Warehouse Order Picking Productivity: How to Cut Travel Time and Pick Faster
Order picking accounts for over 50% of warehouse labor costs—and up to 60% of that time is spent walking. If you want to move the needle on warehouse productivity, picking is where you start.
Yet many operations approach picking optimization backwards. They invest in technology before fixing process fundamentals, or they focus on pick rate without measuring what actually drives it. The result: expensive systems that deliver marginal gains.
Here's how to approach order picking productivity the right way—starting with measurement, then moving to strategies that compound over time.
Why Travel Time Is the Real Enemy
The math on picking productivity is straightforward:
Total pick time = Travel time + Search time + Extract time + Documentation time
In most warehouses, travel accounts for 50-65% of total pick time. Search time adds another 15-20%. The actual physical extraction—grabbing the item—is usually less than 10%.
This means the fastest way to improve picks per hour isn't making pickers move faster. It's making them move less.
A picker covering 8 miles per shift at a normal walking pace of 3 mph spends 2.7 hours just walking. Cut that distance by 30% and you've freed up 48 minutes per picker per shift—without changing anything about the picking itself.
Measure Before You Optimize
Before implementing changes, establish baselines for these metrics:
Picks per hour (PPH): Total picks divided by labor hours. This is your headline number, but it hides important details.
Lines per hour (LPH): Order lines completed per hour. More meaningful for operations with variable units per line.
Travel distance per pick: Total distance covered divided by picks completed. This reveals your true optimization opportunity.
Pick accuracy rate: Correct picks divided by total picks. Productivity means nothing if you're shipping errors. If you're experiencing accuracy issues related to measurement, a parcel dimensioning solution can help verify items at the pick station.
Touches per order: How many times is each order handled between pick and ship? Multiple touches indicate consolidation or sortation problems.
Track these daily for at least two weeks before making changes. You need to understand your current state—including variance by shift, by zone, and by picker.
The Slotting Foundation
Slotting—where you place products within the warehouse—determines the upper bound of your picking productivity. Poor slotting means even perfect pick paths can't save you.
Velocity-based slotting: Your fastest-moving SKUs should be in the most accessible locations. This seems obvious, but many warehouses slot by product category or by when items arrived. Analyze your actual pick data: the top 20% of SKUs typically generate 80% of picks.
Ergonomic placement: High-velocity items belong at waist-to-shoulder height. Every time a picker bends or reaches overhead, you lose seconds. For a picker making 150 picks per hour, saving 2 seconds per pick adds 5 hours of productivity per shift.
Pick density zones: Group frequently co-picked items together. If orders often contain items A, B, and C, those items should be in the same zone—even if they're different product categories. Your order data tells this story; use it.
Cube utilization: Don't waste prime pick locations on bulky, slow-moving items. Your golden zone (waist height, near the pick start point) should hold the items that get picked most often—regardless of their physical size. Many fulfillment centers overlook this and end up with oversized packaging in prime real estate.
Re-slotting is disruptive, so do it strategically. Start with your top 100 SKUs by pick frequency and work outward.
Pick Path Optimization
Once items are slotted correctly, the pick path determines how efficiently pickers move through the warehouse.
Serpentine (S-shaped) routing: Pickers travel down one aisle and up the next, never backtracking. Simple to implement and easy for pickers to follow. Works well for low-to-medium pick density.
Return routing: Pickers enter and exit each aisle from the same end, only going as deep as needed. Better for sparse picks where items are concentrated near aisle entrances.
Combined routing: The algorithm chooses serpentine or return for each aisle based on pick locations. Requires a WMS but delivers 15-30% travel reduction over pure serpentine.
Zone picking with consolidation: Pickers stay in assigned zones; orders are consolidated downstream. Reduces travel to near-zero within zones but adds sortation complexity. The dimensional data you capture for carrier compliance can also help optimize your consolidation staging.
The right approach depends on your order profile. High picks per order favor serpentine or combined. Low picks per order favor zone picking. Most WMS platforms can model different strategies using your historical data.
