One Day, Four Depots: What Alternative Route Plans Reveal About Fleet Cost

Route planning becomes more complex when delivery work can leave from four different depots.

The planner is no longer deciding only which stop should follow another. The operation must also decide which depot should serve each mission, which vehicle should carry the work and how demand should be distributed across the available fleet.

A one-day route-planning comparison based on real operational data shows how different answers can change modeled vehicle use and total distance.

It also shows why the lowest number is not automatically the best plan.

The One-Day, Four-Depot Planning Comparison

The following figures come from an **anonymized historical route-planning analysis using real operational data**. The alternatives were modeled route plans, not completed live routes.

This was not an Australian deployment, an audited post-implementation result or a guarantee of fleet savings. Australian outcomes will vary according to geography, traffic, delivery density, fleet mix, customer windows, service duration, driver conditions, depot rules and data quality.

The percentages are calculated from the listed values and may vary slightly if the source totals were rounded.

The alternative ranges should not be treated as paired results. The available summary does not establish that the 47-vehicle scenario was also the 2,939-kilometre scenario.

That distinction matters because one model may minimize vehicles while another minimizes distance.

What This Comparison Actually Shows

The benchmark shows that the same day of delivery demand produced several materially different route-planning scenarios.

Depending on the planning approach, the model used:

• Between 47 and 51 vehicles
• Between 2,939 and 3,201 kilometers
• Four to eight fewer vehicles than the baseline
• Between 418 and 680 fewer planned kilometers than the baseline

These are planning opportunities, not realized operating results.

Before managers treat one scenario as superior, they need to understand:

• Whether every mission was included
• Whether missions were assigned to appropriate depots
• Whether customer time windows were respected
• Whether service duration were realistic
• Whether vehicle capacities and skills were represented
• Whether routes fitted within driver working windows
• Whether any missions remained unassigned
• Whether dispatchers considered the routes practical
• Whether the plan could be executed under normal conditions

The benchmark begins a cost discussion. It does not complete one.

Why Four Depots Change the Route-Planning Problem

With one depot, every route generally begins from the same operating point. With four depots, each mission may have several possible starting contexts.

The planning process must consider questions such as:

• Which depot is suitable for each mission?
• Which depot has an appropriate vehicle available?
• Does the required driver or vehicle skill exist at that branch?
• Can assigning a mission to another depot reduce distance?
• Would that assignment create an impractical workload elsewhere?
• Does the vehicle need to return to the same depot?
• Are customer or operational rules tied to a particular branch?

This makes multi-depot route planning both a routing problem and an allocation problem.

Rouptimize’s multi-branch delivery operations workflow helps keep branch context connected to missions, vehicles and route planning.

Depot Assignment Can Change Total Distance

A customer may be geographically closer to one depot but operationally assigned to another.

There may be a valid reason:

• Product or stock availability
• Vehicle suitability
• Driver skills
• Customer ownership
• Branch capacity
• Loading arrangements
• Service-area policy
• Working-hour limits

A route model should not reassign work between depots without respecting these conditions.

However, when depot allocation is flexible, testing alternative assignments may reveal duplicated travel or overlapping service areas.

The distance range in the benchmark, from 2,939 to 3,201 kilometers across the modeled alternatives, shows that route objectives and depot decisions can produce meaningfully different plans from the same day of work.

It does not identify which scenario offered the best customer or operational outcome.

Fewer Vehicles Do Not Automatically Mean Lower Cost

The modeled alternatives used between 47 and 51 vehicles, compared with 55 in the existing baseline.

That may indicate that the selected work could be consolidated across fewer routes.

It does not mean the business could immediately remove four to eight vehicles from its fleet.

The financial effect depends on questions such as:

• Are the vehicles owned, leased or hired?
• Are drivers already rostered?
• Is the difference repeated across normal operating days?
• Are the vehicles needed for peak demand?
• Would fewer routes create overtime?
• Does the business retain spare vehicles for breakdowns or urgent work?
• Are specialized vehicles interchangeable?
• Would another depot need additional loading capacity?
• Can the modeled routes be executed reliably?

