Picking

Warehouse management systems (WMS) orchestrate all picking activities, generating pick tasks, optimizing paths, directing workers, and tracking progress. Modern WMS platforms use labor management modules to measure productivity and balance workload, slotting optimization to position fast-moving items optimally, and wave planning algorithms to maximize throughput. Mobile devices with barcode scanning provide real-time task direction and confirmation, while voice picking systems enable hands-free operation through headset commands and verbal confirmations.

Pick-to-light and put-to-light systems use illuminated displays at storage locations or sorting positions to guide workers, eliminating paper pick lists and reducing errors. Pick-to-light shows workers which locations to pick from and quantities needed, while put-to-light indicates where to place items during sorting or packing. These systems improve productivity by 30-50% while achieving 99.9%+ accuracy through visual confirmation. Alphanumeric displays show quantities, while button confirmation verifies completion before advancing to the next task.

Goods-to-person (G2P) systems bring products to stationary workers rather than having workers travel to products, eliminating 60-70% of labor time spent walking. Automated storage and retrieval systems (AS/RS) deliver totes or trays to pick stations, shuttle systems retrieve bins from high-density storage, and autonomous mobile robots (AMRs) transport shelving units to workers. These systems improve productivity by 2-4x while reducing worker fatigue and improving accuracy through ergonomic workstations with integrated verification.

Robotic picking systems automate product retrieval using vision systems and grippers to handle items. Robotic piece picking uses articulated arms with specialized end-effectors to grasp individual items from bins or shelves, handling 300-600 picks per hour for suitable products. Robotic case picking handles full cases or cartons, achieving 800-1,200 picks per hour. Collaborative robots (cobots) work alongside human pickers, handling repetitive or heavy items while humans manage exceptions. Current technology handles 60-80% of typical SKU mixes, with humans managing difficult items.

Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) transport products or workers through the facility, eliminating manual cart pushing or forklift travel. Cart-pulling AGVs follow fixed paths to deliver picked items to packing areas, while AMRs navigate dynamically around obstacles using sensors and mapping. Collaborative mobile robots follow workers during picking, carrying totes or cartons and eliminating bending and lifting. These systems improve productivity by 20-40% while reducing worker fatigue and injury risk.

Voice picking systems provide hands-free operation through headset-based commands and verbal confirmations, improving productivity by 10-25% while maintaining 99.5%+ accuracy. Workers receive spoken instructions for locations and quantities, verbally confirm picks using check digits, and keep hands free for product handling. Multi-modal systems combine voice with scanning or vision for enhanced verification. Voice-directed work extends beyond picking to putaway, replenishment, and loading operations.

Sortation systems automate the consolidation of picked items to orders, particularly valuable for batch or zone picking operations. Tilt-tray sorters, cross-belt sorters, and sliding-shoe sorters divert items to order lanes or packing stations at rates of 5,000-15,000 items per hour. Put-to-light walls provide manual sortation with visual guidance, handling 200-400 items per hour per worker. Robotic sortation uses mobile robots to transport items to designated positions, providing flexible capacity that scales with volume.

Pick accuracy measures the percentage of picks completed correctly (right product, right quantity), with targets of 99.5%+ for most operations and 99.9%+ for automated systems. Line accuracy (percentage of order lines picked correctly) and order accuracy (percentage of complete orders picked correctly) provide different perspectives, as a single error affects line accuracy minimally but may make an entire order inaccurate. Root cause analysis identifies whether errors occur from wrong locations, wrong products, or wrong quantities, enabling targeted improvements.

Picks per hour measures productivity, varying dramatically by picking method and product characteristics. Discrete picking typically achieves 40-80 picks per hour, batch picking 80-150 picks per hour, zone picking 100-200 picks per hour, and goods-to-person systems 200-400 picks per hour. Automation can improve productivity by 2-4x, while process improvements (better slotting, optimized paths, training) deliver 20-40% gains. Tracking productivity by method, zone, and worker identifies improvement opportunities.

Pick travel distance measures how far workers walk per pick or per hour, with targets typically under 50 feet per pick for optimized operations. Slotting optimization placing fast-moving items near packing areas, path optimization calculating efficient routes, and zone picking limiting travel areas all reduce distance. Goods-to-person systems eliminate travel entirely, while collaborative robots reduce walking by carrying items alongside workers.

Order cycle time measures elapsed time from order release to pick completion, with targets varying by order profile and service requirements. Same-day orders may require completion within 2-4 hours, while standard orders allow 12-24 hours. Wave planning balances cycle time against efficiency, as larger waves improve productivity but increase cycle time. Real-time monitoring identifies bottlenecks or delays requiring intervention.

Pick labor cost per line combines direct labor (pickers), indirect labor (supervision, training), and equipment costs (forklifts, mobile devices, automation) divided by lines picked. This metric enables benchmarking against industry standards and comparison of different picking methods. Cost per order provides an alternative view for operations with varying lines per order. Automation ROI calculations compare current costs against projected costs with automation, considering capital investment and ongoing operating costs.

By implementing effective picking processes supported by appropriate automation technologies, warehouses maximize productivity while maintaining accuracy, creating efficient fulfillment operations that meet customer expectations at minimal cost.