Batch and Wave Strategy
How you release work to the floor has as much impact as how pickers move.
Batch picking: Multiple orders picked simultaneously. A picker collects all units of a SKU across several orders in one trip, then sorts them at a consolidation station. Dramatically reduces travel for operations with order overlap.
The optimal batch size depends on order similarity and sorter capacity. Start with batches of 10-15 orders and adjust based on pick density and sorter throughput.
Wave planning: Orders are grouped into waves released at set intervals. Each wave is designed to balance workload across zones and ensure downstream processes (packing, shipping) aren't overwhelmed.
Good wave planning considers:
- Carrier pickup times (work backwards from ship deadlines)
- Zone workload balance (avoid starving or flooding any zone)
- Sortation capacity (don't release more than your sorter can handle)
- Labor availability (match wave size to staffed hours)
Waveless (continuous) picking: Orders are released continuously based on priority and workload. More complex to manage but enables faster response to urgent orders. Requires sophisticated WMS logic and real-time visibility.
Technology That Actually Helps
Not all picking technology delivers equal ROI. Focus on these high-impact tools:
Pick-to-light: Lights at pick locations guide pickers to the correct bin. Eliminates search time and reduces errors. ROI is clear for high-velocity pick faces with small items.
Voice picking: Audio instructions free both hands for picking. Particularly valuable for case picking and environments where pickers wear gloves. Studies show 10-25% productivity gains over paper or RF.
RF scanning: Confirms picks at the bin level. Table stakes for accuracy but doesn't directly improve productivity. The value is in error prevention.
Mobile dimensioning: For operations where picked items need measurement verification, mobile dimensioning at the pick station catches errors before they propagate. This matters especially for 3PL operations with strict SLAs.
Goods-to-person automation: Robots or conveyors bring products to stationary pickers. Eliminates travel entirely but requires significant capital and facility changes. Right for high-volume operations with suitable product profiles.
The common mistake is implementing technology before optimizing process. A poorly slotted warehouse with voice picking is still a poorly slotted warehouse. Fix the fundamentals first.
Labor and Training
Picking productivity ultimately comes down to people. Two areas matter most:
Onboarding time: How long before a new picker reaches average productivity? If it takes 4 weeks, you're losing substantial labor value every time someone leaves or joins. Reduce this through better training materials, buddy systems, and simplified pick processes.
Performance visibility: Pickers should know their productivity in real-time. Gameification works for some cultures; others respond better to team-based goals. The universal principle is feedback—people improve what they can see.
Incentive design: Pay-for-productivity programs can boost output by 15-25%, but design them carefully. Incentivizing pure speed creates accuracy problems. Tie bonuses to picks per hour and accuracy rate.
Building a Continuous Improvement Loop
One-time optimization projects decay. Slotting drifts as SKU velocity changes. Pick paths that worked last quarter don't fit this quarter's order mix. Labor performance regresses without reinforcement.
Build a rhythm of continuous improvement:
Weekly: Review pick productivity by zone and shift. Identify outliers—both high and low performers—and understand why.
Monthly: Re-analyze SKU velocity and compare to current slotting. Flag items that have moved out of the correct velocity tier.
Quarterly: Model alternative pick strategies using recent order data. Test changes in a single zone before rolling out.
Annually: Evaluate technology investments against actual productivity gains. Kill underperforming systems rather than letting them limp along.
Start With What You Can Measure
Order picking productivity isn't a single project—it's an ongoing operational discipline. The operations that achieve 200+ picks per hour do so through relentless attention to detail: measuring everything, fixing the fundamentals, and continuously adjusting as conditions change.
If you're looking to baseline your current performance, start by tracking travel distance and pick accuracy alongside your picks per hour. These three metrics reveal where the opportunities are.
For operations where dimensional accuracy impacts both picking efficiency and shipping costs, Sizelabs' AI-powered dimensioning integrates directly with pick stations—validating items in real-time and eliminating downstream errors before they reach the dock.