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THE TECHNICAL LANGUAGE OF DIGITAL B2B INFRASTRUCTURE

PRACTICE
INTELLIGENT AUTOMATION

Process Mining

The analysis of event logs to reconstruct, measure and improve real operational processes.


BUSINESS RELEVANCE

Process mining shows how work actually flows through ERP, CRM and ticketing systems, so leaders can target automation, remove bottlenecks and check compliance with evidence instead of workshop assumptions.


IMPLEMENTATION EXAMPLE

An order-to-cash team extracts 12 months of SAP event logs (case ID, activity, timestamp), discovers 340 process variants and finds that orders routed through manual credit checks take a median 4 extra days to invoice.


LIMITATIONS

Results depend on event log quality: missing timestamps, inconsistent case IDs and work done outside systems, such as email or phone calls, stay invisible. A discovered model shows what happened, not why it happened.


TECHNICAL EXPLANATION

Each event record needs at least a case identifier, an activity name and a timestamp. Discovery algorithms such as the alpha, heuristic or inductive miner turn the ordered events of each case into a process model, often a Petri net or BPMN diagram. Conformance checking replays the log against a reference model to measure fitness and deviations, and enhancement overlays timing and frequency data to expose bottlenecks.


Secondary Topics

Data Engineering, Systems Architecture

Sources

IEEE Task Force on Process Mining — Process Mining Manifesto — https://www.tf-pm.org/resources/manifesto; IBM — What is process mining? — https://www.ibm.com/think/topics/process-mining