Forecasting: native AI vs basic replenishment math
Cin7's planning surface — forecasting, demand projections, replenishment suggestions — is functional but basic. It's enough to keep replenishment ticking if your demand is stable and your SKUs aren't highly seasonal. It struggles where most consumer brands actually live: promo cycles, channel-mix shifts, launches, returns spikes, retailer sell-through that moves on its own clock. Lumina is built around forecasting from day one. The model trains on your real data — Shopify orders, Amazon velocity, retail sell-through, returns, promo calendars — and adapts as the brand's mix changes. New SKUs, peak season, and promo planning happen inside the same forecast view, not in a side spreadsheet that nobody trusts. For a planner whose week starts with 'what's the forecast actually saying,' that gap matters every single day.
- AI model trained directly on your channel-level demand data
- Handles new SKUs, promos, and seasonality without manual tuning
- Forecast updates roll into replenishment and cash flow automatically
- Stable SKUs with consistent monthly demand
- Wholesale / B2B replenishment with predictable reorder rhythms
- Brands that primarily need stock records, not forecast accuracy