Forecasting: AI on your real data vs statistical models on ERP data
NetStock's forecasting heritage is statistical — solid models running on the structured data inside an ERP. That's a strength when your master data is clean and your business is stable, distribution-heavy, and not seasonal. It's a weakness when you're a consumer brand with promo cycles, omnichannel demand, and product launches that don't fit the historical curve. Lumina is built around AI from day one, applied directly to the data you actually have — Shopify orders, Amazon velocity, retail sell-through, returns, promo calendars. The model adapts to seasonality, launches, and channel-shift patterns without requiring you to rebuild master data first. For scaling brands, that's the difference between a forecast you trust and a forecast you 'override in the spreadsheet anyway.'
- Trains on Shopify, Amazon, and retail data directly — no ERP intermediary
- Handles new SKUs, promos, and seasonality without manual model tuning
- Forecasts roll up to channel, region, and SKU views without rework
- Distribution and B2B SKUs with deep, stable history
- Master data that already lives cleanly in NetSuite or SAP
- Planners comfortable tuning statistical model parameters by hand