About us
Our experience, turned into Stocktimal
At RAW Analytics we have spent decades building models that turn uncertainty into better decisions. That experience comes from the financial sector, where forecasting, assessing risk and optimising have been the core of our work for years. With Stocktimal we apply that same knowledge to demand forecasting and inventory optimisation. It started with a practical need: something that not only does the maths well, but is also flexible enough for different products, forecast horizons, ordering windows, lead times and the other constraints that real operations come with. Stocktimal came out of that: not a standalone algorithm, but our knowledge of forecasting and optimisation turned into software that can be used every week.
Who is behind stocktimal
Stocktimal is built by RAW Analytics. Making decisions under uncertainty has been our trade for decades, including for large banks and insurers in the Netherlands.
In that world the sums are large, and the quality of the models and the decisions has to hold up every day. It teaches you that a single forecast number never tells the whole story: the uncertainty around it matters just as much.
Ordering stock comes down to the same questions. What is likely to happen? How uncertain is that? And what does it mean today for the decision in front of you?
We have seen this go wrong too often
In our consulting work we regularly see ordering decisions resting on a handful of fixed assumptions and parameters. After a while nobody quite remembers why they were set that way — or whether they still match reality.
The context differs from company to company: different data, different suppliers, different constraints. But the heart of the decision stays the same: how much stock do you need to avoid lost sales without tying up more money than you have to?
So we worked that approach out, tested it on real catalogues and turned it into software. Not an analysis that has to be redone now and then, but a process that can run again every week.
You solve something like that once, properly. Not badly, over and over, per customer. So we wrote the method down, tested it on real catalogues, and turned it into software that runs every week instead of by hand every quarter.
Talk to the people who built it
Questions about a model, a calculation or an order proposal are answered by the people who built Stocktimal. No support department in between.
Richard Plat
Forecasting methodology
Developed the forecasting methodology behind Stocktimal, from choosing the model to estimating the uncertainty around the forecast. That uncertainty is ultimately what decides how much buffer each SKU needs.
Corentin Mrejen
Implementation
Involved in implementing Stocktimal from the start. For customers he focuses on analysing the data, choosing the forecasting models and turning that into a working setup.
Bram Jochems
Product
Built most of the product — the ordering engine, the planning environment and the platform underneath.
A free trial run on your own catalogue
One export is enough: sales history, current stock and open orders. We forecast your demand per SKU and show you what Stocktimal would order on that basis — next to what you actually ordered, with the differences explained. You keep the output, even if nothing comes of it. No integration, no access to your ERP and no implementation project. Just one file.