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BSS Asset-Centric Asset Manager Workflow Specification

Workflow Diagram

BSS Asset Manager Workflow

Complete sequence diagram showing the asset manager journey through six workflow areas: AM1 (Fleet Performance Analytics), AM2 (Asset Lifecycle Management), AM3 (Strategic Asset Deployment), AM4 (Energy Management Optimization), AM5 (Asset Financial Management), and AM6 (Fleet Rebalancing Operations).

Purpose

This document defines the comprehensive workflow experience for asset-centric asset managers overseeing asset fleet lifecycles, asset performance optimization, and strategic asset deployment. It focuses on the asset manager's role in maximizing asset utilization, ensuring fleet health, and coordinating with swap-network operators for optimal service delivery.

Source Traceability

  • Parent Document: bss-technical-specs.md
  • Target Implementation: bss-agent-v2.ts asset management functions + asset manager portal interfaces
  • Focus Area: Asset-centric asset lifecycle management and fleet optimization

Overview: Asset Manager Strategic Journey

The asset-centric asset manager experience follows strategic workflows designed for optimal asset lifecycle management:

  1. 📊 Fleet Performance Analytics: Monitor asset performance across entire fleet
  2. 🔋 Asset Lifecycle Management: Manage asset lifecycle from deployment to retirement
  3. 📍 Strategic Asset Deployment: Optimize asset placement and allocation across locations
  4. ⚡ Energy Management Optimization: Optimize charging strategies and energy costs
  5. 💰 Asset Financial Management: Track asset ROI, depreciation, and financial performance
  6. 🔄 Fleet Rebalancing Operations: Coordinate asset movements and redistribution

AM1: Fleet Performance Analytics

Workflow: "Monitor and analyze fleet performance metrics"

Agent Function: analyzeFleetPerformance

AM1.1: Comprehensive Fleet Monitoring

  1. Asset Health Assessment: Monitor individual asset health metrics and degradation patterns
  2. Utilization Analysis: Track asset utilization rates across different locations and time periods
  3. Performance Benchmarking: Compare asset performance against fleet averages and targets
  4. Predictive Maintenance Planning: Identify assets requiring maintenance based on performance data
  5. Financial Performance Tracking: Monitor asset ROI and revenue generation efficiency

AM1.2: Advanced Analytics and Insights

  1. Fleet Optimization Modeling: Use analytics to identify fleet optimization opportunities
  2. Demand Pattern Analysis: Analyze usage patterns to inform strategic decisions
  3. Asset Lifecycle Predictions: Predict asset lifecycle stages and replacement timing
  4. Location Performance Comparison: Compare asset performance across different locations
  5. Seasonal Trend Analysis: Identify seasonal patterns affecting asset performance

AM1.3: Performance Data Aggregation

Topic Pattern: emit/abs/bss/fleet/{fleet_id}/performance_analytics

Fleet Analytics Payload: 

{
  "fleet_id": "fleet-asset-standard-kenya",
  "analysis_period": {
    "start_time": "2025-01-01T00:00:00Z",
    "end_time": "2025-01-31T23:59:59Z",
    "period_type": "monthly"
  },
  "fleet_summary": {
    "total_assets": 500,
    "active_assets": 485,
    "maintenance_assets": 12,
    "retired_assets": 3,
    "fleet_health_score": 0.92
  },
  "performance_metrics": {
    "average_utilization_rate": 0.75,
    "total_energy_delivered": 125000,
    "total_swap_cycles": 15000,
    "average_cycles_per_asset": 30,
    "fleet_efficiency_score": 0.88
  },
  "financial_performance": {
    "total_revenue_generated": 125000.00,
    "average_revenue_per_asset": 250.00,
    "operating_costs": 35000.00,
    "net_asset_roi": 0.72,
    "depreciation_expense": 12500.00
  },
  "asset_health_distribution": {
    "excellent": {"count": 200, "percentage": 0.40},
    "good": {"count": 185, "percentage": 0.37},
    "fair": {"count": 85, "percentage": 0.17},
    "poor": {"count": 15, "percentage": 0.03},
    "critical": {"count": 0, "percentage": 0.00}
  },
  "location_performance": [
    {
      "location_id": "STATION_001",
      "asset_count": 24,
      "utilization_rate": 0.82,
      "revenue_per_asset": 280.00,
      "health_score": 0.95
    }
  ],
  "predictive_insights": {
    "maintenance_required_next_30_days": 8,
    "replacement_recommended_next_90_days": 3,
    "optimization_opportunities": [
      {
        "type": "rebalancing",
        "description": "Move 5 assets from low-utilization to high-demand location",
        "projected_revenue_increase": 2500.00
      }
    ]
  },
  "timestamp": "2025-01-31T23:59:59Z"
}

