# Cost Optimization Engine ## Enterprise-Scale Cost Optimization and Financial Intelligence for Enhanced BMAD System The Cost Optimization Engine provides sophisticated enterprise-grade cost analysis, optimization, and financial intelligence capabilities that enable organizations to maximize operational efficiency, reduce costs, and optimize resource allocation across all business and technology domains with AI-powered cost analytics and automated optimization strategies. ### Cost Optimization Architecture #### Comprehensive Cost Optimization Framework ```yaml cost_optimization_engine: cost_analysis_domains: infrastructure_cost_optimization: - cloud_cost_analysis_and_optimization: "Cloud infrastructure cost analysis and optimization" - on_premise_infrastructure_optimization: "On-premise infrastructure cost optimization" - hybrid_infrastructure_cost_management: "Hybrid infrastructure cost management and optimization" - resource_utilization_optimization: "Resource utilization analysis and optimization" - capacity_planning_cost_optimization: "Capacity planning with cost optimization focus" application_cost_optimization: - application_resource_optimization: "Application resource usage optimization" - licensing_cost_optimization: "Software licensing cost analysis and optimization" - development_cost_optimization: "Development process cost optimization" - maintenance_cost_optimization: "Application maintenance cost optimization" - performance_cost_correlation: "Performance and cost correlation analysis" operational_cost_optimization: - process_efficiency_cost_analysis: "Process efficiency and cost correlation analysis" - workflow_optimization_cost_impact: "Workflow optimization cost impact analysis" - automation_roi_analysis: "Automation return on investment analysis" - quality_cost_optimization: "Quality management cost optimization" - compliance_cost_optimization: "Compliance management cost optimization" human_resource_cost_optimization: - workforce_optimization_analysis: "Workforce allocation and optimization analysis" - skill_development_roi: "Training and skill development ROI analysis" - productivity_cost_correlation: "Productivity and cost correlation analysis" - outsourcing_vs_insourcing_analysis: "Outsourcing vs insourcing cost analysis" - contractor_optimization: "Contractor and vendor cost optimization" vendor_and_supplier_cost_optimization: - vendor_cost_analysis: "Vendor and supplier cost analysis" - contract_optimization: "Contract terms and pricing optimization" - procurement_cost_optimization: "Procurement process cost optimization" - supplier_performance_cost_impact: "Supplier performance and cost impact analysis" - alternative_vendor_analysis: "Alternative vendor cost comparison analysis" optimization_strategies: predictive_cost_modeling: - cost_forecasting_models: "Predictive cost forecasting and modeling" - budget_variance_prediction: "Budget variance prediction and analysis" - cost_trend_analysis: "Historical cost trend analysis and projection" - scenario_based_cost_modeling: "Scenario-based cost modeling and analysis" - risk_adjusted_cost_projections: "Risk-adjusted cost projections and planning" automated_cost_optimization: - automated_resource_scaling: "Automated resource scaling based on cost efficiency" - intelligent_resource_allocation: "AI-driven intelligent resource allocation" - automated_cost_controls: "Automated cost controls and spending limits" - dynamic_pricing_optimization: "Dynamic pricing and cost optimization" - automated_cost_anomaly_detection: "Automated cost anomaly detection and alerting" cost_efficiency_optimization: - efficiency_ratio_optimization: "Cost efficiency ratio optimization and improvement" - waste_elimination_strategies: "Waste identification and elimination strategies" - lean_operations_implementation: "Lean operations implementation and optimization" - value_stream_cost_optimization: "Value stream cost analysis and optimization" - continuous_improvement_cost_focus: "Continuous improvement with cost optimization focus" investment_optimization: - capital_allocation_optimization: "Capital allocation optimization and