Most systems treat demand and inventory as separate problems. This one doesn’t. It models them together — continuously analyzing how fast products are actually consumed, how stock is distributed across variants, and how stable that supply really is. Instead of static stock numbers, you get a living model that reflects true inventory health and its impact on performance.
Don’t wait until you run out of stock to understand demand — predict it in advance with a system that models real consumption, seasonal peaks, and unexpected traffic surges in one unified layer. Because when supply and demand move together, growth becomes something you can control — not react to.
At the core, the system calculates stock depletion and turnover using multi-layered signals. Recent sales velocity, historical patterns, and trend shifts are combined into a weighted consumption model — allowing accurate estimation of when stock will run out, even under changing conditions. At the same time, stockout risk is dynamically classified (critical, warning, safe) based on real days-of-cover and buffer logic — so you don’t just see stock, you understand urgency.
Inventory is not just about how much you have — it’s about how fast it moves, when it spikes, and how stable it really is under pressure. This system reveals all of it in advance, so you can align stock, timing, and strategy with precision instead of intuition.
What makes the system materially different is its supply calendar intelligence. It doesn’t assume demand is stable. Instead, it detects and models seasonality and anomaly-driven spikes — such as campaign periods, special days, or unusual traffic surges — and adjusts forecasts accordingly. Peak season analysis identifies when a product naturally performs best, while real-time signals capture deviations from normal behavior. This means your planning adapts not only to historical patterns, but also to unexpected demand shocks.
Finally, inventory quality is evaluated holistically. Stock consistency, variant distribution, and restock events are all factored into the system — ensuring that a product isn’t just “in stock,” but operationally reliable. Turnover calculations adapt based on data confidence, restocking behavior, and signal quality, preventing misleading conclusions in low-data or volatile scenarios. The result is a forward-looking intelligence layer where supply and demand are aligned — enabling you to plan campaigns, allocate stock, and scale products with confidence instead of guesswork.
Ready to transform your e-commerce business?
Join hundreds of Shopify stores using AI to grow smarter.
Get Started Free