Introduction to ARIS Process Mining
ARIS Process Mining is a powerful business process analysis tool developed by Software AG that transforms event log data into visual process models, revealing how processes actually work in reality. By analyzing digital footprints from IT systems, it helps organizations discover inefficiencies, bottlenecks, and compliance issues that would otherwise remain hidden in traditional process analysis. ARIS Process Mining bridges the gap between perceived processes and actual execution, enabling data-driven process optimization, automation opportunities, and continuous improvement.
Core Concepts & Terminology
Key Process Mining Terms
| Term | Definition |
|---|---|
| Event Log | Digital record of system activities containing case ID, activity name, timestamp, and other attributes |
| Case | Single instance of a process from start to finish (e.g., one customer order) |
| Activity | Individual step or task within a process |
| Variant | Specific sequence of activities representing one possible path through a process |
| Conformance Checking | Comparison between discovered process model and expected/designed model |
| Frequency | How often a specific path or activity occurs in the process |
| Performance | Time metrics associated with process execution (duration, waiting time, etc.) |
| Process Discovery | Automatic generation of process models from event logs |
| Root Cause Analysis | Investigation to identify factors causing process deviations or inefficiencies |
| BPMN | Business Process Model and Notation – standard visualization format for processes |
| Happy Path | Most common or ideal process variant |
ARIS Process Mining Architecture
| Component | Purpose |
|---|---|
| Data Extraction | Connects to source systems and extracts event logs |
| Data Transformation | Converts raw data into process-centric format |
| Process Discovery Engine | Analyzes event logs to discover actual process flows |
| Analysis Workbench | Interactive environment for process investigation |
| Dashboards | Visualization of KPIs and process metrics |
| Process Repository | Storage for process models and analysis results |
| Collaboration Tools | Features for sharing insights and coordinating improvement initiatives |
Step-by-Step Process Mining Methodology
1. Project Setup & Data Extraction
Define process scope and objectives
- Identify target process and key stakeholders
- Establish clear business questions to be answered
- Define success criteria and expected outcomes
Identify relevant data sources
- ERP systems (SAP, Oracle, etc.)
- CRM platforms (Salesforce, Microsoft Dynamics, etc.)
- Custom applications and databases
- Workflow management systems
- IoT devices and operational technology
Extract and prepare data
- Connect to source systems using ARIS connectors
- Extract event logs with minimum required attributes:
- Case ID – unique identifier for process instance
- Activity name – name of process step
- Timestamp – when activity occurred
- Resource – who/what performed the activity (optional)
- Navigate to: Data Management → Import Wizard → Select Source System
2. Data Transformation & Process Discovery
Configure data mapping
- Map source fields to ARIS Process Mining fields
- Configure data types and formats (especially date/time fields)
- Define case ID, activity, timestamp columns
- Path: Data Management → Edit Connection → Mapping
Apply data filtering and enrichment
- Filter out irrelevant records or time periods
- Enrich with additional business context
- Handle missing data and outliers
- Navigation: Data Management → Data Preparation
Generate initial process model
- Run automated process discovery algorithm
- Set appropriate discovery parameters:
- Activity threshold (minimum frequency to include)
- Edge threshold (minimum path frequency to include)
- Navigate to: Process Discovery → Generate Model
3. Analysis & Insights
Explore process visualization
- Examine discovered process flow
- Identify main process variants
- Toggle between frequency and performance views
- Navigation: Process Explorer → Process Flow
Analyze process metrics
- Review key performance indicators:
- Process cycle time (total duration)
- Activity frequencies
- Bottlenecks and waiting times
- Rework loops and deviations
- Navigation: Process Explorer → Statistics
- Review key performance indicators:
Perform root cause analysis
- Isolate problematic cases or variants
- Compare different process segments
- Identify factors influencing performance
- Navigation: Process Explorer → Filtering → Advanced Analysis
4. Optimization & Monitoring
Identify improvement opportunities
- Detect automation candidates
- Find redundant activities
- Identify bottlenecks for resolution
- Navigation: Process Explorer → Improvement
Define target process model
- Create optimized process model
- Set performance targets
- Define conformance rules
- Navigation: Process Design → Target Model
Implement continuous monitoring
- Set up automated dashboards
- Configure alerts for deviations
- Schedule regular refresh of process analysis
- Navigation: Monitoring → Dashboard Configuration
Key ARIS Process Mining Features
Process Discovery Capabilities
| Feature | Description | Navigation Path |
|---|---|---|
| Automated Process Discovery | Generates BPMN-compliant process models from event logs | Process Discovery → Create Model |
| Variant Explorer | Identifies and compares different process execution paths | Process Explorer → Variants |
| Social Network Analysis | Visualizes interactions between process participants | Analysis → Social Network |
| Pattern Recognition | Detects common sequences and recurring patterns | Analysis → Patterns |
| Process Comparison | Compares processes across time periods or organizational units | Analysis → Compare |
Analysis Tools
| Feature | Description | Navigation Path |
|---|---|---|
| Process Funnel | Visualizes process flows with drop-offs at each step | Analysis → Process Funnel |
