7 Signs Your Warehouse Has a Problem (And What to Do About Each One)

Your Pick Rates Are Below 50 Units Per Labor Hour

Pick rate (units picked per labor hour) is the first number we check when we walk into a new warehouse. In a well-organized ecommerce operation, 80 to 120 units per labor hour is a reasonable benchmark. Below 50, we're almost always looking at something structural wrong with either the layout or the process.

What's causing it

We've traced low pick rates back to travel time more times than we can count. Pickers spend more time walking than picking. The three leading causes we see on client floors:

1. Poor slotting

   Fast-moving SKUs are stored far from the pack station, or worse, spread across the warehouse with no velocity-based organization. A picker running 40 orders per day can walk 8 to 12 miles in a poorly slotted warehouse. In a well-slotted one, the same order volume covers 2 to 3 miles.

2. No batch picking

   Pickers are running one order at a time through the warehouse instead of consolidating 5, 10, or 20 orders into a single pass. Each individual order trip takes nearly as long as a batch trip, but a batch trip delivers 10× the output.

3. Bin labeling and location clarity

   If pickers have to slow down or search to find a location, every pick gets slower. Inconsistent labeling, unlabeled overflow locations, and SKUs that don't match their bin assignments add seconds to every pick, which compounds across thousands of picks per week.

The fix

Start with a slotting analysis. Pull 90 days of order data and rank every SKU by pick frequency. Your top 20 percent of SKUs by frequency (typically 50 to 100 SKUs in a mid-sized operation) should be slotted in the golden zone: waist to shoulder height, within 30 to 50 feet of the pack station. Move them there first. We consistently see 20 to 30 percent pick rate improvement from re-slotting the top movers alone, with no other changes. Add batch picking as a second pass: it requires sequenced pick lists or pick-to-cart infrastructure but can push rates above 100 units per hour on its own.

If your pick rates are below 30 units per labor hour, we can tell you the problem before we walk the floor: it's the physical layout, not training, not effort, not staffing level. Adding pickers to a poorly laid out warehouse adds cost without fixing throughput. The Rack Layout Planner can model alternative configurations before any physical changes are made.

Your Inventory Accuracy Is Below 98 Percent

Inventory accuracy below 98 percent means one in fifty locations has the wrong count. That sounds small until you're the one chasing it: picks that fail because the bin is empty, orders that ship incomplete, customer service contacts, and hours of management time spent tracing discrepancies after every cycle count.

What's causing it

The fix

We move accuracy from the 95-to-97 percent range into the 99-plus range with three changes: a formal receiving verification step (count and scan every inbound unit before put-away), a weekly cycle counting program that covers every location at least once per quarter, and locking down informal inventory movements with a system transaction requirement. Together, these typically add 1.5 to 2.5 percentage points of accuracy within 60 days. Getting from 99 to 99.5 percent and above usually requires barcode scanning at the pick step: confirming the barcode of the unit being picked matches the pick list before the picker moves on.

You're Running Out of Space but Volume Hasn't Grown

When a client tells us they're out of space but their SKU count and inventory volume haven't grown, we already suspect the diagnosis before we walk the floor: it's a layout or slotting problem, not an actual shortage. The warehouse has the capacity. It's just not accessible in its current configuration.

What's causing it

Three structural issues create the illusion of a space shortage, and we see all three constantly:

1. Vertical space not being used

   Most warehouses have 20 to 40 percent of their usable cube sitting empty above the top rack level. If your racking tops out at 12 feet in a 24-foot clear-height building, you're using half the vertical space you're paying for. Adding rack levels or transitioning to higher-density storage above the pick face often solves a space problem without adding square footage.

2. Aisles wider than necessary

   Standard pallet racking with a sit-down counterbalance forklift requires 12-foot aisles. A reach truck operates in 8 to 9 feet. A very narrow aisle (VNA) system works in 5 to 6 feet. If your warehouse was configured for one type of equipment and you're now running another, or if the original layout was simply overbuilt on aisle width, you may have 15 to 25 percent of your floor space in aisles that could be recovered with a layout change.

3. Dead and slow stock occupying active storage

   Inventory that hasn't moved in 90 or 180 days is sitting in prime pick locations or forward rack positions that active SKUs need. A dead stock audit that clears out non-moving inventory from the pick face often recovers 10 to 20 percent of usable locations without moving a single rack.