prioritization" - project_portfolio_cost_optimization: "Project portfolio cost optimization" - technology_investment_optimization: "Technology investment ROI optimization" - infrastructure_investment_planning: "Infrastructure investment planning and optimization" - innovation_investment_optimization: "Innovation and R&D investment optimization" financial_intelligence: cost_analytics_and_insights: - cost_driver_analysis: "Cost driver identification and analysis" - cost_benchmarking: "Industry and internal cost benchmarking" - cost_variance_analysis: "Cost variance analysis and root cause identification" - profitability_analysis: "Profitability analysis by products, services, and segments" - total_cost_of_ownership_analysis: "Total cost of ownership analysis and optimization" financial_performance_optimization: - margin_optimization: "Profit margin optimization and improvement" - cash_flow_optimization: "Cash flow optimization and management" - working_capital_optimization: "Working capital optimization and efficiency" - asset_utilization_optimization: "Asset utilization optimization and ROI improvement" - financial_risk_cost_analysis: "Financial risk and cost correlation analysis" budgeting_and_planning_optimization: - intelligent_budget_planning: "AI-powered intelligent budget planning and allocation" - dynamic_budget_optimization: "Dynamic budget optimization and reallocation" - zero_based_budgeting_support: "Zero-based budgeting implementation and support" - activity_based_costing: "Activity-based costing implementation and analysis" - budget_performance_optimization: "Budget performance tracking and optimization" cost_governance_and_control: - cost_governance_framework: "Cost governance framework implementation" - spending_control_automation: "Automated spending control and approval workflows" - cost_accountability_tracking: "Cost accountability tracking and reporting" - cost_policy_enforcement: "Cost policy enforcement and compliance monitoring" - financial_control_automation: "Financial control automation and validation" automation_and_intelligence: ai_powered_cost_optimization: - machine_learning_cost_models: "Machine learning cost prediction and optimization models" - intelligent_cost_recommendations: "AI-generated cost optimization recommendations" - automated_cost_anomaly_detection: "Automated cost anomaly detection and investigation" - predictive_cost_analytics: "Predictive cost analytics and forecasting" - cognitive_cost_analysis: "Cognitive cost analysis and pattern recognition" automated_cost_management: - automated_cost_allocation: "Automated cost allocation and chargeback" - dynamic_resource_optimization: "Dynamic resource optimization based on cost efficiency" - automated_contract_optimization: "Automated contract and pricing optimization" - intelligent_procurement_automation: "Intelligent procurement and sourcing automation" - automated_financial_controls: "Automated financial controls and validation" real_time_cost_optimization: - real_time_cost_monitoring: "Real-time cost monitoring and alerting" - instant_cost_optimization_recommendations: "Instant cost optimization recommendations" - real_time_budget_tracking: "Real-time budget tracking and variance analysis" - dynamic_cost_adjustments: "Dynamic cost adjustments and optimization" - continuous_cost_optimization: "Continuous cost optimization and improvement" ``` #### Cost Optimization Engine Implementation ```python import asyncio import pandas as pd import numpy as np from typing import Dict, List, Any, Optional, Tuple, Union from dataclasses import dataclass, field from enum import Enum from datetime import datetime, timedelta import json import uuid from collections import defaultdict, deque import logging from abc import ABC, abstractmethod from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error, r2_score from sklearn.preprocessing import StandardScaler import plotly.graph_objects as go import plotly.express as px from scipy.optimize import minimize, differential_evolution import warnings warnings.filterwarnings('ignore') class CostCategory(Enum): INFRASTRUCTURE = "infrastructure" APPLICATION = "application" OPERATIONAL = "operational" HUMAN_RESOURCES = "human_resources" VENDOR_SERVICES = "vendor_services" LICENSING = "licensing" COMPLIANCE = "compliance" class OptimizationType(Enum): IMMEDIATE = "immediate" SHORT_TERM = "short_term" MEDIUM_TERM = "medium_term" LONG_TERM = "long_term" STRATEGIC = "strategic" class CostImpact(Enum): CRITICAL = "critical" HIGH = "high" MEDIUM = "medium" LOW = "low" MINIMAL = "minimal" class OptimizationStrategy(Enum): REDUCE_USAGE = "reduce_usage" OPTIMIZE_ALLOCATION = "optimize_allocation" NEGOTIATE_PRICING = "negotiate_pricing" CHANGE_PROVIDER = "change_provider" AUTOMATE_PROCESS = "automate_process" ELIMINATE_WASTE = "eliminate_waste" @dataclass class CostItem: """ Represents a cost item with detailed breakdown and metadata """ cost_id: str name: str category: CostCategory amount: float currency: str period: str # monthly, quarterly, annually description: str cost_center: str allocation_rules: Dict[str, Any] = field(default_factory=dict) tags: List[str] = field(default_factory=list) last_updated: datetime = field(default_factory=datetime.utcnow) trend_data: List[Dict[str, Any]] = field(default_factory=list) benchmarks: Dict[str, float] = field(default_factory=dict) @dataclass class OptimizationOpportunity: """ Represents a cost optimization opportunity with impact analysis """ opportunity_id: str title: str description: str category: CostCategory optimization_type: OptimizationType strategy: OptimizationStrategy current_cost: float potential_savings: float implementation_cost: float roi_percentage: float impact_level: CostImpact implementation_timeline: str risk_assessment: Dict[str, Any] = field(default_factory=dict) prerequisites: List[str] = field(default_factory=list) success_metrics: List[str] = field(default_factory=list) stakeholders: List[str] = field(default_factory=list) @dataclass class CostOptimizationPlan: """ Comprehensive cost optimization plan with timeline and metrics """ plan_id: str name: str description: str total_current_cost: float total_target_savings: float implementation_cost: float net_savings: float roi_percentage: float timeline_months: int opportunities: List[OptimizationOpportunity] = field(default_factory=list) milestones: List[Dict[str, Any]] = field(default_factory=list) risk_mitigation: List[Dict[str, Any]] = field(default_factory=list) success_criteria: List[str] = field(default_factory=list) created_date: datetime = field(default_factory=datetime.utcnow) class CostOptimizationEngine: """ Enterprise-scale cost optimization and financial intelligence engine """ def __init__(self, claude_code_interface, config=None): self.claude_code = claude_code_interface self.config = config or { 'optimization_frequency_days': 30, 'cost_analysis_depth': 'comprehensive', 'automated_optimization': True, 'predictive_analytics': True, 'real_time_monitoring': True, 'budget_variance_threshold': 0.05, # 5% 'roi_threshold_percentage': 15.0, 'payback_period_threshold_months': 12 } # Core optimization components self.cost_analyzer = CostAnalyzer(self.claude_code, self.config) self.optimization_engine = OptimizationEngine(self.config) self.financial_intelligence = FinancialIntelligence(self.config) self.prediction_engine = CostPredictionEngine(self.config) # Specialized optimizers self.infrastructure_optimizer = InfrastructureCostOptimizer(self.config) self.application_optimizer = ApplicationCostOptimizer(self.config) self.operational_optimizer = OperationalCostOptimizer(self.config) self.vendor_optimizer = VendorCostOptimizer(self.config) # Analytics and intelligence self.cost_analytics = CostAnalytics(self.config) self.benchmarking_engine = CostBenchmarkingEngine(self.config) self.roi_calculator = ROICalculator(self.config) self.scenario_modeler = CostScenarioModeler(self.config) # Automation and control self.automation_engine = CostAutomationEngine(self.config) self.control_system = CostControlSystem(self.config) self.alert_manager = CostAlertManager(self.config) self.workflow_engine = CostOptimizationWorkflowEngine(self.config) # State management self.cost_repository = CostRepository() self.optimization_history = [] self.active_optimizations = {} self.cost_models = {} # Reporting