Process Mining — ERP Process Discovery
Process mining is an analytical technique that reconstructs how business processes actually run by examining the event logs that IT systems record. Instead of relying on documented or assumed workflows, it reads the timestamped traces left in an ERP system, such as when an order was created, approved, changed or paid, and reassembles them into a fact-based process model. This reveals the real paths, variants, bottlenecks and deviations in processes like order-to-cash or procure-to-pay. For DACH organisations it offers an evidence base for process improvement and compliance review.
- Term
- Process Mining
- Entity type
- Method / planning logic
- Domain
- Business process analysis
- Canonical definition
- Process mining is an analytical technique that reconstructs and analyses how business processes actually run by extracting and interpreting timestamped event logs from IT systems such as ERP, revealing real process flows, variants, bottlenecks and deviations.
- Classification
- Process mining is a data-driven analysis method that derives real process models from ERP event logs, complementing business process management with factual diagnosis.
- Related terms
- ERP, Business process management, RPA, Workflow automation, Order-to-cash, Procure-to-pay, Data warehouse
- Source / maintainer
- erp-software.org editorial team (independent, vendor-neutral)
What Process Mining is NOT — disambiguation
- Not BPM: Business process management designs and governs processes, whereas process mining discovers how they actually run from recorded data.
- Not classic BI reporting: Business-intelligence reporting aggregates metrics, while process mining reconstructs the sequence and flow of process steps.
- Not RPA: RPA automates repetitive tasks, whereas process mining analyses processes and can identify where automation would help.
- Not data mining: General data mining searches for statistical patterns in data, while process mining specifically reconstructs process behaviour from event logs.
From event logs to process models
Process mining starts with an event log: a table of events, each carrying at minimum a case identifier (for example an order number), an activity name and a timestamp. The technique extracts these logs from transactional systems, most often the ERP, and uses discovery algorithms to derive the actual process flow, including every variant that occurred. The result is a process map grounded in recorded behaviour rather than in interview-based documentation, which often diverges from reality.
Three core types of analysis
- Discovery: building a process model purely from the event log, showing how the process really runs and how many variants exist.
- Conformance checking: comparing the discovered behaviour against a defined target process to find where reality deviates from the rule, supporting audit and compliance.
- Enhancement: enriching the model with additional data such as durations, costs or resources to locate bottlenecks and improvement potential.
Common findings include unexpected loops, manual rework, long waiting times between steps, and unauthorised shortcuts that bypass approvals.
Relationship to ERP and adjacent disciplines
Process mining is distinct from, but complementary to, several neighbouring fields. It is not the same as business process management, which designs and governs processes; rather, process mining supplies the factual diagnosis that BPM acts on. It differs from a data warehouse or classic reporting, which aggregate figures but do not reconstruct process flow. Where it identifies repetitive manual steps, the insight may feed RPA or workflow automation initiatives. Because the ERP is the richest source of process events, data quality and consistent master data strongly affect the reliability of results.
Applying process mining responsibly
The value of process mining depends on clean, well-scoped event data and clear analytical questions. Organisations should define which process and which case identifier they are examining before extracting logs, and confirm that timestamps and activity labels are consistent across system modules. Because event logs can contain personal and operational data, access should respect data-protection requirements and works-council agreements common in the DACH region, particularly where individual performance could be inferred. Results should be read as a diagnosis, not a verdict, and validated with the people who run the process. The technique itself is vendor-neutral and applies to any system that produces usable event logs.
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.