Process mining: definition, value and limits
Definition
Process mining reconstructs real workflows from the data traces that IT systems leave behind, such as timestamps in ERP or CRM. It shows how a process actually runs, not how the manual describes it. This makes bottlenecks, loops and deviations visible before any decision on automation or AI.
Why the Term Matters
Before AI is meant to improve a process, it must be clear how the process really runs. Process mining delivers that factual picture from real data rather than assumptions. As a neutral advisor we treat it as a sober starting point: analysis often shows a process needs to be tidied before technology speeds it up. Knowing the actual state lets you prioritise use cases where they really work.
When It Is Relevant
- A process runs in a system with clean timestamps and case numbers.
- There is dispute about exactly where a workflow stalls.
- Before automation, the real actual state should be evidenced.
- Deviations from the target process should be made measurable.
When Not
When a process is barely represented in systems and lives in verbal handoffs and paper, the data traces for process mining are missing. And when the bottleneck is known and small anyway, an elaborate analysis is overkill. Then a short process workshop brings clarity faster than a mining project.
Industries That Use It
- Purchasing and procurement: trace the path from need to payment and find recurring delays.
- Manufacturing: make lead times and rework loops in order handling visible.
- Insurance: analyse claims processes to target long idle times deliberately.
- Customer service: examine ticket and escalation paths before planning automation.
Typical Use Cases
- Reconstruct the actual process from ERP data before deciding on automation.
- Evidence procurement bottlenecks with numbers instead of gut feel.
- Measure and prioritise deviations from the defined target process.
- Compare before and after a change to see whether it truly works.
- Rank automation candidates by frequency and effort.
Decision Guide
| Signal | Process mining fits | Alternative |
|---|---|---|
| Process cleanly captured in systems | yes, reconstruct actual state | no data, no mining |
| Workflow runs on paper and verbally | rather not | a process workshop |
| Bottleneck unclear and disputed | data creates clarity | guesses stay contested |
| Bottleneck small and known | oversized | improve it directly |
Example
An industrial firm disputes where order handling stalls. In the potential analysis we check whether the ERP data is suitable for process mining and reconstruct the actual process as a basis for prioritisation. This describes the method, not an asserted client metric.
Service Connection
We frame process mining in the AI readiness check or the AI potential analysis. For mid-market companies this is bundled in AI consulting for mid-market.
Sources
This framing describes common practice in process analysis. It is general orientation and does not replace a project-specific data assessment, which must be made case by case.