A vehicle that is not required for one modeled day may still be necessary for the wider operation.

The Missing Working-Time Comparison Matters

The existing baseline recorded 530 total working hours.

The available alternative-plan summary does not provide equivalent working-time figures. Therefore, no valid time reduction can be calculated.

This limits the financial conclusion.

A scenario using fewer vehicles could still require:

• Longer individual routes
• More driver overtime
• Earlier departures
• Later returns
• Greater loading pressure
• Tighter customer-window risk
• More difficult dispatcher intervention

Distance and vehicle count should never be used as substitutes for working time.

A financially attractive-looking route plan can become expensive if labor requirements or delivery risk increase.

Four Layers of Fleet Cost

The following table is a management framework, not a measured saving from the benchmark.

A fleet-cost analysis should examine all four layers.

For example, a plan using 47 vehicles may minimize route count but place more pressure on working time. A 51-vehicle plan may require more vehicles while producing shorter or more resilient routes.

Without scenario-level working time and feasibility data, neither can be declared the lower-cost option.

Why Route Optimization Should Produce Alternatives

Route optimization is most useful when it helps managers compare valid operating choices.

Possible planning objectives include:

• Lower total distance
• Lower total working time
• Fewer vehicles
• More balanced driver workloads
• Stronger protection of customer time windows
• Better alignment between missions and depots
• Greater capacity utilization
• Lower planning costs based on configured vehicle assumptions

These objectives can conflict.

The plan with the fewest vehicles may not have the shortest distance. The shortest-distance plan may not distribute work fairly. A highly consolidated plan may provide less room for delivery exceptions.

Rouptimize can generate routes around mission and vehicle constraints, allowing dispatch teams to review the operational effect of different planning choices.

What Managers Should Compare Between Scenarios

Mission Coverage

Every scenario should begin with the same delivery requirement.

Check:

• Total missions
• Assigned missions
• Unassigned missions
• Customer groups
• Delivery and pickup requirements

A scenario should not appear efficient because difficult missions were excluded.

Fleet Requirements

Compare:

• Vehicles used
• Vehicle types
• Capacity utilization
• Working windows
• Required skills
• Depot assignments

Rouptimize’s fleet management capabilities keep capacity, skills, working hours, costs and depot context available to the planning process.

Route Structure

Review:

• Total and average distance
• Total and average working time
• Missions per route
• Stop order
• Workload distribution
• Route start and end points

Customer Feasibility

Confirm:

• Time windows
• Service duration
• Priority missions
• Customer-specific requirements
• Delivery or pickup order
• Proof-of-delivery requirements

Operational Flexibility

Ask:

• How much spare time remains?
• Can the route absorb a customer delay?
• Are routes dependent on one vehicle or driver?
• Can dispatchers make practical adjustments?
• Does one depot carry an excessive share of the risk?

Lower Distance Is Not Always the Lowest-Cost Scenario

The modeled alternatives ranged from 2,939 to 3,201 kilometers.

The 2,939-kilometre plan appears more attractive if distance is the only measure. But the available comparison does not show whether that plan:

• Used 47 or 51 vehicles
• Required less working time
• Produced balanced routes
• Respected every customer cut-off
• Used the correct depots
• Left missions unassigned
• Required more manual changes

The lower-distance plan is a candidate for review, not an automatic winner.

This is especially important for Australian delivery teams serving a mixture of metropolitan, outer-suburban and regional areas. A small distance difference may be less important than route resilience, vehicle suitability or protection of customer commitments.

Modeled Vehicle Use Versus Fleet Size

Three terms should remain separate.

Vehicles Used in a Model

The number of vehicles assigned by a route-planning scenario.

Vehicles Used Operationally

The number of vehicles actually dispatched to complete the work.

Fleet Size

The total number of vehicles the business owns, leases or can access.

