Process Mining is the visualization and analysis of business processes on the basis of event logs using algorithms and mathematical procedures. Event logs are protocols of IT-based processes. Here the events – individual activities in the IT system – are listed together with their attributes. Attributes that are typically listed are the case ID, the time stamps of the start and end times, and other attributes of the event that are dependent on the system, such as the processor or the location.
Process mining methods are Process Discovery, Conformance Checking and Model Enhancement. Process Discovery describes the data-based visualization of a process. The model is usually generated automatically from the event logs and displayed as a direct follower graph.
In conformance checking, the actual process presented is compared with an existing reference model (or target model) of the same process. This comparison can be used to determine the correspondence between the target process and the lived process (actual process). Skipped or added process steps or paths can also be visualized:
Another method in Process Mining is Model Enhancement. It describes the analysis of the process model that uncovers optimization potentials. Optimization potentials can, for example, be derived from long processing or idle times. Identified bottlenecks or unforeseen process sequences can thus be identified and eliminated.
How does this fit in with business process management?
Traditional business process management usually follows the process management lifecycle. Process Mining helps to document the processes, since the system processes can be generated directly and easily from the log files. This means that neither do employees have to be questioned nor processes have to be modeled manually. Another advantage is that the processes are always modeled at the same level of detail during generation.
The documented processes are then optimized on the basis of the defined process strategy. Since analyses are necessary for the optimization, Process Mining is also a meaningful support here. Process Mining tools are able to automate parts of the process analysis, which significantly increases the efficiency of the analyses.
The optimized processes are introduced in the next phase and implemented by employees, making it possible to record and measure the performance of the processes. Performance measurement can be facilitated by re-analyzing the event logs. Afterwards, the evaluation of the first event logs simply needs to be compared with the evaluation of the new event logs. This means that you do not have to measure and calculate key performance indicators for each controlling run. Instead, the system analyzes the data as soon as event logs are imported. It is also conceivable that the Process Mining Tool reads the event logs independently.
The recorded performance data and key figures can be used in the last phase of the process management lifecycle to check whether guideline values are adhered to or whether there are bottlenecks and thus optimization potentials. In addition, conformance checking in this phase can check whether the processes continue to run according to conformance.
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