Forecast demand through seasonal swings

Don't guess peak demand, build it from last year's curve times this year's growth. Then work backwards from lead times to set order deadlines and size safety stock by how bumpy each SKU is.

Seasonal demand is predictable if you anchor to your own history. Last year's monthly shape times this year's growth gives a per-month forecast, and lead-time-adjusted deadlines tell you when to order so peak stock arrives in time. Safety stock scales with each SKU's variability. AI builds the forecast; you supply the growth rate and lead times.

Peak demand isn't a mystery. It's last year's curve times this year's growth. The move: build a per-month forecast from your own history, work backwards from lead times to set order deadlines, size safety stock by how bumpy each SKU is, and plan the post-season sell-down.

AI can turn a sales export into the forecast. You supply the growth rate and the lead times, because only you know them.

What you'll learn

Start from last year's shape, then scale
Work backwards from lead time to an order deadline
Size safety stock by variability, not a flat percent
Plan the sell-down before the season ends