Transforming fleet capacity planning for a high-volume delivery operation
CASE STUDY AT A GLANCE:
THE CHALLENGE:
A delivery operation with fluctuating daily demand needed a better way to match vehicle capacity to pallet volumes. The client’s static fleet structure could not adapt to changing order volumes, which created rolling backlogs and inefficient planning decisions.
LOOP'S SOLUTION:
Loop modelled multiple fleet scenarios and built a lightweight planning tool that used recent and projected order volumes to recommend the most efficient vehicle mix for the next day.
CUSTOMER OUTCOME:
The client moved from a static, reactive operating model to a flexible, predictive planning approach, resulting in the identification of an optimal dynamic three-vehicle fleet and a 99.7 percent reduction in total backlog volume.
Customer and project overview
A delivery operation with fluctuating daily demand needed a better way to match vehicle capacity to pallet volumes. The client’s static fleet structure could not adapt to changing order volumes, which created rolling backlogs and inefficient planning decisions.
To address this, we modelled multiple fleet scenarios and built a lightweight planning tool that used recent and projected order volumes to recommend the most efficient vehicle mix for the next day. This enabled the client to move from reactive planning to predictive, data-driven fleet management.
The challenge was driven by highly variable daily demand, ranging from low-volume to high-volume operating days.
The challenge
Aligning fleet capacity with volatile daily demand
CHALLENGE 1
Static fleet limitations
A fixed two-vehicle fleet could not handle order volumes that fluctuated significantly from day to day.
CHALLENGE 2
Rolling backlogs
When capacity fell short, orders spilled into subsequent days, creating service risk and operational instability.
CHALLENGE 3
Manual planning
Fleet decisions were based on manual judgement rather than predictive analysis, resulting in inefficient vehicle selection.
The primary objective was to identify the most effective fleet size and vehicle mix to reduce backlog risk, improve utilisation, and support reliable next-day delivery performance.
Loop's solution
Predictive fleet modelling and daily vehicle mix planning
Loop analysed recent and projected order volumes, pallet patterns, and fleet performance under different operational scenarios.
Using this, loop built a lightweight planning tool that recommended the best vehicle mix for the following day based on expected demand.
The model also surfaced weekly and seasonal demand patterns, allowing the client to plan more proactively for high-volume periods rather than reacting once backlogs had already formed.
The process involved
Demand and pallet volume analysis
Fleet scenario simulation
Vehicle mix modelling
Backlog risk assessment
Daily planning tool development
Scenario modelling
We tested multiple fleet structures to understand how each one would perform under different demand conditions.
This helped identify not only the minimum fleet required, but the most efficient combination of vehicles to reduce waste while protecting service levels.
Customer outcome
Dynamic fleet planning with major backlog reduction
The client moved from a static, reactive operating model to a more flexible and predictive planning approach.
As a result:
Identification of a dynamic three-vehicle fleet as the optimal model
69.2 percent reduction in backlog days
99.7 percent reduction in total backlog volume
Better alignment between fleet capacity and daily demand
Practical daily decision-making, such as swapping smaller vehicles for larger ones on heavy-volume days
Improved visibility of the busiest days of the week for proactive planning
Let's build your last-mile advantage together.
Speak to Loop's team and discover how we can tailor our delivery management software to your needs.