and visualization self.reporting_engine = CostReportingEngine(self.config) self.dashboard_service = CostDashboardService(self.config) self.visualization_engine = CostVisualizationEngine(self.config) async def perform_comprehensive_cost_analysis(self, analysis_scope, time_period="12m"): """ Perform comprehensive cost analysis across all domains """ cost_analysis_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'analysis_scope': analysis_scope, 'time_period': time_period, 'cost_breakdown': {}, 'optimization_opportunities': [], 'financial_insights': {}, 'recommendations': [] } try: # Collect and categorize cost data cost_data = await self.cost_analyzer.collect_comprehensive_cost_data( analysis_scope, time_period ) cost_analysis_session['cost_data_collected'] = len(cost_data) # Perform cost breakdown analysis cost_breakdown = await self.cost_analyzer.analyze_cost_breakdown( cost_data ) cost_analysis_session['cost_breakdown'] = cost_breakdown # Identify optimization opportunities optimization_opportunities = await self.identify_optimization_opportunities( cost_data, cost_breakdown ) cost_analysis_session['optimization_opportunities'] = optimization_opportunities # Generate financial insights financial_insights = await self.financial_intelligence.generate_financial_insights( cost_data, cost_breakdown, optimization_opportunities ) cost_analysis_session['financial_insights'] = financial_insights # Perform benchmarking analysis benchmarking_results = await self.benchmarking_engine.perform_cost_benchmarking( cost_breakdown, analysis_scope ) cost_analysis_session['benchmarking_results'] = benchmarking_results # Generate predictive cost models predictive_models = await self.prediction_engine.create_cost_prediction_models( cost_data, time_period ) cost_analysis_session['predictive_models'] = predictive_models # Generate optimization recommendations recommendations = await self.generate_cost_optimization_recommendations( cost_analysis_session ) cost_analysis_session['recommendations'] = recommendations except Exception as e: cost_analysis_session['error'] = str(e) finally: cost_analysis_session['end_time'] = datetime.utcnow() cost_analysis_session['analysis_duration'] = ( cost_analysis_session['end_time'] - cost_analysis_session['start_time'] ).total_seconds() return cost_analysis_session async def identify_optimization_opportunities(self, cost_data, cost_breakdown): """ Identify cost optimization opportunities across all categories """ optimization_opportunities = [] # Infrastructure cost optimization opportunities infrastructure_opportunities = await self.infrastructure_optimizer.identify_opportunities( cost_data, cost_breakdown ) optimization_opportunities.extend(infrastructure_opportunities) # Application cost optimization opportunities application_opportunities = await self.application_optimizer.identify_opportunities( cost_data, cost_breakdown ) optimization_opportunities.extend(application_opportunities) # Operational cost optimization opportunities operational_opportunities = await self.operational_optimizer.identify_opportunities( cost_data, cost_breakdown ) optimization_opportunities.extend(operational_opportunities) # Vendor cost optimization opportunities vendor_opportunities = await self.vendor_optimizer.identify_opportunities( cost_data, cost_breakdown ) optimization_opportunities.extend(vendor_opportunities) # Calculate ROI and prioritize opportunities prioritized_opportunities = await self.prioritize_optimization_opportunities( optimization_opportunities ) return prioritized_opportunities async def create_cost_optimization_plan(self, optimization_opportunities, constraints=None): """ Create comprehensive cost optimization plan with timeline and implementation strategy """ plan_creation_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'opportunities_count': len(optimization_opportunities), 'constraints': constraints or {}, 'optimization_plan': None, 'implementation_strategy': {}, 'risk_analysis': {} } try: # Filter and select opportunities based on constraints selected_opportunities = await self.select_optimization_opportunities( optimization_opportunities, constraints ) # Calculate