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Reverse Logistics Capacity Estimator

The Reverse Logistics Capacity Estimator calculates how many returned units a reverse-logistics operation can process over a week based on workstation count, processing rate, scheduled hours, operating days, and expected uptime. It is designed for returns centers, refurbishment operations, repair depots, and ecommerce teams planning inspection, triage, grading, or disposition capacity.

The estimator converts station-level throughput into a weekly practical capacity rather than assuming every scheduled minute is productive. Applying an uptime factor accounts for breaks, changeovers, minor stoppages, system downtime, and other losses that reduce available processing time. The result is useful for comparing expected return volume with current capability, evaluating staffing or workstation additions, and testing different shift patterns. It assumes the entered processing rate is sustainable across the workload mix; highly variable inspection or repair times may require separate capacity models by return type.

Inputs

stations
units/hr
hr/day
days
%
Result
—
Estimated weekly reverse-logistics capacity
Theoretical weekly capacity—
Effective productive hours—
Capacity lost to downtime—

1. Enter active processing stations
Count stations that can work on returns in parallel.

2. Set the processing rate
Use the sustainable average units processed per station per hour.

3. Enter the daily schedule
Provide scheduled operating hours per day and active days per week.

4. Apply expected uptime
Reduce theoretical capacity for breaks, downtime, and other productivity losses.

5. Review weekly capacity
The main result shows practical weekly units, while the breakdown shows theoretical capacity and lost capacity.

Theoretical weekly capacity = Stations × Units per station per hour × Hours per day × Days per weekEffective weekly capacity = Theoretical weekly capacity × Uptime % ÷ 100Effective productive hours = Stations × Hours per day × Days per week × Uptime % ÷ 100Capacity lost to downtime = Theoretical weekly capacity − Effective weekly capacity

The processing rate should represent an average sustainable rate for the workload being planned.

The model assumes stations operate independently and that inbound supply, staffing, systems, and downstream disposition do not become separate bottlenecks.

What the result means

The result estimates how many returned units the operation can process in a typical week under the entered schedule and uptime assumptions.

If return types have very different processing times, estimate capacity by work type and combine the results with an appropriate workload mix.

Given:
6 stations, 11 units per station-hour, 8 hours per day, 5 days per week, and 85% uptime.

Calculation:
Theoretical capacity = 6 × 11 × 8 × 5 = 2,640 units/week. Effective capacity = 2,640 × 0.85 = 2,244 units/week. Effective productive hours = 6 × 8 × 5 × 0.85 = 204 station-hours. Capacity lost = 396 units/week.

Result:
Estimated weekly reverse-logistics capacity = 2,244 units.

Interpretation:
At the stated uptime, the operation gives up capacity equivalent to 396 units per week versus the theoretical schedule.

What should the processing rate include?

Use an average rate that reflects the actual mix of inspection, grading, data entry, repackaging, or disposition tasks performed at a station. A best-case rate will overstate practical capacity.

Why include uptime if scheduled hours are already entered?

Scheduled hours describe when the operation is open. Uptime reduces those hours for breaks, stoppages, system issues, changeovers, and other losses that prevent productive processing.

Can I use this for a daily capacity estimate?

Yes, but the displayed model is weekly. Set operating days to 1 to model one active day, or divide the weekly result by the number of operating days for an average daily figure.

What if returns require very different processing times?

Create separate scenarios for major return classes or use a weighted average rate based on the expected mix. A single average can hide bottlenecks when one class takes much longer than another.

How can I estimate whether more stations are needed?

Compare expected weekly return volume with the effective capacity result. If demand consistently exceeds capacity, test additional stations, higher uptime, longer schedules, or process-rate improvements.