1002 lines
42 KiB
Markdown
1002 lines
42 KiB
Markdown
# Cost Optimization Engine
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## Enterprise-Scale Cost Optimization and Financial Intelligence for Enhanced BMAD System
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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.
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### Cost Optimization Architecture
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#### Comprehensive Cost Optimization Framework
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```yaml
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cost_optimization_engine:
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cost_analysis_domains:
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infrastructure_cost_optimization:
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- cloud_cost_analysis_and_optimization: "Cloud infrastructure cost analysis and optimization"
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- on_premise_infrastructure_optimization: "On-premise infrastructure cost optimization"
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- hybrid_infrastructure_cost_management: "Hybrid infrastructure cost management and optimization"
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- resource_utilization_optimization: "Resource utilization analysis and optimization"
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- capacity_planning_cost_optimization: "Capacity planning with cost optimization focus"
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application_cost_optimization:
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- application_resource_optimization: "Application resource usage optimization"
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- licensing_cost_optimization: "Software licensing cost analysis and optimization"
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- development_cost_optimization: "Development process cost optimization"
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- maintenance_cost_optimization: "Application maintenance cost optimization"
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- performance_cost_correlation: "Performance and cost correlation analysis"
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operational_cost_optimization:
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- process_efficiency_cost_analysis: "Process efficiency and cost correlation analysis"
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- workflow_optimization_cost_impact: "Workflow optimization cost impact analysis"
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- automation_roi_analysis: "Automation return on investment analysis"
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- quality_cost_optimization: "Quality management cost optimization"
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- compliance_cost_optimization: "Compliance management cost optimization"
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human_resource_cost_optimization:
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- workforce_optimization_analysis: "Workforce allocation and optimization analysis"
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- skill_development_roi: "Training and skill development ROI analysis"
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- productivity_cost_correlation: "Productivity and cost correlation analysis"
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- outsourcing_vs_insourcing_analysis: "Outsourcing vs insourcing cost analysis"
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- contractor_optimization: "Contractor and vendor cost optimization"
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vendor_and_supplier_cost_optimization:
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- vendor_cost_analysis: "Vendor and supplier cost analysis"
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- contract_optimization: "Contract terms and pricing optimization"
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- procurement_cost_optimization: "Procurement process cost optimization"
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- supplier_performance_cost_impact: "Supplier performance and cost impact analysis"
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- alternative_vendor_analysis: "Alternative vendor cost comparison analysis"
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optimization_strategies:
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predictive_cost_modeling:
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- cost_forecasting_models: "Predictive cost forecasting and modeling"
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- budget_variance_prediction: "Budget variance prediction and analysis"
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- cost_trend_analysis: "Historical cost trend analysis and projection"
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- scenario_based_cost_modeling: "Scenario-based cost modeling and analysis"
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- risk_adjusted_cost_projections: "Risk-adjusted cost projections and planning"
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automated_cost_optimization:
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- automated_resource_scaling: "Automated resource scaling based on cost efficiency"
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- intelligent_resource_allocation: "AI-driven intelligent resource allocation"
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- automated_cost_controls: "Automated cost controls and spending limits"
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- dynamic_pricing_optimization: "Dynamic pricing and cost optimization"
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- automated_cost_anomaly_detection: "Automated cost anomaly detection and alerting"
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cost_efficiency_optimization:
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- efficiency_ratio_optimization: "Cost efficiency ratio optimization and improvement"
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- waste_elimination_strategies: "Waste identification and elimination strategies"
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- lean_operations_implementation: "Lean operations implementation and optimization"
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- value_stream_cost_optimization: "Value stream cost analysis and optimization"
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- continuous_improvement_cost_focus: "Continuous improvement with cost optimization focus"
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investment_optimization:
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- capital_allocation_optimization: "Capital allocation optimization and prioritization"
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- project_portfolio_cost_optimization: "Project portfolio cost optimization"
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- technology_investment_optimization: "Technology investment ROI optimization"
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- infrastructure_investment_planning: "Infrastructure investment planning and optimization"
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- innovation_investment_optimization: "Innovation and R&D investment optimization"
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financial_intelligence:
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cost_analytics_and_insights:
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- cost_driver_analysis: "Cost driver identification and analysis"
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- cost_benchmarking: "Industry and internal cost benchmarking"
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- cost_variance_analysis: "Cost variance analysis and root cause identification"
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- profitability_analysis: "Profitability analysis by products, services, and segments"
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- total_cost_of_ownership_analysis: "Total cost of ownership analysis and optimization"
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financial_performance_optimization:
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- margin_optimization: "Profit margin optimization and improvement"
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- cash_flow_optimization: "Cash flow optimization and management"
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- working_capital_optimization: "Working capital optimization and efficiency"
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- asset_utilization_optimization: "Asset utilization optimization and ROI improvement"
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- financial_risk_cost_analysis: "Financial risk and cost correlation analysis"
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budgeting_and_planning_optimization:
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- intelligent_budget_planning: "AI-powered intelligent budget planning and allocation"
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- dynamic_budget_optimization: "Dynamic budget optimization and reallocation"
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- zero_based_budgeting_support: "Zero-based budgeting implementation and support"
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- activity_based_costing: "Activity-based costing implementation and analysis"
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- budget_performance_optimization: "Budget performance tracking and optimization"
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cost_governance_and_control:
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- cost_governance_framework: "Cost governance framework implementation"
