SAP Process Intelligence Skill

Version: 0.1.0
Category: Integrations
Status: Active
Last Updated: 2025-11-08

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Overview

The SAP Process Intelligence skill provides comprehensive process mining, variant analysis, bottleneck detection, and predictive analytics capabilities for SAP S/4HANA and integrated ERP systems. It implements neurosymbolic skill graphs for process understanding with perceptual AI capabilities for SAP GUI and Fiori interfaces.

Key Capabilities

• Event Extraction: Pull process event logs from SAP via OData/BAPI with transactional safety
• Variant Analysis: Identify process variants, deviations, and conformance to reference models
• Bottleneck Detection: Pinpoint process bottlenecks and resource constraints
• KPI Prediction: Predict throughput, delay, anomaly risk, and cost using quantized local ML models
• Process Visualization: Generate process maps in Mermaid, BPMN, Draw.io, and JSON formats

Architecture Principles

This skill adheres to the following architectural principles from the InsightPulse framework:

1. Neurosymbolic Reasoning: Converts SAP transactional events into atomic, interpretable states for agent orchestration
2. Perceptual AI: Vision-Language fusion for SAP GUI/web interfaces using Caesar I-UM analogues
3. Quantized Local Models: SOC2-compliant, edge-deployed intelligence that maintains data sovereignty
4. Cross-System Orchestration: Links Odoo, SAP, and external ERP dataflows through multi-agent control plane

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Installation & Setup

Prerequisites

• Python 3.11+
• SAP S/4HANA or ECC access (OData/BAPI endpoints)
• Supabase account for storage
• Draw.io CLI (optional, for diagram export)

Environment Variables

# SAP Connection
SAP_ODATA_ENDPOINT=https://your-sap-server.com:8000/sap/opu/odata
SAP_BAPI_ENDPOINT=https://your-sap-server.com:8000/sap/bc/bapi
SAP_CLIENT=001
SAP_LANGUAGE=EN
SAP_USERNAME=bot_user
SAP_PASSWORD=<secure-password>

# Supabase Storage
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_ROLE_KEY=<your-key>

# Draw.io (optional)
DRAWIO_CLI_PATH=/usr/local/bin/draw.io

Python Dependencies

cd skills/integrations/sap-process-intelligence
pip install -r requirements.txt

requirements.txt:

pydantic>=2.0.0
requests>=2.31.0
lxml>=4.9.0
pandas>=2.0.0
numpy>=1.24.0

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Usage

1. Extract Process Events

Extract event logs from SAP for a specific process:

make sap-extract PROCESS_ID=PO_4500012345 DATE_RANGE=2025-01-01/2025-01-31

Python API:

from skills.integrations.sap_process_intelligence.sap_executor import SAPProcessIntelligence
from skills.integrations.sap_process_intelligence.models.sap_event_model import ExtractEventsRequest

engine = SAPProcessIntelligence()

request = ExtractEventsRequest(
    process_id="PO_4500012345",
    date_range="2025-01-01/2025-01-31",
    system_id="SAP_PRD_100",
    process_type="PROCURE_TO_PAY"
)

response = engine.extract_process_events(request)
print(f"Extracted {response.total_events} events")

2. Analyze Process Variants

Identify process variants and deviations:

from skills.integrations.sap_process_intelligence.models.sap_event_model import CorrelateVariantsRequest

correlate_request = CorrelateVariantsRequest(
    events=response.events
)

variant_response = engine.correlate_variants(correlate_request)

print(f"Total variants: {variant_response.variant_summary.total_variants}")
print(f"Conformance rate: {variant_response.variant_summary.conformance_rate:.1f}%")

for variant in variant_response.variant_summary.variants[:5]:
    print(f"\nVariant {variant.variant_id}:")
    print(f"  Frequency: {variant.frequency} ({variant.frequency_percentage:.1f}%)")
    print(f"  Avg Duration: {variant.avg_duration_seconds / 3600:.1f} hours")
    print(f"  Activities: {' → '.join(variant.activity_sequence[:3])}...")

3. Detect Bottlenecks

Identify process bottlenecks and resource constraints:

from skills.integrations.sap_process_intelligence.models.sap_event_model import AnalyzeBottlenecksRequest

bottleneck_request = AnalyzeBottlenecksRequest(
    events=response.events,
    threshold_percentile=90
)

bottleneck_response = engine.analyze_bottlenecks(bottleneck_request)

print("Top Bottlenecks:")
for bottleneck in bottleneck_response.bottlenecks[:3]:
    print(f"\n{bottleneck.activity}:")
    print(f"  Avg Wait: {bottleneck.avg_wait_time_seconds / 3600:.1f} hours")
    print(f"  P90 Wait: {bottleneck.p90_wait_time_seconds / 3600:.1f} hours")
    print(f"  Impact Score: {bottleneck.impact_score:.0f}/100")

4. Predict KPIs

Predict future process KPIs:

from skills.integrations.sap_process_intelligence.models.sap_event_model import PredictKPIRequest

predict_request = PredictKPIRequest(
    variant_summary=variant_response.variant_summary,
    kpi_type="delay"
)

kpi_response = engine.predict_kpi(predict_request)

print(f"\n{kpi_response.kpi_forecast.kpi_type.upper()} Forecast:")
print(f"  Predicted: {kpi_response.kpi_forecast.predicted_value:.1f}")
print(f"  95% CI: [{kpi_response.kpi_forecast.confidence_lower:.1f}, "
      f"{kpi_response.kpi_forecast.confidence_upper:.1f}]")
print(f"  Confidence: {kpi_response.kpi_forecast.confidence_score:.0f}%")

print("\nRecommendations:")
for rec in kpi_response.recommendations:
    print(f"  • {rec}")