The fix

Before you sign a lease on additional space, run the numbers on your current facility. Use the Warehouse Space Calculator to model your current pallet count against your ceiling height and aisle configuration. Most operations that think they're out of space are actually sitting at 60 to 70 percent of theoretical capacity in their existing footprint. They've filled the easy-to-access positions and the remaining capacity just requires a layout change to reach.

The decision to add space versus reconfigure existing space is one of the highest-stakes calls in warehouse management, and we've watched clients get burned on both sides of it. Adding space you don't need costs $8 to $15 per square foot per year in rent and utilities. Reconfiguring to use existing space typically costs less than one month of additional rent, and it solves the layout problem instead of postponing it.

Your Labor Cost Per Order Keeps Rising

Labor cost per order (total labor spend divided by total orders shipped) should stay flat or decline as volume grows. Fixed costs spread across more throughput. If yours is trending up quarter over quarter, something in your operation is getting less efficient as it scales (the opposite of what should happen).

What's causing it

We've yet to see a rising labor cost per order that turned out to be a wage problem. It's almost always throughput. Adding staff is the instinctive response when order volume grows, but if the process is inefficient, more staff amplifies the inefficiency instead of solving it. The specific culprits we look for:

1. Non-value-adding time that scales with headcount

   If pickers spend 40 percent of their time searching, waiting for equipment, or re-handling mis-put product, adding another picker adds 40 percent wasted time to the payroll. The inefficiency comes with the new hire.

2. Receiving re-work

   Inbound inventory that isn't properly sorted, labeled, or counted at receiving gets put away wrong and has to be found, moved, or relabeled later, by the same labor pool that should be picking and packing. A weak receiving process creates a tax on every downstream operation.

3. No labor standards or productivity tracking

   Without a measurable expectation of units per hour, labor time fills to fit the available hours. Operators who track pick rate per person per shift typically run 15 to 25 percent more efficient than those who don't, not because of pressure, but because visibility creates accountability.

The fix

Measure before you hire. Pull labor hours against units shipped for the past 90 days and calculate your actual units per labor hour. Compare it to the benchmark for your operation type. If you're below benchmark, run a time study next: 2 to 4 hours of observing and timing picker movements to see where the time is actually going. In our experience, the root cause is almost always in the top 3 time consumers the study surfaces, and fixing those 3 things moves the number faster than any hiring decision.

More Than 1–2 Percent of Shipments Are Going Out Late

An on-time shipment rate below 98 percent means more than 1 in 50 orders is missing its ship window. For ecommerce sellers, late shipments trigger customer service contacts, negative reviews, and (on Amazon) seller performance metrics that directly hit listing visibility. We've seen a persistent late-ship pattern do more damage to a platform-dependent business than almost any other operational problem.

What's causing it

The fix

Map the order flow from receipt to carrier pickup and time each step. The bottleneck (the step where orders queue up) is where the late ships originate. Most operations have one primary constraint. Fix that constraint and the on-time rate improves disproportionately. Secondary fix: add a daily ship-rate dashboard that shows orders received, orders picked, orders packed, and orders labeled against the carrier pickup time. Make it visible to everyone working the floor.

Your Damage or Mispick Rate Is Above 0.5 Percent

In a well-run operation, damage and mispick rates below 0.5 percent of units shipped are achievable. We hold ourselves to that standard every day. Above 1 percent, the cost compounds quickly: replacement product, return shipping, customer service labor, and the reputational cost of a customer who got the wrong item or a broken one. Above 2 percent, we're not looking at bad luck: that's a systemic process failure.

What's causing it

Mispicks and damage have different root causes, and we track them separately for that reason.

Mispicks (sending the wrong item) almost always come from one of three places: commingled SKUs in adjacent or shared locations (pickers grab the visually similar wrong one), pick lists that aren't specific enough (no barcode confirmation, just a location reference), or locations with multiple SKUs that the picker has to sort through manually. The fix for commingled SKUs is a slotting change that creates visual separation. The fix for unconfirmed picks is a scan-verify step, requiring the picker to scan the item barcode before it goes in the tote.

