A swimwear brand bought each season on instinct — and paid for it in markdowns. We built an AI forecasting pipeline that turns sales history and live sell-through into monthly buy proposals with confidence ranges. End-of-season overstock dropped 27%.
Typical results
2 hrs
To review a monthly buy
320
SKUs forecast every month
-64%
Best-seller stockouts
Figures reflect typical results for this kind of automation.
The challenge
Swimwear is unforgiving: order too little and best-sellers vanish in June; order too much and January clearance eats the margin. Buying decisions leaned on gut feel and a week of spreadsheet archaeology. Roughly 22% of every season's buy ended as overstock, while fourteen best-selling SKUs still ran out mid-season.
Before and after
End-of-season overstock
−27%Hours to build a buy proposal
−95%Best-seller stockouts per season
−64%The automation
We synced three years of sales, returns and inventory data into a forecasting pipeline. Every month, an AI model blends seasonality, trend and current sell-through to project demand for each of 320 SKUs. The output is a ready-to-edit buy proposal — recommended quantities, confidence ranges, and the cash impact of every line. Buyers adjust what they disagree with, and approved rows convert straight into supplier purchase orders.
How it works
- 01
Sync the history
Three years of sales, returns and inventory data flow into the pipeline nightly from the store.
- 02
Forecast every SKU
An AI model blends seasonality, trend and live sell-through to project demand for all 320 SKUs.
- 03
Propose the buy
A monthly sheet lands with recommended quantities, confidence ranges and the cash impact of every line.
- 04
Edit and approve
Buyers adjust the lines they disagree with; approved rows convert straight into supplier purchase orders.
- 05
Score against actuals
Each forecast is graded against real sales, so the model sharpens month after month.
Starts from
- Sales & sell-through history
- Live inventory levels
Delivers
- Monthly buy proposal sheet
- Cash-impact summary
- Stockout early warnings
The impact
End-of-season overstock fell from 22% of the buy to 16% — a 27% reduction that freed real cash. Best-seller stockouts dropped from fourteen SKUs a season to five. Building a buy proposal now takes two hours of review instead of forty hours of spreadsheets, and forecasts sharpen every month against actuals.
Questions about this automation
What data does the forecast use?
The pipeline syncs sales, returns and inventory history from the store every night, ideally covering at least two or three seasons. The AI model weighs seasonality, recent trend and live sell-through for each SKU, and it can include known factors such as planned promotions or new launches when buyers add them.
Do buyers have to accept the recommended quantities?
No. The monthly proposal is a starting point with a confidence range for every line, plus the cash impact of the full buy. Buyers edit any quantity they disagree with, and only approved rows become supplier purchase orders. Their edits are kept, so later forecasts can be compared with both the model and the final decision.
How does the forecast improve over time?
After each month, every forecast is scored against actual sales, and the error is tracked by SKU and category. Patterns the model misses, such as a weather-driven spike, show up in that review. It is a multi-step build, usually rolled out in stages, starting with forecasts and adding purchase-order creation later.