Process Mining — ERP Process Discovery
Process mining is a data-science discipline that reconstructs actual operational process flows from event logs in source systems — primarily ERP — rather than relying on idealised process maps drawn in BPMN. By analysing the timestamps and case identifiers in every transaction (order created, picked, packed, shipped, invoiced, paid), process mining reveals what truly happens in your operations: how often the happy path is followed, where bottlenecks form, which steps create rework, where automation pays off. For ERP-centric organisations, process mining has become a standard tool for transformation programmes since the late 2010s.
How process mining works
Process mining requires three columns of data from the source system: Case ID (the thing being processed — order number, invoice number, customer ID), Activity (the step that happened — created, approved, modified), Timestamp (when it happened). With these three columns across hundreds of thousands of records, the process mining tool reconstructs the actual process flow graph, computes case-throughput times, identifies variants (deviations from the happy path), and pinpoints where cycle time and rework concentrate. Modern tools layer in machine learning to recommend improvements: automation candidates, parameter tuning, root-cause analysis.
Leading process-mining platforms
Celonis — the German-built market leader, with native connectors for SAP, Oracle, Microsoft Dynamics, Salesforce. Dominant in DACH enterprise and mid-market. Homeing around 80,000 EUR per year. UiPath Process Mining — integrated with the UiPath automation platform, popular in companies already invested in UiPath RPA. Microsoft Process Mining (formerly Minit and Process Advisor) — bundled with Microsoft Power Platform Premium, accessible price point for mid-market. SAP Signavio Process Intelligence — SAP's own platform, tightly integrated with S/4HANA. QPR ProcessAnalyzer, Apromore, MEHRWERK ProcessMining — mid-market and open-source alternatives.
High-impact use cases in ERP
- Order-to-cash optimisation — identify where orders get stuck between created and invoiced, reduce DSO
- Purchase-to-pay efficiency — spot maverick buying, late approvals and duplicate invoices
- Accounts-payable automation candidates — find which invoice categories are best suited for AP automation
- Production-flow analysis — uncover schedule deviations and unplanned scrap rework
- Customer-service insight — trace ticket paths to find resolution-rate killers
- Compliance verification — prove that segregation-of-duties controls are actually working in practice
- Pre-ERP-migration assessment — document the as-is processes before a new ERP rollout
What it takes to deploy
A typical process-mining first project: 8-16 weeks from kick-off to first insights. Effort: 50-150 person-days from a process-mining consultant team plus internal IT support for data extraction. Cost for licence plus first project: 100,000-400,000 EUR. Critical success factor: choose a high-value process (procurement, order-to-cash) where leadership commits to act on findings. Process mining without an action loop becomes a one-off slide deck. Process mining with executive sponsorship and a clear improvement backlog can deliver 5-25% improvement in cycle time, working capital or compliance metrics within 12 months.
Related Topics
Frequently Asked Questions
How big does my data need to be?
Minimum: tens of thousands of cases over a representative time window (typically 6-24 months). Below that, the statistical patterns are too thin to reveal meaningful variants. Most mid-market ERP operations easily clear this threshold: a manufacturer with 50,000 production orders per year and 18 months of history has 75,000 cases — plenty for process mining.
Will process mining replace BPM tools?
No. BPM tools (signavio, ARIS, Visio) design and document target processes; process mining reveals what actually happens. Used together: BPM describes what should happen, process mining checks whether it does, and the gap drives improvement projects.
Is process mining GDPR-compliant?
Yes, when implemented carefully. Event logs typically include personal data (user IDs, timestamps, case attributes). Process mining tools should anonymise or pseudonymise user identifiers, restrict access to the project team, document data-retention policies and obtain works-council consent in Germany before deploying broadly. Major platforms (Celonis, UiPath, Microsoft) provide GDPR-aligned data-handling features.