Fleet Performance Analytics Outcomes

  • 📊 ANALYTICS_COMPLETED: Fleet performance analytics successfully generated
  • 🎯 OPTIMIZATION_OPPORTUNITIES: Asset optimization opportunities identified
  • ⚠️ MAINTENANCE_ALERTS: Assets requiring maintenance attention identified
  • 💡 STRATEGIC_INSIGHTS: Strategic insights for fleet management generated
  • 📈 PERFORMANCE_TRENDS: Performance trends and patterns identified
  • 🔍 ANOMALIES_DETECTED: Performance anomalies requiring investigation
  • 📋 REPORTS_GENERATED: Comprehensive performance reports created

Implementation Requirements

// Target function: analyzeFleetPerformance
const analyzeFleetPerformance = async (requestData: RequestData, planState: PlanState): Promise<PartialOutcome> => {
  const fleetId = requestData.fleet_id;
  const analysisPeriod = requestData.analysis_period;
  const analyticsScope = requestData.analytics_scope;

  // Collect comprehensive asset performance data
  // Analyze utilization patterns and efficiency metrics
  // Calculate financial performance and ROI
  // Identify optimization opportunities and maintenance needs
  // Generate predictive insights and recommendations
  // Create performance reports and dashboards
  // Broadcast analytics via MQTT for stakeholder access

  return {
    signals: ['ANALYTICS_COMPLETED' | 'OPTIMIZATION_OPPORTUNITIES' | 'MAINTENANCE_ALERTS' | 'STRATEGIC_INSIGHTS' | 'PERFORMANCE_TRENDS' | 'ANOMALIES_DETECTED' | 'REPORTS_GENERATED'],
    metadata: {
      fleet_analysis: {
        performance_summary: {
          fleet_health_score: 'overall fleet health assessment',
          utilization_efficiency: 'fleet utilization rate analysis',
          financial_performance: 'ROI and revenue analysis',
          operational_efficiency: 'operational effectiveness metrics'
        },
        asset_health_monitoring: {
          individual_asset_scores: 'health score for each asset',
          degradation_patterns: 'asset degradation trend analysis',
          lifecycle_stage_distribution: 'assets by lifecycle stage',
          maintenance_requirements: 'assets requiring maintenance'
        },
        utilization_analysis: {
          location_utilization: 'utilization rates by location',
          temporal_patterns: 'time-based usage patterns',
          demand_forecasting: 'predicted future demand',
          capacity_optimization: 'optimal capacity allocation'
        }
      },
      strategic_insights: {
        optimization_recommendations: 'strategic optimization opportunities',
        rebalancing_suggestions: 'asset rebalancing recommendations',
        investment_priorities: 'areas for strategic investment',
        fleet_expansion_guidance: 'fleet growth recommendations'
      },
      reporting_outputs: {
        mqtt_broadcast: {
          topic: `emit/abs/bss/fleet/${fleetId}/performance_analytics`,
          payload: 'comprehensive fleet analytics',
          frequency: 'daily, weekly, and monthly analytics'
        },
        dashboard_updates: 'asset manager dashboard updates',
        stakeholder_reports: 'reports for executives and partners'
      }
    }
  };
};

