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

CUSTOMER

Penske

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. The case study demonstrates how simulation technology can transform complex operational planning challenges into strategic advantages through virtual testing and data-driven decision making.

Introduction

Company Background

Penske Truck Leasing operates as part of Penske Transportation Solutions, managing a fleet of more than 439,000 vehicles across the United States, Canada, and Mexico. The company’s operations span multiple service areas including truck rental, leasing, maintenance services, and used vehicle sales. This extensive operation is supported by nearly 1,000 service facilities and approximately 22,000 employees, with roughly half being technicians who perform critical maintenance functions.

Maintenance represents a core component of Penske’s business model, with services ranging from preventative maintenance and state inspections to complex mechanical, electrical, and body repairs. The company’s ability to efficiently maintain this massive fleet directly impacts customer satisfaction, operational costs, and overall business performance.

The Challenge

In 2020, Penske’s Operational Excellence department—an internal consulting group specializing in Six Sigma, Lean methodologies, and process improvement—identified a significant challenge facing one of their facility managers. The manager needed to accommodate 500 additional vehicles over a five-year period (100 vehicles annually) while already operating at what appeared to be maximum capacity.

As Morgan Swink from Penske’s Operational Excellence team explained: “Our growth plan includes 500 vehicles over the next five years, but I feel like I’m already out of space. What do I do?”

This growth plan created a complex planning challenge that extended beyond simple space calculations. The facility manager needed to understand:

  1. Which capacity constraints would be encountered first during the growth period
  2. What specific solutions should be implemented to address each constraint
  3. When each solution should be implemented to support sustainable growth
  4. How to balance cost, timing, safety, and customer satisfaction in the implementation plan

Traditional analytical methods proved insufficient for modeling these complex, interrelated factors across a multi-year planning horizon.

Problem Statement

Capacity Constraints Analysis

The capacity challenge extended far beyond simple space limitations. As Swink noted, “Capacity in its simplest form is pretty straightforward. It’s the number of workspaces you have multiplied by how many people and hours you have to operate them, divided by the throughput time of the unit moving through the system.” However, the reality at Penske was far more complex.

Adding 500 vehicles created cascading pressures across multiple dimensions:

  1. Physical Space Constraints:
  • Limited parking space for additional vehicles
  • Insufficient service bays to handle increased maintenance volume
  • Restricted yard space for vehicle movement and staging
  1. Resource Requirements:
  • Increased demand for parts, tires, fluids, and other consumables
  • Additional storage space needed for inventory
  • More tools and equipment required for maintenance
  1. Staffing Implications:
  • Need for additional technicians to handle increased workload
  • Support facilities (break rooms, bathrooms, locker rooms) for expanded staff
  • Management capacity to oversee larger operations
  1. Operational Complexity:
  • Different vehicle types required vastly different maintenance frequencies
  • Local delivery trucks might need service once annually
  • Long-haul tractors could require maintenance up to 15 times per year
  • Varying service durations based on vehicle type and maintenance needs

The facility manager faced difficult decisions about how to address these constraints. Options included expanding physical facilities, adding shifts, implementing mobile maintenance, restructuring technician roles, or some combination of these approaches. Each option carried different costs, implementation timelines, and operational impacts.

Solution Approaches

Penske identified several potential approaches to address capacity constraints:

  1. Workspace Expansion Options:
  • Adding new bays within existing buildings
  • Creating external service areas with aprons and canopies
  • Building entirely new facilities
  • Implementing mobile maintenance units for off-site service
  1. Resource and Working Hours Optimization:
    • Hiring additional technicians
    • Adding second, third, or weekend shifts
    • Creating specialized technician roles (triage, diagnostic, quick service)
    • Optimizing scheduling to balance workload
  2. Throughput Time Reduction:
  • Implementing lean process improvements
  • Enhancing visual management and inventory systems
  • Leveraging technology for more efficient maintenance

The challenge was determining which of these “switches” to flip and when to flip them over the five-year growth period. The facility manager needed a comprehensive plan that outlined specific actions for each year based on anticipated capacity constraints.

