Purti
purti - fulfilment, the completion of a need
Supply-chain disruption prediction.
Purti sees supply-chain disruption while there is still time to act. It forecasts risk across the network, lets planners model responses before an event lands, and routes early warnings to the people who own them – turning supply chain from reactive firefighting into planned response.
Workflow
Problem
Supply-chain disruptions are seen too late, when options are already expensive or gone.
Outcome
Early prediction of disruptions with enough lead time to act, not just react.
01
Predict disruption
Risk is forecast across the supply network.
02
Plan scenarios
Responses are modeled before disruption lands.
03
Alert owners
Early warnings route to the right people.
04
Act with lead time
Decisions are made while options are still open.
Modules
Disruption Prediction
Forecast risk across the supply network.
Scenario Planning
Model responses before disruption lands.
Alerting
Route early warnings to the right owners.
Representative use cases
Early Detection of Port Congestion Shocks & Dynamic Ocean Freight Rerouting
A global consumer electronics manufacturer importing component sub-assemblies from Asian supplier hubs into North American and European manufacturing plants.
Raw Material Price Volatility & Supply Disruption Mitigation in Steel Fabrication
A heavy industrial equipment manufacturer consuming over 100,000 tons of structural steel plate and alloy tubing annually across four fabrication facilities.
Predicting Tier-2 & Tier-3 Supplier Insolvency & Operational Risk in Automotive
An automotive OEM sourcing critical precision castings, rubber seals, and specialized fasteners from a complex network of 1,200 Tier-2 and Tier-3 suppliers.
Dynamic Safety Stock & Reorder Point Optimization in Retail Distribution
A national retail chain operating 12 regional distribution centers (DCs) supplying 400 retail store locations with 30,000 SKUs.
Geopolitical Trade Risk Monitoring & Vendor Re-Allocation for Aerospace
An aerospace defense contractor sourcing specialized titanium forgings, rare-earth magnets, and electronic components across global international suppliers.
Cold-Chain Perishable Logistics Disruption Interception in Food Distribution
A global fresh food and produce distributor shipping temperature-sensitive agricultural goods from South American growers to European supermarket chains.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
Early
disruption lead time
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
6 agent roles
Act
Execute an approved step in the system of record and keep the evidence.
Covered by the platform
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
Covered by the platform
Pack Architecture & Agent Composition Graph
6 Composed AgentsControl gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
Capability atoms
- forecasting
- system-connectors
- scenario-simulation
- alert-routing
- risk-scoring
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- SAP Ariba
- Coupa
- Snowflake
- Dun and Bradstreet API
- SAP S/4HANA
- Workday
- Oracle NetSuite
- SAP MCP
Designed for
logistics, retail-cpg, manufacturing
Business case
Model a Purti scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 9% | — | — | — | — |
| Expected | 18% | — | — | — | — |
| Upside | 24% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Pairs well with