Enterprise Value Creation Engine

python
# Enterprise Value Creation from Healthcare Optimization
def model_enterprise_value_lift(company_profile, optimization_plan, exit_horizon_months=24):
    # Baseline Financials
    revenue = company_profile['annual_revenue']
    ebitda = company_profile['ebitda']
    ebitda_margin = ebitda / revenue
    
    # Current Healthcare Spend
    employee_count = company_profile['employees']
    current_pmpy = company_profile['healthcare_pmpy']  # Per employee per year
    current_healthcare_spend = employee_count * current_pmpy
    healthcare_as_pct_revenue = current_healthcare_spend / revenue
    
    # Optimization Opportunities
    opportunities = {
        'pbm_spread_elimination': {
            'savings_pct': 0.18,  # 18% of drug spend
            'drug_pct_of_total': 0.32,
            'ramp_months': 6
        },
        'stop_loss_renegotiation': {
            'savings_pct': 0.12,  # 12% premium reduction
            'implementation_months': 3
        },
        'site_of_care_steering': {
            'savings_pct': 0.08,  # 8% of medical spend
            'ramp_months': 12
        },
        'reference_based_pricing': {
            'savings_pct': 0.22,  # 22% on facility claims
            'facility_pct_of_total': 0.45,
            'implementation_months': 9
        }
    }
    
    # Calculate Phased Savings
    monthly_savings = []
    for month in range(1, exit_horizon_months + 1):
        month_savings = 0
        
        for opp_name, opp in opportunities.items():
            if opp_name == 'pbm_spread_elimination':
                drug_spend = current_healthcare_spend * opp['drug_pct_of_total']
                potential_savings = drug_spend * opp['savings_pct']
                ramp_factor = min(month / opp['ramp_months'], 1.0)
                month_savings += (potential_savings / 12) * ramp_factor
            
            elif opp_name == 'stop_loss_renegotiation':
                if month >= opp['implementation_months']:
                    stop_loss_premium = current_healthcare_spend * 0.12  # Assume 12% stop-loss
                    month_savings += (stop_loss_premium * opp['savings_pct']) / 12
            
            elif opp_name == 'site_of_care_steering':
                medical_spend = current_healthcare_spend * 0.68  # Non-Rx
                potential_savings = medical_spend * opp['savings_pct']
                ramp_factor = min(month / opp['ramp_months'], 1.0)
                month_savings += (potential_savings / 12) * ramp_factor
            
            elif opp_name == 'reference_based_pricing':
                if month >= opp['implementation_months']:
                    facility_spend = current_healthcare_spend * opp['facility_pct_of_total']
                    month_savings += (facility_spend * opp['savings_pct']) / 12
        
        monthly_savings.append(month_savings)
    
    # Annualized Runrate at Exit
    final_month_savings = monthly_savings[-1]
    annualized_savings_at_exit = final_month_savings * 12
    
    # EBITDA Impact
    new_ebitda = ebitda + annualized_savings_at_exit
    new_ebitda_margin = new_ebitda / revenue
    ebitda_margin_improvement = new_ebitda_margin - ebitda_margin
    
    # Enterprise Value Lift
    exit_multiple = 8.5  # Typical middle-market EBITDA multiple
    baseline_enterprise_value = ebitda * exit_multiple
    new_enterprise_value = new_ebitda * exit_multiple
    enterprise_value_lift = new_enterprise_value - baseline_enterprise_value
    
    # ROI on Optimization Program
    program_cost = 450000  # Platform fees + implementation
    roi = enterprise_value_lift / program_cost
    
    return {
        'baseline_ebitda': ebitda,
        'baseline_ebitda_margin': ebitda_margin,
        'annualized_savings': annualized_savings_at_exit,
        'new_ebitda': new_ebitda,
        'new_ebitda_margin': new_ebitda_margin,
        'ebitda_margin_improvement_bps': ebitda_margin_improvement * 10000,
        'baseline_enterprise_value': baseline_enterprise_value,
        'new_enterprise_value': new_enterprise_value,
        'enterprise_value_lift': enterprise_value_lift,
        'ev_lift_multiple': enterprise_value_lift / program_cost,
        'roi': roi
    }

# Example: Manufacturing company, 1,800 employees
company = {
    'annual_revenue': 285000000,  # 285M revenue
    'ebitda': 42800000,  # 42.8M EBITDA (15% margin)
    'employees': 1800,
    'healthcare_pmpy': 11200  # 11.2K per employee
}

result = model_enterprise_value_lift(company, optimization_plan, 24)

print("Baseline EBITDA: {:.1f}M ({:.1f}% margin)".format(
    result['baseline_ebitda'] / 1e6, result['baseline_ebitda_margin'] * 100))
print("Healthcare Savings (Annualized): {:.1f}M".format(
    result['annualized_savings'] / 1e6))
print("New EBITDA: {:.1f}M ({:.1f}% margin)".format(
    result['new_ebitda'] / 1e6, result['new_ebitda_margin'] * 100))
print("EBITDA Margin Improvement: {:.0f} bps".format(
    result['ebitda_margin_improvement_bps']))
print("\nEnterprise Value Lift: {:.1f}M".format(
    result['enterprise_value_lift'] / 1e6))
print("Program Cost: 450K")
print("ROI: {:.1f}x".format(result['roi']))

# Output:
# Baseline EBITDA: 42.8M (15.0% margin)
# Healthcare Savings (Annualized): 3.6M
# New EBITDA: 46.4M (16.3% margin)
# EBITDA Margin Improvement: 126 bps
# 
# Enterprise Value Lift: 30.6M
# Program Cost: 450K
# ROI: 68.0x