plan financials plan_financials = await self.calculate_plan_financials(selected_opportunities) # Create optimization plan optimization_plan = CostOptimizationPlan( plan_id=generate_uuid(), name=f"Cost Optimization Plan - {datetime.utcnow().strftime('%Y-%m')}", description="Comprehensive cost optimization plan based on identified opportunities", total_current_cost=plan_financials['total_current_cost'], total_target_savings=plan_financials['total_target_savings'], implementation_cost=plan_financials['implementation_cost'], net_savings=plan_financials['net_savings'], roi_percentage=plan_financials['roi_percentage'], timeline_months=plan_financials['timeline_months'], opportunities=selected_opportunities ) # Generate implementation strategy implementation_strategy = await self.generate_implementation_strategy( optimization_plan ) plan_creation_session['implementation_strategy'] = implementation_strategy # Perform risk analysis risk_analysis = await self.perform_plan_risk_analysis( optimization_plan, implementation_strategy ) plan_creation_session['risk_analysis'] = risk_analysis # Create milestones and timeline milestones = await self.create_plan_milestones( optimization_plan, implementation_strategy ) optimization_plan.milestones = milestones # Define success criteria success_criteria = await self.define_plan_success_criteria( optimization_plan ) optimization_plan.success_criteria = success_criteria plan_creation_session['optimization_plan'] = optimization_plan except Exception as e: plan_creation_session['error'] = str(e) finally: plan_creation_session['end_time'] = datetime.utcnow() plan_creation_session['creation_duration'] = ( plan_creation_session['end_time'] - plan_creation_session['start_time'] ).total_seconds() return plan_creation_session async def execute_cost_optimization_plan(self, optimization_plan): """ Execute cost optimization plan with monitoring and tracking """ execution_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'plan_id': optimization_plan.plan_id, 'execution_phases': [], 'completed_opportunities': [], 'cost_savings_realized': 0.0, 'current_status': 'in_progress' } try: # Initialize plan execution await self.initialize_plan_execution(optimization_plan) # Execute opportunities in phases execution_phases = await self.organize_execution_phases(optimization_plan) for phase in execution_phases: phase_result = await self.execute_optimization_phase( phase, optimization_plan ) execution_session['execution_phases'].append(phase_result) # Update cost savings execution_session['cost_savings_realized'] += phase_result.get('savings_realized', 0.0) # Check for phase completion and success if phase_result.get('status') == 'completed': execution_session['completed_opportunities'].extend( phase_result.get('completed_opportunities', []) ) # Monitor and adjust if needed if phase_result.get('requires_adjustment'): adjustment_result = await self.adjust_execution_plan( optimization_plan, phase_result ) execution_session['adjustments'] = execution_session.get('adjustments', []) execution_session['adjustments'].append(adjustment_result) # Calculate final results final_results = await self.calculate_execution_results( execution_session, optimization_plan ) execution_session['final_results'] = final_results # Update plan status execution_session['current_status'] = 'completed' except Exception as e: execution_session['error'] = str(e) execution_session['current_status'] = 'failed' finally: execution_session['end_time'] = datetime.utcnow() execution_session['execution_duration'] = ( execution_session['end_time'] - execution_session['start_time'] ).total_seconds() # Store execution history self.optimization_history.append(execution_session) return execution_session async def monitor_cost_optimization_performance(self, optimization_plan): """ Monitor ongoing cost optimization performance and adjust as needed """ monitoring_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'plan_id': optimization_plan.plan_id, 'performance_metrics': {}, 'variance_analysis': {}, 'corrective_actions': [], 'optimization_adjustments': [] } try: # Collect current performance data performance_data = await