Fleet Repair & Vehicle Loss Minimization

Overview

The Fleet Repair & Vehicle Loss Minimization Idea is a decision-support tool designed to help manage vehicles and fleets by determining whether to keep repairing a vehicle or sell it before repair costs exceed its value.

It uses Monte Carlo simulations to model uncertainty in repair costs, fuel/operating expenses, seasonal events, and vehicle-specific factors (type, mileage, age). This allows data-driven decisions for fleet maintenance and asset management.

---

Features

• Loss-Minimization Decision: Determine SELL or KEEP for individual vehicles.
• Monte Carlo Simulation: Handles uncertainty in repair costs, fuel, and events.
• Seasonal & Regional Adjustments: Includes weather, disasters, and parts delays (tailored for DFW).
• Vehicle-Specific Adjustments: Accounts for mileage, vehicle type, and depreciation.
• Fleet Management: Supports multiple vehicles.
• Risk Profiling: Outputs expected loss, min/max, and standard deviation.
• Extensible Design: Add vehicle types, seasonal events, or regional adjustments.

---

InputsFuture Enhancements

Fleet-wide batch analysis to rank vehicles by expected loss

GPU-accelerated Monte Carlo for large fleets

Seasonal resale value modeling

CLI or web dashboard for visualization

Integration with labor/fuel cost estimators for total operating cost

Input	Description
vehicle_type	Type of vehicle (light, truck, offroad)
current_value	Current resale value
mileage	Vehicle mileage
repair_cost_range	Tuple (low, high) for repair cost estimates
fuel_cost	Expected operating/fuel cost per period
insurance_coverage	Amount covered by insurance
month	Current month (for seasonal adjustments)
num_samples	Monte Carlo simulations (default: 1000)
future_periods	Optional: number of periods to simulate ahead

---

Outputs

• decision: "SELL" or "KEEP"
• expected_loss: Average cost of repairs and operations
• resale_value: Current vehicle value
• risk_profile: Dictionary with min_loss, max_loss, std_dev

Example Output:

{
  "decision": "SELL",
  "expected_loss": 3200.50,
  "resale_value": 3000,
  "risk_profile": {
    "min_loss": 2800,
    "max_loss": 4100,
    "std_dev": 250
  }
}

Possible Future Enhancements

Fleet-wide batch analysis to rank vehicles by expected loss

GPU-accelerated Monte Carlo for large fleets

Seasonal resale value modeling

CLI or web dashboard for visualization

Integration with labor/fuel cost estimators for total operating cost