Your forecast decides your inventory. And it's running on averages.
Stratalytic builds demand forecasts on your own sales data, seasonality, promos and channels included, and proves the improvement in a benchmark before you commit to anything. No planning suite, no subscription per SKU.
What is a better forecast worth?
Even a modest improvement compounds. Slide to your own situation.
A 15% better forecast is worth roughly
€ 8.925 per year
Plus € 31.500 in working capital freed from safety stock.
20-40%
Less forecast error vs baseline
€475K
Untracked stock gap found at one client
Weeks
Benchmark turnaround
100%
Yours, code and model
You talk directly to a solution architect.
At Stratalytic you talk to the solution architect who also builds the models, no sales team. In 30 minutes we look at your assortment and your current method, and you'll hear honestly whether a benchmark on your data is worth doing. If your current forecast is already fine, you'll hear exactly that.
Measured at these companies
Sound familiar?
Purchasing runs on last year's average, and it shows
Too much capital in slow movers, empty shelves on risers. The forecast error is invisible on a dashboard but very visible in working capital and lost sales.
The planning suite quote made your eyes water
Enterprise planning software bills per SKU per month, forever. And your promo weeks and marketplace channels still don't fit the standard model.
Every supplier order is a gut-feeling decision
Long lead times and minimum order quantities make every purchase order expensive to get wrong, in both directions.
How it works, measured instead of promised
30 minutes with the solution architect
You talk directly to the solution architect who builds the models, no layer in between. Together you look at your assortment, channels and current method, and you get an honest answer on whether a better forecast is realistic for your data.
Forecast benchmark on your own history
Under NDA we run our models against your current method and a naive baseline, on your real sales data. You see the improvement per product group in writing, measured, not promised.
From forecast to purchasing decision, you own it
Model plus the translation into reorder points and purchasing advice, integrated with what you already use. Fixed price, running in your own environment, code and model are yours.
Why not a planning suite or a spreadsheet?
| Excel | Planning suite | Custom forecast | |
|---|---|---|---|
| Handles your deviations (promos, channels, season) | Average-based formulas | Standard model, config limits | Trained on your data |
| Proof before you commit | None | Vendor demo | Benchmark on your own history |
| Cost model | Free, but error is expensive | Subscription per SKU, forever | One-off, fixed price |
| Ownership | Yours | Vendor platform | You own code and model |
Curious what this looks like on your data?
Book a free 30-minute call with the solution architect who also builds the work. You walk through your processes together and get an honest answer on where the numbers are for you, and where they are not.
Free intro call
30 minutes, no obligation, directly with the builder.
Before you book
Why not Slimstock, Optiply or another planning suite?
Those packages are strong for companies that fit their standard profile. If your reality deviates (promo-driven sales, marketplaces next to your own channels, import lead times, hard seasonality), a standard model calculates with averages. A custom forecast trains on your data and your deviations, and you pay once instead of a per-SKU subscription.
How do I know it will actually be better?
We measure it before you decide anything. In a forecast benchmark we put our prediction next to your current method and a naive baseline, on your own sales history, in black and white per product group. If we don't demonstrably beat your current approach, it stops there and you got a free insight.
We already built something ourselves and it works. What do you add?
Good sign. Then the question is no longer whether it can be done, but whether you are getting everything out of it. That is where most of it stalls: Gartner expects 30% of AI projects to be dropped after the proof of concept, and MIT found that 95% of pilots deliver no result that shows up in the numbers. Rarely because the model does not work, usually because nobody owns the number. On top of that, what is possible shifts quickly, and keeping up with that does not fit alongside running an operation. We calculate up front on your own data what there is to gain, free, and then make sure it actually runs. On a forecast running on averages that came to €778,000 a year.
What does it cost?
The benchmark is fixed-scope and costs little to nothing; the full project (model + integration with your inventory/purchasing process) typically runs €15,000-€40,000 one-off. For comparison: at an inventory value of a few hundred thousand euros, 10-15% less overstock is already a multiple of that, check the calculator on this page.
What data is needed?
Sales history per item per week (at least ~2 years for seasonality), ideally plus the promo calendar and stock levels. That almost always already sits in your webshop, POS, ERP or fulfilment system, extracting it is part of the work.
What happens with the forecast in practice?
A prediction nobody uses is worthless. We translate it into the decision: reorder points, purchasing advice per supplier, promo weeks planned separately. Your buyer gets a list, not a chart: order this, skip that, and why.