self.collect_optimization_performance_data( optimization_plan ) # Calculate performance metrics performance_metrics = await self.calculate_optimization_performance_metrics( optimization_plan, performance_data ) monitoring_session['performance_metrics'] = performance_metrics # Perform variance analysis variance_analysis = await self.perform_optimization_variance_analysis( optimization_plan, performance_metrics ) monitoring_session['variance_analysis'] = variance_analysis # Identify corrective actions if needed if variance_analysis['requires_correction']: corrective_actions = await self.identify_corrective_actions( optimization_plan, variance_analysis ) monitoring_session['corrective_actions'] = corrective_actions # Execute corrective actions for action in corrective_actions: action_result = await self.execute_corrective_action( action, optimization_plan ) monitoring_session['optimization_adjustments'].append(action_result) except Exception as e: monitoring_session['error'] = str(e) finally: monitoring_session['end_time'] = datetime.utcnow() monitoring_session['monitoring_duration'] = ( monitoring_session['end_time'] - monitoring_session['start_time'] ).total_seconds() return monitoring_session async def generate_cost_optimization_recommendations(self, cost_analysis_session): """ Generate intelligent cost optimization recommendations """ recommendations = [] # Analyze optimization opportunities for high-impact recommendations high_impact_opportunities = [ opp for opp in cost_analysis_session['optimization_opportunities'] if opp.impact_level in [CostImpact.CRITICAL, CostImpact.HIGH] ] for opportunity in high_impact_opportunities: if opportunity.roi_percentage > self.config['roi_threshold_percentage']: recommendation = { 'recommendation_id': generate_uuid(), 'title': f"Implement {opportunity.title}", 'description': opportunity.description, 'category': 'cost_optimization', 'priority': 'high' if opportunity.impact_level == CostImpact.CRITICAL else 'medium', 'potential_savings': opportunity.potential_savings, 'implementation_cost': opportunity.implementation_cost, 'roi_percentage': opportunity.roi_percentage, 'timeline': opportunity.implementation_timeline, 'risk_level': opportunity.risk_assessment.get('overall_risk', 'medium'), 'implementation_steps': await self.generate_implementation_steps(opportunity), 'success_metrics': opportunity.success_metrics, 'dependencies': opportunity.prerequisites } recommendations.append(recommendation) # Generate strategic recommendations strategic_recommendations = await self.generate_strategic_cost_recommendations( cost_analysis_session ) recommendations.extend(strategic_recommendations) # Prioritize recommendations prioritized_recommendations = await self.prioritize_recommendations(recommendations) return prioritized_recommendations class CostAnalyzer: """ Comprehensive cost analysis and data collection engine """ def __init__(self, claude_code, config): self.claude_code = claude_code self.config = config async def collect_comprehensive_cost_data(self, analysis_scope, time_period): """ Collect comprehensive cost data from all sources """ cost_data = [] # Infrastructure costs infrastructure_costs = await self.collect_infrastructure_costs( analysis_scope, time_period ) cost_data.extend(infrastructure_costs) # Application costs application_costs = await self.collect_application_costs( analysis_scope, time_period ) cost_data.extend(application_costs) # Operational costs operational_costs = await self.collect_operational_costs( analysis_scope, time_period ) cost_data.extend(operational_costs) # Human resource costs hr_costs = await self.collect_human_resource_costs( analysis_scope, time_period ) cost_data.extend(hr_costs) # Vendor and licensing costs vendor_costs = await self.collect_vendor_and_licensing_costs( analysis_scope, time_period ) cost_data.extend(vendor_costs) return cost_data async def collect_infrastructure_costs(self, analysis_scope, time_period): """ Collect infrastructure-related costs """ infrastructure_costs = [] # Cloud infrastructure costs cloud_costs = [ CostItem( cost_id="cloud_compute_001", name="Cloud Compute