Damage (sending a broken or poorly protected item) almost always comes from the pack station, not the pick floor. Common causes: packing material that doesn't match the product's fragility (bubble wrap used where foam is needed, or no cushioning at all for glass items), box sizes too large for the product being packed (too much void, product shifts in transit), and no standardized packing SOP that specifies the correct materials and method for each product type.

The fix

Build a packing SOP that specifies, by SKU or product category: the correct box size, the correct void fill material and quantity, the required cushioning type, and any required exterior markings (Fragile, This Side Up, etc.). Post it at the pack station. Add a visual quality check before the box is sealed: one look at the contents against the SOP before the tape gun comes out. We've watched this single step, done consistently, drop damage rates by 50 to 70 percent within the first two weeks.

Simple Distribution runs at 99.5 percent order accuracy (a mispick rate of 0.5 percent or below) across all outbound shipments. The systems behind that aren't expensive or complicated. They're just consistent: a slotting strategy that separates similar SKUs, scan-confirm at pick, and a packing SOP with a closing check. We can put all three in place in any warehouse in 30 days.

Your Staff Turnover Is Above 40 Percent Annually

Warehouse turnover above 40 percent per year is common enough that a lot of operators shrug it off as normal. It isn't. High turnover is expensive (typically $3,000 to $5,000 per replacement once recruiting, onboarding, and productivity ramp costs are counted) and it feeds every other problem on this list. A warehouse that replaces 40 percent of its staff every year never builds the institutional knowledge that keeps operations running smoothly. Every process improvement has to be re-trained constantly. Quality and throughput suffer while new hires come up to speed.

What's causing it

We see two primary drivers behind high warehouse turnover, and both sit within management's control:

1. The work is harder than it needs to be

   A poorly laid out warehouse with no batch picking, excessive walking, and unclear expectations makes a physically demanding job more frustrating than it needs to be. Staff who feel like the operation is chaotic or that their effort doesn't translate to results leave faster than those working in a well-organized environment. The operational fixes in Signs 1 through 6 (better slotting, clearer processes, visible metrics) also reduce turnover by making the work more manageable and the expectations more legible.

2. No visible path to better roles

   Warehouses that promote from within and give floor staff a defined path (picker to lead picker to supervisor) retain people who would otherwise leave for a lateral move at a competitor. Warehouses that hire all supervisors externally signal to the floor that there's no future in staying. This costs more in turnover than most operators realize.

The fix

Fix the operation first. High turnover in a chaotic environment will persist regardless of what you do on culture and compensation. Once the operation is running more smoothly, measure and share throughput metrics with the floor. People who can see their own performance data engage differently with the work. Add a defined internal promotion path. These three (an operation that works, visible metrics, and a path forward) move warehouse turnover from 50 to 60 percent annually down to 20 to 30 percent in most operations within 12 to 18 months.

When to Solve It Internally vs. Bring In Outside Help

Most of what's above, an internal team can solve, if they have the time, the expertise, and the authority to make physical and process changes. The case for bringing in outside help is specific:

1. You've tried the obvious fixes and the problem persists

   If you've re-slotted, retrained, and reorganized and pick rates are still below 50 or accuracy is still below 97 percent, the root cause is something you haven't identified yet. We've solved the same problem in dozens of other facilities, and that pattern recognition finds it faster than an internal team working through it from first principles.

2. The problem is getting worse as volume grows

   A problem that was manageable at 500 orders per day and is breaking things at 1,000 is a structural problem, not a scaling problem. Scaling a broken process produces a bigger broken process. Fix the structure before adding volume.

3. You need it solved fast

   Internal process improvement takes time: building consensus, training staff, waiting to see if changes hold. We work hands-on in the facility, which compresses a 6-month internal improvement project into 2 to 4 weeks. When a late-ship pattern is generating customer service escalations or Amazon account health warnings, speed matters more than the learning that comes from solving it internally.

4. You're considering adding space or staff before fixing the process

   Adding space to a warehouse that's using 65 percent of its theoretical capacity, or hiring staff to compensate for a 40-unit-per-hour pick rate, are the two most expensive ways to avoid fixing the underlying problem. A short outside assessment (even a one-day facility audit) tells you whether you have a layout problem or a real capacity problem before you sign a lease or expand payroll.

For a deeper look at the specific efficiency levers (the eight changes that move warehouse performance metrics most reliably), see How to Improve Warehouse Efficiency: The 2026 Playbook.

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