WUNN Labs

// Demonstration Report

This report showcases the depth of insights that WUNN Labs delivers to clients. Figures based on synthetic data; patterns mirror real-world behavior.

Route Optimization and Cost Efficiency Analysis: Identifying 18% Hidden Savings in Fleet Operations

Executive Summary

This analysis of 20,000 trips across 8 regional routes over 6 months reveals a significant optimization opportunity: $2.19M in annual savings (12.4% of operating costs) through data-driven route optimization, predictive maintenance, and operational improvements.

The fleet currently operates with an 18.4% distance inefficiency rate—more than double the industry benchmark of 8-12%. Three underperforming routes (South Loop, Midwest Express, and Delta Line) account for $483K in annual opportunity, while preventable maintenance events represent an additional $1.1M in avoidable costs.

Implementation ROI: 280% in Year 1, with full payback in just 3.2 months.

TL;DR

Key Insights

Immediate Recommendations

  1. Consolidate South Loop route (0-30 days) - Primary target with 44.4% inefficiency and $177K opportunity; quick win with highest ROI
  2. Deploy route optimization pilot (30-90 days) - $150K investment, $1.0M annual savings, 1.8-month payback period
  3. Implement predictive maintenance AI (30-90 days) - $350K investment targeting high-risk vehicles, $1.1M annual savings, 3.8-month payback
  4. Retrain/reassign worst 4 drivers (0-30 days) - Immediate intervention for drivers 35, 6, 19, 16 costing $63K annually

Abstract

Our comprehensive analysis of fleet operations across 20,000 trips, 55 vehicles, and 45 drivers reveals systemic inefficiencies costing the organization $2.19M annually. The root causes are threefold: (1) route planning inefficiencies leading to 18.4% excess mileage, (2) reactive maintenance practices resulting in 65% preventable maintenance events, and (3) operational gaps including 9.4% idle time and driver performance variance.

The data demonstrates that three underperforming routes—South Loop, Midwest Express, and Delta Line—operate at 32-44% distance inefficiency, well above both company average and industry benchmarks. These routes alone represent $483K in annual optimization opportunity. Meanwhile, preventable maintenance events occurring at nearly twice the rate of non-preventable events indicate significant opportunity for predictive intervention.

A three-pillar optimization strategy targeting route efficiency, predictive maintenance, and dispatch optimization can capture this opportunity with rapid payback. Route optimization shows the strongest ROI (567% Year 1, 1.8-month payback), while predictive maintenance offers the largest absolute savings ($1.1M annually). The full optimization package delivers 280% Year 1 ROI with 3.2-month payback, making this a compelling investment for immediate implementation.

This report provides detailed analysis, actionable recommendations, and a phased implementation roadmap suitable for presentation to private equity portfolio operations teams and CFO audiences.

Operational Overview

Key Metrics (6-Month Period):

The fleet is currently operating at a loss due to systemic inefficiencies. The analysis below quantifies specific opportunities to reverse this trend.

Problem 1: Route Inefficiency - 18.4% Above Optimal

Route Performance Comparison

Critical Finding: Three routes operate at 2-4x industry benchmark inefficiency:

  1. South Loop: 44.4% inefficiency, $177K annual opportunity
  2. Midwest Express: 38.7% inefficiency, $170K annual opportunity
  3. Delta Line: 32.8% inefficiency, $136K annual opportunity

Together, these three routes account for $483K in annual savings potential through route consolidation, frequency optimization, and improved planning.

Annual Optimization Opportunity by Route

Problem 2: Preventable Maintenance - $1.1M Annual Opportunity

Maintenance Cost Breakdown

Critical Insight: Preventable maintenance represents:

Root Causes:

Problem 3: Fuel Efficiency Variance by Route

Fuel Efficiency by Route

Key Finding: Fuel efficiency varies by 24% across routes (6.26 to 7.74 MPG), with underperforming routes showing lowest efficiency:

The 1.48 MPG gap between worst and best routes represents additional fuel costs directly attributable to route inefficiency.

Problem 4: Driver Performance Variance

Driver Performance Distribution

Top 10 Underperforming Drivers

Critical Finding:

Opportunity: Targeted retraining or reassignment of worst 10 drivers could recover $120K-$180K annually (30-50% improvement).

Three-Pillar Optimization Strategy

ROI Analysis by Initiative

Savings Waterfall Chart

Implementation Timeline and Expected Returns

Phase 1: Quick Wins (0-30 days)

Phase 2: Core Optimization (30-90 days)

Phase 3: Scale and Refine (90-180 days)

Investment Analysis

ROI Comparison by Initiative

Key Investment Insights:

  1. Route Optimization: Strongest ROI (567% Year 1)

    • Lowest implementation cost ($150K)
    • Fastest payback (1.8 months)
    • High-confidence returns based on proven algorithms

  2. Predictive Maintenance: Largest absolute impact ($1.1M)

    • Moderate implementation cost ($350K)
    • Fast payback (3.8 months)
    • 214% Year 1 ROI

  3. Full Optimization Package: Best overall value

    • $575K total investment
    • $2.19M annual savings
    • 280% Year 1 ROI, 1040% Year 3 ROI
    • 3.2-month payback period

Risk Mitigation:

Data Quality and Methodology

Data Sources

All analysis based on 6-month operational data (20,000 trips) from:

Key Calculations

Benchmarks Used