Process Mining

Systematic and data-supported evaluation of business processes

Process mining belongs to the data mining techniques and uses transaction data generated by IT systems in the company. The data is log and event data, for example from operational systems.

Process mining aims to close the gap between BPM (Business Process Management) and BI (Business Intelligence).

In process mining, data sets are evaluated with the help of software and mathematical and static methods. Artificial intelligence (AI) methods now also play a decisive role in this area.

The goal is to automatically derive business process models based on data. Through the model, business processes can be monitored, reconstructed, examined and, above all, optimised.

The prerequisite, however, is that the data is processed accordingly beforehand. Otherwise, the generated models are useless.

What are log files?

Log files record events on computer systems or networks, for example system accesses, system interactions, process and data changes etc.. In this way, the many different processes within IT systems are documented in a comprehensible way. The generated data contain attributes such as time stamps, locations or agents and are usually available in chronological order. Log files are used, for example, to trace what caused a system to crash. Log files are automatically created, filled and saved by the system.

What information should log files contain for process mining?

• Each entry in a log file must be associated with process activities.
• The entries must be ordered in time to show the execution sequence of the activities.
• It should be obvious to which case (e.g. customer order) an activity is assigned.
• Only process-relevant data should be included in the event log format.

Possible applications

• Representation and optimisation of ticket processing in support
• Presentation and optimisation of medical treatment paths of patients
• Optimisation of ordering processes
• Optimisation of production processes
• Optimisation of development processes
• Compliance testing of financial processes and financial transactions
• Optimisation of financial flows

Advantages of process mining

• Mapping of realistic business process models based on actual and objective data.
• Comparability of the actual and the theoretical process becomes possible.
• A high degree of automation minimises the manual analysis effort.
• The visualisation of processes facilitates the understanding of processes by employees.
• Managers can make better decisions through continuous monitoring.
• By looking at the process from end to end, the influence of changes in individual process steps on the overall process can be assessed. For this purpose, process change scenarios can be carried out in the process mining software.
• Process mining can be used to reconstruct, visualise and evaluate processes.
• Through process mining, processes can be optimised, efforts can be reduced and thus the efficiency of the process can be increased. This leads to lower costs and thus to improved competitiveness.
• Process mining supports companies in the preparation phase for RPA.
• Using AI and machine learning, a root cause analysis can be carried out automatically. This automatically uncovers hidden connections between the influencing attributes and the problems/disruptions. Answers are provided to the following questions, for example: Why are some process runs slower than others? Why do some process runs get stuck in rework? Why do some process runs have more waiting time?
• Helps to identify process steps that are suitable for automation.