Inventory Automation
Close the loop between demand signals, warehouse truth, and ledger impact.
Inventory error is margin error—usually before finance sees it.
PrimeAxiom connects sales velocity, supplier lead times, and location-level balances. Automation proposes purchases within guardrails, flags anomalies, and posts adjustments with approvals—not silent spreadsheet edits.
Why this department matters
Stockouts cost revenue; overstocks cost capital and obsolescence. Most businesses oscillate between both because signals arrive late.
Multi-channel brands multiply SKUs and aliases—without canonical identity, “available” is a guess.
Physical counts that disagree with books are symptoms of process gaps, not just “warehouse mistakes.”
Common pain points
Lag between sell and decrement
E-commerce, POS, and ERP disagree intraday. Oversells and cancellations erode trust.
Receiving not matched to POs
Partial receipts and blind receipts pile up; AP pays against PO while stock sits unlocated.
Multi-location visibility fragmented
Transfers are manual; safety stock is per-site guesswork; transfers hide shrink.
Reorder rules are static spreadsheets
Seasonality and promotions break min/max logic; buyers override without systemic feedback.
What we automate
SKU and alias governance
Canonical product IDs across channels; pack size conversions; bundle explosion rules.
Receiving workflows
Scan-to-PO matching with tolerances; putaway tasks; exception queues for variances.
Stock allocation and reservations
Reserve against orders; release on pick; prevent double allocation across systems.
Reorder suggestions
Blend lead time, service level targets, and demand forecasts—bounded by cash and space constraints.
Cycle count triggers
ABC stratification; auto-schedule counts when drift indicators fire.
Shrink and adjustment governance
Approval paths for write-offs; root-cause tags for operations review.
Typical workflow / system flow
Example chain from trigger to reporting—your exact shape depends on stack and policy, but the control pattern stays consistent.
Signal
Sales order, transfer request, or min threshold breach.
Check
ATP/CTP across locations; substitute suggestions if configured.
Commit
Reservation in WMS; pick list generation.
Fulfill
Ship confirm decrements stock; carrier events update customer comms.
Replenish
PO suggestion with vendor constraints; buyer approval if over cap.
Reconcile
Ledger alignment; variance tasks if counts diverge.
Systems & integrations
- Commerce: Shopify, BigCommerce, Amazon Seller patterns.
- POS: Square, Toast, Clover-class feeds.
- WMS/IMS: Cin7, Fishbowl, NetSuite WMS, custom Postgres inventory tables.
- ERP: NetSuite, Dynamics, SAP Business One integrations via APIs or staged imports.
- Suppliers: EDI or CSV ASN where vendors support it.
AI intelligence layer
AI is not a replacement for your ERP—it is an accelerator for extraction, classification, prioritization, and surfacing exceptions before they become rework.
- Demand forecasting: blend statistical baselines with promotional calendars and external signals.
- Anomaly detection: sudden spikes in returns, negative on-hands, or phantom inventory.
- Classification: auto-tag slow movers for markdown campaigns.
- Optimization: multi-echelon hints when you have hubs and stores—not only single-site min/max.
- Summarization: weekly inventory health brief for ops leadership.
Outcomes clients care about
Higher fill rates with less safety stock
Better signals reduce both stockouts and carrying cost.
Purchasing aligned to consumption
Suggestions reflect reality, not last year’s spreadsheet.
Auditable movement
Every adjustment has actor, reason, and before/after.
Faster financial close on inventory
Fewer surprise adjustments at period end.
Example use cases
Omnichannel retail with ship-from-store
Location-aware ATP; prevent oversell; prioritize nearest ship points for margin.
Contract manufacturer raw materials
MRP-style suggestions with batch constraints and shelf-life rules.
Spare parts for field service
Truck stock min/max with consumption analytics by technician skill region.
Cold chain with expiry
FEFO picking prompts; waste tracking tied to supplier quality issues.
3PL visibility
Inbound ASNs reconcile to receipts; billing disputes drop when timestamps align.
FAQs
- We have a WMS—what do you automate around it?
- The orchestration: cross-system identity, exception workflows, purchasing handoffs, and analytics that the WMS does not own end-to-end.
- How do you handle kitting and BOM explosion?
- We encode BOM rules in a governed layer so components decrement correctly when finished goods ship.
- Can buyers override suggestions?
- Yes—with reason codes and budgets. Overrides feed back into model features so the system learns organizational bias safely.
- What about consignment inventory?
- We model ownership and settlement triggers so reports separate your stock from supplier-held units.
See how this fits your stack
Request a workflow review: we map bottlenecks, integrations, and a phased plan—no generic pitch deck.