The Solution

Methodology

Simulation Approach Selection

Penske’s Operational Excellence team selected Simio simulation software to model the facility’s operations and test potential solutions. The simulation approach offered several advantages over traditional planning methods:

  1. Complex System Modeling: Ability to represent the intricate interactions between different capacity constraints
  2. Risk Mitigation: Testing solutions virtually before committing resources to implementation
  3. Time Compression: Simulating five years of growth in minutes
  4. Scenario Testing: Evaluating multiple solution combinations to identify optimal approaches

Model Development Process

The team followed a structured approach to develop the simulation model:

  1. Project Scoping: The team broke down the complex challenge into manageable components, focusing initially on parking capacity as a representative example while building toward a comprehensive model.
  2. Process Documentation: Before building the simulation, the team documented current processes using tools like PowerPoint, OneNote, and Visio to create detailed process maps. As Swink explained, “I like to document my process outside of Simio first, because it gives me the opportunity to work alongside my stakeholders who are not familiar with the software, and propose my plan in something that is recognizable to them.”
  3. Process Analysis: For the parking lot component, the team identified key processes:
  • Vehicle arrival for in-service at the start of its life
  • Periodic arrivals for preventative and regular maintenance
  • Arrivals for out-servicing when exiting the fleet
  • Employee parking requirements
  1. Data Collection and Analysis: The team gathered data from multiple sources:
  • Historical operational data from Penske’s databases
  • Time studies for processes lacking historical data
  • Subject matter expert input for validation
  1. Data Analysis Methodology: The team used a three-pronged approach to analyze data:
  • Graphical analysis to identify data patterns and distributions
  • Statistical analysis using tools like Minitab
  • Practical interpretation to ensure business relevance
  1. Model Construction: The team built the simulation model in Simio, incorporating:
  • Accurate facility layout
  • Process flows based on documented procedures
  • Data-driven parameters from their analysis
  • Boolean controls to test different solution combinations
  1. Validation and Verification: The team verified that the model operated as intended and validated its accuracy against observed real-world outcomes. As Swink noted, “I don’t want them to be perfect matches. If I was returned all of my same inputs as outputs, I would have to think that my model is overfit and I lose the benefit of randomness that modeling is meant to provide.”

Solution Testing Framework

The team incorporated multiple potential solutions into the model using Boolean controls (ones and zeros) to activate or deactivate different process modifications. This allowed them to test combinations of solutions to identify optimal approaches for each capacity constraint.

For example, they created controls to test:

  • Routing in-service work to another facility (0 = no, 1 = yes)
  • Routing out-service work to another facility (0 = no, 1 = yes)
  • Adding second shift (0 = no, 1 = yes)
  • Implementing mobile maintenance (0 = no, 1 = yes)

By running experiments with different combinations of these controls, the team could identify which solutions or combination of solutions most effectively addressed capacity constraints at different points in the growth timeline.

Visualization for Stakeholder Engagement

To enhance stakeholder understanding and buy-in, the team created a 3D rendering of the facility that visually represented the simulation results. As Swink emphasized, “I take the time to craft around a rendering that looks and operates like the shop in question to make it digestible to anyone who does not have simulation or similar background.”

This visualization approach proved critical for communicating complex simulation results to decision-makers who might not have expertise in simulation technology.

Results

Capacity Constraint Identification

The simulation model successfully identified when specific capacity constraints would be encountered during the five-year growth period:

  • Parking Capacity: The model determined exactly when parking capacity would be exceeded based on the growth rate of 100 vehicles per year.
  • Bay Utilization: The simulation identified when service bay capacity would become a constraint, accounting for the varying maintenance frequencies of different vehicle types.
  • Technician Staffing: The model determined when additional technicians would be needed based on increased maintenance volume.
  • Support Resources: The simulation highlighted when constraints in parts storage, tool availability, and support facilities would emerge.

