Process mining: definition, value and limits
Process mining Updated
Definition
Process mining reconstructs the actual flow of a process from the log data of the systems involved, instead of relying on drawn target diagrams. From timestamps, case IDs and activities it builds a picture that reveals order, waiting times, loops and deviations. For the mid-market it is the data-based foundation for seeing which step is worth automating.
Why the Term Matters
Many automation projects fail because they automate the assumed flow, not the real one. Process mining uses the system traces to show where time is truly lost, which cases deviate from the standard and where rework happens. Who sees this before building picks the step with the fastest provable value and avoids expensive automation in the wrong place.
When It Is Relevant
- The process runs across several systems and nobody knows the full path.
- There are many deviations and special cases of unclear extent.
- Before an automation effort the biggest bottleneck should be found.
- The systems log timestamps and case IDs cleanly enough.
When Not
When a process is small, clear and already known, process mining yields little new insight and does not repay the effort. And when the log data is patchy or lacks reliable case IDs, it produces a distorted picture that should not be trusted before data quality is fixed.
Industries That Use It
- Machinery and plant engineering: trace the path from quote through order to delivery in the ERP and expose idle times between departments.
- Logistics and freight forwarding: analyse shipment and order runs to find recurring delays at handover points.
- Insurance: examine claims handling to make query loops and manual rework visible.
- Utilities: review connection and switching processes where several specialist systems interlock.
Typical Use Cases
- Reconstruct the real order-to-cash process from SAP documents and quantify bottlenecks.
- Count deviations from the standard path and sort them by frequency and cost.
- Measure waiting time between two activities to name the biggest time sink.
- Before an RPA build, check whether a flow is stable enough for rules.
- After an automation, re-measure whether the new path is really shorter.
Decision Guide
| Signal | Process mining fits | Alternative |
|---|---|---|
| Flow across many systems, path unclear | analyse system data | rough documentation is enough |
| Many special cases suspected | measure deviations | sample via interview |
| Find bottleneck before automating | data-based prioritisation | gut-feel selection |
| Log data patchy | secure data quality first | no analysis on assumption |
Example
A mid-market company exports the document data of its purchasing process from the ERP and has the actual flow reconstructed from it. The analysis shows that an approval step is run twice in many cases. Exactly this step is measured as a baseline and then relieved with RPA and an ERP connection. This describes the method, not an asserted client metric.
Service Connection
Process mining is usually the first step of process automation consulting, before an RPA build or process automation for the mid-market follows.
Sources
This framing describes common mid-market process mining practice. Concrete analysis, data extraction and data protection depend on the case, the systems and the tools and must be checked case by case.