Supporting Event Log Extraction based on Matching

Process mining allows organizations to obtain relevant insights into the execution of their processes. However, the starting point of any process mining analysis is an event log, which is typically not readily available in practice. The extraction of event logs from the relevant databases is a manual and highly time-consuming task, and often a hurdle for the application of process mining altogether. Available support for event log extraction comes with different assumptions and requirements and only provides limited automated support. In this paper, we therefore take a novel angle at supporting event log extraction. The core idea of our paper is to use an existing process model as a starting point and automatically identify to which database tables the activities of the considered process model relate to. Based on the resulting mapping, an event log can then be extracted in an automated fashion. We use this paper to define a first approach that is able to identify such a mapping between a process model and a database. We evaluate our approach using three real-world databases and five process models from the purchase-to-pay domain. The results of our evaluation show that our approach has the potential to successfully support event log extraction based on matching

Progressing from Process Mining Insights to Process Improvement: Challenges and Recommendations

Many organizations have adopted process mining to analyze their business processes, gain insights into their performance, and identify improvement opportunities. Several academic case studies and reports from practice leave no doubt that process mining tools can deliver substantial value to organizations and help them to realize improvements. However, both organizations and academics have also realized that the path from obtaining insights via process mining to realizing the desired improvements is far from trivial. Existing process mining methodologies pay little to no attention to this matter and mainly focus on how to obtain insights through process mining. In this paper, we address this research gap by conducting a qualitative study based on 17 semi-structured interviews. We identify seven challenges pertaining to translating process mining insights into process improvements. Furthermore, we provide five specific recommendations for practitioners and stakeholders that should be considered before starting a new process mining initiative. By doing so, we aim to close the gap between insights and action and help organizations to effectively use process mining to realize process improvements

Simulation model of the military rescue chain in combat scenarios – a conceptional design

The intensity of current military combats far exceeds the ones of recent conflicts. Since medical resources are limited during battle, the available capacities must be used optimally. This requires distributing patients to available resources, such as medical facilities and transporters. Therefore, the effective planning and coordination of a complex, constantly changing logistics network is of utmost importance. Due to limited data, current planning is often based on expert assumptions. To evaluate current and future concepts, we propose constructive simulation to analyse the interplay of assumptions and planning decisions. For this purpose, we study the military rescue chain and review existing optimization approaches. A conceptual simulation model design is presented and an outlook of upcoming research is given

From Process Mining Insights to Process Improvement: All Talk and No Action?

Organizations from various domains use process mining to better understand, analyze, and improve their business processes. While the overall value of process mining has been shown in several contexts, little is known about the specific actions that are taken to move from process mining insights to process improvement. In this work, we address this research gap by conducting a systematic literature review. Specifically, we investigate which types of actions have been taken in existing studies and to which insights these actions are linked. Our findings show that there exists a large variety of actions. Many of these actions do not only relate to changes to the investigated process but also to the associated information systems, the process documentation, the communication between staff members, and personnel training. Understanding the diversity of the actions triggered by process mining insights is important to instigate future research on the different aspects of translating process mining insights into process improvement. The insights-to-action realm presented in this work can inform and inspire new process mining initiatives and prepare for the effort required after acquiring process mining insights