> For the complete documentation index, see [llms.txt](https://help.modelreef.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.modelreef.io/use-cases/logistics-transport-and-fleet/fleet-utilisation-and-cost-modelling.md).

# Fleet Utilisation & Cost Modelling

This use case explains how to model fleet utilisation and operating cost for logistics, transport and fleet based businesses in Model Reef.

You will:

* Represent vehicles and fleets in the branch structure.
* Define utilisation drivers such as kilometres, hours, drops or loads.
* Build cost models covering fuel, maintenance, tyres, tolls, drivers and overheads.
* Connect fleet utilisation and cost into P\&L, Cashflow and Cash Waterfall.

Model Reef is not a telematics or route planning system. It sits above those tools as a planning and financial modelling layer.

## When to use this pattern

Use this pattern when:

* You operate a fleet of trucks, vans, buses or specialist vehicles.
* Vehicle utilisation and operating cost are major drivers of profitability.
* You want to compare routes, contracts, depots or customer groups on a consistent economic basis.
* You need a full three statement view of fleet operations, not just unit metrics.

You will often combine this pattern with:

* **Route or Region Profitability**
* **Fuel and Maintenance Forecasting**
* **Asset Replacement Planning**

## Architecture overview

Fleet utilisation and cost modelling uses four main layers:

* Fleet structure
  * Branches per region, depot or fleet segment.
  * Optional sub branches for vehicle types or contracts.
* Utilisation drivers
  * Kilometres, hours, loads or drops per vehicle or fleet group.
  * Load factors and empty running assumptions.
* Cost drivers
  * Fuel burn per kilometre or hour.
  * Maintenance, tyres and tolls.
  * Driver wages and oncosts.
  * Insurance and registration.
* Financial outputs
  * Cost per kilometre, per hour, per drop or per tonne.
  * Fleet cost and margin per route, depot, contract or customer.
  * Integration with P\&L, Cashflow and valuation.

{% stepper %}
{% step %}

### Set up branches for fleet and depots

Start by deciding how you want to view performance. Common approaches are:

* Branches per depot or operating centre.
* Branches per region (for example North, South, Metro, Regional).
* Branches per fleet type (linehaul, last mile, refrigerated, bulk).

For example:

* `Group`
  * `Region - North`
  * `Region - South`
  * `Region - Metro`

Within each region, you can add sub branches for depots or specific fleets if needed.

Keep branch structure aligned with how operations and finance think about accountability and reporting.
{% endstep %}

{% step %}

### Define utilisation drivers

In the Data Library, create drivers that describe how your fleet is used, for example:

* `Kilometres per Period - Linehaul Fleet - North`.
* `Kilometres per Period - Last Mile Fleet - Metro`.
* `Operating Hours per Period - Bus Fleet`.
* `Drops per Period - Last Mile Fleet`.
* `Tonne Kilometres per Period` where relevant.

You may also define:

* Load factors (for example average trailer fill percentage).
* Empty running percentage (how many kilometres are unproductive).

These drivers can be created from:

* Historical telematics or odometer data.
* Forecast route volumes and network plans.
* Contract commitments.

Store them as time series so they can grow, shrink or respond to scenario changes.
{% endstep %}

{% step %}

### Build fuel and variable operating cost per kilometre or hour

For each fleet group, define unit cost drivers such as:

* `Fuel Burn per 100 km - Linehaul Fleet`.
* `Fuel Price per Litre`.
* `Maintenance Cost per km`.
* `Tyre Cost per km`.
* `Tolls per km` where toll roads are used heavily.

Compute a composite variable cost per kilometre or hour, for example:

* `Variable Cost per km - Linehaul Fleet = Fuel Burn per km × Fuel Price per Litre plus Maintenance Cost per km plus Tyre Cost per km plus Tolls per km`.

Then create COGS or Opex variables such as:

* `COGS - Variable Fleet Cost - Linehaul North = Kilometres per Period - Linehaul North × Variable Cost per km - Linehaul Fleet`.

