A classic story among brands running through a fulfiller: a new batch of stock is on its way, and nobody knows precisely when they can start selling again.
You've put a container on a ship. Okay, arrival date is fixed. But when it's unloaded, counted, booked in, and placed in the right location — that's another story. It can take a day. It can take a week. At busy times, two weeks.
Meanwhile: your marketing is queued up, your ads are waiting, your cash flow depends on it.
What we built
We now pull inbound data from our 3PL partners and layer forecasts on top. For every expected shipment, brands see in their admin:
- When the shipment is expected to arrive at the fulfiller
- When it is projected to be booked in (and therefore sellable)
- How much uncertainty sits in that projection
The forecast isn't magic. We look at historical turnaround times per fulfiller, per season, per volume. We factor in public holidays. We watch the fulfiller's current workload. And we let brands approve or adjust the forecast, so it becomes a commitment rather than a guess.
Connected to cash flow
The real payoff: we feed this forecast into the cash flow projection in our admin. If you know when your stock becomes sellable, you can model when you'll actually generate revenue from it. For brands on a tight cash flow — and that's plenty of brands at our scale — that's the difference between reordering now or waiting.
Supply chain forecasting, for smaller players
This is the kind of feature brands assume only enterprise players do. SAP. Oracle. Large ERP suites with consultants on retainer. But you don't need those to do it well. What you need: the right data (from your fulfiller), a place to bring that data together (that's us), and a few sensible rules to turn it into something useful.
What we took from building it
Most "AI forecasting" stories in e-commerce are, if you look closely, just well-structured data plus a handful of reasonable heuristics. No black box, no magic. Just: data that always existed, finally in the right place, with someone who took the time to draw conclusions from it.
That's what we did here. And it works surprisingly well.