AM2: Asset Lifecycle Management

Workflow: "Manage complete asset lifecycle"

Agent Function: manageAssetLifecycle

AM2.1: Asset Deployment Management

  1. New Asset Integration: Coordinate deployment of new assets to fleet
  2. Asset Commissioning: Oversee initial testing and certification of new assets
  3. Location Assignment: Optimize initial placement of assets based on demand analysis
  4. Performance Baseline Establishment: Set performance baselines for new assets
  5. Warranty and Service Plan Setup: Configure warranty tracking and service schedules

AM2.2: Lifecycle Stage Monitoring

  1. Performance Degradation Tracking: Monitor asset performance degradation over time
  2. Lifecycle Stage Classification: Classify assets by lifecycle stage (new, mature, aging, end-of-life)
  3. Maintenance Schedule Optimization: Optimize maintenance schedules based on asset condition
  4. Replacement Planning: Plan asset replacements based on performance and financial criteria
  5. End-of-Life Management: Coordinate asset retirement and disposal processes

AM2.3: Asset State Management Signals

Topic Pattern: emit/abs/bss/asset/{asset_type}/{asset_id}/lifecycle_update

Lifecycle Update Payload: 

{
  "asset_id": "AST-001",
  "fleet_id": "fleet-asset-standard-kenya",
  "lifecycle_event": "deployment|performance_update|maintenance_scheduled|replacement_recommended|retirement_initiated",
  "asset_manager_context": {
    "manager_id": "AM-001",
    "organization": "KenyaAssetFleet Ltd",
    "management_region": "East Africa"
  },
  "lifecycle_details": {
    "current_stage": "mature|new|aging|end_of_life",
    "stage_transition_reason": "performance_threshold|time_based|maintenance_requirement",
    "expected_remaining_lifecycle": "months_or_cycles",
    "next_milestone": "next_maintenance|replacement_evaluation|retirement"
  },
  "performance_context": {
    "current_health_score": 0.85,
    "degradation_rate": 0.02,
    "utilization_efficiency": 0.78,
    "total_cycles_completed": 1250,
    "energy_efficiency": 0.92
  },
  "financial_context": {
    "acquisition_cost": 5000.00,
    "current_book_value": 3500.00,
    "total_revenue_generated": 4200.00,
    "maintenance_costs_to_date": 350.00,
    "projected_roi": 0.68
  },
  "operational_instructions": {
    "location_assignment": "STATION_001",
    "service_restrictions": ["premium_service_only"],
    "maintenance_priority": "normal|urgent|critical",
    "replacement_timeline": "2025-06-15T00:00:00Z"
  },
  "timestamp": "2025-01-15T16:00:00Z"
}

Asset Lifecycle Management Outcomes

  • 🔋 ASSET_DEPLOYED: New asset successfully deployed to fleet
  • 📈 LIFECYCLE_UPDATED: Asset lifecycle stage successfully updated
  • 🔧 MAINTENANCE_SCHEDULED: Asset maintenance successfully scheduled
  • 🔄 REPLACEMENT_PLANNED: Asset replacement planned and scheduled
  • 📊 PERFORMANCE_TRACKED: Asset performance tracking updated
  • 💰 FINANCIAL_UPDATED: Asset financial performance updated
  • 🏁 RETIREMENT_INITIATED: Asset retirement process initiated

Implementation Requirements

// Target function: manageAssetLifecycle
const manageAssetLifecycle = async (requestData: RequestData, planState: PlanState): Promise<PartialOutcome> => {
  const assetId = requestData.asset_id;
  const lifecycleEvent = requestData.lifecycle_event;
  const managerId = requestData.asset_manager_id;

