Every successful eaches picking operation relies on tracking the right metrics. Here are the critical KPIs warehouse managers need to monitor performance, identify bottlenecks, and drive improvements.
Productivity Metrics
1. Picks Per Hour (PPH)
Why it matters: Your primary labour cost metric. Higher PPH directly reduces cost per pick and improves profitability.
Calculation: Total items picked ÷ Total labour hours
Benchmarks:
- Manual picking: 80-120 PPH
- Pick-to-light: 150-200 PPH
- Voice picking: 120-160 PPH
- Robotic assistance: 200-300 PPH
2. Lines Per Hour
Why it matters: Better reflects order complexity than raw picks. One line equals one SKU on an order, regardless of quantity.
Calculation: Total order lines picked ÷ Total labour hours
Typical ranges:
- Experienced pickers: 60-85 lines per hour
- New workers: 35-50 lines per hour
3. Orders Per Hour
Why it matters: Shows how many customers you’re serving per labour hour. Most relevant metric for customer satisfaction and revenue generation.
Calculation: Total orders completed ÷ Total labour hours
Benchmarks by order size:
- Single-item orders: 40-60 per hour
- 2-5 items: 20-35 per hour
- 6+ items: 12-20 per hour
4. Travel Time Percentage
Why it matters: Non-productive time that directly impacts all other metrics. Typically represents 40-60% of picker time in poorly organised warehouses.
Calculation: (Travel time ÷ Total shift time) × 100
Targets:
- Well-optimised layout: 25-35%
- Poor layout: 50-65%
Quality Metrics
5. Pick Accuracy Rate
Why it matters: Errors cost £25-50 each to correct and damage customer relationships. Even 1% improvement saves significant money.
Calculation: (Total picks – Errors) ÷ Total picks × 100
Industry standards:
- Manual picking: 97-99%
- Pick-to-light: 99.5-99.8%
- Voice picking: 99.2-99.6%
6. Damage Rate
Why it matters: Product damage during picking creates write-offs and customer complaints. Higher rates indicate training or equipment issues.
Calculation: Damaged units ÷ Total units picked × 100
Typical rates:
- Acceptable: <0.5%
- Needs attention: >1%
7. Mispicks by Category
Why it matters: Identifies specific error patterns to target training and process improvements.
Track:
- Wrong quantity
- Wrong item
- Wrong location
- Missed items
Efficiency Metrics
8. Order Cycle Time
Why it matters: Measures end-to-end picking speed and identifies bottlenecks in your process flow.
Calculation: Time from pick list generation to order completion
Components to track:
- Pick time
- Travel time
- Verification time
- Queue time
9. Batch Completion Rate
Why it matters: Shows how effectively you’re utilising batch picking to improve productivity.
Calculation: Batches completed on time ÷ Total batches × 100
Target: >95% completion rate
10. Pick Path Adherence
Why it matters: Deviations from optimised paths increase travel time and reduce productivity.
Measurement: Compare actual pick sequence to system-recommended sequence
Target: >90% adherence
Cost Metrics
11. Cost Per Pick
Why it matters: The ultimate measure of picking efficiency. Combines productivity and labour costs into one actionable metric.
Calculation: Total labour costs ÷ Total picks
Typical ranges:
- Manual systems: £0.75-1.25 per pick
- Semi-automated: £0.45-0.75 per pick
- Highly automated: £0.25-0.45 per pick
12. Labour Cost as Percentage of Revenue
Why it matters: Shows whether picking productivity keeps pace with business growth and pricing.
Calculation: (Picking labour costs ÷ Revenue) × 100
Benchmarks:
- E-commerce: 8-15%
- B2B: 4-8%
13. Storage Cost Per Pick
Why it matters: Measures how efficiently your storage layout supports picking operations.
Calculation: Storage area costs ÷ Total picks
Factors affecting cost:
- Storage density
- Location accessibility
- Equipment utilisation
Utilisation Metrics
14. Picker Utilisation Rate
Why it matters: Shows how much of shift time is spent on productive picking vs. waiting, training, or other activities.
Calculation: Productive picking time ÷ Total shift time × 100
Targets:
- Standard operations: 75-85%
- Peak efficiency: 85-95%
15. Equipment Utilisation
Why it matters: Ensures picking equipment (carts, scanners, etc.) are being used effectively.
Track:
- Equipment downtime
- Usage hours per shift
- Equipment per picker ratio
16. Storage Location Utilisation
Why it matters: Identifies unused or underutilised picking locations that could be optimised.
Calculation: Active locations ÷ Total available locations × 100
Target: 80-90% utilisation
Technology and Automation Metrics
17. System Response Time
Why it matters: Slow WMS or picking system response times reduce productivity and frustrate workers.
Measurement: Average time for system to respond to picker inputs
Target: <2 seconds for most transactions
18. Scanner First-Pass Read Rate
Why it matters: Poor scan rates slow down picking and increase errors.
Calculation: Successful first scans ÷ Total scan attempts × 100
Target: >95% first-pass read rate
19. Pick-to-Light Hit Rate
Why it matters: Measures how effectively technology is guiding pickers to correct locations.
Calculation: Correct picks on first attempt ÷ Total picks × 100
Target: >98% hit rate
Seasonal and Capacity Metrics
20. Peak Season Performance Retention
Why it matters: Shows how well your operation scales during high-volume periods.
Calculation: (Peak season PPH ÷ Normal PPH) × 100
Good performance: 80-90% retention Excellent performance: >90% retention
21. Training Ramp-Up Time
Why it matters: Measures how quickly new pickers reach productivity standards. Critical during seasonal hiring.
Measurement: Days to reach 80% of standard productivity
Typical ranges:
- Simple operations: 3-7 days
- Complex operations: 10-14 days
22. Temporary Worker Performance Gap
Why it matters: Quantifies the productivity difference between permanent and temporary staff.
Calculation: (Permanent staff PPH – Temporary staff PPH) ÷ Permanent staff PPH × 100
Typical gap: 20-40% lower for temporary staff
Setting Up Your KPI Dashboard
Essential Daily Metrics
- Picks per hour
- Pick accuracy rate
- Orders completed
- Cost per pick
Weekly Review Metrics
- Travel time percentage
- Training progress
- Equipment utilisation
- Storage location utilisation
Monthly Strategic Metrics
- Labour cost as percentage of revenue
- Seasonal performance trends
- Technology ROI
- Process improvement impact
Key Implementation Tips
Start Small: Begin with 5-7 core metrics rather than trying to track everything at once.
Benchmark First: Establish current performance levels before making changes.
Set Realistic Targets: Aim for 10-15% improvement initially, not dramatic overnight changes.
Focus on Trends: Look for patterns over time rather than daily fluctuations.
Connect to Storage Solutions: Many KPI improvements come from better storage organisation and equipment. PIX storage systems are specifically designed to improve picking productivity and accuracy.
Taking Action on Your KPIs
The best KPIs drive action, not just measurement. Use these metrics to:
- Identify training needs through individual picker performance
- Optimise storage layouts based on travel time and productivity data
- Justify equipment investments with clear ROI calculations
- Plan capacity for seasonal and growth demands
Ready to improve your eaches picking performance? Calculate your current metrics and discover how optimised storage solutions can drive measurable improvements in your operation.
