Process Mining is a data-driven analysis method that reconstructs, visualizes, and analyzes actually executed business processes from digital traces in IT systems (event logs). This innovative technique combines data mining, algorithms, and business process management for objective evaluation of process efficiency, compliance, and optimization potential based on real execution data.
Process Discovery algorithms like Alpha Algorithm, Heuristic Miner, and Inductive Miner automatically extract process models from event logs. These algorithms identify activity sequences, parallelisms, and decision points.
Conformance Checking compares discovered processes with predefined reference models and identifies deviations, rule violations, or compliance breaches. Fitness, precision, and generalization measure model quality.
Process Enhancement uses event log data to improve existing process models through performance annotation, bottleneck identification, and optimization suggestions.
Purchase-to-Pay Processes: Procurement processes from purchase requisition to invoice payment are analyzed for approval cycles, maverick buying, and supplier performance. Three-way-matching violations are automatically identified.
Order-to-Cash Processes: Sales processes from quote creation to payment receipt show delays, price deviations, and customer behavior. Quote-to-cash times are systematically optimized.
Incident Management: IT Service Management processes analyze ticket processing, escalation behavior, and resolution times. SLA compliance is continuously monitored.
Production Planning: Manufacturing Execution Systems generate event logs for production analyses. Setup times, machine availability, and quality problems are identified.
Data extraction collects event logs from various IT systems like ERP, CRM, BPM, or workflow systems. ETL processes transform heterogeneous data sources into standardized event log formats.
Data preprocessing cleanses, filters, and enriches raw data. Missing values are handled, outliers identified, and case IDs consistently assigned.
Process discovery applies mining algorithms for automatic process model extraction. Alternative algorithms are compared to select the best model.
Variant Analysis: Identification of different process variants and their frequency distribution. Happy path and exceptional cases are distinguished.
Performance Analysis: Throughput time analyses, waiting time identification, and resource utilization measurements. Service level evaluations are based on actual performance data.
Social Network Analysis: Analysis of handoff patterns between processors and organizational units. Collaboration patterns and workload distribution are visualized.
Commercial tools like Celonis, ProcessGold, and Disco offer comprehensive process mining platforms with self-service analytics. Cloud-based solutions enable rapid implementation.
Open source alternatives like ProM, PM4Py, and Apromore provide flexible, customizable analysis frameworks for specialized applications.
Integration into BI platforms like Power BI, Tableau, or Qlik enables process mining as part of comprehensive analytics strategies.
Event log quality assessment evaluates completeness, accuracy, and relevance of the data foundation. Data quality issues can significantly impair mining results.
Model validation by domain experts ensures mining results deliver business-relevant insights. Statistical significance testing validates findings.
Continuous process monitoring establishes process mining as permanent monitoring instance. Real-time dashboards warn of process deviations.
Process automation opportunities are identified through mining analyses. RPA implementations are based on evidence-based automation potential.
Predictive process analytics uses historical patterns for predictions of future process flows and proactive interventions.
Machine learning integration extends process mining with predictive and prescriptive analytics. Deep learning recognizes complex patterns in process data.
Real-time process mining analyzes processes in real-time for immediate optimization measures. Stream processing enables live process analytics.
Process Mining evolves into a central discipline for data-driven process excellence that systematically advances objective process insights, continuous optimization, and intelligent automation.