Stockout risk on autopilot:
see the shortage before the cart does
A reorder alert that fires when stock hits a fixed number is often too late for a fast-selling SKU and too early for a slow one. We build a model that reads current sell-through against supplier lead time and flags the SKUs genuinely at risk of running out, days before the fixed-threshold alert would have noticed.
The process today
Most inventory alerts use one fixed number: stock drops below 10 units, someone gets pinged. That number was set once, often by guesswork, and never adjusted for how fast a particular SKU actually sells or how long its supplier takes to deliver. A SKU that sells five units a day and has a six-week lead time needs a very different trigger point than one that sells five units a month with next-day delivery, and a flat threshold treats them the same.
The result is predictable: fast-moving SKUs run out while their alert is still sitting comfortably above the threshold, and slow-moving SKUs trigger alerts that waste a buyer’s attention on stock that was never actually at risk. Over a full catalogue, both failure modes happen constantly, and a buyer learns to ignore the alerts because too many of them are noise.
The deeper cost is that supplier lead time is rarely tracked as data at all. It lives in someone’s memory as “they’re usually fast” or “that one’s always slow,” and when a supplier quietly starts taking an extra two weeks, nobody notices until an order that should have arrived does not.
What the agent does
The model combines each SKU’s recent sell-through velocity with the actual lead time of its supplier, tracked from past orders rather than assumed, to produce a days-to-stockout estimate that updates as both numbers change. SKUs are ranked by how soon they are likely to run out relative to when a reorder placed today would arrive, so the list a buyer sees every morning is sorted by genuine urgency, not alphabetically or by a static rule.
When a supplier’s actual delivery time drifts from its historical average, the model adjusts the risk score for everything that supplier provides, catching a slow-down before it causes a stockout rather than after. Urgent cases, SKUs likely to run out before a reorder could plausibly arrive even if placed today, trigger an immediate alert in Telegram or Slack instead of waiting for the next daily review.
The ranked list integrates with your existing reorder workflow: a purchase order can be drafted automatically for review, or the SKU simply surfaces at the top of your buying sheet with the reasoning attached, so a buyer does not have to re-derive why it is urgent.
What stays with humans
Committing budget to a purchase order stays with a buyer, along with any judgment about a supplier relationship, a minimum order quantity, or a reason to delay a reorder that the model has no visibility into. The model ranks risk and explains it; it does not sign a purchase order on its own unless you explicitly set a low-risk category to full autopilot after a trial period.
Guards
Every risk score is logged with the sell-through and lead-time numbers behind it, so a buyer can see exactly why a SKU was flagged. The model runs a backtest against your past stockouts before go-live, so you can see whether it would have caught them earlier, and a kill switch reverts to your previous fixed-threshold alerts in one message if something looks wrong.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $800 | One warehouse or category, risk model, ranked alerts | 7 to 12 days |
| Department package | from $2,800 | Stockout risk plus demand forecasting and reorder automation across your buying team | 3 to 5 weeks |
Running cost is usually $20 to $80 a month depending on SKU count and alert volume.
Related
Pair this with demand forecasting so the reorder quantity itself is grounded in a real forecast, not a guess, and with inventory alerts and reorder for the simpler, rule-based version of this on categories that do not need a predictive model. The full package breakdown is on the AI agents service page and the automation-everything overview; for a real retailer’s inventory work, see the Thailand D2C rebuild case study and the Balkans supplements store case study.
Ready to stop finding out about a stockout from an empty cart? Get in touch and we will look at your SKU list in the first call.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does stockout risk prediction cost?
From $800 for one warehouse or category with risk scoring across your current SKU list, live in 7 to 12 days. A department package combining this with demand forecasting and reorder automation usually starts at $2,800.
How is this different from a simple low-stock alert?
A fixed threshold ('alert at 10 units') ignores how fast a SKU sells and how long the supplier takes to deliver. This model combines both, so a slow SKU with 10 units left is fine and a fast SKU with 50 units left and a six-week lead time gets flagged first.
Does it place the reorder automatically?
It ranks risk and can draft a purchase order for review, but committing budget and choosing the final quantity stays with your buyer unless you explicitly want a low-risk category on full autopilot.
What data does the model need?
Sales history, current stock levels and supplier lead times, typically from your e-commerce platform, POS or ERP. The more consistent your lead-time data, the sharper the risk ranking.
What happens if a supplier suddenly gets slower?
Lead-time drift is tracked per supplier and feeds back into the risk score automatically, so a supplier that starts taking an extra two weeks raises the risk on everything they supply, not just the one SKU someone happened to notice.