Process mining: definition, value and approach
Why the Term Matters
Before you automate a process, you should know how it really runs. Interviews and flowcharts often show only the believed version. Process mining delivers the measured version and reveals bottlenecks where automation pays off. For you that means targeted instead of broad. From the findings, a sharply cut use case can be derived in days, tested first as a prototype and then built.
When It Is Relevant
- A process runs through several systems and nobody knows the real end-to-end flow.
- Cycle times vary widely and the cause is unclear.
- Before automation, the best starting point should be determined objectively.
- Compliance requires proof of where flows deviate from the specification.
When Not
When a process is small, stable and already transparent, process mining adds little and the data preparation is not worth it. And when the systems log no usable event data with timestamp and case ID, the basis is missing. Then a short clarification workshop is the more honest first step.
Industries That Use It
- Banking and financial services: measure lending and onboarding processes and evidence bottlenecks before investing in automation.
- Manufacturing: make order flow and rework visible from MES and ERP data and prioritise the starting point.
- Telecommunications: reconstruct order-to-activation across many systems and address wait points as a fixed package.
- Utilities: check outage and billing processes for deviations and improve them with a clear deliverable.
Typical Use Cases
- Reconstruct the real order-to-cash flow and locate bottlenecks.
- Uncover waiting times and rework loops in approval processes.
- Evidence deviations from the target process for audit and compliance.
- Select the best candidate for AI automation on a data basis.
- Measure the effect of a process change before and after.
- Identify handovers between departments as loss points.
Decision Guide
| Signal | Process mining fits | Alternative |
|---|---|---|
| Real flow across many systems is unclear | reconstruction from event data | process interviews for a simple flow |
| Starting point for automation wanted | data-based prioritisation | gut feel at an obvious bottleneck |
| Systems log no event data | rather not | first create logging and a baseline |
| Only a small, stable process | too much effort | test direct automation |
Example
A financial services provider suspects delays in the lending process but does not know the cause. In the workshop, event data from two systems is merged and the actual flow reconstructed. A recurring rework loop at one approval becomes visible. From this finding, a narrowly cut automation prototype emerges. This describes the method, not an asserted client metric.
Service Connection
The usual entry is the AI workshop, then AI automation and AI consulting.
Sources
This framing describes common practice in data-based process analysis. Significance and effort depend on the quality of the event data and the systems involved and should be checked there.