Every Supply Chain Tool Needs One Thing: Inventory Record Accuracy
Forecasting, planning, order promising and robots all read the same stock number. If it’s wrong, every one of them gets it wrong faster.
The dashboard says 40 on hand. The control tower is green. A picker’s standing at the location looking at an empty slot, and those 40 have already been promised to customers.
Nobody bought a bad tool. Every system involved did what it was told. They all read the same number, and the number was wrong.
I read a list recently of 32 modern supply chain tools: AI forecasting, IBP, control towers, inventory planning, warehouse automation, the lot. It’s a fair list, and I’d happily use plenty of them. But nearly all of them need one thing that didn’t make the list, and it’s older than any of them.
The short version: most supply chain tools take the stock number in your system as fact. When it’s wrong, forecasts learn the wrong lesson, reorders don’t fire, orders get promised against empty slots and robots get sent to the wrong place. Before you buy the next tool, measure how often your records match your shelves, location by location.
Why does every tool need inventory record accuracy?
Inventory record accuracy. That’s how closely the quantity in your system matches what’s physically at that location, checked by counting it. It isn’t the same as a stock value that looks right at month end, because gains and losses can cancel each other out in the total while individual locations are wrong.
It sounds like housekeeping, and that’s why it never makes the list. But it’s the input most of the modern stack reads first and never questions.
What one wrong number does across the stack
Take that same item: 40 in the system, none on the shelf. People call that phantom inventory, because the system treats it as real.
Forecasting goes first. Sales stop because there’s nothing to sell, but no stockout gets recorded, so the model has no reason to think supply was the problem. It learns that nobody wants the item, and the next forecast comes down.
Inventory planning is next. With 40 on hand, the item sits above its reorder point, so the reorder never fires. We’ve written about how reorder points work, and they’re only as good as the count behind them. MIT research by Yun Kyu Kang found that a small level of undetected stock loss, left to build up over time, can disrupt replenishment and cause severe out-of-stocks.
Order promising doesn’t know any better. It sees 40, promises 40, and someone finds out at the pick face. Now you’ve got a short pick, a call to the customer and maybe a split shipment, on an order that looked fine at 9am.
Automation takes it harder than people do. A picker who finds an empty slot will often look one bay over and ask around. A robot sent to the wrong location raises an exception and waits, and that exception lands on a person who now has to go and find the pallet.
And the control tower shows all of this in real time, in green. Better visibility of a wrong number is still a wrong number. It’s just on a bigger screen.
How wrong are stock records, really?
Often worse than people assume, and you won’t know yours until you measure it. The study everyone quotes is by Nicole DeHoratius and Ananth Raman, who looked at nearly 370,000 inventory records across 37 stores of one retailer and found 65% of them inaccurate.
That’s retail stores, not your DC, so don’t read it as your number. Read it as a warning about how a system can look from the inside while nobody’s checking. The same paper found that inventory auditing practices reduce record errors, which is the cheerful part.
AI has raised the price of getting this wrong. In February 2025, Gartner predicted that through 2026, organisations would abandon 60% of AI projects unsupported by AI-ready data. That covers all data, not only stock, but a record that doesn’t match the shelf isn’t ready for anything.
Why can’t better software fix it?
Because the errors don’t start in the software. They start on the floor. A pallet goes one bay over without a scan. A short delivery gets booked at the PO quantity because the driver’s waiting. Returns sit in a cage for two weeks. Twelve cases get keyed as 12 eaches.
None of that shows up as an error, because the system can’t know what it wasn’t told.
Good tools can help. Anomaly detection can flag a SKU that’s stopped selling or a slot that keeps throwing exceptions, and that’s useful. But a flag only tells you where to look. Something still has to check what’s physically there, whether that’s a person with a scanner, an RFID read or a drone. That’s a count, whatever you call it.
How to check before you buy the next tool
None of this means putting the tool on hold for a year. It means knowing your starting point, and that takes a couple of weeks.
Count a random sample of locations, a couple of hundred if you can, and compare each one with the system. Score it by location, not by total.
Split the misses by cause: receiving, putaway, picking, returns or data entry. There’s a good chance one of them is doing most of the damage.
Fix that cause first, then put your A items on a regular cycle count.
Ask each vendor what their tool does when the stock number it’s given is wrong, and ask to see it in the demo.
Put your result in the business case. If the payback assumes accurate stock, say what accuracy you’ve got today.
This won’t apply to everyone. If your records already hold up, go and buy the tools. If you’re not sure, I’d count before I sign. And look at who’s doing the counting, because everyone needing the number while one person counts it is a risk of its own.
Questions people ask about inventory accuracy
What is inventory record accuracy?
How closely the quantity in your system matches what’s physically at that location, checked by counting it.
What is phantom inventory?
Stock the system says you have but the shelf doesn’t. Systems treat it as available, so reorders don’t fire and orders get promised against it.
Can AI fix bad inventory data?
It can flag suspicious records, but it can’t see the shelf. Something still has to check what’s physically there.
What’s the difference between inventory accuracy and inventory visibility?
Visibility shows you what the system thinks you have. Accuracy tells you whether that’s true.
How do you measure inventory record accuracy?
Count a random sample of locations, then divide the number of locations that match the system by the number you counted.
Start with a count
Every business case for those 32 tools starts with knowing what you’ve got. Counting is the least modern thing in the stack, and it’s still holding up the rest.
CountIt is the app we built for that job. Every count is logged with the user, date and time, it reports the financial impact, and it runs on phones and tablets, online or off, with no hardware or WMS to buy. See how it works at count-inventory.com. If you’d rather have people who do this for a living run the count with you, talk to us at Yuneva.
Comments