Typing these as **COGS** ensures they reduce gross margin correctly and flow through to the Cashflow Statement and Cash Waterfall when timing is applied.
{% endstep %}

{% step %}

### Model fixed and semi fixed fleet costs

In addition to variable costs, fleets incur fixed or semi fixed costs, for example:

* Vehicle lease or finance payments.
* Depreciation if you own vehicles, modelled as Asset variables.
* Insurance and registration.
* Driver base salaries (often Staff variables).
* Depot rent and overheads (Opex).

For each region or depot, create variables such as:

* `Staff - Drivers - North`.
* `Opex - Insurance and Registration - Fleet`.
* `Opex - Depot Rent - North`.
* `Asset - Vehicles - North` for capitalised vehicles with depreciation.

Ensure timing rules reflect when cash actually leaves the bank (for example monthly lease payments, annual insurance or registration).
{% endstep %}

{% step %}

### Compute fleet unit economics

With utilisation and cost variables in place, build unit economic metrics such as:

* `Cost per km` by fleet type and region.
* `Cost per hour` where relevant.
* `Cost per drop` for last mile operations.
* `Cost per tonne kilometre` for bulk freight.

You can implement these as:

* Custom chart formulas, or
* Variables that divide total cost by total utilisation drivers.

For example:

* `Cost per km - North = Total Fleet Cost - North ÷ Total Kilometres - North`.

Connect these to route or region level revenue where you have **Route or Region Profitability** configured, so you can view:

* Margin per km.
* Margin per trip or route.
* Margin per contract or lane.
  {% endstep %}

{% step %}

### Link fleet modelling to P\&L, cashflow and valuation

Because all fleet costs are represented as variables with correct types and timing:

* P\&L will show fleet cost lines in COGS, staff and Opex.
* Balance Sheet will capture vehicles as Assets and associated debt as Liabilities.
* Cashflow and Cash Waterfall will show fuel, maintenance, lease, debt and capex payments over time.

You can then:

* Test different utilisation and cost assumptions in scenarios.
* See how fleet decisions affect overall cash requirements and valuation.
* Compare the economics of owning versus leasing or outsourcing routes.
  {% endstep %}

{% step %}

### Use scenarios to test fleet strategies

Clone the model into scenario models to represent different fleet strategies, for example:

* Higher utilisation of existing fleet with minimal growth in fleet size.
* Fleet expansion to support new contracts or geographies.
* Increased outsourcing of certain routes to third party carriers.
* Electrification or alternative fuels with different capex and operating cost profiles.

In each scenario, adjust:

* Utilisation drivers (kilometres, hours, drops).
* Variable cost per kilometre or hour (fuel, maintenance, tyres).
* Fixed cost structures (leases, assets, staff).
* Revenue per route or region in the connected route profitability models.

Compare scenarios using:

* Fleet cost and margin per route, region or customer.
* Required capex and financing.
* Cashflow and funding needs.
* Valuation and risk metrics.
  {% endstep %}
  {% endstepper %}

{% hint style="info" %}

### Check your work

* Fleet groupings in branches match how operations view the network.
* Kilometre, hour and drop drivers align with historical patterns when calibrated.
* Unit cost assumptions for fuel, maintenance and other running costs are realistic.
* P\&L and cashflow outputs look plausible relative to recent financial results.
  {% endhint %}

## Troubleshooting

<details>

<summary>Cost per km looks too high or too low</summary>

Check that you have captured all relevant cost components and that you are dividing by the correct utilisation driver for that fleet.

</details>

<details>

<summary>Fleet looks over utilised in the model</summary>

Compare kilometres and hours per vehicle to realistic limits and adjust utilisation or fleet size assumptions.

</details>

<details>

<summary>Difficult to maintain at individual vehicle level</summary>

Aggregate vehicles into homogeneous fleets and only model individual units where they are unusually large or important.

</details>

## Related guides

* [Build a Staffing Cost Model](/how-tos/operations-and-unit-economics/build-a-staffing-cost-model.md)
* [Build a Central Assumption Library](/how-tos/data-workflows-and-automation/build-a-central-assumption-library.md)
* [Differences vs Ticker Fundamentals Import](/help/importing-from-google-finance-and-yahoo-apis/differences-vs-ticker-fundamentals-import.md)
* [Operational Driver Fields](/syntax/drivers-syntax/operational-driver-fields.md)