---
name: fleet-analytics-dashboard-a435250dabb1
description: Comprehensive fleet performance analytics skill tracking KPIs across fuel, utilization, maintenance, and driver performance
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
  - WebFetch
metadata:
  specialization: logistics
  domain: business
  category: fleet-management
  priority: medium
graph:
  domains: [domain:logistics]
  skillAreas: [skill-area:procurement-management, skill-area:data-analytics, skill-area:business-analysis]
  roles: [role:supply-chain-analyst, role:operations-analyst, role:business-analyst]
---

# Fleet Analytics Dashboard

## Overview

The Fleet Analytics Dashboard provides comprehensive fleet performance analytics tracking KPIs across fuel efficiency, utilization, maintenance, and driver performance. It consolidates data from multiple sources to provide actionable insights and benchmarking capabilities for fleet optimization.

## Capabilities

- **Fuel Efficiency Analysis**: Track and analyze fuel consumption patterns, identify inefficiencies, and benchmark performance
- **Utilization Rate Tracking**: Monitor vehicle utilization rates and identify underutilized assets
- **Cost Per Mile Calculation**: Calculate and track total cost per mile including fuel, maintenance, and labor
- **Driver Scorecard Generation**: Generate driver performance scorecards based on safety, efficiency, and compliance
- **Idle Time Monitoring**: Track idle time and identify opportunities for reduction
- **Benchmark Comparison**: Compare fleet performance against industry benchmarks and internal targets
- **Trend Analysis and Alerting**: Identify trends and generate alerts for performance deviations

## Tools and Libraries

- Telematics Platforms
- BI Tools (Tableau, Power BI)
- Fuel Card Integration
- Data Analytics Libraries

## Used By Processes

- Fleet Performance Analytics
- Vehicle Maintenance Planning
- Driver Scheduling and Compliance

## Usage

```yaml
skill: fleet-analytics-dashboard
inputs:
  fleet:
    fleet_id: "FLEET001"
    vehicles: 50
    analysis_period:
      start: "2026-01-01"
      end: "2026-01-24"
  data_sources:
    telematics: true
    fuel_cards: true
    maintenance_system: true
    eld_data: true
  benchmarks:
    fuel_efficiency_mpg: 7.0
    utilization_percent: 85
    cost_per_mile: 1.85
outputs:
  fleet_summary:
    total_miles: 875000
    total_fuel_gallons: 131250
    average_mpg: 6.67
    total_cost: 1575000
    cost_per_mile: 1.80
  utilization_analysis:
    average_utilization: 82.5
    vehicles_underutilized: 8
    idle_hours_total: 2500
    idle_cost_estimate: 12500
  fuel_analysis:
    average_mpg: 6.67
    mpg_trend: "improving"
    best_performing_vehicles: ["VH012", "VH023", "VH045"]
    worst_performing_vehicles: ["VH008", "VH031", "VH019"]
    fuel_cost_total: 525000
  driver_scorecards:
    - driver_id: "DRV001"
      overall_score: 92
      safety_score: 95
      efficiency_score: 88
      compliance_score: 94
      miles_driven: 8500
      mpg: 6.9
    - driver_id: "DRV002"
      overall_score: 78
      safety_score: 75
      efficiency_score: 82
      compliance_score: 78
      miles_driven: 7200
      mpg: 6.4
  alerts:
    - type: "utilization"
      message: "8 vehicles below 70% utilization target"
      severity: "warning"
    - type: "fuel"
      message: "Vehicle VH008 fuel efficiency 15% below fleet average"
      severity: "attention"
  benchmark_comparison:
    fuel_efficiency: { actual: 6.67, benchmark: 7.0, variance: -4.7 }
    utilization: { actual: 82.5, benchmark: 85, variance: -2.9 }
    cost_per_mile: { actual: 1.80, benchmark: 1.85, variance: 2.7 }
```

## Integration Points

- Telematics Systems
- Fuel Card Providers
- Maintenance Management Systems
- ELD Providers
- Business Intelligence Platforms

## Performance Metrics

- Fleet MPG
- Cost per mile
- Vehicle utilization rate
- Driver safety score
- Maintenance cost ratio


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Marketing, Operations, Product Management, Sales
Roles: Business Development, Data Analyst, Growth Marketer, Operations Manager, Product Manager
Specialties: API, Database
Tools: Power Bi, Tableau
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 8fc520b61a2756a4f257387e356e719ef8609b39aa05348c5d92aec310f79142