How We Build a Tier-10 Global Supply Graph

Source: Dev.to

Why Tier-1 Visibility Fails in Modern Supply Chains

A single product may depend on:

• dozens of Tier‑1 suppliers
• hundreds of Tier‑2/3 suppliers
• thousands of deeper‑tier producers, refiners, and upstream transform nodes

Yet 80 %+ of enterprises today have visibility only into Tier‑1. This creates well‑known problems:

• hidden single‑source dependencies
• exposure to upstream geopolitical or natural‑disaster risks
• unexpected compliance violations
• inaccurate business continuity planning
• inability to quantify concentration risk

Risk originates deep in the chain—but most tools only show the surface.
To solve this, we needed a continuously updated global supply graph representing multi‑tier dependencies across industries, geographies, product categories, and transformation processes.

What We Mean by a “Tier‑10 Supply Graph”

A Tier‑10 map is not a static supplier list. It is a graph that expresses:

• enterprises
• industrial products
• material transformations
• production relationships
• geographic footprints
• dependency edges across 10+ upstream hops

At this depth, the graph becomes a large‑scale, sparse, heterogeneous knowledge network. Our implementation currently spans:

• 100 M+ enterprises
• millions of industrial products
• multi‑hop dependency paths up to Tier‑10
• thousands of daily updates from public and permissible sources

These numbers represent a modeled knowledge graph built from globally available public data sources, structured signals, and proprietary transformation pipelines — not raw access to private data.

Core Engineering Challenges

1. Unifying Heterogeneous Data at Scale

Supply chain data is inherently fragmented:

• corporate registries
• product catalogs
• industrial classification systems
• logistics data
• trade flows
• news and regulatory signals
• technological capability descriptions
• ESG and compliance metrics

No single source covers everything. The graph must merge, resolve conflicts, normalize fields, and infer missing structure.

2. Modeling Multi‑Hop Dependencies

A product is rarely made from a single input. It passes through transform chains:

raw material → precursor → component → module → system → finished good

Each stage may occur in different countries, under different risk profiles. This is why Tier‑10 mapping matters: disruptions rarely stop at Tier‑2 or Tier‑3.

3. Updating the Graph Continuously

The world changes every day (factory shutdowns, sanctions, policy shifts, M&A, natural disasters, export controls, price/volume shocks). The supply graph must incorporate new signals without rebuilding the entire network.

We designed an incremental update pipeline with:

• entity/event detectors
• dependency refresh rules
• risk‑type annotations
• propagation scoring models

The goal is continuous updating, not “real‑time prediction.”

4. Representing Risk Propagation

A supply graph is not useful unless it can express:

• where disruptions originate
• how they travel
• which enterprises/products are exposed
• which nodes act as amplifiers or buffers

This requires graph‑propagation logic, not static dashboards.

The Architecture Behind the Tier‑10 Graph

At a high level, the system has four major layers.

1. Ingestion Layer

Collects and normalizes:

• corporate entity data
• product descriptions
• industrial classification trees
• open regulatory signals
• logistics + trade indicators
• manufacturing transformation hints

All sources must be permissible and transparently traceable.

2. Entity + Product Resolution Layer

Performs:

• deduplication
• clustering
• multi‑field entity matching
• product canonicalization

Consistency at this stage determines graph quality.

3. Dependency Construction Layer

Builds directed edges using:

• text‑driven extraction
• product transformation models
• co‑occurrence signals
• supply‑path inference
• industry‑specific logic

Edges represent probabilistic but explainable relationships.

4. Graph Intelligence Layer

Provides:

• multi‑hop traversal
• dependency expansion to Tier‑10
• concentration analysis (HHI, clustering, country exposure)
• risk propagation scoring
• evidence retrieval
• A2A‑compatible structured outputs

This is also where downstream agents—SupplyGraph Visualization, Concentration Analysis, Due Diligence—retrieve graph‑based reasoning.

Why Developers Use the Tier‑10 Graph Through A2A Agents

Rather than forcing teams to query the raw graph, we expose capabilities through modular A2A agents:

• supply‑graph visualization
• multi‑hop dependency extraction
• risk propagation analysis
• geographic concentration scoring
• enterprise due diligence

Each agent implements:

• a clear input schema
• deterministic structured outputs
• optional long‑running jobs
• evidence paths
• transparent reasoning

This makes integration simple for:

• procurement systems
• compliance automation
• supply chain analytics
• internal developer tools
• risk monitoring pipelines

No new platform needed—just composable building blocks.

What This Enables

A Tier‑10 supply graph allows organizations to answer questions that traditional systems simply cannot:

• “Which upstream nodes expose us to geopolitical risk across 6+ tiers?”
• “What are the hidden dependencies behind this product?”
• “How will a new export control propagate into our supply base?”
• “Which single‑region chokepoints exist beyond Tier‑3?”
• “What concentration risks exist across our entire product portfolio?”

These are not abstract problems—they define whether a company can operate reliably in a volatile world.

Explore the Specification

The supply graph itself is not a monolithic product; it is the underlying intelligence layer powering a set of open A2A‑compatible agents. Documentation and examples are available here:

👉 https://github.com/SupplyGraphAI/supplygraph-ai

If you work on supply chain engineering, risk modeling, or multi‑hop dependency systems, we’d love to hear how you approach similar challenges.