Instances", category=CostCategory.INFRASTRUCTURE, amount=15000.0, currency="USD", period="monthly", description="EC2 and compute instance costs", cost_center="IT Infrastructure", tags=["cloud", "compute", "aws"] ), CostItem( cost_id="cloud_storage_001", name="Cloud Storage", category=CostCategory.INFRASTRUCTURE, amount=3500.0, currency="USD", period="monthly", description="S3 and EBS storage costs", cost_center="IT Infrastructure", tags=["cloud", "storage", "aws"] ), CostItem( cost_id="cloud_network_001", name="Cloud Networking", category=CostCategory.INFRASTRUCTURE, amount=2000.0, currency="USD", period="monthly", description="VPC, Load Balancer, and data transfer costs", cost_center="IT Infrastructure", tags=["cloud", "networking", "aws"] ) ] infrastructure_costs.extend(cloud_costs) # On-premise infrastructure costs onprem_costs = [ CostItem( cost_id="datacenter_001", name="Data Center Operations", category=CostCategory.INFRASTRUCTURE, amount=25000.0, currency="USD", period="monthly", description="Data center facilities, power, and cooling", cost_center="IT Infrastructure", tags=["datacenter", "facilities", "onpremise"] ), CostItem( cost_id="hardware_001", name="Server Hardware", category=CostCategory.INFRASTRUCTURE, amount=8000.0, currency="USD", period="monthly", description="Server hardware depreciation and maintenance", cost_center="IT Infrastructure", tags=["hardware", "servers", "onpremise"] ) ] infrastructure_costs.extend(onprem_costs) return infrastructure_costs async def analyze_cost_breakdown(self, cost_data): """ Analyze cost breakdown by categories and dimensions """ cost_breakdown = { 'by_category': {}, 'by_cost_center': {}, 'by_time_period': {}, 'total_costs': 0.0, 'trends': {} } # Calculate costs by category category_costs = defaultdict(float) for cost_item in cost_data: category_costs[cost_item.category.value] += cost_item.amount cost_breakdown['by_category'] = dict(category_costs) # Calculate total costs cost_breakdown['total_costs'] = sum(category_costs.values()) # Calculate costs by cost center cost_center_costs = defaultdict(float) for cost_item in cost_data: cost_center_costs[cost_item.cost_center] += cost_item.amount cost_breakdown['by_cost_center'] = dict(cost_center_costs) # Calculate percentage breakdown total_costs = cost_breakdown['total_costs'] cost_breakdown['category_percentages'] = { category: (amount / total_costs) * 100 for category, amount in category_costs.items() } return cost_breakdown class InfrastructureCostOptimizer: """ Specialized optimizer for infrastructure costs """ def __init__(self, config): self.config = config async def identify_opportunities(self, cost_data, cost_breakdown): """ Identify infrastructure cost optimization opportunities """ opportunities = [] # Analyze cloud cost optimization opportunities cloud_opportunities = await self.identify_cloud_optimization_opportunities( cost_data, cost_breakdown ) opportunities.extend(cloud_opportunities) # Analyze on-premise optimization opportunities onprem_opportunities = await self.identify_onpremise_optimization_opportunities( cost_data, cost_breakdown ) opportunities.extend(onprem_opportunities) return opportunities async def identify_cloud_optimization_opportunities(self, cost_data, cost_breakdown): """ Identify cloud infrastructure cost optimization opportunities """ cloud_opportunities = [] # Right-sizing opportunity rightsizing_opportunity = OptimizationOpportunity( opportunity_id="cloud_rightsizing_001", title="Cloud Instance Right-sizing", description="Optimize cloud instance sizes based on actual utilization patterns", category=CostCategory.INFRASTRUCTURE, optimization_type=OptimizationType.SHORT_TERM, strategy=OptimizationStrategy.OPTIMIZE_ALLOCATION, current_cost=15000.0, potential_savings=3000.0, implementation_cost=500.0, roi_percentage=600.0, # (3000-500)/500 * 100 impact_level=CostImpact.HIGH, implementation_timeline="2-4 weeks", risk_assessment={ 'overall_risk': 'low', 'performance_impact': 'minimal', 'availability_impact': 'none' }, prerequisites=[ "Performance monitoring data collection", "Application performance baseline" ], success_metrics=[ "20% reduction in compute costs", "Maintained application performance", "No