Fuel & Maintenance Forecasting

This use case explains how to forecast fuel and maintenance costs for logistics, transport and fleet based businesses using Model Reef.

You will:

• Use utilisation drivers such as kilometres or hours to drive fuel and maintenance.

• Separate price and volume effects on fuel cost.

• Model routine and major maintenance events.

• Connect fuel and maintenance to route profitability, asset planning and cashflow.

The aim is to move fuel and maintenance from a single line guess to a driver based, transparent structure that can be stress tested in scenarios.

When to use this pattern

Use this pattern when:

• Fuel and maintenance are major cost categories.

• You want to understand sensitivity to fuel price, efficiency and utilisation.

• You need to plan major services, overhauls or rebuilds.

• You want fuel and maintenance decisions visible in P&L, cash and valuation.

You will usually combine this with:

• Fleet Utilisation and Cost Modelling

• Route and Region Profitability

• Asset Replacement Planning

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Architecture overview

Fuel and maintenance forecasting uses:

1. Utilisation drivers

   • Kilometres or hours per fleet group, region or vehicle type.

2. Fuel cost structure

   • Fuel burn per kilometre or hour.

   • Fuel price and taxes.

   • Efficiency improvements or deterioration.

3. Maintenance cost structure

   • Routine maintenance cost per kilometre or hour.

   • Major services or overhauls based on intervals.

   • Tyres and other wear items.

4. Financial integration

   • COGS and Opex variables for fuel and maintenance.

   • Cashflow impact through payment timing.

   • Scenario analysis for price and utilisation shocks.

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1

Define utilisation drivers

Ensure your model already has utilisation drivers from fleet modelling, for example:

• Kilometres per Period - Linehaul Fleet.

• Kilometres per Period - Last Mile Fleet.

• Operating Hours per Period - Bus Fleet.

If not, create these based on:

• Historical odometer or telematics data.

• Planned schedule and route patterns.

• Contract commitments and forecasts.

These drivers will be the base for both fuel and maintenance calculations.

2

Build fuel burn and price drivers

In the Data Library, define fuel related drivers such as:

• Fuel Burn per 100 km - Linehaul Fleet.

• Fuel Burn per 100 km - Last Mile Fleet.

• Fuel Price per Litre by region or country.

• Fuel Efficiency Modifier to capture improvements or deterioration over time.

Calculate fuel cost per kilometre as:

• Fuel Cost per km = (Fuel Burn per 100 km ÷ 100) × Fuel Price per Litre × Fuel Efficiency Modifier.

You can vary fuel price over time to reflect known or expected changes in fuel markets or tax regimes.

3

Create fuel cost variables

For each fleet group or region, create COGS variables such as:

• COGS - Fuel - Linehaul North.

• COGS - Fuel - Last Mile Metro.

Define formulas such as:

• COGS - Fuel - Linehaul North = Kilometres per Period - Linehaul North × Fuel Cost per km - Linehaul Fleet.

Set appropriate timing:

• Occurrence when kilometres are driven.

• Payment delay based on supplier terms (for example 14 or 30 days).

This ensures that:

• Fuel expense hits P&L when utilisation occurs.

• Cash outflows follow supplier payment terms.

• AP and cash effects are visible in Balance Sheet and Cashflow outputs.

4

Model routine maintenance cost

Routine maintenance often scales with utilisation. In the Data Library, define drivers such as:

• Routine Maintenance Cost per km - Linehaul Fleet.

• Routine Maintenance Cost per km - Last Mile Fleet.

• Or per hour where that is more appropriate.

Then create COGS or Opex variables such as:

• COGS - Routine Maintenance - Linehaul North.

• COGS - Routine Maintenance - Last Mile Metro.

Formulas typically look like:

• Routine Maintenance Cost = Kilometres per Period × Maintenance Cost per km.

Again, use timing to reflect payment terms with workshops or internal recharge arrangements.

5

Model major maintenance, overhauls and tyres

Major maintenance events and tyres do not always scale linearly. To capture these, you can:

• Schedule major services at intervals based on utilisation, for example every X kilometres.

• Use schedules or seasonality style drivers to reflect expected timing.

• Represent costs as one off or lumpy variables in the periods where major work is expected.

Examples:

• COGS - Major Overhaul - Truck Group A scheduled every three to five years.

• COGS - Tyres - Linehaul Fleet based on expected tyre life in kilometres.

You can approximate this by:

• Averaging major costs into a cost per kilometre if you want a smooth view.

• Or explicitly scheduling major events if you want to see lumpy cash outflows.

6

Integrate with route profitability and asset planning

Once fuel and maintenance costs are driven by utilisation:

• Route and region level profitability can include realistic running costs.

• You can compare routes with different distance, speed and road conditions.

• Asset Replacement Planning can incorporate the rising cost of maintaining older vehicles.

For example, use a driver such as:

• Maintenance Escalation for Vehicles Over X Years Old.

Apply this modifier to maintenance cost per kilometre as the fleet ages to reflect higher maintenance costs for older units.

7

Use scenarios for fuel price and utilisation shocks

Clone your base model into scenario models to explore:

• Fuel price spikes or reductions.

• Efficiency improvements from newer vehicles or driver training.

• Changes in utilisation due to new contracts, lost work or network redesign.

• Accelerated or deferred major maintenance.

In each scenario, adjust:

• Fuel price and efficiency drivers.

• Maintenance cost per kilometre and major event schedules.

• Utilisation drivers per fleet group and region.

Compare scenarios using:

• Total fuel and maintenance cost.

• Cost per kilometre or per route.

• Margin and cash impacts.

• Required pricing changes to maintain profitability.

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Check your work

• Utilisation drivers align with historical data when the model is calibrated.

• Fuel burn and unit cost assumptions are based on real fleet performance and current fuel prices.

• Maintenance cost patterns and major events roughly match workshop records or fleet manager expectations.

• The split between routine and major maintenance is useful for planning but not unnecessarily complex.

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Troubleshooting

Fuel cost in the model is much higher or lower than actuals

Review fuel burn, price and utilisation assumptions and confirm that distance units are consistent across all drivers and variables.

Maintenance cost appears too smooth or too lumpy

Adjust the balance between cost per kilometre and explicit major events to reflect the level of detail you need.

Scenario results are hard to interpret

Use dashboards that separate fuel and maintenance from other costs and focus on unit cost metrics such as cost per kilometre to clarify patterns.

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Related guides

• Build a Dilution / Cap Table Impact Model

• Build a Discount Rate Scenario Model

• Divisional Structures

• Overriding Variables