Cutting Picking Errors in Peak Season: Fixing Human Error in Pick/Pack/Ship and Shortening the Pick Path
During campaigns, orders surge, new staff arrive and everyone rushes, so wrong-item picks climb. This guide brings together research-backed fixes: three checkpoints across pick, pack and ship, shorter pick paths, smarter slotting, a four-week peak readiness plan and the metrics to watch.
Short answer: Picking errors rise during peak sales because order volume jumps, many staff are new, and everyone is rushing. What actually works is not telling people to "be more careful" but designing the work so mistakes are hard to make: scan the location and every item when picking, scan against the order again when packing, count parcels onto the truck, slot items so they are easy to pick, keep look-alike products apart, and let the system sequence the pick path so it is as short as possible. Research estimates order picking can account for as much as 55% of warehouse operating cost, and walking is usually the biggest share of a picker's time, so cutting both errors and travel pays off most when the warehouse is at full stretch.
- 55%
- Order picking's share of warehouse operating cost (upper estimate)
- 6.86%
- Error rate of new pickers using paper lists (2025 experiment)
- 1.18%
- Error rate with a device pointing to the pick location (same study)
- 3
- Checkpoints to have: pick, pack, ship
Why do picking errors rise during peak sales?
Anyone who has worked a warehouse during 11.11, 12.12 or the year-end rush knows the scene. Orders arrive at several times the normal rate, temporary staff are brought in, everyone races to make the next truck, and stock that is usually tidy starts ending up in the wrong place. Most errors don't come from careless people; they come from working conditions that make mistakes easy.
New staff don't know the warehouse
Temporary workers haven't learned locations or what products look like. Reading a paper pick list and searching on their own, they make far more mistakes than experienced staff.
Rushing and fatigue
Long shifts and a constant stream of orders wear down concentration. Errors tend to climb toward the end of a shift.
Look-alike products
Slightly different colors, size S next to size M, or old and new versions side by side are the easiest way to pick the wrong item.
Stock in the wrong location
Rushed replenishment puts items somewhere the system doesn't expect, so the next picker can't find them or grabs the item next door.
Promotion bundles
Buy-two-get-one deals and gift sets add lines to each order, making it easy to forget the free item or pack the wrong set.
Long walks and backtracking
When the pick list order doesn't match the walking route, pickers double back, losing time and getting more tired.
A literature review by Grosse, Glock and Neumann (2017) found that human characteristics such as fatigue, learning and perception are a major driver of picking performance, yet picking system design has paid them little attention (International Journal of Production Research). Designing the work around the people who actually do it leaves a lot of room for improvement.
A Japanese approach: don't blame people, make errors hard to make (poka-yoke)
Poka-yoke (ポカヨケ) is a Japanese term meaning "error-proofing". The Lean Enterprise Institute describes it as simple, inexpensive devices that stop operators making mistakes, in two forms: shutdown devices that won't let the next step happen until things are right, and warning devices that signal with a sound or light (Lean Enterprise Institute).
In a warehouse, a good WMS works as poka-yoke: scan the wrong item and the system won't let you continue; reach the right quantity and the scanner tells you at once. The idea is a cornerstone of Japanese manufacturing, and it is the same thinking C.NET (Thailand), part of the Japanese C.NET group, brings when designing warehouse processes with customers.
Three checkpoints: what to verify at pick, pack and ship
Pick: scan the location, then the item
Have pickers scan the location label first so the system knows they're at the right slot, then scan every item and confirm the quantity. Mistakes are flagged on the spot, not discovered at packing.
Pack: scan against the order again
Before closing the carton, scan every item against the order. If anything is missing, extra or wrong, the system won't print the shipping label. This catches forgotten free items and wrong bundles.
