Spare parts forecast against the actual failure curve, not the calendar. Multi-warehouse balancing, vendor lead-time aware, tied to the truck rolls Asset Intelligence is about to dispatch.
Real-time stock with floor markers. Depot allocation heatmap. Pipeline-aware reorder triggers.
Safety stock based on usage averages, not forecasted failure curves. Half your warehouse is junk you'll never need; the other half stocks out at the wrong time.
The six capabilities that make this module work end-to-end. Pick any one as your starting point — they compound.
Forecasts driven by Asset Intelligence's failure curves — not historical averages. The right SKU, in the right warehouse, before the truck dispatches.
Per-SKU, per-vendor lead-time distribution. Expedite, alternate-source, and dual-source policies trigger automatically when slips appear.
Continuous re-balancing across warehouse network. Stock follows demand, not yesterday's geography.
Inventory reserves at dispatch — Asset Intelligence sees the part is on a truck, not in a warehouse. No double-allocation.
Carry vs stockout cost modelled per SKU. Slow-movers retire automatically; critical SKUs over-stocked deliberately. Total cost minimized.
Lead time, fill rate, RMA rate, recall events — scorecarded per vendor. Procurement leverage at the next negotiation.
Asset Intelligence forecasts failures. Inventory pre-positions parts. Truck rolls with the diagnosis and the right SKU. POs fire from real demand, not min/max.
Asset Intelligence's failure model becomes the demand forecast — per SKU, per region.
DailyNetwork-aware balancing redistributes parts before trucks roll.
ContinuousDispatch reserves the part; inventory state visible to all modules in real time.
< 1sPOs fire with expected demand, lead-time variance, alternate-source policy.
Per cycleCritical safety SKUs (breakers, contactors) overstock deliberately. High-velocity commodities (fasteners) run lean. Long-lead vendor-locked items dual-sourced where possible.
Each SKU class has its own policy. Total carry minimized. Stockout risk capped per criticality.
# Stock Policy · SKU classes class "safety-critical": service_level = 99.9% cover_days = 21 expedite_when_below = 7d dual_source = required class "high-velocity-commod": service_level = 99% cover_days = 5 blanket_po = annual class "long-lead-vendor-locked": service_level = 98% cover_days = vendor.lead_time × 1.5 forecast_horizon = 90d class "slow-mover": retire_when = usage.last_180d == 0 liquidate_via = "secondary-market"
NetSuite, SAP, Oracle — Allometry layers on top, no forklift.
Manhattan, Blue Yonder, native — pick, pack, transfer in your tool.
Coupa, Ariba, native — POs fire with full demand context.
ServiceTitan, FieldEdge, native — parts reserved at dispatch.
Give us your top 200 SKUs and 12 months of usage. We'll return the carry-cost reduction and stockout drop you'd see — modelled on your data.