The real cost of guessing
- Overstock: frozen money (with its financial cost), busy warehouse, loss due to expiration or obsolescence, and panic sales to "remove" what does not rotate.
- Out of stock: today's lost sale and, worse, the customer who learned to buy from someone else. In retail, bankruptcies typically cost 4-8% of sales.
- The middle ground by hand is impossible at scale: with 500 SKUs, two branches and seasonality, no human recalculates reorder points every week. That's why almost no one does it — and that's why AI here is not a luxury, it's the only way to do it well.
What demand forecasting does with AI
Modern forecasting learns from your sales history by product and detects what intuition misses: trend (grow or die), seasonality (the fan in April, the jacket in November), day and week patterns (the fortnight exists), and the effect of your past promotions.
With that, it projects the expected demand per SKU for the coming weeks — not as a magic number, but as a range with confidence. And from there everything else comes: how much to order, when to order it and how much safety stock to maintain depending on how variable each product is.
From prediction to action: reordering and alerts
- Purchasing suggestion by supplier: "with the expected demand and delivery time from this supplier, order these quantities this week" — ready to be converted to a purchase order with one click.
- Imminent stockout alerts: SKUs that at this rate are sold out before the replenishment arrives, with days in advance to react.
- Detection of anomalies: the product that suddenly sells 5x (did it go viral? price error?) or the one that stopped short (display problem? competitor?), indicated on the day it passes and not in the monthly report.
- Redistribution between branches: the same product left over in one store and broken in another is a recoverable sale with a timely transfer.
What you need to make it work (and what to expect)
Inventory AI eats data: sales recorded by SKU (your POS and online store powering it alone), reliable stock (cycle counts matter), and clean catalog without duplicates. If you sell on slips of paper, first digitize the sale — there is no forecast without history.
Typical results well implemented: 20-30% less capital tied up in stock, bankruptcies reduced by half and hours of "ordering" converted into reviewing a suggestion. At Aura, inventory, POS, ecommerce, and purchasing live together, so AI forecasts your actual sales from all channels and suggests reorders by supplier — without integrations or data science involved. Try 14 days for $14 USD.
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Start your trial →Frequently asked questions
How much sales history do I need to forecast?
With 3-6 months of sales per SKU there is already a useful signal; with 12+ months, annual seasonality enters the model. New products are estimated by similarity to comparable products while generating their own story.
Does it work for irregularly sold products?
Yes, with the right approach: for sporadic selling SKUs, the system works with ranges and safety stock instead of misleading averages. There is no such thing as a perfect forecast — the goal is to be much less wrong than intuition, consistently.
Does the AI make the orders itself or does it ask me?
As you define it: healthy startup mode is suggestion with approval (you review and adjust in minutes), and full automation only for stable SKUs where you trust the system. Limit control is always yours.
What about events that history does not know about (a pandemic, a viral)?
No model predicts the unprecedented — but the AI DETECTS it in days (demand anomaly) and recalculates. The real advantage is not guessing the future: it is reacting in 3 days to what you previously discovered at the end of the month.