Buyers manually forecast and place replenishment orders weekly across thousands of SKUs. Stock-outs and over-stock both cost money.
The AI approach
Demand forecast per SKU per location → reorder-point calc with lead-time and safety-stock → auto-create POs for confident SKUs, route the rest to buyer.
The outcome
~−30% stock-outs
Stock-out rate down ~30%; over-stock down ~15%. Buyers focus on new launches and exceptions, not the long tail.
Try itInput → Process → Output
Input — history + stock + lead-time
SKU CL-117 · pipe-4"plant A · 80 wks history
avg/wk 412stock 580 (5d)lead 14d
SKU CL-204 · elbowplant B · stocky · sparse
avg/wk 14stock 38 (3w)lead 21d
Process — AI pipeline
1Forecast demand per SKU × locationReadPredictive
2Compute reorder pointRulesSymbolic
3Decide auto-PO vs review queueRulesSymbolic
4Generate PO drafts for buyerWriteGenerative
Output — PO recommendations
Click Run demo to forecast demand and propose reorder POs.
2 PO recommendations 1 auto · 1 review
SKU CL-117plant A · pipe-4"
+1,200 units
฿42,000
Auto-PO
SKU CL-204plant B · elbow · sparse demand
+50 units
฿1,900
Buyer review
CL-117: demand +14% MoM; stock cover <7 days; auto-PO of 1,200 covers 3 weeks. CL-204: sparse intermittent demand — Croston forecast unsure, route to buyer for judgement call.
Three AI types in this use case
SymbolicReorder formula (ROP = avg demand × lead time + safety stock); buyer approval thresholds; supplier MOQ rules.