  // Process lifecycle event for specific asset
  // Update asset lifecycle stage and performance metrics
  // Calculate financial performance and depreciation
  // Schedule maintenance or replacement as needed
  // Coordinate with ARM for asset state updates
  // Update fleet analytics and reporting
  // Broadcast lifecycle updates via MQTT

  return {
    signals: ['ASSET_DEPLOYED' | 'LIFECYCLE_UPDATED' | 'MAINTENANCE_SCHEDULED' | 'REPLACEMENT_PLANNED' | 'PERFORMANCE_TRACKED' | 'FINANCIAL_UPDATED' | 'RETIREMENT_INITIATED'],
    metadata: {
      lifecycle_management: {
        asset_details: {
          asset_id: assetId,
          current_lifecycle_stage: 'new|mature|aging|end_of_life',
          stage_transition_trigger: 'what caused the lifecycle stage change',
          next_lifecycle_milestone: 'next significant lifecycle event'
        },
        performance_tracking: {
          health_score_trend: 'asset health score over time',
          degradation_analysis: 'rate and pattern of performance degradation',
          utilization_efficiency: 'how effectively asset is being used',
          maintenance_history: 'record of all maintenance activities'
        },
        financial_management: {
          acquisition_cost: 'original asset acquisition cost',
          current_book_value: 'current depreciated value',
          revenue_performance: 'total revenue generated by asset',
          roi_calculation: 'return on investment analysis',
          total_cost_of_ownership: 'comprehensive cost analysis'
        }
      },
      operational_coordination: {
        location_management: {
          current_assignment: 'current location assignment',
          assignment_optimization: 'optimal location for asset performance',
          relocation_recommendations: 'suggested asset relocations'
        },
        maintenance_coordination: {
          scheduled_maintenance: 'upcoming maintenance activities',
          maintenance_priority: 'urgency of maintenance needs',
          maintenance_cost_estimates: 'estimated maintenance costs'
        },
        replacement_planning: {
          replacement_criteria: 'criteria for asset replacement',
          replacement_timeline: 'recommended replacement timing',
          replacement_cost_analysis: 'cost-benefit analysis of replacement'
        }
      },
      stakeholder_communication: {
        arm_integration: {
          asset_state_updates: 'updates to ARM asset management system',
          location_inventory_updates: 'location inventory adjustments',
          availability_status_updates: 'asset availability status changes'
        },
        operator_notifications: {
          operational_instructions: 'instructions for swap-network operators',
          service_restrictions: 'any limitations on asset usage',
          maintenance_scheduling: 'coordination of maintenance activities'
        },
        mqtt_broadcasts: {
          topic: `emit/abs/bss/asset/{asset_type}/${assetId}/lifecycle_update`,
          payload: 'comprehensive lifecycle update information',
          stakeholder_targeting: 'relevant stakeholders for this update'
        }
      }
    }
  };
};

AM3: Strategic Asset Deployment

Workflow: "Optimize asset placement and allocation"

Agent Function: optimizeAssetDeployment

AM3.1: Demand Analysis and Forecasting

  1. Location Demand Assessment: Analyze demand patterns across different locations
  2. Seasonal Demand Modeling: Model seasonal variations in asset requirements
  3. Growth Projection Analysis: Forecast future demand based on market trends
  4. Competitive Analysis: Assess competitive landscape impact on demand
  5. Customer Behavior Analysis: Understand customer preferences and usage patterns

AM3.2: Strategic Placement Optimization

  1. Asset Allocation Modeling: Optimize asset distribution across locations
  2. Revenue Maximization Analysis: Identify placements that maximize revenue
  3. Risk Assessment: Evaluate risks associated with different deployment strategies
  4. Capacity Planning: Plan capacity to meet projected demand
  5. Geographic Expansion Strategy: Identify opportunities for geographic expansion

AM3.3: Deployment Coordination Signals

Topic Pattern: emit/abs/bss/fleet/{fleet_id}/deployment_strategy

Deployment Strategy Payload: 