Ship: scan parcels onto the truck
Scan every carton at handover, match the count to the pickup, and close the shipment in the system, so parcels don't get left behind or loaded onto the wrong truck.
| Common problem | Checkpoint | How to stop it recurring |
|---|---|---|
| Wrong color or size | Pick | Scan every item's barcode and never slot look-alike products side by side |
| Short picks | Pick | Let the system count from scans instead of people counting |
| Picked from the next slot | Pick | Scan the location label first, and make labels easy to read |
| Free item missing or wrong bundle | Pack | Scan against the order before sealing, or pre-pack promotion bundles |
| Swapped shipping labels | Pack | Print labels only after a complete scan, one order per bench at a time |
| Parcels left behind or on the wrong truck | Ship | Scan at handover and close each pickup in the system |
How much do picking aids reduce errors? What the research says
A study published in Sensors in 2025 tested people new to order picking, a close match for peak-season temporary staff. With paper pick lists they made errors on 6.86% of picks, with pick-by-light 2.31%, and with pick-by-point, where a device points to the exact slot, just 1.18% (Sensors 2025).
Weaver and colleagues (CHI 2010) found the same direction: participants using a head-mounted display averaged 0.104 errors per task, versus 0.448 with a paper list (CHI 2010). Both are small experiments, and results in your warehouse will depend on products and layout, but the message is clear: the more precisely the system guides the picker, the fewer mistakes they make, especially new staff.
What is a pick path, and why does a shorter one mean fewer errors?
A pick path is the route and sequence a picker follows to visit each location in a pick round. A literature review by de Koster and colleagues (2007) estimates that order picking can cost as much as 55% of total warehouse operating expense, that travel is often the dominant part of a picker's time, and that in manual picking travel time grows with travel distance (European Journal of Operational Research).
A shorter path isn't only faster. It also means less fatigue and less confusion from doubling back, both of which drive mis-picks late in a shift.
| Routing strategy | How the picker walks | Best for |
|---|---|---|
| S-shape (traversal) | Walk the full length of every aisle with picks, then move to the next | Aisles with many picks; easy to learn and teach |
| Return | Walk to the farthest pick and come back out the same end | Picks near the aisle entrance, or dead-end aisles |
| Midpoint | Pick the front half from the front and the back half from the back | Layouts with cross aisles at front and back |
| Largest gap | Enter from front or back, never walking the largest empty stretch | Aisles with few picks where changing aisles is quick |
| Combined | Decide aisle by aisle whether to go through or turn back | Used with a system that calculates it |
| Optimal | Calculate the shortest route from actual locations | The system sequences the work; pickers just follow it |
Researchers at Erasmus University describe S-shape as "very simple to use and to understand", largest gap as especially useful when aisles have few picks and changing aisles is quick, and note that truly optimal routes usually look like a mix of the two (ERIM). In practice, pickers shouldn't have to work out the route themselves; the system should sequence each pick list into a single pass.
Slot stock for easy picking before peak
- Move items expected to sell well in the campaign close to packing, between waist and shoulder height
- Separate look-alike products, such as different colors or sizes, so they are never in adjacent slots
- Make location labels large, easy to read and scannable in every slot
- Replenish pick faces before each shift so pickers never wait or pick from elsewhere
- Pre-pack promotion bundles and give each bundle its own single code
- Count key locations before the campaign so system stock matches what's on the shelf
A four-week plan to get the warehouse ready for peak
Four weeks out: review data and reslot
Look at last year's campaign sales and this year's promotion items. Move fast movers near packing and separate look-alike items.
Three weeks out: labels and people
Check every location label, plan headcount, and train new staff on real tasks in the warehouse rather than in a classroom.
Two weeks out: full rehearsal
Release a large batch of mock orders to find the bottlenecks. Check scanners, batteries, label printers and warehouse Wi-Fi.
One week out: count and pre-pack
Count key locations, pre-pack promotion bundles and confirm pickup times with carriers.
During peak: check numbers every hour
Track backlog, mis-pick rate and truck departures. Where errors cluster, fix it straight away, for example by moving a confusing item or adding staff at packing.
After peak: capture the lessons
Collect the causes of mis-picks and returns by item and location, and fix them before the next campaign.
