Returns, damage, and fulfillment errors can quietly drive e-commerce inventory shrinkage The industry average for inventory shrinkage sits around 1.6% of retail sales, according to the National Retail Federation's most recent loss prevention survey. Doesn't sound like much. But run that against a $10M operation and you're looking at $160,000 walking out the door annually — in missing product, miscounted stock, vendor shorts, and internal error that nobody caught in time. Shri

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

A buyer I talked to last year told me they'd just written off $340,000 in seasonal overstock because their forecast was built on the previous two years of sales data — which included a pandemic year and a supply shock year. Two bad inputs, one very expensive mistake. That's not a technology problem, that's a data problem disguised as a process problem, and it's more common than people admit. What good AI-powered forecasting actually does is pull in signals that a spreadsheet

These three fulfillment models sound like acronyms someone invented to fill a slide deck, but they have real consequences for how you count and replenish stock. If your operation is running more than one of them at once — and a lot of mid-size retailers are — your inventory is probably absorbing hits you haven't fully traced yet. So here's the plain version. BOPIS (Buy Online, Pick In Store) means a customer paid for something online and is showing up at your location expecti