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