{
  "fleet_id": "fleet-asset-standard-kenya",
  "strategy_period": {
    "start_date": "2025-02-01T00:00:00Z",
    "end_date": "2025-12-31T23:59:59Z",
    "strategy_type": "quarterly|annual|expansion"
  },
  "demand_analysis": {
    "total_projected_demand": 18000,
    "growth_rate": 0.15,
    "seasonal_factors": {
      "peak_season": "June-August",
      "peak_multiplier": 1.3,
      "low_season": "January-March",
      "low_multiplier": 0.8
    },
    "market_expansion_opportunities": [
      {
        "location": "Mombasa",
        "projected_demand": 2500,
        "investment_required": 125000.00,
        "projected_roi": 0.85
      }
    ]
  },
  "deployment_recommendations": [
    {
      "action_type": "deploy_new_assets",
      "location_id": "STATION_003",
      "asset_count": 15,
      "deployment_timeline": "2025-03-01T00:00:00Z",
      "investment_required": 75000.00,
      "projected_revenue_increase": 18750.00
    },
    {
      "action_type": "relocate_assets",
      "from_location": "STATION_002",
      "to_location": "STATION_004",
      "asset_count": 8,
      "relocation_timeline": "2025-02-15T00:00:00Z",
      "projected_revenue_impact": 5200.00
    }
  ],
  "optimization_metrics": {
    "current_fleet_utilization": 0.75,
    "optimized_fleet_utilization": 0.88,
    "revenue_improvement_potential": 0.17,
    "capacity_optimization_score": 0.82
  },
  "risk_assessment": {
    "demand_volatility_risk": "medium",
    "competitive_pressure_risk": "low",
    "regulatory_risk": "low",
    "technology_obsolescence_risk": "medium"
  },
  "implementation_plan": {
    "phase_1": "Asset rebalancing - February 2025",
    "phase_2": "New asset deployment - March 2025",
    "phase_3": "Market expansion - Q3 2025",
    "total_investment_required": 200000.00,
    "projected_annual_revenue_increase": 45000.00
  },
  "timestamp": "2025-01-31T18:00:00Z"
}

Strategic Asset Deployment Outcomes

  • 📊 DEMAND_ANALYSIS_COMPLETED: Demand analysis and forecasting completed
  • 🎯 DEPLOYMENT_STRATEGY_DEVELOPED: Strategic deployment plan developed
  • 📍 OPTIMAL_PLACEMENT_IDENTIFIED: Optimal asset placements identified
  • 💰 ROI_PROJECTIONS_CALCULATED: Return on investment projections completed
  • 🔄 REBALANCING_RECOMMENDED: Asset rebalancing recommendations generated
  • 🌍 EXPANSION_OPPORTUNITIES: Market expansion opportunities identified
  • 📋 IMPLEMENTATION_PLAN_CREATED: Detailed implementation plan created

Implementation Requirements

// Target function: optimizeAssetDeployment
const optimizeAssetDeployment = async (requestData: RequestData, planState: PlanState): Promise<PartialOutcome> => {
  const fleetId = requestData.fleet_id;
  const strategyPeriod = requestData.strategy_period;
  const optimizationCriteria = requestData.optimization_criteria;

  // Analyze demand patterns across locations
  // Model seasonal variations and growth projections
  // Optimize asset allocation for revenue maximization
  // Assess risks and competitive factors
  // Generate deployment recommendations
  // Create implementation timeline and investment plan
  // Broadcast strategy via MQTT for stakeholder alignment

