04 JAN, 2026 · food manufacturing, frozen products
Predictive warehouse logistics: slotting, stock-level validation and order forecasting
A manufacturer of frozen food under its own brands, selling through wholesale, retail chains and foodservice. 28,000 SKUs, an in-house WMS and ERP, hundreds of partner webshops. An AI layer of four predictive use cases that optimises warehouse operations from the data in existing systems, through database-level integration.
- SKUs the solution is sized for
- 28,000+
- predictive use cases on one shared data layer
- 4
- warehouse operation it is designed for
- 55-person
The data is already there, it just isn't working
The manufacturer's own-brand products ship from its finished-goods warehouse to wholesale, retail chains and foodservice. The company had integrated ERP, WMS and webshop systems, yet what went where in the warehouse, and the minimum–maximum stock at pick locations, were decided by experience. The task was to turn existing transactional data into decision support without introducing a new system.
Technical implementation and business impact
Direct database integration across three systems
The solution would connect directly to the ERP, warehouse management system and webshop databases, with no web scraping and no manual exports. The analytics layer would run separately, so the load on production systems would not increase.
Four predictive models
1. Seasonal slotting: items with seasonal peaks are moved closer to the dispatch lane.
2. AI validation of min–max stock levels at pick locations: fewer replenishments and fewer empty pick locations.
3. Order forecasting: workload can be planned ahead from orders already visible in the ERP but not yet released to the warehouse.
4. Cart abandonment prediction: based on cart data from the company's own and partner webshops.
Expected business impact and ROI
Picking routes get shorter, overtime and peak-period bottlenecks decrease, and shift capacity can be matched to expected workload. In the PoC phase we base the ROI on the client's own baseline data (overtime, payroll cost, cart abandonment rate), not on industry averages.
Decision support, not a black box
Every recommendation appears on a dashboard with its reasoning. Warehouse management approves slotting and stock-level changes. The modules can be rolled out independently, so you can start with the highest-impact use case.