Today, every organization collects large amounts of data through systems, websites, surveys or production lines. This data can be very valuable to gain insight into how your business processes actually work. But how can you gain insight from this data so that you actually discover patterns, pitfalls or opportunities for improving your processes? A suitable method for this is process mining. In this insight, we take you through this technique and show you step by step how you can best approach this.
What is process mining?
Process mining is a technique for automatically mapping and analyzing the actual course of business processes. It is a powerful tool to visualize how the current process actually runs and thus provides insight into potential areas of improvement. Process mining can be used for various issues. Consider, for example, providing insight into bottlenecks in a process, measuring the effectiveness of a process improvement, or checking compliance with procedures.
Process mining offers many advantages because the insights:
- Are transparent, they are based on actual recorded data;
- Are available quickly and easily, thanks to powerful (mostly) automated tools;
- Are of high quality, thanks to the possibility of zooming in on detail;
- Are easy to interpret, thanks to the visualization of the results.
Precondition for process mining
A prerequisite for process mining is the availability of data about the process. This data is called an “event log,” or event log. Each event (line in the log) refers to a case (for example, a batch in a production process), activity (process step) and time. Thus, an event log is a collection of events. If none or too little of this data is available, the first step is to implement a (measurement) system that can collect the necessary data. Without data, process mining is not an appropriate tool for solving your issue.
Seven steps from data to insight with process mining
We use seven steps to use process mining to gain insight into your problem and formulate concrete follow-up actions. In short, these seven steps are as follows:
- Problem description: Start with a clear, unambiguous and (quantitative) problem description.
- Project team and support: Make sure priority and attention is given to the process mining project.
- Process mining components: Determine which parts of process mining are relevant.
- Tooling: Choose the right software tool.
- Data: Use existing data on the process and collect additional data as needed.
- Analyses: Perform the analyses in the software tool.
- Output: Analyze the output and determine improvement actions.
Example of step 6: analysis via software tool
Data source: processmining.org
Want to know more?
Want to learn more about the seven steps of process mining, help to apply process mining in practice or help translate your insights into concrete improvement plans? Then download our comprehensive insight below.