Solution Evaluation

For each capacity constraint, the model evaluated multiple potential solutions:

  1. Parking Solutions:
  • Routing in-service or out-service work to other facilities
  • Adding parking spaces through property expansion
  • Implementing mobile maintenance to reduce on-site vehicle presence
  • Optimizing scheduling to balance vehicle flow
  1. Bay Utilization Solutions:
  • Adding new bays within the existing building
  • Creating external service areas with aprons and canopies
  • Implementing specialized technician roles to improve throughput
  1. Staffing Solutions:
  • Adding second or third shifts
  • Implementing weekend shifts
  • Creating specialized roles (triage technician, diagnostic technician, quick service technician)

Solution Assessment Framework

For each potential solution, the model assessed multiple factors:

  1. Timing Requirements: Lead time needed for implementation
  2. Cost Implications: Capital and operational expenses
  3. Customer Impact: Effect on service quality and vehicle uptime
  4. Safety Considerations: Potential safety implications for employees
  5. Effectiveness: Capacity improvement potential

This comprehensive assessment enabled the team to develop a phased implementation plan that balanced multiple objectives.

Implementation Roadmap

The simulation provided facility managers with a clear roadmap showing:

  • Year 1 Actions: Immediate steps to address initial capacity constraints
  • Years 2-3 Actions: Medium-term solutions as growth continued
  • Years 4-5 Actions: Longer-term investments needed to support full growth

This phased approach allowed for a strategic implementation plan, with lower-cost, quicker-turnaround solutions implemented in early phases, building toward larger investments as needed.

As Swink explained: “How great would it be to come back with a plan specific to the parameters and details of their shop that outlines specifically what to do and when to do it along their five-year path to 500 new units.”

Validation Through Multiple Approaches

While the simulation provided valuable insights, Penske recognized its limitations as a statistical estimate rather than a perfect prediction. As Swink noted, “The one thing about simulation, and truly this is the case for any simulation, is the best that you get is a statistical estimate of the actual outcome.”

Consequently, Penske integrated simulation results with other testing methodologies, including proof of concept and pilot testing, to account for behavioral aspects that simulation cannot capture.

The Business Impact

Conclusion

Business Impact

Penske’s application of Simio simulation software to capacity planning demonstrates the significant value of simulation-based decision making in complex operational environments. The approach allowed Penske to:

  • Reduce Risk: Test solutions virtually before committing resources to implementation
  • Optimize Timing: Identify precisely when different interventions would be needed
  • Enhance Understanding: Provide stakeholders with clear visualizations of complex capacity challenges
  • Support Strategic Planning: Create a multi-year roadmap for sustainable growth
  • Improve Resource Allocation: Direct investments to the most critical capacity constraints
  • Maintain Service Quality: Ensure customer satisfaction throughout the growth period

Broader Applications

The success of this project has led Penske to expand its use of simulation to other areas:

  • Rental Operations: Modeling pickup and drop-off processes
  • Logistics: Optimizing warehouse and distribution operations
  • Call Center Staffing: Determining optimal staffing levels based on call volumes

As Swink noted, “You can probably make a case for nearly every part of the business to use it in some way.”

Lessons Learned

Key insights from Penske’s simulation journey include:

  1. Start Simple: Begin with focused models addressing specific challenges before expanding to more complex systems
  2. Document Thoroughly: Maintain detailed documentation of assumptions, data sources, and model logic
  3. Balance Detail: Find the appropriate level of model granularity to address the specific questions at hand
  4. Engage Stakeholders: Use visualization to build understanding and buy-in from decision-makers
  5. Integrate Methods: Combine simulation with other analytical approaches for comprehensive solutions

Future Directions

Penske continues to evolve its simulation capabilities, with aspirations to develop true digital twins that “live and breathe with their real-time counterparts.” This evolution represents the next frontier in their simulation journey, potentially enabling even more dynamic and responsive operational planning.

By combining simulation with other analytical approaches and leveraging advanced visualization techniques, Penske has transformed a complex capacity planning challenge into a strategic advantage, supporting sustainable growth while maintaining operational excellence across its extensive transportation network.