AI Inventory Optimization
Inventory levels are the most expensive over-correction in retail and manufacturing — too much capital tied up, too many stock-outs lost. OctOpus is the AI data scientist that models the entire decision: demand forecast → service-level target → optimal reorder point + safety stock, per SKU per location.
The full chain, end-to-end
Step 1: demand forecast per SKU × location with proper time-series validation. Step 2: lead-time uncertainty model from your supplier-performance data. Step 3: service-level driven safety-stock calculation that honors your target fill rate (typically 95-98%). Step 4: reorder-point recommendation rolled up to the replenishment system. OctOpus owns the analytical layers; you keep your existing ERP / replenishment software.
Multi-echelon when relevant
For warehouses + stores, single-echelon optimization undersizes safety stock. OctOpus detects multi-echelon structure (your data has both warehouse_id and store_id columns) and switches to multi-echelon optimization with risk-pooling math — typically frees 15-25% of stock-level capital while maintaining service.
Outputs you can act on
Per-SKU reorder point and order quantity, formatted for direct paste into your ERP. Sensitivity reports showing how recommendations shift under different service-level targets. Top SKUs by inventory-cost-saving opportunity, ranked so the merchandising team knows where to focus. All re-runnable monthly as fresh sales data lands.
Key capabilities
- Demand forecast → safety stock → reorder point, end-to-end.
- Multi-echelon when warehouse + store data is present.
- Service-level driven (95% / 98% / 99% fill-rate targets).
- Lead-time uncertainty modelled, not assumed.
- Output formatted for direct paste into your ERP.
Frequently asked questions
What inputs does OctOpus need for inventory optimization?
Sales history (date, SKU, location, units_sold), lead-time data (PO date, receipt date, supplier), and your target service level. Optional but powerful: promotions calendar, weather, holidays, inbound-supply pipeline.
Can it handle multi-echelon (warehouse + store)?
Yes. When OctOpus detects both warehouse_id and store_id columns in the data it switches to multi-echelon optimization with risk-pooling — which typically frees 15-25% of inventory capital while maintaining service. Single-echelon (one-level) is the default when the data has only one location dimension.
Does it integrate with my ERP?
Output formats for direct paste into SAP, Oracle, NetSuite, and most replenishment-system CSV imports. OctOpus also hosts a prediction API so your ERP can pull live reorder-point recommendations programmatically.
How does it handle seasonality?
Demand forecasting (the first step) automatically detects seasonality via the NeuralForecast / Chronos models. Safety stock is computed under the demand-distribution at the relevant horizon, so seasonal peaks get larger buffers and troughs get less — capital isn't tied up out of season.