| Conformance Checking | Compares actual execution against reference models | Analysis → Conformance |
| Bottleneck Analysis | Identifies process slowdowns and constraints | Analysis → Bottlenecks |
| Rework Detection | Finds loops and repeated activities | Analysis → Rework |
| Decision Point Analysis | Examines factors influencing path selection at decision points | Analysis → Decision Mining |
| Simulation | Tests impact of process changes before implementation | Process Optimization → Simulate |
Dashboarding & Visualization
| Feature | Description | Navigation Path |
|---|---|---|
| KPI Dashboards | Customizable visualizations of process metrics | Dashboards → Create New |
| Process Cockpit | Real-time monitoring of ongoing processes | Monitoring → Process Cockpit |
| Heatmaps | Color-coded visualization of performance metrics | Analysis → Heatmap |
| Timeline Analysis | Chronological view of process execution | Analysis → Timeline |
| Custom Reports | Scheduled or on-demand process reports | Reporting → Generate Report |
Comparison of Analysis Views
| View Type | Best For | Configuration Path |
|---|---|---|
| Process Flow | Understanding overall process structure and main paths | Process Explorer → Flow View |
| BPMN View | Standard process documentation and communication | Process Explorer → BPMN View |
| Swimlane View | Visualizing handoffs between departments/systems | Process Explorer → Swimlane View |
| Statistical View | Quantitative analysis of process metrics | Analysis → Statistics |
| Tabular View | Detailed examination of individual cases | Case Explorer → Table View |
| Chart View | Trend analysis and pattern recognition | Analysis → Charts |
Common Challenges & Solutions
| Challenge | Symptoms | Solution |
|---|---|---|
| Incomplete Event Logs | Missing steps in discovered process | • Identify data gaps and additional sources<br>• Configure case linking to connect fragmented cases<br>• Path: Data Management → Data Quality Check |
| Data Quality Issues | Unrealistic timestamps, duplicates | • Apply data cleaning transformations<br>• Set up validation rules<br>• Path: Data Management → Data Preparation → Transformations |
| Complex Process Structure | Unreadable “spaghetti” process diagrams | • Apply process simplification filters<br>• Focus on main variants<br>• Path: Process Explorer → Simplification |
| Performance Problems | Slow analysis with large datasets | • Implement data sampling<br>• Optimize hardware resources<br>• Path: System Administration → Performance Tuning |
| Lack of Business Context | Difficulty interpreting patterns | • Enrich data with business attributes<br>• Include contextual information<br>• Path: Data Management → Enrichment |
Best Practices & Tips
Data Preparation Best Practices
- Validate timestamps – Ensure consistent format and time zones
- Define clear case boundaries – Properly identify start and end events
- Include contextual attributes – Add business dimensions for deeper analysis
- Handle missing values – Develop a consistent approach for gaps
- Document data transformations – Maintain traceability of changes
Analysis Strategy Tips
- Start with high-level overview, then drill down to details
- Compare top and bottom performers to identify differentiating factors
- Use filtering to isolate specific segments or behaviors
- Combine process mining with business domain knowledge
- Create hypothesis and test with data before drawing conclusions
Implementation Tips
- Begin with a well-defined, high-impact process
- Involve both IT and business stakeholders from the start
- Link findings to quantifiable business outcomes
- Establish regular review cycles for continuous improvement
- Build a center of excellence to share knowledge and best practices
Integrations & Extensions
ARIS Ecosystem Integration
| Component | Integration Benefit |
|---|---|
| ARIS Business Designer | Link discovered processes to enterprise architecture |
| ARIS Risk & Compliance | Enhance compliance monitoring with actual process data |
| ARIS Simulation | Test process improvements before implementation |
| ARIS Document Storage | Connect process documentation to discovered models |
Third-Party System Connections
| System | Connection Method | Configuration Path |
|---|---|---|
| SAP | Direct connector, table extraction | Connectors → SAP Connector |
| Oracle | Database connector, SQL queries | Connectors → Database → Oracle |
| Salesforce | API connector, SOQL queries | Connectors → Cloud → Salesforce |
| ServiceNow | REST API integration | Connectors → Cloud → ServiceNow |
| Excel/CSV | File import | Data Management → Import → Files |
| Custom Systems | REST API, database connection, file export | Connectors → Custom |
Advanced Techniques
Predictive Process Analytics
// Pseudocode for setting up predictive analytics
1. Navigate to: Analytics → Predictive → Configure Model
2. Select target metric (e.g., "ProcessDuration")
3. Choose predictor variables
4. Select algorithm (Random Forest, Regression, etc.)
5. Set training/test split percentage
6. Run model training
7. Apply model to ongoing processes
8. Configure alerts for predicted deviations
Process Enhancement with Machine Learning
Automatic Activity Classification
- Path: ML Configuration → Activity Classifier
- Use cases: Categorizing free-text descriptions, grouping similar activities
Anomaly Detection
- Path: ML Configuration → Anomaly Detection
- Use cases: Identifying unusual patterns, fraud detection, compliance monitoring
Next-Best-Action Prediction
- Path: ML Configuration → Next Action Predictor
- Use cases: Process guidance, training, workflow optimization
Advanced Conformance Checking
// Setting up token-based conformance checking
1. Navigate to: Conformance → Token-Based Analysis
2. Load reference model (BPMN format)
3. Configure token replay settings:
- Fitness threshold: 0.85
- Move-on-log cost: 1
- Move-on-model cost: 5
4. Run analysis
5. Review metrics:
- Fitness score
- Precision
- Generalization
- Simplicity