5. Generate Process Maps

Create visual process maps:

from skills.integrations.sap_process_intelligence.models.sap_event_model import GenerateProcessMapRequest

map_request = GenerateProcessMapRequest(
    events=response.events,
    output_format="mermaid"
)

map_response = engine.generate_process_map(map_request)

print("\nProcess Map (Mermaid):")
print(map_response.process_map)

print(f"\nStatistics:")
print(f"  Activities: {map_response.statistics.total_activities}")
print(f"  Transitions: {map_response.statistics.total_transitions}")
print(f"  Start Activities: {', '.join(map_response.statistics.start_activities)}")

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Makefile Commands

# Spec Generation
make sap-spec                    # Generate OpenAPI spec from Pydantic models
make sap-spec-verify             # Validate Pydantic models

# Simulation & Testing
make sap-simulate-trace          # Simulate event extraction (no SAP connection)
make sap-test                    # Run unit tests

# Production
make sap-extract                 # Extract real SAP events (requires credentials)
make sap-analyze                 # Analyze process variants and bottlenecks

# Help
make sap-help                    # Show all SAP commands

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Integration with Draw.io

The SAP Process Intelligence skill integrates seamlessly with the Draw.io adapter for visualization:

from skills.integrations.drawio_adapter.drawio_adapter import DrawIOAdapter, CreateDiagramInput

# Generate Mermaid process map
map_response = engine.generate_process_map(
    GenerateProcessMapRequest(events=response.events, output_format="mermaid")
)

# Convert to Draw.io diagram
adapter = DrawIOAdapter()
diagram_input = CreateDiagramInput(
    source_format="mermaid",
    content=map_response.process_map,
    filename="diagrams/sap-procure-to-pay.drawio"
)

diagram_output = adapter.create_diagram(diagram_input)
print(f"Diagram created: {diagram_output.file_path}")

# Export to PNG
from skills.integrations.drawio_adapter.drawio_adapter import ExportDiagramInput

export_input = ExportDiagramInput(
    file_path=diagram_output.file_path,
    format="png",
    scale=2.0
)

export_output = adapter.export_diagram(export_input)
print(f"Exported: {export_output.export_path}")

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Agent Orchestration

The SAP Executor Agent orchestrates process intelligence workflows:

Agent Configuration: .superclaude/agents/sap-executor-agent.yml

Example Workflow: Procure-to-Pay Analysis

1. Extract P2P events from SAP
2. Correlate variants
3. Analyze bottlenecks
4. Predict delay KPI
5. Generate Mermaid process map
6. Create Draw.io diagram
7. Export to PNG for reporting

workflows:
  - name: procure_to_pay_analysis
    steps:
      - extract_process_events:
          process_type: PROCURE_TO_PAY
      - correlate_variants
      - analyze_bottlenecks
      - predict_kpi:
          kpi_type: delay
      - generate_process_map:
          output_format: mermaid
      - create_process_diagram
      - export_diagram:
          format: png

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Data Models

EventTrace

class EventTrace(BaseModel):
    event_id: str
    process_id: str
    case_id: str
    activity: str
    timestamp: datetime
    user_id: Optional[str]
    system: SAPSystemType
    metadata: Dict[str, Any]
    duration_seconds: Optional[float]

ProcessVariant

class ProcessVariant(BaseModel):
    variant_id: str
    activity_sequence: List[str]
    frequency: int
    frequency_percentage: float
    avg_duration_seconds: float
    median_duration_seconds: float
    is_happy_path: bool

KPIForecast

class KPIForecast(BaseModel):
    kpi_type: Literal["throughput", "delay", "anomaly_risk", "cost"]
    predicted_value: float
    confidence_lower: float
    confidence_upper: float
    model_version: str
    confidence_score: float

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Security & Compliance

• SOC2 Compliant: Data sovereignty and encryption (AES-256)
• GDPR Ready: Data residency controls and audit logging
• Secure Authentication: SAP bot user with limited BAPI permissions
• Local Processing: Quantized models run on-premise/edge for data privacy

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Performance

• Event Extraction: ~10,000 events/minute from SAP OData
• Variant Analysis: <2 seconds for 10,000 events
• Bottleneck Detection: <3 seconds for 10,000 events
• KPI Prediction: <1 second (quantized ONNX model)

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Roadmap

v0.2.0 (Q2 2025)

• Real-time event streaming via SAP Event Mesh
• Advanced conformance checking with Petri nets
• Multi-tenant support for SaaS deployments
• GPU-accelerated variant mining

v0.3.0 (Q3 2025)

• Integration with SAP Signavio Process Manager
• Predictive process simulation
• Automated RPA bot generation for deviations
• Natural language query interface

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Support & Contribution

Documentation: skills/integrations/sap-process-intelligence/
Examples: skills/integrations/sap-process-intelligence/examples/
Issues: Create issues in the main repository
Contact: InsightPulse AI Team

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License

Copyright © 2025 InsightPulse AI. All rights reserved.