Process management technology for the evaluation of business processes with the help of IT systems
In many companies, large amounts of data are generated every day – in ERP systems, machine controllers, logistics systems and other IT solutions. This data is usually stored so that it can be used at a later point in time for evaluations or optimisations. In practice, however, this often does not happen or only to a very limited extent. The reason: the actual utilisation of the data is often complex and involves a great deal of effort.
The challenges are manifold:
- The relevant data is distributed across different systems
- There are different data formats and interfaces
- Information is often only stored as simple log files – without context
- Process correlations are missing or not directly apparent
- The data usually does not contain any information about the causes or control backgrounds
The result: although you often know what happened – but not why.
A comparison from everyday life: Similar to GPS tracking, you can see who was where and when, but not why certain decisions were made or why delays occurred.
This is precisely where process mining comes in: This method of data-based process management makes it possible to read and link real process data and transfer it into a visual process model. To do this, the data must contain at least a temporal component (WHEN) and a reference to the unit (WHO). The position (WHERE) is derived from the data source itself. With the help of modern tools, process maps, material flow views or KPIs that were previously not visible are automatically created.
SimPlan supports companies in the introduction and application of process mining.
Our experts combine the method with existing analysis and simulation solutions. Process Mining is particularly powerful in combination with dynamic simulation models. This not only allows processes to be visualised, but also to be tested for possible improvement measures – risk-free, in advance, on the digital twin.
For example, SimPath – our solution for analysing automated logistics systems – enables companies to uncover data-driven weaknesses and make informed decisions about optimisation. Changes to control rules, throughput or resource utilisation can be simulated to ensure they are effective before investments are made.