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- spending_control_automation: "Automated spending control and approval workflows"
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- cost_accountability_tracking: "Cost accountability tracking and reporting"
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- cost_policy_enforcement: "Cost policy enforcement and compliance monitoring"
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- financial_control_automation: "Financial control automation and validation"
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automation_and_intelligence:
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ai_powered_cost_optimization:
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- machine_learning_cost_models: "Machine learning cost prediction and optimization models"
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- intelligent_cost_recommendations: "AI-generated cost optimization recommendations"
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- automated_cost_anomaly_detection: "Automated cost anomaly detection and investigation"
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- predictive_cost_analytics: "Predictive cost analytics and forecasting"
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- cognitive_cost_analysis: "Cognitive cost analysis and pattern recognition"
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automated_cost_management:
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- automated_cost_allocation: "Automated cost allocation and chargeback"
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- dynamic_resource_optimization: "Dynamic resource optimization based on cost efficiency"
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- automated_contract_optimization: "Automated contract and pricing optimization"
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- intelligent_procurement_automation: "Intelligent procurement and sourcing automation"
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- automated_financial_controls: "Automated financial controls and validation"
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real_time_cost_optimization:
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- real_time_cost_monitoring: "Real-time cost monitoring and alerting"
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- instant_cost_optimization_recommendations: "Instant cost optimization recommendations"
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- real_time_budget_tracking: "Real-time budget tracking and variance analysis"
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- dynamic_cost_adjustments: "Dynamic cost adjustments and optimization"
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- continuous_cost_optimization: "Continuous cost optimization and improvement"
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```
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#### Cost Optimization Engine Implementation
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```python
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import asyncio
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import pandas as pd
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import numpy as np
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from typing import Dict, List, Any, Optional, Tuple, Union
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from dataclasses import dataclass, field
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from enum import Enum
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from datetime import datetime, timedelta
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import json
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import uuid
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from collections import defaultdict, deque
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import logging
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from abc import ABC, abstractmethod
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from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.preprocessing import StandardScaler
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import plotly.graph_objects as go
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import plotly.express as px
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from scipy.optimize import minimize, differential_evolution
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import warnings
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warnings.filterwarnings('ignore')
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class CostCategory(Enum):
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INFRASTRUCTURE = "infrastructure"
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APPLICATION = "application"
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OPERATIONAL = "operational"
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HUMAN_RESOURCES = "human_resources"
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VENDOR_SERVICES = "vendor_services"
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LICENSING = "licensing"
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COMPLIANCE = "compliance"
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class OptimizationType(Enum):
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IMMEDIATE = "immediate"
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SHORT_TERM = "short_term"
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MEDIUM_TERM = "medium_term"
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LONG_TERM = "long_term"
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STRATEGIC = "strategic"
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class CostImpact(Enum):
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CRITICAL = "critical"
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HIGH = "high"
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MEDIUM = "medium"
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LOW = "low"
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MINIMAL = "minimal"
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class OptimizationStrategy(Enum):
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REDUCE_USAGE = "reduce_usage"
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OPTIMIZE_ALLOCATION = "optimize_allocation"
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NEGOTIATE_PRICING = "negotiate_pricing"
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CHANGE_PROVIDER = "change_provider"
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AUTOMATE_PROCESS = "automate_process"
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ELIMINATE_WASTE = "eliminate_waste"
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@dataclass
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class CostItem:
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"""
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Represents a cost item with detailed breakdown and metadata
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"""
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cost_id: str
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name: str
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category: CostCategory
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amount: float
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currency: str
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period: str # monthly, quarterly, annually
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description: str
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cost_center: str
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allocation_rules: Dict[str, Any] = field(default_factory=dict)
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tags: List[str] = field(default_factory=list)
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last_updated: datetime = field(default_factory=datetime.utcnow)
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trend_data: List[Dict[str, Any]] = field(default_factory=list)
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benchmarks: Dict[str, float] = field(default_factory=dict)
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@dataclass
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class OptimizationOpportunity:
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"""
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Represents a cost optimization opportunity with impact analysis
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"""
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opportunity_id: str
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title: str
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description: str
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category: CostCategory
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optimization_type: OptimizationType
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strategy: OptimizationStrategy
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current_cost: float
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potential_savings: float
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implementation_cost: float
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roi_percentage: float
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impact_level: CostImpact
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implementation_timeline: str
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risk_assessment: Dict[str, Any] = field(default_factory=dict)
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prerequisites: List[str] = field(default_factory=list)
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success_metrics: List[str] = field(default_factory=list)
