Quick Summary

Bronson developed predictive maintenance cost models and a 10-year capital replacement plan for a fleet-intensive transportation organization operating a 2,800-vehicle fleet.

The engagement used Alteryx AutoML to model maintenance cost trajectories across vehicle classes, identifying the optimal replacement window for each class.

The 10-year capital plan balances replacement timing, lifecycle cost minimization, and the organization’s green fleet transition objectives.

Maintenance cost forecasts fed an optimized 10-year capital fleet replacement plan designed to minimize total operational costs over the planning horizon.

Outputs support both annual budgeting and long-horizon capital strategy, giving fleet managers a defensible basis for replacement and renewal decisions.

Project Overview

A fleet-intensive transportation organization operating approximately 2,800 vehicles retained Bronson to develop predictive maintenance cost modelling and a 10-year fleet capital replacement plan. The fleet spans multiple vehicle classes, including light-duty vehicles, medium and heavy-duty trucks, and specialized operational equipment. Maintenance and replacement decisions across that portfolio carry significant capital and operating cost implications, and the organization wanted an analytical foundation that connected maintenance data directly to capital planning.

Two questions sat at the centre of the engagement: when does it make economic sense to replace a vehicle rather than continue maintaining it, and how does the answer change when the organization also wants to transition toward a greener fleet? Bronson was engaged to build the models and the 10-year plan that would answer both questions defensibly.

The work needed to integrate historical maintenance records, vehicle age and mileage, operational duty cycle, replacement cost data, and assumptions about green fleet transition timing. Outputs needed to support both annual budget conversations and longer-horizon capital strategy.

The Challenge

A 2,800-vehicle fleet is large enough that intuition-based replacement decisions leave significant value on the table. But the fleet is also operationally diverse enough that a single rule of thumb does not apply across every vehicle class. The work had to deliver class-level analytical rigour at fleet-wide scale.

The main challenges Bronson tackled:

  • Fleet scale and diversity. 2,800 vehicles across multiple classes meant the analysis had to operate at vehicle-class granularity while still rolling up cleanly to fleet-wide capital plans.
  • Predictive cost modelling. Maintenance costs are non-linear over a vehicle’s life. Costs typically rise as vehicles age and accumulate mileage, but the curve varies meaningfully by vehicle class, duty cycle, and operating conditions. The models had to capture those differences rather than apply uniform aging assumptions.
  • Replacement timing optimization. The economic replacement point is not always when a vehicle physically can no longer operate. It is when the annualized cost of continuing to maintain the vehicle exceeds the annualized cost of replacing it. The analysis had to identify that crossover point per class.
  • 10-year capital horizon. Replacement decisions in any given year affect the capital plan for the next decade. The plan had to sequence replacements across the 10-year horizon in a way that smoothed capital demand while still respecting the class-by-class economic replacement points.
  • Green fleet transition overlay. The organization wanted to transition toward greener vehicles where possible. The plan had to layer that transition objective onto the lifecycle cost analysis, identifying where replacement opportunities also represented green transition opportunities.
  • Defensibility for capital approval. Replacement and renewal decisions of this scale require capital approval. The analysis had to be traceable back to source maintenance records, replacement cost data, and modelling logic that could withstand internal review.
  • Practical adoption by fleet managers. The outputs had to be usable by the existing fleet management team, not require specialized data science skills, and integrate with their existing capital planning cycle.

The organization needed a predictive maintenance and fleet replacement capability that operated at the granularity of vehicle classes, optimized over a 10-year horizon, and accommodated green fleet transition objectives, all while remaining defensible enough to anchor

Our Solution

Bronson designed and delivered the engagement as a structured predictive maintenance and capital planning program. The work was organized into the following streams:

1. Maintenance and Fleet Data Integration

Bronson worked with the fleet management team to integrate historical maintenance records, vehicle attributes (class, age, mileage, duty cycle), replacement cost data, and operational assumptions into a unified analytical dataset.

2. Alteryx AutoML Predictive Cost Modelling

Bronson used Alteryx AutoML to model maintenance cost trajectories at the vehicle class level. The models captured how maintenance costs evolve as a function of vehicle age, mileage, and duty cycle, surfacing class-specific cost curves rather than applying uniform aging assumptions across the fleet.

3. Economic Replacement Point Analysis

For each vehicle class, Bronson identified the economic replacement point: the age and condition at which the annualized cost of continuing to maintain the vehicle exceeds the annualized cost of replacing it. The analysis treated each class on its own terms rather than applying a single fleet-wide replacement age.

4. 10-Year Capital Replacement Plan

Bronson developed the 10-year capital replacement plan, sequencing replacements across the horizon in a way that respected class-by-class economic replacement points while smoothing annual capital demand. The plan supports both annual budgeting and long-horizon capital strategy.

5. Green Fleet Transition Overlay

Bronson layered the green fleet transition objective onto the capital plan, identifying where replacement opportunities also represented green transition opportunities and where lifecycle cost trade-offs warranted closer evaluation.

6. Defensibility and Audit Trail

The analytical workflow links outputs back to source maintenance records, replacement cost data, and modelling logic, supporting capital approval review and internal audit requirements.

7. Adoption Support for Fleet Managers

Bronson supported the fleet management team in adopting the analysis, providing documentation and walkthroughs that enabled fleet managers to interpret the models, apply the 10-year plan, and adjust assumptions for scenario analysis without specialized data science skills.

Key Deliverables

Integrated Maintenance and Fleet Dataset – The unified analytical dataset bringing together historical maintenance records, vehicle attributes, replacement cost data, and operational assumptions.

Economic Replacement Point Analysis – Class-by-class identification of the economic replacement point, anchoring replacement decisions in lifecycle cost analysis rather than fixed age thresholds.

10-Year Fleet Capital Replacement Plan – The sequenced 10-year plan balancing class-by-class replacement timing with smoothed annual capital demand across the 2,800-vehicle fleet.

The Impact

Bronson’s work gave the organization an analytical foundation that connects day-to-day maintenance data directly to multi-year capital decisions. Specifically, the engagement delivered:

  • Predictive maintenance cost models at the vehicle class level, replacing fleet-wide aging assumptions with class-specific cost trajectories grounded in the fleet’s own historical data.
  • Economic replacement point analysis per class, anchoring replacement decisions in lifecycle cost analysis rather than fixed age or mileage thresholds.
  • A 10-year capital replacement plan sequencing replacements across the 2,800-vehicle fleet in a way that respects class-by-class economic replacement points while smoothing annual capital demand.
  • A green fleet transition overlay identifying where replacement opportunities align with the organization’s green objectives, integrating sustainability into the capital plan rather than treating it as a separate exercise.
  • Defensibility for capital approvals, with outputs traceable to source data and modelling logic that withstands internal review.
  • Adoption by the fleet management team without requiring specialized data science skills, integrating with the team’s existing capital planning cycle.

The result is a fleet planning capability built for the scale of a 2,800-vehicle operation. Maintenance data, replacement cost data, and green transition objectives now feed into the same analytical foundation, giving fleet managers a defensible basis for replacement and renewal decisions across the next decade and beyond.

6 min read
Project Themes:
, ,

Similar Projects

Let’s work together.

Don’t let data challenges hold back your operations. Explore how data, analytics, and AI can drive success in your business processes. Contact us today for a consultation and unlock the full potential of your data.