How Many Orders Should Your Team Pick Per Hour?
Guest blog by MHI member Rebus
See How You Compare to Industry Benchmarks
In warehouse operations, labor productivity is not just a metric. It directly affects cost, throughput, and customer service. Yet many leaders cannot confidently answer a basic question: how many orders does our team pick per hour, and how does that compare to top performers?
Without a clear benchmark, it is easy to overestimate performance or overlook inefficiencies that quietly erode margins.
Why Labor Productivity Deserves Attention
Labor often represents more than half of a warehouse’s operating budget. Every unnecessary step, poorly slotted SKU, or unbalanced workload increases cost per order.
At the same time, customer expectations continue to rise. Faster fulfillment and near-perfect accuracy are no longer differentiators. They are baseline requirements. Orders picked and shipped per person per hour has become one of the clearest indicators of operational health.
When productivity improves, costs per unit decline, throughput increases, and service levels strengthen. When it slips, expenses climb and delivery performance suffers.
The Benchmark Gap
Industry benchmarking data highlights a significant spread between top performers and the rest of the field when measuring orders picked and shipped per person per hour:
• Best-in-class: 35 or more orders per hour
• Median: 10 orders per hour
• Typical range: approximately 6 to 12 orders per hour
The gap is striking. Best-in-class operations move more than three times the orders of the median facility.
It is important to note that no single benchmark fits every operation. Product size, order complexity, facility layout, and fulfillment model all influence achievable rates. A high-volume e-commerce operation with small, fast-moving SKUs will look very different from a facility handling oversized or regulated goods.
Still, benchmarks provide a valuable reference point. They help leaders understand whether their current performance reflects operational constraints or untapped opportunity.
Why Similar Warehouses Perform So Differently
When two facilities of comparable size and order volume produce dramatically different productivity rates, the difference usually lies in execution.
Several factors consistently influence pick rates:
- Warehouse layout – Travel time is one of the largest hidden costs in picking. Poor slotting, congestion, or inefficient storage strategies reduce output per hour.
- Technology utilization – Tools such as warehouse management systems, RF scanning, voice-directed picking, and pick-to-light can streamline task execution and reduce errors when implemented effectively.
- Workforce training and discipline – Well-trained associates understand process expectations, move with confidence, and make fewer mistakes. Clear standards and consistent coaching reinforce performance.
- Order profile complexity – High variability in order size or product characteristics can reduce average pick rates. Grouping similar orders or refining batching strategies often improves consistency.
- Performance visibility – Monitoring key performance indicators daily rather than monthly allows managers to correct small declines before they become systemic issues.
Rarely is productivity determined by a single element. It is the interaction of layout, process, technology, and management discipline that drives results.
The Hidden Cost of “Average”
Operating in the middle of the pack may not seem alarming. However, even modest productivity gaps can translate into significant cost differences.
If one team averages 10 orders per hour and another averages 20 under similar conditions, the lower-performing operation is effectively paying double in labor per order. Over time, that difference compounds.
Lower productivity also affects customer experience. Slower internal processing can extend order cycle time, increase the risk of backlogs during peak periods, and reduce flexibility when demand spikes.
Practical Ways to Improve Pick Rates
Improving productivity does not always require major capital investment. Many gains come from disciplined evaluation and targeted changes.
• Revisit slotting strategy. Position high-velocity SKUs to minimize travel distance and reduce congestion.
• Analyze pick paths. Eliminate unnecessary steps and refine routing logic where possible.
• Standardize work. Clear, documented processes reduce variability between associates and shifts.
• Cross-train employees. A flexible workforce can shift resources to relieve pressure points during peak demand.
• Review performance regularly. Short feedback cycles help managers identify trends and reinforce expectations.
The most effective operations treat productivity as an ongoing management focus rather than a periodic initiative.
Context Matters
While 35 orders per hour may serve as a useful aspiration for some operations, context should guide goal setting.
Facilities handling fragile, hazardous, or highly customized products may prioritize accuracy and compliance over raw speed. In those environments, reducing errors and rework may generate more value than pushing pick rates higher.
The key is consistency in measurement. Define how orders per hour is calculated, apply that definition uniformly, and compare performance against relevant peers or internal targets.
A Continuous Benchmarking Mindset
Productivity is not static. Seasonal volume shifts, workforce changes, new product introductions, and technology upgrades all influence results.
Tracking orders per hour alongside related metrics such as inventory accuracy, order cycle time, and cost per order provides a more complete picture of operational performance. Together, these indicators help leaders identify where process improvements will have the greatest impact.
Ultimately, knowing your current pick rate is the first step. Understanding how it compares to credible benchmarks is the second. From there, consistent measurement and focused process improvement can close the gap between average and high-performing operations.