How do you know errors are really going down?
| Metric | How to calculate | What it tells you |
|---|---|---|
| Order accuracy | Orders shipped complete and correct ÷ all orders × 100 | What the customer actually experiences |
| Mis-pick rate | Lines picked wrong ÷ all lines picked × 100 | Which items or locations cause problems |
| Errors caught at packing | Errors found at the pack bench per day | Where the pick step is leaking |
| Returns due to wrong shipments | Returns caused by wrong item or quantity | Damage that has already reached customers |
| Lines picked per hour | Lines picked ÷ hours worked | The effect of slotting and routing |
Start recording these before you change anything, then compare the same season before and after. That shows the real effect far more clearly than comparing with an ordinary month.
Where does a WMS help reduce human error?
Scans instead of eyesight
The system checks location, product code and quantity every time, so people don't have to remember what each item looks like.
Pick lists in walking order
The system groups and sequences locations into a single pass, cutting backtracking.
Storage location suggestions
Real movement data helps decide which items belong where.
Real-time stock by location
Pickers aren't sent to empty slots, and pick work isn't released against locations with no stock.
Faster onboarding
The screen shows step by step where to go, what to pick and how many, so temporary staff are productive sooner.
Data to look back on
You can see which checkpoint, item and picker an error came from, and fix the root cause.
Tools help a lot, but results depend on designing the process for each warehouse. The C.NET (Thailand) team, part of the C.NET group that develops warehouse management systems in Japan, helps review your site, design pick, pack and ship processes, and choose tools that fit your volume, from barcode scanners to voice picking. For small and mid-sized businesses see our SME WMS; for larger warehouses see WMS Standard.
FAQ: cutting picking errors during peak sales
Why do picking errors increase during peak sales?
Order volume multiplies, new staff don't yet know the warehouse, everyone is rushed and tired, promotions make orders more complex, and stock often ends up in the wrong place during hurried replenishment.
What is the most effective way to reduce mis-picks?
Let the system check instead of relying on eyesight: scan the location and every item when picking, scan against the order again when packing, and keep look-alike products out of adjacent slots.
What is a pick path?
A pick path is the route and sequence a picker follows to visit each location in a pick round. Common strategies are S-shape, return, midpoint, largest gap, combined, and optimal routes calculated by a system.
Which pick path strategy is best?
No single strategy is best for every warehouse. S-shape is easy to learn and suits aisles with many picks, largest gap suits aisles with few picks, and routes calculated by a system from actual locations are usually shortest without pickers having to think about it.
How much difference is there between paper lists and guided picking?
A 2025 study in Sensors found that new pickers using paper lists made errors on 6.86% of picks, versus 1.18% to 2.31% with devices that indicate the location. Your results will vary, but guided picking clearly cuts errors, especially for new staff.
How far ahead should we prepare for peak?
At least four weeks, to reslot items, label locations, train new staff, rehearse with mock orders, count key locations and pre-pack promotion bundles.
Which metrics should we track?
Order accuracy, mis-pick rate, errors caught at packing, returns caused by wrong shipments, and lines picked per hour, comparing the same season before and after improvements.
How can we get an expert to look at our warehouse?
Contact the C.NET (Thailand) team through the website's contact page. We will ask about your order volume, products and the problems you face, then recommend ways to cut errors and suitable tools.
Sources
- de Koster, Le-Duc & Roodbergen (2007), Design and control of warehouse order picking: A literature review, European Journal of Operational Research 182(2)
- Erasmus University (ERIM): Routing strategies for order pickers
- Łopuszyński, Janusz & Karwat (2025), Comparative Study of Selected Order-Picking Methods, Sensors 25(3), 923
- Weaver et al. (2010), An empirical task analysis of warehouse order picking using head-mounted displays, CHI 2010
- Grosse, Glock & Neumann (2017), Human factors in order picking: a content analysis of the literature, International Journal of Production Research 55(5)
- Lean Enterprise Institute: Poka-yoke
Updated November 2026 by the C.NET (Thailand) team · Research figures are from the sources listed; results in a real warehouse depend on products and layout · Illustrations are conceptual.
