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.

This turns pure data analysis into real added value for process optimisation.

Frequently Asked Questions

What is the difference between process mining and simulation?
Process mining analyses real data from existing IT systems and shows what actually happened within the process. A simulation goes one step further and illustrates why a process runs the way it does and how it would behave under different conditions. SimPlan frequently uses process mining results as the data basis for a subsequent simulation model.

What is process mining?
Process mining is a process management technique that enables business processes to be analysed and visualised using data traces from existing IT systems, such as ERP systems. This reveals how a process actually runs, not just how it is intended to run according to the documentation.

What role does machine learning play in process optimisation using process mining?
Process mining first provides a data-driven snapshot of the actual process flow. Building on this, machine learning methods can identify patterns – such as which factors typically lead to delays – and thus help to prioritise optimisation approaches before these are further examined in a simulation model.