Workflow 01
Inventory + Demand Agent
Stock level monitoring → reorder triggering → demand-prediction adjustments → stockout-risk flagging → carrying-cost optimization. Continuous.
By function · Logistics
Inventory that auto-corrects. Supplier scorecards that stay current. Shipping decisions per order.
Inventory levels that auto-correct against demand. Supplier performance tracked continuously, not annually. Shipping decisions optimized per order. Returns processed without manual triage. Warehouse productivity that responds to bottlenecks before they cascade. That's the agentic logistics shape, and the cross-functional signals make it compound.
Agent workflows we deploy
Five concrete workflows, not abstract claims.
Each workflow is a multi-step orchestration with a visible task log and an outcome metric. Productized inside AaaS.
Workflow 02
Supplier Performance Agent
Delivery data ingestion → on-time rate calculation → quality scorecard → risk flagging → renegotiation prep → alternative-supplier recommendations.
Workflow 03
Shipping Optimization Agent
Order intake → carrier + service-level selection → cost optimization → tracking → exception handling → customer notification. Per-order, not per-RFP.
Workflow 04
Returns Handling Agent
Return request → reason classification → resolution path (replacement / refund / repair / restock) → customer communication → root-cause feed to product / quality.
Workflow 05
Warehouse Productivity Agent
Pick rate monitoring → bottleneck detection → resource reallocation suggestions → shift-planning recommendations → exception escalation.
BI signals
What this function generates, and what it consumes from the rest of the business.
The cross-functional integration is what makes agents compound. A signal generated here often triggers action somewhere else.
Generated
- Stockout rates
- Carrying cost trends
- Supplier on-time rates
- Shipping cost per order
- Returns rate by category
- Warehouse productivity metrics
Consumed
- Sales demand signals
- Finance cost data
- IT system telemetry from warehouse systems
- Customer-success feedback on delivery experience
Software + integrations
The stack we integrate.
WMS, TMS, ERP, supplier portals, carrier APIs, demand-planning platforms, integrated through Zyos OS. Cross-function dependence on Sales (demand) + Finance (cost) + Operations (process changes). Capability language only.
Operations changes
What the team experiences in the first 90 days.
- 01Inventory decisions: weekly planning → continuous adjustment.
- 02Supplier reviews: annual → continuous scorecards.
- 03Shipping decisions: rate-table-driven → per-order optimization.
- 04Warehouse work: static shift plans → responsive reallocation.
Measured business outcomes
Typical 90-day movement.
Ranges, not promises. Actual outcomes depend on the starting state surfaced by PI Implementation.
| Outcome | Typical 90-day movement |
|---|---|
| Stockout rate | −30–50% |
| Carrying cost | −10–20% |
| Shipping cost per order | −8–15% |
| Returns processing time | −50–70% |
| Warehouse productivity (units per hour) | +10–25% |
Customer Success cadence
How the results stay proven, quarter over quarter.
QBR slide tracks stockout trend, supplier scorecards, shipping cost trend, returns rate, warehouse productivity. OKRs typically tie to working capital + customer delivery experience + cost-to-serve.
Ready when you are
Start with a measurement.
The Opportunity Engine intake routes by function. Pick this one in the bottleneck question and the diagnostic will identify the two or three workflows worth deploying first. Want it run for you as a managed service? Agent as a Service is productized on zyos.io.