Initial commit - production deployment

services/alert_processor/app/enrichment/business_impact.py

@@ -0,0 +1,156 @@
+"""
+Business impact analyzer for alerts.
+
+Calculates financial impact, affected orders, customer impact, and other
+business metrics from event metadata.
+"""
+
+from typing import Dict, Any
+import structlog
+
+logger = structlog.get_logger()
+
+
+class BusinessImpactAnalyzer:
+    """Analyze business impact from event metadata"""
+
+    def analyze(self, event_type: str, metadata: Dict[str, Any]) -> dict:
+        """
+        Analyze business impact for an event.
+
+        Returns dict with:
+        - financial_impact_eur: Direct financial cost
+        - affected_orders: Number of orders impacted
+        - affected_customers: List of customer names
+        - production_delay_hours: Hours of production delay
+        - estimated_revenue_loss_eur: Potential revenue loss
+        - customer_impact: high/medium/low
+        - waste_risk_kg: Potential waste in kg
+        """
+
+        impact = {
+            "financial_impact_eur": 0,
+            "affected_orders": 0,
+            "affected_customers": [],
+            "production_delay_hours": 0,
+            "estimated_revenue_loss_eur": 0,
+            "customer_impact": "low",
+            "waste_risk_kg": 0
+        }
+
+        # Stock-related impacts
+        if "stock" in event_type or "shortage" in event_type:
+            impact.update(self._analyze_stock_impact(metadata))
+
+        # Production-related impacts
+        elif "production" in event_type or "delay" in event_type or "equipment" in event_type:
+            impact.update(self._analyze_production_impact(metadata))
+
+        # Procurement-related impacts
+        elif "po_" in event_type or "delivery" in event_type:
+            impact.update(self._analyze_procurement_impact(metadata))
+
+        # Quality-related impacts
+        elif "quality" in event_type or "expired" in event_type:
+            impact.update(self._analyze_quality_impact(metadata))
+
+        return impact
+
+    def _analyze_stock_impact(self, metadata: Dict[str, Any]) -> dict:
+        """Analyze impact of stock-related alerts"""
+        impact = {}
+
+        # Calculate financial impact
+        shortage_amount = metadata.get("shortage_amount", 0)
+        unit_cost = metadata.get("unit_cost", 5)  # Default €5/kg
+        impact["financial_impact_eur"] = float(shortage_amount) * unit_cost
+
+        # Affected orders from metadata
+        impact["affected_orders"] = metadata.get("affected_orders", 0)
+
+        # Customer impact based on affected orders
+        if impact["affected_orders"] > 5:
+            impact["customer_impact"] = "high"
+        elif impact["affected_orders"] > 2:
+            impact["customer_impact"] = "medium"
+
+        # Revenue loss (estimated)
+        avg_order_value = 50  # €50 per order
+        impact["estimated_revenue_loss_eur"] = impact["affected_orders"] * avg_order_value
+
+        return impact
+
+    def _analyze_production_impact(self, metadata: Dict[str, Any]) -> dict:
+        """Analyze impact of production-related alerts"""
+        impact = {}
+
+        # Delay minutes to hours
+        delay_minutes = metadata.get("delay_minutes", 0)
+        impact["production_delay_hours"] = round(delay_minutes / 60, 1)
+
+        # Affected orders and customers
+        impact["affected_orders"] = metadata.get("affected_orders", 0)
+
+        customer_names = metadata.get("customer_names", [])
+        impact["affected_customers"] = customer_names
+
+        # Customer impact based on delay
+        if delay_minutes > 120:  # 2+ hours
+            impact["customer_impact"] = "high"
+        elif delay_minutes > 60:  # 1+ hours
+            impact["customer_impact"] = "medium"
+
+        # Financial impact: hourly production cost
+        hourly_cost = 100  # €100/hour operational cost
+        impact["financial_impact_eur"] = impact["production_delay_hours"] * hourly_cost
+
+        # Revenue loss
+        if impact["affected_orders"] > 0:
+            avg_order_value = 50
+            impact["estimated_revenue_loss_eur"] = impact["affected_orders"] * avg_order_value
+
+        return impact
+
+    def _analyze_procurement_impact(self, metadata: Dict[str, Any]) -> dict:
+        """Analyze impact of procurement-related alerts"""
+        impact = {}
+
+        # Extract potential_loss_eur from reasoning_data.parameters
+        reasoning_data = metadata.get("reasoning_data", {})
+        parameters = reasoning_data.get("parameters", {})
+        potential_loss_eur = parameters.get("potential_loss_eur")
+
+        # Use potential loss from reasoning as financial impact (what's at risk)
+        # Fallback to PO amount only if reasoning data is not available
+        if potential_loss_eur is not None:
+            impact["financial_impact_eur"] = float(potential_loss_eur)
+        else:
+            po_amount = metadata.get("po_amount", metadata.get("total_amount", 0))
+            impact["financial_impact_eur"] = float(po_amount)
+
+        # Days overdue affects customer impact
+        days_overdue = metadata.get("days_overdue", 0)
+        if days_overdue > 3:
+            impact["customer_impact"] = "high"
+        elif days_overdue > 1:
+            impact["customer_impact"] = "medium"
+
+        return impact
+
+    def _analyze_quality_impact(self, metadata: Dict[str, Any]) -> dict:
+        """Analyze impact of quality-related alerts"""
+        impact = {}
+
+        # Expired products
+        expired_count = metadata.get("expired_count", 0)
+        total_value = metadata.get("total_value", 0)
+
+        impact["financial_impact_eur"] = float(total_value)
+        impact["waste_risk_kg"] = metadata.get("total_quantity_kg", 0)
+
+        if expired_count > 5:
+            impact["customer_impact"] = "high"
+        elif expired_count > 2:
+            impact["customer_impact"] = "medium"
+
+        return impact