When orders are backing up and dispatch is falling behind, the obvious answer is more staff. More pickers, more packers, more people in the warehouse.
Sometimes that's right. Often, it's not.
The fastest warehouses aren't the ones with the most people. They're the ones where people aren't fighting bad data, fixing errors, and doing rework that shouldn't exist.
Where Warehouse Time Actually Goes
Watch a warehouse for a day. Time how long orders actually take, and you'll find something interesting: the physical picking and packing is fast. Everything else is slow.
Decoding unclear orders: What did the customer actually want? Is "blue widget 10mm" the same as "widget small blue"? Which product code is that?
Chasing missing information: Delivery instructions buried in email threads. Special handling requirements nobody logged. Customer preferences that exist only in a sales rep's head.
Fixing data entry errors: Wrong quantities, wrong products, wrong addresses. Someone typed "10" instead of "100" and now there's a short shipment to fix.
Handling exceptions: Orders that need manual intervention because the system couldn't process them. Holds, approvals, credit checks that weren't done upfront.
Rework from mistakes: Pick again because the first pick was wrong. Repack because the wrong items were boxed. Redeliver because the address was bad.
None of this is picking product. It's dealing with the consequences of bad upstream systems.
The Real Bottleneck Is Usually Data
When orders arrive in the warehouse messy, the warehouse has to clean them up. That cleaning takes time—time that looks like the warehouse is slow.
Orders From Phone and Email
Sales rep takes a call, scribbles notes, enters the order later. Maybe they get it right. Maybe they don't. Either way, that order hits the warehouse hours after the customer wanted it, with whatever information the rep remembered to include.
Manual Re-keying
Order comes in via email. Someone types it into the ERP. Then someone else types it into the WMS. Each transcription is another chance for error.
When the pick sheet says "10" but the customer email said "100," whose fault is it? Doesn't matter—the warehouse still has to fix it.
Inconsistent Product Information
Customers order by their internal codes, by description, by "the same thing we got last time." Someone has to translate that into actual SKUs before picking can start.
Missing Delivery Details
"Deliver to site" isn't an instruction. Which site? What gate? Who's receiving? Does it need a tail-lift? Can they take a full pallet or only cartons?
When this information isn't captured at order, the warehouse guesses or the driver calls from the driveway.
What Clean Orders Look Like
An order that's ready for the warehouse should have:
Validated product codes: Actual SKUs, not descriptions or customer codes that need translation.
Confirmed quantities: Ideally from a system where the customer selected quantities themselves, not from someone interpreting a handwritten note.
Complete delivery information: Address, contact, phone, delivery window, access instructions, special handling—captured once, at order time.
Resolved exceptions: Credit checks passed, stock allocated, approvals obtained. The warehouse gets orders ready to ship, not orders to investigate.
Logical pick sequences: Orders organised by location, priority, delivery route—whatever helps your warehouse flow.
The Upstream Fix
The fastest path to warehouse efficiency isn't warehouse investment. It's fixing what happens before orders reach the warehouse.
Customer Self-Service
When customers enter their own orders through a B2B ordering portal, they're selecting from your actual products, entering quantities themselves, confirming their delivery address.
No translation. No re-keying. No "I think they meant..."
They can see their order history, reorder previous purchases, access their pricing. The data is clean because it was clean from the start.
Structured Data Capture
A good ordering system forces completeness. Can't submit without a delivery address. Can't checkout without selecting a delivery window. Special instructions have a dedicated field, not a free-text note someone might miss.
This feels like friction to the customer, but it's seconds of their time that saves hours of warehouse time.
Validation at Entry
Check stock at order time, not at pick time. Validate addresses when entered, not when the driver can't find them. Run credit checks before the order hits the warehouse, not when it's packed and waiting.
Every exception caught early is an exception the warehouse never sees.
Direct System Integration
Orders flow from the portal to your ERP to your WMS automatically. No export files, no manual imports, no "the systems don't talk to each other."
What the customer ordered is what the picker sees. No translation, no transcription, no "updated order" emails that may or may not have been actioned.
The Numbers
Warehouses operating with clean order data typically see:
30-50% fewer picking errors: Because pickers are working from accurate information, not interpreting messy instructions.
20-30% faster order processing: Because orders arrive ready to pick, not ready to decode.
Significant reduction in returns: Because customers got what they actually ordered.
Lower training requirements: Because the system is clear, not tribal knowledge passed between staff.
These aren't efficiency gains from working faster. They're efficiency gains from not doing unnecessary work.
Signs Your Warehouse Problems Are Actually Data Problems
- Pickers constantly asking "what does this mean?"
- Same orders getting picked multiple times
- Credit holds discovered after packing
- Delivery failures due to bad address data
- Customers complaining about wrong items when your team is sure they picked correctly
- Staff who "just know" things that aren't in any system
If your experienced warehouse staff are spending time on translation and correction rather than picking and packing, your problem isn't warehouse capacity. It's upstream data quality.
The Investment Case
More warehouse staff is an ongoing cost that scales linearly. Ten percent more orders, roughly ten percent more staff.
Fixing your ordering systems is a capital investment that scales sublinearly. Ten percent more orders might be zero percent more warehouse staff, because the time savings come from eliminated rework, not faster picking.
The question isn't "can we afford to fix our ordering systems?" It's "can we afford to keep paying for the warehouse inefficiency they create?"
What Good Looks Like
Customer places order at 2pm. Order validated, stock allocated, credit checked—automatically. Order appears in WMS with complete pick instructions and delivery details. Picked, packed, manifested, on the truck by 4pm.
No calls to clarify. No re-keying between systems. No exceptions queue. No "we'll sort it out tomorrow."
That's not a fantasy warehouse with unlimited staff. That's a normal warehouse with clean data.
Warehouse bottlenecks that feel like capacity problems? Book a call and we'll help you figure out if the real issue is upstream.