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stakeholders: List[str] = field(default_factory=list)
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@dataclass
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class CostOptimizationPlan:
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"""
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Comprehensive cost optimization plan with timeline and metrics
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"""
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plan_id: str
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name: str
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description: str
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total_current_cost: float
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total_target_savings: float
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implementation_cost: float
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net_savings: float
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roi_percentage: float
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timeline_months: int
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opportunities: List[OptimizationOpportunity] = field(default_factory=list)
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milestones: List[Dict[str, Any]] = field(default_factory=list)
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risk_mitigation: List[Dict[str, Any]] = field(default_factory=list)
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success_criteria: List[str] = field(default_factory=list)
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created_date: datetime = field(default_factory=datetime.utcnow)
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class CostOptimizationEngine:
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"""
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Enterprise-scale cost optimization and financial intelligence engine
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"""
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def __init__(self, claude_code_interface, config=None):
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self.claude_code = claude_code_interface
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self.config = config or {
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'optimization_frequency_days': 30,
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'cost_analysis_depth': 'comprehensive',
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'automated_optimization': True,
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'predictive_analytics': True,
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'real_time_monitoring': True,
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'budget_variance_threshold': 0.05, # 5%
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'roi_threshold_percentage': 15.0,
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'payback_period_threshold_months': 12
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}
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# Core optimization components
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self.cost_analyzer = CostAnalyzer(self.claude_code, self.config)
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self.optimization_engine = OptimizationEngine(self.config)
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self.financial_intelligence = FinancialIntelligence(self.config)
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self.prediction_engine = CostPredictionEngine(self.config)
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# Specialized optimizers
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self.infrastructure_optimizer = InfrastructureCostOptimizer(self.config)
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self.application_optimizer = ApplicationCostOptimizer(self.config)
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self.operational_optimizer = OperationalCostOptimizer(self.config)
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self.vendor_optimizer = VendorCostOptimizer(self.config)
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# Analytics and intelligence
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self.cost_analytics = CostAnalytics(self.config)
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self.benchmarking_engine = CostBenchmarkingEngine(self.config)
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self.roi_calculator = ROICalculator(self.config)
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self.scenario_modeler = CostScenarioModeler(self.config)
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# Automation and control
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self.automation_engine = CostAutomationEngine(self.config)
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self.control_system = CostControlSystem(self.config)
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self.alert_manager = CostAlertManager(self.config)
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self.workflow_engine = CostOptimizationWorkflowEngine(self.config)
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# State management
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self.cost_repository = CostRepository()
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self.optimization_history = []
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self.active_optimizations = {}
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self.cost_models = {}
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# Reporting and visualization
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self.reporting_engine = CostReportingEngine(self.config)
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self.dashboard_service = CostDashboardService(self.config)
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self.visualization_engine = CostVisualizationEngine(self.config)
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async def perform_comprehensive_cost_analysis(self, analysis_scope, time_period="12m"):
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"""
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Perform comprehensive cost analysis across all domains
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"""
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cost_analysis_session = {
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'session_id': generate_uuid(),
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'start_time': datetime.utcnow(),
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'analysis_scope': analysis_scope,
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'time_period': time_period,
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'cost_breakdown': {},
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'optimization_opportunities': [],
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'financial_insights': {},
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'recommendations': []
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}
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try:
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# Collect and categorize cost data
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cost_data = await self.cost_analyzer.collect_comprehensive_cost_data(
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analysis_scope,
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time_period
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)
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cost_analysis_session['cost_data_collected'] = len(cost_data)
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# Perform cost breakdown analysis
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cost_breakdown = await self.cost_analyzer.analyze_cost_breakdown(
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cost_data
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)
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cost_analysis_session['cost_breakdown'] = cost_breakdown
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# Identify optimization opportunities
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optimization_opportunities = await self.identify_optimization_opportunities(
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cost_data,
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cost_breakdown
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)
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cost_analysis_session['optimization_opportunities'] = optimization_opportunities
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# Generate financial insights
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financial_insights = await self.financial_intelligence.generate_financial_insights(
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cost_data,
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cost_breakdown,
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optimization_opportunities
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)
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cost_analysis_session['financial_insights'] = financial_insights
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# Perform benchmarking analysis
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benchmarking_results = await self.benchmarking_engine.perform_cost_benchmarking(
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cost_breakdown,
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analysis_scope
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)
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cost_analysis_session['benchmarking_results'] = benchmarking_results
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# Generate predictive cost models
