How engineers can surface hidden upstream dependencies without access to proprietary supplier data.
Most supply-chain visibility tools stop at Tier-1. But the structural patterns that matter most — country-level concentration, single-region dependencies, and upstream bottlenecks — often emerge far deeper in the network, sometimes at Tier-4, Tier-6, or even Tier-10.
This raises a practical engineering question:
How can we algorithmically detect where a company’s upstream supply chain is dominated by one geography, even when no supplier lists or ERP data are available?
This article explains how SupplyGraph AI’s Geographic Concentration Analysis Agent uses graph inference, multi-hop traversal, and quantitative concentration metrics to surface these patterns — and how you can integrate it directly into your own system through our A2A API.
1. The Core Problem: Geographic Risk You Cannot See
Most enterprises do not have enough visibility to answer questions like:
- Which upstream tiers depend heavily on a single country?
- Which components lack geographic alternatives?
- Where could a regional disruption propagate into upstream bottlenecks?
Even companies with globally diversified Tier-1 vendors may still have deep-tier segments showing very high geographic concentration — something not detectable through traditional supplier-declaration approaches.
These insights are often needed for:
- compliance and ESG workflows
- due-diligence automation
- internal supply-chain risk dashboards
- strategic sourcing assessments
This is where graph-based inference becomes useful.
2. The Graph-Based Approach (How We Model Upstream Structure)
Instead of relying on declared supplier lists, the agent uses a global enterprise–product knowledge graph, encoding:
- enterprises
- products
- product-inclusion structures
- geographic attributes
By chaining product-inclusion relationships, we can infer likely multi-tier upstream structures.
⚠ Important Clarification
This graph does not represent:
- transactional supplier relationships
- purchase-order data
- proprietary vendor lists
- verified supply-chain certifications
It reflects inferred, probabilistic upstream pathways, not a reconstruction of any company’s official supply chain.
An abstraction of the graph:
Enterprise → makes → Product → includes → Component → made_by → Enterprise
Different industries exhibit different reachable depths; some may reach eight or more tiers, while others are shallower.
3. What the Geographic Concentration Analysis Agent Does
1) Multi-hop traversal
Builds an inferred multi-tier upstream structure based on product-inclusion logic.
2) Geographic segmentation
Maps each enterprise node to country and region attributes.
3) Quantitative concentration scoring
Using models such as:
HHI = Σ (share_i²)
dominance_ratio = count(country) / total
4) Substitutability scan
Identifies whether other regions appear in the graph for the same upstream segment.
This reflects only graph-level presence — not commercial availability, qualification status, or procurement feasibility.
5) Structured, transparent reporting
Including geographic dominance, HHI metrics, segment summaries, and evidence chains.
The output is analytical and informational; it is not a regulatory audit or compliance determination.
4. Example Output (Simplified)
{
"segment": "Tier-4 Electronics Components",
"dominant_region": "China",
"dominance_ratio": 0.91,
"hhi": 0.87,
"alternatives_exist": true,
"evidence": [
"enterprise_A → component_X → product_Y",
"enterprise_B → component_X → product_Y"
]
}
In practice, the agent produces a rich Markdown report with detailed explanations.
5. Try the Agent Interactively
You can explore the agent with no integration required:
https://supplygraph.ai/zk_chat_os/agentic/dialog.html?name=sg_chokepoint
Just enter a company name.
Entity matching is performed automatically.
6. A2A API Integration Guide
Developers can integrate the agent into analytics pipelines, internal dashboards, ETL flows, or risk-modeling services.
🔹 Manifest: Retrieve schema & pricing
curl -X GET https://agent.supplygraph.ai/api/v1/agents/sg_chokepoint/manifest \
-H "Authorization: Bearer <YOUR_API_KEY>"
🔹 Run Analysis Task
curl -X POST https://agent.supplygraph.ai/api/v1/agents/sg_chokepoint/run \
-H "Authorization: Bearer <YOUR_API_KEY>" \
-H "Content-Type: application/json" \
-d '{
"text": "Company Name",
"stream": true
}'
Notes:
-
"text"accepts a natural-language company name - Entity resolution is handled internally
- Streaming may return:
THINKING,TASK_ACCEPTED,TASK_RUNNING
🔹 Check Task Status
curl -X POST https://agent.supplygraph.ai/api/v1/agents/sg_chokepoint/run \
-H "Authorization: Bearer <YOUR_API_KEY>" \
-d '{"mode":"status","task_id":"<id>"}'
🔹 Retrieve Final Results
curl -X POST https://agent.supplygraph.ai/api/v1/agents/sg_chokepoint/run \
-H "Authorization: Bearer <YOUR_API_KEY>" \
-d '{"mode":"results","task_id":"<id>"}'
Example:
{
"success": true,
"data": {
"content": "## Concentration Analysis Report ..."
},
"metadata": {
"credits_used": 10
}
}
7. Where This Fits in Engineering & Analytics Work
✔ Compliance & ESG automation
Support documentation workflows with explainable metrics.
(Not a certification or regulatory determination.)
✔ Risk dashboards
Incorporate upstream geographic concentration metrics into internal UIs.
✔ Procurement analytics
Highlight hidden dependencies during supplier onboarding or category planning.
✔ Geopolitical risk modeling
Use concentration metrics as inputs to scenario simulations.
✔ Data science pipelines
Engineer HHI-based features for ML or forecasting models.
8. One Sentence Summary
If your visibility stops at Tier-1, you’re blind to the most important geographic risks in your supply chain.
This agent exposes those deep-tier dependencies — analytically, transparently, and at scale — using graph-based inference.
9. GitHub (A2A SDK + Examples)
➡️ https://github.com/SupplyGraphAI/supplygraphai_a2a_sdk
If this project is useful, consider leaving a ⭐ to help others discover it.
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