Simio Case Studies

Optimizing Fleet Growth Through Simulation: Penske Truck Leasing’s Capacity Planning Journey

Written by Simio | Feb 11, 2026 10:05:41 AM

The Challenge

Abstract

This case study examines how Penske Truck Leasing leveraged Simio simulation software to solve complex capacity planning challenges associated with significant fleet growth. Facing the addition of 500 vehicles over five years to an already space-constrained facility, Penske’s Operational Excellence team developed a comprehensive simulation model to identify capacity constraints and evaluate potential solutions.

The model analyzed multiple capacity dimensions including parking space, service bays, technician staffing, and support resources. Through simulation, Penske identified specific capacity ceilings, determined optimal timing for implementing various solutions, and provided facility managers with a data-driven roadmap for supporting growth while maintaining operational efficiency.

This approach enabled Penske to make informed decisions about resource allocation, facility modifications, and staffing adjustments without the risks associated with real-world implementation.

Introduction

Company Background

Penske Truck Leasing operates as part of Penske Transportation Solutions, managing a fleet of more than 414,000 vehicles across the United States, Canada, and Mexico. The company’s operations span truck rental, leasing, maintenance services, and used vehicle sales.

This extensive operation is supported by nearly 1,000 service facilities and over 11,000 maintenance technicians.

Maintenance represents a core component of Penske’s business model, ranging from preventative maintenance and inspections to complex mechanical, electrical, and body repairs.

Problem Statement

Capacity Constraints Analysis

Adding 500 vehicles created cascading pressures across multiple dimensions.

Physical Space Constraints

  • Limited parking space for additional vehicles
  • Insufficient service bays
  • Restricted yard space

Resource Requirements

  • Increased demand for parts, tires, and fluids
  • Additional storage space for inventory
  • More tools and equipment required

Staffing Implications

  • Need for additional technicians
  • Expanded break rooms and support facilities
  • Increased management oversight

Operational Complexity

  • Different vehicle types require different maintenance frequencies
  • Local delivery trucks may need annual service
  • Long-haul tractors may require maintenance up to 15 times per year
  • Varying service durations

Solution Approaches Considered

Workspace Expansion Options

  • Adding new bays
  • Creating external service areas
  • Building new facilities
  • Implementing mobile maintenance units

Resource and Hours Optimization

  • Hiring additional technicians
  • Adding second or third shifts
  • Weekend shifts
  • Specialized technician roles

Throughput Time Reduction

  • Lean process improvements
  • Enhanced inventory systems
  • Technology enhancements

The Solution

Methodology

Simulation Approach Selection

  • Complex system modeling
  • Risk mitigation through virtual testing
  • Time compression (simulate 5 years quickly)
  • Scenario testing for multiple solution combinations

Model Development Process

  1. Project scoping
  2. Process documentation
  3. Data collection and statistical analysis
  4. Model construction in Simio
  5. Validation and verification

Solution Testing Framework

The team used Boolean controls to activate or deactivate solutions such as routing work to other facilities, adding shifts, or implementing mobile maintenance.

Results

Capacity Constraint Identification

  • Parking capacity limits identified
  • Service bay utilization constraints
  • Technician staffing requirements
  • Support resource limitations

Implementation Roadmap

  1. Year 1 – Immediate adjustments
  2. Years 2–3 – Medium-term solutions
  3. Years 4–5 – Long-term investments

Validation

The team validated results through pilot testing and proof-of-concept initiatives to supplement simulation insights.

The Business Impact

Strategic Benefits

  • Reduced implementation risk
  • Optimized investment timing
  • Improved stakeholder understanding
  • Data-driven multi-year roadmap
  • Maintained service quality during growth

Broader Applications

  • Rental operations modeling
  • Logistics and warehouse optimization
  • Call center staffing analysis

Lessons Learned

  • Start with focused models
  • Document assumptions thoroughly
  • Balance model detail appropriately
  • Engage stakeholders visually
  • Integrate simulation with other analytical methods

Future Direction

Penske aims to evolve toward real-time digital twin capabilities that integrate simulation with live operational data.

By combining simulation, analytics, and visualization, Penske transformed a complex capacity planning challenge into a strategic growth advantage.