Forecasting: AI on your real data vs statistical models from the 2010s
Inventory Planner's forecasting heritage is statistical — the same family of models that have been around since the early 2010s, tuned by hand. They work, especially on stable, Shopify-only DTC data. But they don't gracefully handle the things that increasingly define modern consumer brands: new product launches with no history, promo cycles that warp the baseline, channel-shift between Shopify and Amazon, returns volatility, or the noise that comes from selling on TikTok Shop one quarter and not the next. 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 an analyst to tune parameters by hand. For scaling brands, that's the difference between a forecast you trust and a forecast you 'override in the spreadsheet anyway.'
- AI-native models trained on your Shopify, Amazon, and retail data directly
- Handles new SKUs, promos, and seasonality without manual model tuning
- Forecasts roll up to channel, region, and SKU views without rework
- Mature, well-understood statistical models with a long track record
- Stable, single-channel Shopify DTC SKUs with deep history
- Planners who prefer hand-tuning model parameters over AI-driven defaults