  return {
    signals: ['DEMAND_ANALYSIS_COMPLETED' | 'DEPLOYMENT_STRATEGY_DEVELOPED' | 'OPTIMAL_PLACEMENT_IDENTIFIED' | 'ROI_PROJECTIONS_CALCULATED' | 'REBALANCING_RECOMMENDED' | 'EXPANSION_OPPORTUNITIES' | 'IMPLEMENTATION_PLAN_CREATED'],
    metadata: {
      demand_analytics: {
        location_demand_analysis: 'demand patterns by location',
        temporal_demand_patterns: 'time-based demand variations',
        growth_projections: 'projected demand growth',
        market_opportunity_assessment: 'new market opportunities'
      },
      optimization_analysis: {
        current_allocation_efficiency: 'current asset allocation performance',
        optimal_allocation_model: 'mathematically optimized allocation',
        revenue_maximization_strategy: 'asset placement for maximum revenue',
        capacity_utilization_optimization: 'optimal capacity utilization strategy'
      },
      strategic_recommendations: {
        deployment_actions: 'specific deployment actions recommended',
        rebalancing_operations: 'asset rebalancing recommendations',
        investment_priorities: 'prioritized investment opportunities',
        timeline_and_milestones: 'implementation timeline with key milestones'
      },
      financial_projections: {
        investment_requirements: 'total investment needed for strategy',
        revenue_projections: 'projected revenue impact',
        roi_analysis: 'return on investment calculations',
        risk_adjusted_returns: 'risk-adjusted financial projections'
      },
      implementation_coordination: {
        stakeholder_alignment: 'coordination with operators and ARM',
        resource_allocation: 'allocation of resources for implementation',
        milestone_tracking: 'key performance indicators for tracking progress',
        contingency_planning: 'backup plans for potential issues'
      }
    }
  };
};

AM4: Energy Management Optimization

Workflow: "Optimize charging strategies and energy costs"

Agent Function: optimizeEnergyManagement

AM4.1: Energy Consumption Analysis

  1. Energy Usage Pattern Analysis: Analyze energy consumption patterns across fleet
  2. Cost Structure Assessment: Evaluate energy costs and pricing structures
  3. Peak Demand Management: Optimize charging to minimize peak demand charges
  4. Renewable Energy Integration: Integrate renewable energy sources where available
  5. Grid Load Balancing: Balance electrical loads across charging infrastructure

AM4.2: Cost Optimization Strategies

  1. Time-of-Use Optimization: Schedule charging during low-cost energy periods
  2. Demand Charge Minimization: Optimize charging to minimize demand charges
  3. Energy Contract Negotiation: Optimize energy supply contracts and pricing
  4. Load Forecasting: Predict energy requirements for optimal procurement
  5. Efficiency Improvement: Identify opportunities to improve energy efficiency

Energy Management Outcomes

  • ⚡ ENERGY_ANALYSIS_COMPLETED: Energy consumption analysis completed
  • 💰 COST_OPTIMIZATION_ACHIEVED: Energy cost optimization implemented
  • 📅 CHARGING_SCHEDULE_OPTIMIZED: Optimal charging schedule created
  • 🌱 RENEWABLE_INTEGRATION_ENHANCED: Renewable energy integration improved
  • 📊 LOAD_BALANCING_OPTIMIZED: Electrical load balancing optimized
  • 🎯 DEMAND_CHARGES_MINIMIZED: Peak demand charges minimized
  • 💡 EFFICIENCY_IMPROVEMENTS: Energy efficiency improvements identified

AM5: Asset Financial Management

Workflow: "Track asset ROI, depreciation, and financial performance"

Agent Function: manageAssetFinancials

AM5.1: Financial Performance Tracking

  1. Revenue Attribution: Track revenue generated by individual assets
  2. Cost Allocation: Allocate operating costs to specific assets
  3. ROI Calculation: Calculate return on investment for each asset
  4. Depreciation Management: Manage asset depreciation schedules
  5. Profitability Analysis: Analyze asset profitability across different scenarios

AM5.2: Financial Reporting and Analytics

  1. Asset Portfolio Performance: Analyze overall asset portfolio performance
  2. Financial Benchmarking: Compare asset performance against benchmarks
  3. Investment Decision Support: Provide financial analysis for investment decisions
  4. Risk Assessment: Assess financial risks associated with asset operations
  5. Budget Planning: Support budget planning with asset financial data

