Use case
We use your sales history to anticipate demand for each product and warn you before you run out of stock or overbuy. No endless spreadsheets or gut feeling.
The problem
Falling short means lost sales and customers going to the competition. Overbuying means money sitting in the warehouse and products that expire or become obsolete.
Today
With AI
How it works
You do not need a data science team. We start from the data your ERP or online shop already has.
Sales per day and product, stock, supplier lead times, promotions and calendar.
3 years · 850 items
It learns trends, seasonality and the effect of holidays and campaigns.
Validated on real months
Expected demand per week, with a likely range, not a magic number.
Wk 11: 280-340 units
Quantity to order based on stock, supplier lead time and safety stock.
Order 300 units by Monday
Human review
Reviews, adjusts with what they know (a trade fair, a new customer) and approves.
Approved with +20 adjustment
Examples
Sales history
“Desk fans. Almost no sales in winter; peaks in June and July that vary each year with the heat.”
Forecast
Order planned around the supplier's lead time
Planned promotion
“15% off replacement filters for two weeks in March, with a newsletter to customers.”
Forecast
Buyer alerted 4 weeks ahead
Spare parts catalogue
“600 items that sell a few times a month, with irregular orders from garages.”
Forecast
Weekly review in 15 minutes
Data you can use
Feet on the ground
Ideally two years or more of sales per product. You can start with less, but with more uncertainty.
That is why we give a range, not a number. What matters is being more accurate than today and knowing how much margin to leave.
Buyers know things that are not in the data. The model proposes; the team adjusts and approves.
Visual guide · Sales and stock forecasting
The weather
forecast
for your sales
Like the weather forecast: it does not say exactly what will happen, but what is most likely and with what margin. Enough to buy on time without overdoing it.
01 · The forecast
Actual sales Forecast and likely range
Each item gets a weekly forecast with a range. Next week is forecast closely; two months out, with more margin.
02 · What moves demand
Seasonality
Fans in July, heaters in January.
Holidays
Bank holidays, closures and short weeks.
Promotions
Sales rise, then dip for a while.
Trend
What slowly grows or fades.
03 · From forecast to order
04 · Feet on the ground
A range,
not a magic number
Alerts
The forecast turns into specific alerts: which items will run out before the next delivery and which are building up more stock than will sell.
Method
We check what history exists, its quality and which products account for the most money or the most stock-outs.
Data assessment and priority items
We train the model and compare it with your current method on past months the model has not seen.
Accuracy comparison against the current method
We connect the forecast to the ERP or a shared sheet and review its accuracy every month.
Weekly forecast and monitoring dashboard
We design and build artificial intelligence solutions tailored to your processes, data and tools when an off-the-shelf solution does not fit or falls short.
Anticipate demand, sales or consumption with models you can explain.
Better buying decisions and a supply chain with fewer surprises.
Tell us how many items you manage, where your sales data comes from and how you calculate orders today. We will tell you whether a forecasting pilot makes sense.
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