availability impact" ] ) cloud_opportunities.append(rightsizing_opportunity) # Reserved instance opportunity reserved_instance_opportunity = OptimizationOpportunity( opportunity_id="cloud_reserved_001", title="Reserved Instance Optimization", description="Purchase reserved instances for predictable workloads", category=CostCategory.INFRASTRUCTURE, optimization_type=OptimizationType.MEDIUM_TERM, strategy=OptimizationStrategy.NEGOTIATE_PRICING, current_cost=15000.0, potential_savings=4500.0, implementation_cost=0.0, roi_percentage=float('inf'), # No implementation cost impact_level=CostImpact.HIGH, implementation_timeline="1-2 weeks", risk_assessment={ 'overall_risk': 'low', 'commitment_risk': 'medium', 'flexibility_impact': 'medium' }, prerequisites=[ "Workload predictability analysis", "Capacity planning review" ], success_metrics=[ "30% reduction in compute costs", "Improved cost predictability" ] ) cloud_opportunities.append(reserved_instance_opportunity) return cloud_opportunities def generate_uuid(): """Generate a UUID string""" return str(uuid.uuid4()) # Additional classes would be implemented here: # - OptimizationEngine # - FinancialIntelligence # - CostPredictionEngine # - ApplicationCostOptimizer # - OperationalCostOptimizer # - VendorCostOptimizer # - CostAnalytics # - CostBenchmarkingEngine # - ROICalculator # - CostScenarioModeler # - CostAutomationEngine # - CostControlSystem # - CostAlertManager # - CostOptimizationWorkflowEngine # - CostRepository # - CostReportingEngine # - CostDashboardService # - CostVisualizationEngine ``` ### Cost Optimization Commands ```bash # Comprehensive cost analysis bmad cost analyze --comprehensive --time-period "12m" --all-categories bmad cost breakdown --by-category --by-department --by-project bmad cost benchmark --industry --internal --best-practices # Optimization opportunity identification bmad cost optimize --identify-opportunities --roi-threshold 15 bmad cost opportunities --infrastructure --applications --operations bmad cost savings --potential --quick-wins --strategic # Infrastructure cost optimization bmad cost infrastructure --cloud-optimization --rightsizing --reserved-instances bmad cost cloud --multi-cloud --cost-allocation --usage-optimization bmad cost datacenter --efficiency --consolidation --modernization # Application and licensing optimization bmad cost applications --licensing --resource-optimization --lifecycle bmad cost licensing --audit --optimization --vendor-negotiation bmad cost software --asset-management --usage-tracking --optimization # Operational cost optimization bmad cost operations --process-efficiency --automation-roi --workflow-optimization bmad cost vendor --contract-optimization --supplier-analysis --procurement bmad cost workforce --productivity --skill-optimization --capacity-planning # Predictive cost analytics bmad cost predict --forecasting --trend-analysis --scenario-modeling bmad cost budget --variance-analysis --dynamic-allocation --optimization bmad cost planning --capacity --investment --resource-allocation # Automated cost management bmad cost automate --controls --alerts --optimization --governance bmad cost monitor --real-time --anomaly-detection --budget-tracking bmad cost control --spending-limits --approval-workflows --compliance # Financial intelligence and reporting bmad cost intelligence --insights --recommendations --strategic-analysis bmad cost report --executive --departmental --project-specific bmad cost dashboard --real-time --kpis --optimization-tracking # Cost optimization plan execution bmad cost plan --create --optimize --timeline --milestones bmad cost execute --implementation --monitoring --tracking bmad cost measure --roi --savings --performance --success-metrics ``` This Cost Optimization Engine provides sophisticated enterprise-grade cost analysis, optimization, and financial intelligence capabilities that enable organizations to maximize operational efficiency, reduce costs, and optimize resource allocation across all business and technology domains with AI-powered cost analytics and automated optimization strategies throughout the entire enterprise cost management lifecycle.