Forecasting: built on your channel data vs a mature statistical engine
Inventory Planner's forecasting engine is mature and well understood, and it works, especially on stable products with deep history. Where it struggles is with the things that increasingly define modern consumer brands: new launches with no history, promo cycles that distort the underlying pattern, demand shifting between Shopify and Amazon, and the noise of selling on TikTok Shop one quarter and not the next. Handling those means configuring your way around the engine. Lumina forecasts from the data you have today: Shopify orders, Amazon velocity, retail sell-through, returns, and your promo calendar. Its models are configured to how your range is split and profiled and how your products behave, so the forecast matches your own intuition about the range, with your promotions, launches, and growth plans included and every number visible. For scaling brands, that's the difference between a forecast you trust and a forecast you override in the spreadsheet anyway.
- Forecasts from Shopify, Amazon, and retail data directly
- Configured to your range: how it's split and profiled, how each line behaves, with your promotions and launches included
- Rolls up to channel, region, and SKU views without rework
- A long-established engine with a track record on stable, deep-history SKUs
- A large existing user base, so there's plenty of documentation and community knowledge
- Familiarity: if your planner already knows it, there's no relearning