Financial Management Outcomes

  • 💰 FINANCIALS_CALCULATED: Asset financial performance calculated
  • 📊 ROI_ANALYSIS_COMPLETED: Return on investment analysis completed
  • 📈 PORTFOLIO_PERFORMANCE_ANALYZED: Asset portfolio performance analyzed
  • 💡 INVESTMENT_INSIGHTS: Investment decision insights generated
  • ⚠️ FINANCIAL_RISKS_IDENTIFIED: Financial risks identified and assessed
  • 📋 FINANCIAL_REPORTS_GENERATED: Comprehensive financial reports created

AM6: Fleet Rebalancing Operations

Workflow: "Coordinate asset movements and redistribution"

Agent Function: coordinateFleetRebalancing

AM6.1: Rebalancing Analysis

  1. Utilization Imbalance Detection: Identify locations with asset utilization imbalances
  2. Demand-Supply Gap Analysis: Analyze gaps between asset supply and demand
  3. Rebalancing Opportunity Assessment: Evaluate opportunities for asset redistribution
  4. Cost-Benefit Analysis: Analyze costs and benefits of rebalancing operations
  5. Logistics Coordination: Plan logistics for asset movements

AM6.2: Rebalancing Execution

  1. Asset Movement Planning: Plan specific asset movements between locations
  2. Stakeholder Coordination: Coordinate with operators and logistics providers
  3. Movement Tracking: Track asset movements in real-time
  4. Impact Assessment: Assess impact of rebalancing on performance
  5. Optimization Validation: Validate that rebalancing achieved desired outcomes

Fleet Rebalancing Outcomes

  • 📊 IMBALANCE_DETECTED: Asset utilization imbalances identified
  • 🎯 REBALANCING_PLAN_CREATED: Comprehensive rebalancing plan developed
  • 🚚 LOGISTICS_COORDINATED: Asset movement logistics coordinated
  • 📍 MOVEMENTS_TRACKED: Asset movements tracked in real-time
  • ✅ REBALANCING_COMPLETED: Asset rebalancing operation completed
  • 📈 PERFORMANCE_IMPROVED: Fleet performance improvement validated
  • 🔄 OPTIMIZATION_VALIDATED: Rebalancing optimization results validated

Integration Notes

MQTT Signal Integration for Asset Management

Following the established messaging convention for asset manager workflows:

// Asset Manager Signal Patterns
const assetManagerTopics = {
  fleetAnalytics: `emit/abs/bss/fleet/{fleet_id}/performance_analytics`,
  lifecycleUpdate: `emit/abs/bss/asset/{asset_type}/{asset_id}/lifecycle_update`,
  deploymentStrategy: `emit/abs/bss/fleet/{fleet_id}/deployment_strategy`,
  energyOptimization: `emit/abs/bss/fleet/{fleet_id}/energy_optimization`,
  financialAnalytics: `emit/abs/bss/fleet/{fleet_id}/financial_analytics`,
  rebalancingOperation: `emit/abs/bss/fleet/{fleet_id}/rebalancing_operation`
};

// Asset Manager Subscription Patterns
const assetManagerSubscriptions = [
  'echo/abs/bss/station/+/status_update',           // Station status updates
  'emit/abs/bss/asset/{asset_type}/+/performance_data',  // Individual asset performance
  'meta/abs/bss/fleet/+/market_intelligence',      // Market intelligence data
  'stat/abs/bss/energy/+/cost_data'                // Energy cost and usage data
];

Component Boundaries for Asset Management

Asset manager workflows respect DIRAC component separation:

