Find the source of the error before correcting the number Most inventory errors aren't counting problems. They're process problems that counting eventually exposes. Think about where discrepancies actually come from in a real operation. A pallet gets moved without a transfer being logged. A kit gets partially picked and the remainder gets shoved back on the shelf in the wrong slot. A barcode is damaged and the scanner misreads it, or the counter skips it entirely because they

Cycle counting means checking stock continuously—not once a year Most warehouses pick a counting method early on and never revisit it. The operation grows, SKU count doubles, and they're still running the same full physical they ran when they had a single dock door and twelve employees. That mismatch is where shrink hides. A full physical count stops the building. Everything freezes — receiving, shipping, picks — while your team walks every slot and reconciles every discrepan

Know what you have, where it is, and when it moves Bad stock numbers don't come from laziness. They come from clipboards, spreadsheets that are three days out of date, and count sheets that someone filled in from memory because the scanner died in aisle 9. The gap between what your system says you have and what's actually on the rack is where shrink hides, where stockouts happen, and where your team loses an hour every Tuesday morning reconciling numbers that should never hav

AI helps retailers stock smarter and waste less. Most of what gets written about AI in retail inventory reads like it was written by someone who has never had to recount a 4,000-SKU backroom because the system said 12 units and the shelf said 0. So let me try to be more useful than that. The honest answer is that AI earns its keep in three places. First, demand forecasting. A decent model watching 18 months of sales history, seasonal spikes, and local event calendars will cat