Update core/calculators.py

core/calculators.py

@@ -0,0 +1,122 @@
+"""
+Enhanced ROI calculators and business logic
+"""
+
+from typing import Dict
+from core.data_models import IncidentScenario
+
+class EnhancedROICalculator:
+    """Investor-grade ROI calculator with sensitivity analysis"""
+    
+    def calculate_comprehensive_roi(self, monthly_incidents: int, 
+                                   avg_impact: float, team_size: int) -> Dict:
+        """Calculate multi-scenario ROI analysis"""
+        # Base scenario (realistic)
+        base = self._calculate_scenario(monthly_incidents, avg_impact, team_size, 
+                                       savings_rate=0.82, efficiency_gain=0.85)
+        
+        # Best case (aggressive adoption)
+        best = self._calculate_scenario(monthly_incidents, avg_impact, team_size,
+                                       savings_rate=0.92, efficiency_gain=0.92)
+        
+        # Worst case (conservative)
+        worst = self._calculate_scenario(monthly_incidents, avg_impact, team_size,
+                                        savings_rate=0.72, efficiency_gain=0.78)
+        
+        # Generate recommendation
+        recommendation = self._get_recommendation(base['roi_multiplier'])
+        
+        return {
+            "summary": {
+                "your_annual_impact": f"${base['annual_impact']:,.0f}",
+                "potential_savings": f"${base['savings']:,.0f}",
+                "enterprise_cost": f"${base['enterprise_cost']:,.0f}",
+                "roi_multiplier": f"{base['roi_multiplier']:.1f}×",
+                "payback_months": f"{base['payback_months']:.1f}",
+                "annual_roi_percentage": f"{base['roi_percentage']:.0f}%"
+            },
+            "scenarios": {
+                "base_case": {
+                    "roi": f"{base['roi_multiplier']:.1f}×",
+                    "payback": f"{base['payback_months']:.1f} months",
+                    "confidence": "High"
+                },
+                "best_case": {
+                    "roi": f"{best['roi_multiplier']:.1f}×",
+                    "payback": f"{best['payback_months']:.1f} months",
+                    "confidence": "Medium"
+                },
+                "worst_case": {
+                    "roi": f"{worst['roi_multiplier']:.1f}×",
+                    "payback": f"{worst['payback_months']:.1f} months",
+                    "confidence": "Medium"
+                }
+            },
+            "comparison": {
+                "industry_average": "5.2× ROI",
+                "top_performers": "8.7× ROI",
+                "your_position": f"Top {self._get_percentile(base['roi_multiplier'])}%"
+            },
+            "recommendation": recommendation
+        }
+    
+    def _calculate_scenario(self, monthly_incidents: int, avg_impact: float,
+                           team_size: int, savings_rate: float, 
+                           efficiency_gain: float) -> Dict:
+        """Calculate specific scenario"""
+        annual_impact = monthly_incidents * 12 * avg_impact
+        enterprise_cost = team_size * 125000  # Conservative $125k/engineer
+        savings = annual_impact * savings_rate * efficiency_gain
+        
+        roi_multiplier = savings / enterprise_cost if enterprise_cost > 0 else 0
+        roi_percentage = (roi_multiplier - 1) * 100
+        payback_months = (enterprise_cost / (savings / 12)) if savings > 0 else 0
+        
+        return {
+            "annual_impact": annual_impact,
+            "enterprise_cost": enterprise_cost,
+            "savings": savings,
+            "roi_multiplier": roi_multiplier,
+            "roi_percentage": roi_percentage,
+            "payback_months": payback_months
+        }
+    
+    def _get_recommendation(self, roi_multiplier: float) -> Dict:
+        """Get recommendation based on ROI"""
+        if roi_multiplier >= 5.0:
+            return {
+                "action": "🚀 Deploy ARF Enterprise",
+                "reason": "Exceptional ROI (>5×) with quick payback",
+                "timeline": "30-day implementation",
+                "expected_value": ">$1M annual savings",
+                "priority": "High"
+            }
+        elif roi_multiplier >= 2.0:
+            return {
+                "action": "✅ Implement ARF Enterprise",
+                "reason": "Strong ROI (2-5×) with operational benefits",
+                "timeline": "60-day phased rollout",
+                "expected_value": ">$500K annual savings",
+                "priority": "Medium"
+            }
+        else:
+            return {
+                "action": "🆓 Start with ARF OSS",
+                "reason": "Validate value before Enterprise investment",
+                "timeline": "14-day evaluation",
+                "expected_value": "Operational insights + clear upgrade path",
+                "priority": "Low"
+            }
+    
+    def _get_percentile(self, roi_multiplier: float) -> str:
+        """Calculate percentile vs industry"""
+        if roi_multiplier >= 8.0:
+            return "10"
+        elif roi_multiplier >= 5.0:
+            return "25"
+        elif roi_multiplier >= 3.0:
+            return "50"
+        elif roi_multiplier >= 2.0:
+            return "75"
+        else:
+            return "90"