Year-end counts get all the attention, but the stores that actually stay in stock through the messy middle months — back-to-school, holiday setup, post-holiday returns, spring reset — are the ones that count more often than they have to. Here's what I've seen work. Not theory. Just the stuff that separates a store that's constantly chasing ghosts on the shelf from one that actually knows what it has. Before any seasonal shift, pull your top 50 movers by category and count the

Building a demand forecast from historical sales data sounds like a data science project. It's not. It's mostly just cleaning up your own mess. Here's what I mean. When you pull 12 months of sales history to start a forecast model, the first thing you'll find is that three of those months are lying to you. A stockout in February made it look like demand dropped. A promotional push in Q3 inflated one SKU by 40%. A receiving error in October logged 200 units that never actual