BrainPredict Controlling: ERP Integrations

from brainpredict import ControllingClient

client = ControllingClient(api_key="bp_controlling_live_xxx")

# Step 1: Connect to SAP S/4HANA Controlling
client.integrations.connect(
    platform="sap_s4hana",
    host="your-sap-host.com",
    client="100",
    username="your_username",
    password="your_password",
    language="EN"
)

# Step 2: Configure data sync
client.integrations.configure_sync(
    sync_frequency="real-time",  # or "hourly", "daily"
    sync_entities=["cost_centers", "profit_centers", "actuals", "plans"],
    sync_direction="bidirectional"
)

# Step 3: Map cost centers
client.integrations.map_cost_centers(
    sap_cost_centers=["1000", "2000", "3000"],
    brainpredict_cost_centers=["CC-1000", "CC-2000", "CC-3000"]
)

# Step 4: Sync actuals data
actuals = client.actuals.sync_from_erp()
print(f"Synced {len(actuals)} actual records from SAP")

# Step 5: Run AI-powered variance analysis
results = client.variance.analyze()
print(f"Detected {results.variances_found} significant variances")
print(f"Root causes identified: {results.root_causes_count}")
print(f"Analysis accuracy: {results.accuracy}%")

from brainpredict import ControllingClient

client = ControllingClient(api_key="bp_controlling_live_xxx")

# Step 1: Connect to Oracle ERP Cloud
client.integrations.connect(
    platform="oracle_erp_cloud",
    instance_url="https://your-instance.oraclecloud.com",
    username="your_username",
    password="your_password"
)

# Step 2: Sync budget data
budgets = client.plans.sync_from_erp(
    fiscal_year=2025,
    scenarios=["baseline", "optimistic", "pessimistic"]
)
print(f"Synced {len(budgets)} budget records from Oracle")

# Step 3: Run AI-powered forecast
forecast = client.performance.forecast(
    historical_months=24,
    forecast_months=12,
    kpis=["revenue", "ebitda", "cash_flow"]
)
print(f"Forecast Accuracy: {forecast.accuracy}%")
print(f"Revenue Forecast (12M): €{forecast.revenue_12m:,.0f}")

from brainpredict import ControllingClient

client = ControllingClient(api_key="bp_controlling_live_xxx")

# Step 1: Connect to Anaplan
client.integrations.connect(
    platform="anaplan",
    workspace_id="your_workspace_id",
    model_id="your_model_id",
    username="your_username",
    password="your_password"
)

# Step 2: Sync planning data
plans = client.plans.sync_from_erp(
    modules=["Sales Plan", "OpEx Plan", "CapEx Plan"],
    versions=["Actual", "Budget", "Forecast"]
)
print(f"Synced {len(plans)} planning records from Anaplan")

# Step 3: Run scenario simulation
scenario = client.scenarios.create(
    name="Revenue Growth +10%",
    assumptions={"revenue_growth": 0.10},
    simulation_months=12
)
results = client.scenarios.simulate(scenario.id)
print(f"Scenario Impact: Revenue {results.revenue_impact:+.1f}%, EBITDA {results.ebitda_impact:+.1f}%")

from brainpredict import ControllingClient

client = ControllingClient(api_key="bp_controlling_live_xxx")

# Step 1: Connect to Microsoft Dynamics 365
client.integrations.connect(
    platform="dynamics_365",
    instance_url="https://your-org.crm.dynamics.com",
    tenant_id="your_tenant_id",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Step 2: Sync financial dimensions
dimensions = client.integrations.sync_dimensions(
    dimension_types=["Department", "Project", "Cost Center", "Product Line"]
)
print(f"Synced {len(dimensions)} financial dimensions from Dynamics 365")

# Step 3: Sync actuals and run profitability analysis
actuals = client.actuals.sync_from_erp()
profitability = client.profitability.analyze(
    dimensions=["Product Line", "Customer Segment"],
    period="2025-Q1"
)
print(f"Profitability Analysis: {profitability.insights_count} insights generated")
print(f"Top profitable segment: {profitability.top_segment} ({profitability.top_margin:.1f}% margin)")