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Delivery Robot Task Capacity Estimator

The Delivery Robot Task Capacity Estimator calculates how many completed delivery tasks a robot fleet can reasonably handle in a day from fleet size, operating time, average task duration, and utilization. It is useful for sidewalk delivery, campus logistics, hospitality delivery, and other operations where each robot repeats a similar pickup-to-drop-off cycle.

The estimate turns operating assumptions into an easy daily capacity figure. Teams can use it to compare demand with available fleet capacity, test the effect of shorter task times, or determine whether higher utilization could absorb more orders before adding robots.

Inputs

robots
hours
min
%
Result
—
Estimated delivery tasks per day
Tasks per robot/day—
Theoretical capacity—
Effective active minutes—

1. Enter fleet size
Use the number of delivery robots expected to be available during the operating day.

2. Set operating hours
Enter the scheduled hours each robot can be dispatched, excluding a full-day shutdown.

3. Enter average task time
Use the average minutes from task assignment through completion, including typical travel and handoff time.

4. Set utilization
Enter the share of scheduled time expected to be productive after charging, waiting, repositioning, and other downtime.

5. Review capacity
Compare the estimated daily tasks with expected demand and inspect the per-robot figure for staffing or fleet planning.

Tasks/day = Robots × (Operating hours × 60 ÷ Average task minutes) × Utilization rate

Robots — number of robots available.

Operating hours — scheduled dispatchable hours per robot per day.

Average task minutes — mean elapsed minutes needed for one completed delivery task.

Utilization rate — productive share of scheduled time, entered as a percentage and converted to a decimal.

Assumptions: Tasks are treated as sequential, similarly sized cycles. The model does not simulate queueing, geographic clustering, charging schedules, or simultaneous multi-order routes.

What the result means

At these assumptions, the fleet can support roughly 225 daily tasks; sustained demand above that level suggests a need for faster cycles, higher productive time, or more robots.

Use the result as a planning estimate and validate assumptions with observed operating data before making deployment decisions.

Given
• 12 robots
• 10 operating hours per day
• 24 minutes per task
• 75% utilization

Calculation
Theoretical capacity = 12 × (10 × 60 ÷ 24) = 300 tasks/day
Effective capacity = 300 × 0.75 = 225 tasks/day
Tasks per robot = 225 ÷ 12 = 18.75 tasks/day

Result
About 225 completed delivery tasks per day.

Interpretation
At these assumptions, the fleet can support roughly 225 daily tasks; sustained demand above that level suggests a need for faster cycles, higher productive time, or more robots.

Does utilization include charging time?

Yes. Use utilization as the share of scheduled operating time that remains productive after charging, waiting, repositioning, maintenance, and similar downtime.

Should I use average or worst-case task time?

Use a representative average for routine capacity planning. For a conservative scenario, run a second case with a longer task time based on busy periods or difficult routes.

Can one robot carry multiple orders at once?

This model counts completed task cycles, not individual packages. If one cycle regularly completes several deliveries, convert the result using your average deliveries per cycle.

Why can the estimate be higher than actual throughput?

Traffic, building access, handoff delays, battery constraints, and uneven demand can reduce realized output. Set utilization to reflect those recurring losses.

How is this different from fleet sizing?

Task capacity starts with a known fleet and estimates output. Fleet sizing starts with required demand and estimates how many robots are needed.