Reorder points that optimize themselves
Instead of static ERP parameters: AI calculates the optimal reorder point and order quantity for every item, every day – based on true probability distributions.
What we solve
Excessive safety stock
Because uncertainty is cushioned with blanket buffers instead of being modeled precisely.
Outdated parameters
Reorder points are configured once and never updated – while demand and lead times changed long ago.
Manual effort
Planners spend their time on routine instead of on the exceptions where their experience really counts.
What planning looks like when it works
Not a promise for the future – the everyday reality of our customers.
Monday, 8:15 a.m. You open the order-proposal list. 940 of 1,000 items are green – calculated, justified, approved with a single click. You focus on the 60 exceptions where the system says: here I'm uncertain, here I need a human. By 9:30 the planning run is done. The rest of the day belongs to the work you're actually paid for: suppliers, bottlenecks, assortment.
For planners and schedulers
From data-keeper to decision-maker. You no longer maintain parameters – you judge exceptions. Your experience flows in where it counts: on the 6 % that no model should decide on its own.
For heads of materials management and SCM
Calm instead of firefighting. No escalation rounds over missing parts, no surprises at stocktaking. And when the board asks about AI, you have an answer – with numbers.
For CFOs and management
Capital that works again. An inventory curve that falls. A service-level curve that doesn't. And every ordering decision justifiable from demand.
And control? Stays with you. The system proposes and justifies – humans approve. Every recommendation is traceable, every parameter reversible. No black box, no blind flight.
Not sure it pays off for you?
In the Proof of Value we measure your concrete potential in 5 days at a fixed price – we recompute against your sales data what optimized replenishment would have delivered, before you invest in a project.
Three steps from a file to an optimized warehouse
Demand forecasting
Expected value and standard deviation of demand are quantified for every item – as a full probability distribution, not just a point forecast.
Inventory simulation
Future inventory levels are simulated by convolving the probability densities – including lead times and order cycles.
Parameter optimization
Reorder point and order quantity are recalculated daily and written directly into your ERP – fully automated.
Who it's for
Requirements: Historical sales data (at least 12 months) and an ERP system.
Results our clients achieve
98%+
Material availability
15–30%
Inventory reduction
up to 90%
Less manual effort
5–20%
Transport cost reduction
How AI demand forecasts cut inventory without risking availability:
At Heidelberger Druckmaschinen and Endress+Hauser we found 15–30 %. Either your planning is already better than theirs – or there's something here for you.
Ready for reorder points that think ahead?
Test AutoDispo with your own data, or discuss your use case directly with us.