Last-mile optimisation: how to reduce costs and improve deliveries
Last-mile optimisation helps reduce costs, improve on-time performance and make better use of the fleet in operations with many deliveries, constraints and daily changes. Good planning must take account of mileage, time windows, capacities, service times, incidents and real operational data.
The last mile is one of the most complex and costly parts of distribution.
It refers to the final stage of the logistics process: from the warehouse, platform or distribution point to the end customer. Although it may seem like a small part of the journey, it often concentrates many operational problems.
Scattered deliveries, urban traffic, access restrictions, time windows, absent customers, last-minute changes and rising costs make the last mile particularly difficult to manage.
This is why last-mile optimisation has become a key tool for improving service and reducing costs.
Why the last mile is so complex
In the last mile there are many deliveries, many destinations and little room for error.
Unlike long-distance transport, where routes tend to be more stable, the last mile changes constantly. Orders, schedules, areas, volumes and priorities may vary every day.
In addition, the urban environment adds further difficulty.
There may be streets with restricted access, congested loading and unloading areas, time-based restrictions, variable traffic or parking difficulties.
All this makes manual planning increasingly difficult as the number of deliveries grows.
It is not just about delivering faster
Optimising the last mile does not simply mean going faster.
The real objective is to find the best balance between cost, time and service quality.
A very fast route may be expensive if it uses too many vehicles. A low-cost route may be poor if it misses time windows. A very tight plan may work on paper but fail when any incident occurs.
This is why optimisation must take the whole operation into account.
- Distance travelled.
- Driving time.
- Number of vehicles used.
- Time windows.
- Load capacity.
- Service times.
- Delivery priorities.
- Urban restrictions.
- Cost per route.
- Risk of incidents.
The best solution is not always the shortest. It is the one that can be executed most effectively.
Reducing last-mile mileage
One of the most visible benefits of optimisation is the reduction in mileage.
When deliveries are assigned manually, routes may cross, vehicles may cover similar areas, or stop sequences may be inefficient.
Optimisation helps group orders more effectively, avoid overlaps and arrange visits in a more coherent order.
This makes it possible to reduce unnecessary journeys and improve fleet utilisation.
However, mileage reduction must be approached carefully. If distance is reduced at the cost of missed time windows or additional waiting, the result may not actually be better.
Meeting time windows
In the last mile, time windows are increasingly important.
Many customers expect to receive deliveries within a specific time slot. In professional distribution, there may also be receiving hours, unloading shifts or access restrictions.
Good planning must ensure that deliveries arrive within the agreed interval.
This means organising routes not only according to distance, but also according to the estimated arrival time at each stop.
When time windows are correctly incorporated into optimisation, failed deliveries, waiting times and incidents are reduced.
Making better use of the fleet
The last mile can lead to an unbalanced use of vehicles.
Some vehicles may leave overloaded while others have spare capacity. Some may finish very late and others too early. Some routes may be long while others are unproductive.
Optimisation helps balance the workload more effectively.
In last-mile operations, a route planning software can distribute hundreds of deliveries across several vehicles, balancing areas, capacities, schedules and priorities to avoid unbalanced routes.
This does not mean that every vehicle must do exactly the same work, but that the fleet as a whole should be used more efficiently.
Good planning can reduce the number of vehicles required or improve the performance of those already available.
Adapting to last-minute changes
The last mile is full of changes.
An urgent order may arrive late. A customer may change their availability. A vehicle may be delayed. A delivery may fail. An area may develop access problems.
When this happens, the original plan may no longer be valid.
An optimisation system must make it possible to adjust routes without disrupting the entire operation.
In some cases, changing the order of a few stops will be enough. In others, orders may need to be moved between vehicles or part of the plan recalculated.
The ability to adapt is essential for an efficient last-mile operation.
Data needed for optimisation
To optimise the last mile effectively, the system needs reliable data.
Some of the most important data include:
- Accurate addresses.
- Delivery times.
- Order volume or weight.
- Vehicle capacity.
- Estimated service time.
- Departure and return base.
- Priorities.
- Access restrictions.
- Operating costs.
- Historical times and incidents.
The better the information, the more realistic the generated routes will be.
An incorrectly geocoded address, an inaccurate unloading time or an incomplete time window can affect the entire plan.
Indicators for measuring last-mile performance
Optimisation requires measurement.
Some useful indicators for analysing the last mile are:
- Cost per delivery.
- Miles or kilometres per order.
- Deliveries per route.
- Percentage of on-time deliveries.
- Failed deliveries.
- Average service time.
- Vehicle utilisation.
- Number of incidents.
- Total route time.
These data make it possible to determine whether the operation is genuinely improving and where problems still remain.
Optimisation should not be seen as a one-off action, but as a process of continuous improvement.
Last mile and artificial intelligence
Artificial intelligence can add significant value to last-mile operations.
Machine learning can help estimate more realistic service times, identify areas with a greater risk of delay, or learn patterns from previous routes.
Optimisation algorithms can use this information to generate more reliable routes adapted to real operating conditions.
In this way, the system not only calculates routes but also learns from experience and progressively improves planning.
This is especially important in urban environments, where conditions change frequently and theoretical times do not always match actual times.
Main benefits
Effective last-mile optimisation can deliver significant benefits:
- Fewer miles or kilometres travelled.
- Lower cost per delivery.
- Better on-time performance.
- Fewer failed deliveries.
- Higher fleet productivity.
- Better customer experience.
- Greater ability to absorb changes.
- Less time spent planning.
- Greater control over the operation.
These benefits have a direct impact on costs, service quality and competitiveness.
Conclusion
The last mile is one of the greatest challenges in modern distribution.
Its complexity lies not only in distance, but also in the number of deliveries, constraints, schedules, changes and incidents that must be managed every day.
Optimising the last mile makes it possible to reduce costs, improve service and make better use of available resources.
To achieve this, routes must be planned with the full reality of the operation in mind: vehicles, orders, schedules, capacities, constraints, costs and historical data.
In the last mile, where every minute and every delivery count, LOGISPLAN draws on the experience of Evolution Algorithms to generate more efficient, realistic routes tailored to the constraints of each operation.