A route model may use fewer vehicles without changing the required fleet size. The business may retain capacity for seasonal peaks, maintenance, emergency work or different delivery days.

To support a fleet decision, the pattern should be tested across representative periods rather than one day.

The six-day delivery benchmark provides an example of how vehicle use, working time, distance and missions per vehicle can be reviewed together across several planning days.

How to Test Four-Depot Scenarios With Your Own Data

1. Select a Representative Delivery Day

Include normal operational complexity rather than choosing only an easy or unusually quiet day.

2. Freeze the Mission Set

Use the same missions in the baseline and every alternative scenario.

3. Confirm Depot Rules

Identify which missions can move between depots and which must remain with a specific branch.

4. Validate Fleet Availability

Record the vehicles, capacities, skills, working windows and start or end depots that were genuinely available.

5. Check Customer Constraints

Review time windows, service duration, weight, volume, priority and special handling requirements.

6. Record the Existing Baseline

Capture vehicle use, distance, working time and mission assignments using consistent definitions.

7. Generate Several Route Plans

Test different objectives while keeping the mission and fleet data comparable.

8. Keep Unassigned Missions Visible

Investigate why they could not be assigned rather than removing them from the comparison.

9. Review Routes With Dispatchers

Rouptimize’s dispatcher map supports route review and adjustment before plans are sent to drivers.

10. Test Selected Plans Operationally

A controlled live pilot is needed before modeled differences become operational evidence.

The guide to running a route optimization pilot with existing delivery data provides a complete pilot framework.

Build a Fleet-Cost Scenario

Once a route scenario is considered feasible, managers can apply the business’s own cost assumptions.

Do not apply one modeled percentage to every cost category.

A calculated distance difference may influence distance-related cost. A vehicle-use difference may influence hire or activation cost. Labor depends on working time, which was not available for the modeled alternatives in this benchmark.

Review the Dispatcher Workflow

A multi-depot plan may be mathematically feasible but difficult to manage.

Dispatchers should review:

• Whether depot assignments make operational sense
• Whether loads can be prepared at the selected branches
• Whether drivers have the required mission context
• Whether stop order is practical
• Whether workload is balanced
• Whether cross-branch changes are visible
• Whether exceptions can be managed during the day

Manual route changes should be documented. Repeated changes may reveal missing constraints or data that should influence future plans.

This is part of the continuous route improvement loop, not evidence that human review has failed.

Measure the Live Result Before Claiming Savings

After dispatch, compare the planned scenario with what occurred.

Useful review areas may include:

• Vehicles actually used
• Missions completed
• Distance recorded
• Working time
• Driver or vehicle reassignment
• Unsuccessful missions
• Customer-window exceptions
• Manual route changes
• Differences between depots

Rouptimize’s reports and analytics views help keep route, mission, driver and fleet information close to the operational records.

The final business case should rely on repeated, comparable operational results rather than one modeled day.

What This Four-Depot Benchmark Does Not Prove

The comparison does not prove:

• A guaranteed 7.3%–14.5% reduction in operational vehicle use
• A permanent reduction in fleet size
• A guaranteed 11.6%–18.8% distance reduction
• A reduction in driver working time
• A direct reduction in total delivery cost
• Better customer satisfaction
• That the lowest-distance plan was the lowest-cost plan
• That all modeled routes were executed
• That Australian operations will achieve the same result

It demonstrates that different route and depot decisions can produce significantly different modeled fleet requirements.

That is enough to justify careful testing.

Compare Route Cost as a System

The one-day benchmark shows that multi-depot route planning can change vehicle and distance requirements before a single delivery begins.

It also shows why cost cannot be judged from one number.

Australian delivery teams need to compare distance, working time, vehicle requirements, depot allocation, mission coverage and customer feasibility as one connected operating system.

Rouptimize brings mission data, fleet constraints, branch context, route generation, dispatcher review and operational reporting into the same workflow.

Start testing multi-depot route scenarios with Rouptimize.