mining is one of the most active streams in BPM, numerous approaches have been proposed in the last decade, and various commercial vendors transferred these methods into practice, substantially facilitating event data analysis [9]. At the tip of the iceberg, Celonis expanded in only seven years from start-up to a unicorn, indicating the enormous cross-industry business potential of process mining [10]. By 2023, Markets and Markets predicts a market potential of 1.42 billion US$ for process mining technologies [11]. However, there are still numerous unsolved challenges that hinder the further adoption and usage of process mining at the enterprise level [12]. First, finding, extracting, and preprocessing relevant event data is still challenging and requires a significant amount of time in a process mining project and, thus, remains a bottleneck without providing appropriate support [13]. Second, most process mining approaches operate on a single-process level, but organizations are confronted with a process network covering hundreds of interdependent processes [12]. Third, process managers strongly require forward-directed operational support, but most process mining approaches provide only descriptive ex-post insights, e.g., discovered models or performance analysis of a past period [8]. Since these challenges mainly drive this doctoral thesis, they will be discussed in detail below. First, finding, extracting, and preprocessing relevant event data is still challenging. This is most frequently due to the lack of domain knowledge about the process, the distributed storage of required data in different databases and tables, and the requirement of advanced data engineering skills [13]. Most recent process mining approaches assume high-quality event logs without describing how such logs can be extracted from process-aware (PAIS) and particularly non-process-aware information