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predictive_models = await self.prediction_engine.create_cost_prediction_models(
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cost_data,
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time_period
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)
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cost_analysis_session['predictive_models'] = predictive_models
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# Generate optimization recommendations
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recommendations = await self.generate_cost_optimization_recommendations(
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cost_analysis_session
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)
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cost_analysis_session['recommendations'] = recommendations
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except Exception as e:
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cost_analysis_session['error'] = str(e)
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finally:
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cost_analysis_session['end_time'] = datetime.utcnow()
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cost_analysis_session['analysis_duration'] = (
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cost_analysis_session['end_time'] - cost_analysis_session['start_time']
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).total_seconds()
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return cost_analysis_session
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async def identify_optimization_opportunities(self, cost_data, cost_breakdown):
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"""
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Identify cost optimization opportunities across all categories
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"""
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optimization_opportunities = []
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# Infrastructure cost optimization opportunities
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infrastructure_opportunities = await self.infrastructure_optimizer.identify_opportunities(
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cost_data,
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cost_breakdown
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)
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optimization_opportunities.extend(infrastructure_opportunities)
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# Application cost optimization opportunities
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application_opportunities = await self.application_optimizer.identify_opportunities(
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cost_data,
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cost_breakdown
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)
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optimization_opportunities.extend(application_opportunities)
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# Operational cost optimization opportunities
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operational_opportunities = await self.operational_optimizer.identify_opportunities(
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cost_data,
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cost_breakdown
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)
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optimization_opportunities.extend(operational_opportunities)
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# Vendor cost optimization opportunities
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vendor_opportunities = await self.vendor_optimizer.identify_opportunities(
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cost_data,
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cost_breakdown
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)
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optimization_opportunities.extend(vendor_opportunities)
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# Calculate ROI and prioritize opportunities
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prioritized_opportunities = await self.prioritize_optimization_opportunities(
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optimization_opportunities
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)
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return prioritized_opportunities
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async def create_cost_optimization_plan(self, optimization_opportunities, constraints=None):
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"""
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Create comprehensive cost optimization plan with timeline and implementation strategy
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"""
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plan_creation_session = {
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'session_id': generate_uuid(),
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'start_time': datetime.utcnow(),
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'opportunities_count': len(optimization_opportunities),
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'constraints': constraints or {},
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'optimization_plan': None,
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'implementation_strategy': {},
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'risk_analysis': {}
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}
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try:
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# Filter and select opportunities based on constraints
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selected_opportunities = await self.select_optimization_opportunities(
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optimization_opportunities,
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constraints
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)
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# Calculate plan financials
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plan_financials = await self.calculate_plan_financials(selected_opportunities)
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# Create optimization plan
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optimization_plan = CostOptimizationPlan(
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plan_id=generate_uuid(),
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name=f"Cost Optimization Plan - {datetime.utcnow().strftime('%Y-%m')}",
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description="Comprehensive cost optimization plan based on identified opportunities",
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total_current_cost=plan_financials['total_current_cost'],
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total_target_savings=plan_financials['total_target_savings'],
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implementation_cost=plan_financials['implementation_cost'],
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net_savings=plan_financials['net_savings'],
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roi_percentage=plan_financials['roi_percentage'],
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timeline_months=plan_financials['timeline_months'],
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opportunities=selected_opportunities
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)
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# Generate implementation strategy
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implementation_strategy = await self.generate_implementation_strategy(
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optimization_plan
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)
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plan_creation_session['implementation_strategy'] = implementation_strategy
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# Perform risk analysis
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risk_analysis = await self.perform_plan_risk_analysis(
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optimization_plan,
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implementation_strategy
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)
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plan_creation_session['risk_analysis'] = risk_analysis
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# Create milestones and timeline
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milestones = await self.create_plan_milestones(
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optimization_plan,
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implementation_strategy
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)
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optimization_plan.milestones = milestones
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# Define success criteria
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success_criteria = await self.define_plan_success_criteria(
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optimization_plan
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)
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optimization_plan.success_criteria = success_criteria
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plan_creation_session['optimization_plan'] = optimization_plan
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except Exception as e:
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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. |