42 KiB
42 KiB
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
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
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
# 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.