// Asset Manager Agent: Strategic fleet management and optimization
interface AssetManagerAgentWorkflows {
  analyzeFleetPerformance(requestData: RequestData, planState: PlanState): Promise<PartialOutcome>;
  manageAssetLifecycle(requestData: RequestData, planState: PlanState): Promise<PartialOutcome>;
  optimizeAssetDeployment(requestData: RequestData, planState: PlanState): Promise<PartialOutcome>;
  optimizeEnergyManagement(requestData: RequestData, planState: PlanState): Promise<PartialOutcome>;
  manageAssetFinancials(requestData: RequestData, planState: PlanState): Promise<PartialOutcome>;
  coordinateFleetRebalancing(requestData: RequestData, planState: PlanState): Promise<PartialOutcome>;
}

// ARM: Asset state management and operational data
interface ARMAssetManagerServices {
  getFleetPerformanceData(fleetId: string): Promise<FleetPerformanceData>;
  updateAssetLifecycleStage(assetId: string, stage: LifecycleStage): Promise<void>;
  coordinateAssetRebalancing(rebalancingPlan: RebalancingPlan): Promise<void>;
  getAssetFinancialData(assetIds: string[]): Promise<AssetFinancialData[]>;
}

// Analytics Platform: Advanced analytics and reporting
interface AnalyticsAssetManagerServices {
  generateFleetAnalytics(fleetId: string, period: AnalyticsPeriod): Promise<FleetAnalytics>;
  createDeploymentOptimization(criteria: OptimizationCriteria): Promise<DeploymentStrategy>;
  calculateEnergyOptimization(fleetId: string): Promise<EnergyOptimization>;
}

Asset Manager State Management

Asset manager workflows integrate with strategic planning systems:

// Asset Manager State Transitions
const AssetManagerStateTransitions = {
  FLEET_ANALYSIS: {
    from: 'monitoring',
    to: 'analyzing',
    triggers: ['analyzeFleetPerformance']
  },
  STRATEGIC_PLANNING: {
    from: 'analyzing',
    to: 'planning',
    triggers: ['optimizeAssetDeployment']
  },
  REBALANCING_EXECUTION: {
    from: 'planning',
    to: 'executing',
    triggers: ['coordinateFleetRebalancing']
  }
};

Implementation Priority

Phase 1: Core Asset Management (Immediate)

  1. AM2: manageAssetLifecycle - Essential for asset lifecycle tracking
  2. AM1: analyzeFleetPerformance - Critical for performance monitoring
  3. AM5: manageAssetFinancials - Required for financial oversight

Phase 2: Optimization and Strategy (Near-term)

  1. AM3: optimizeAssetDeployment - Strategic asset placement
  2. AM6: coordinateFleetRebalancing - Operational optimization

Phase 3: Advanced Analytics (Future)

  1. AM4: optimizeEnergyManagement - Energy cost optimization and sustainability

Integration Dependencies

  1. Operator Workflows: Coordination with swap-network operators
  2. ARM Integration: Asset state management and tracking
  3. Analytics Platform: Advanced analytics and reporting capabilities
  4. Financial Systems: Integration with accounting and billing systems
  5. Logistics Systems: Coordination with asset movement and transportation

Expected Asset Manager Signal Flow

📊 Fleet performance monitoring ➡️ AM1: Analytics generation ➡️ Strategic insights
                 ⬇️
🔋 Asset lifecycle events ➡️ AM2: Lifecycle management ➡️ ARM state updates
                 ⬇️
📍 Deployment optimization ➡️ AM3: Strategic placement ➡️ Implementation planning
                 ⬇️
⚡ Energy usage analysis ➡️ AM4: Energy optimization ➡️ Cost reduction strategies
                 ⬇️
💰 Financial performance ➡️ AM5: Financial management ➡️ Investment decisions
                 ⬇️
🔄 Utilization imbalances ➡️ AM6: Fleet rebalancing ➡️ Operator coordination

This asset manager workflow architecture ensures optimal fleet performance through strategic asset management, financial optimization, and data-driven decision making while maintaining seamless integration with operational teams and systems.