Supply Chain Management with ArcadeDB

Supply Chain Disruptions Cost 8% of Annual Revenue

In 2024, 80% of organizations experienced supply chain disruptions. The average financial impact? 8% of annual revenue — and for aerospace and defense manufacturers, losses reach $184 million per year. The Red Sea crisis alone disrupted $6 billion in weekly trade flows, adding 10–14 days to shipping lead times.

The root cause isn't a lack of data — it's a lack of connected visibility. Supply chains are networks: suppliers feed into manufacturers, who ship through logistics providers, to warehouses, to customers. Every node depends on others. A disruption at a Tier 3 supplier in Shenzhen can halt a production line in Stuttgart — but only if you can see the connection.

Relational databases model supply chains as flat tables with JOIN-heavy queries that break down at multi-tier depth. Graph databases model supply chains the way they actually work: as interconnected networks where relationships are first-class citizens.

Multi-Tier Supply Network

-- Model the supply chain as a graph
CREATE VERTEX TYPE Supplier
CREATE VERTEX TYPE Component
CREATE VERTEX TYPE Product
CREATE VERTEX TYPE Warehouse
CREATE VERTEX TYPE Customer
CREATE VERTEX TYPE ShippingRoute

CREATE EDGE TYPE SUPPLIES
CREATE EDGE TYPE CONTAINS
CREATE EDGE TYPE SHIPS_VIA
CREATE EDGE TYPE STORED_AT
CREATE EDGE TYPE ALTERNATIVE_FOR

-- Multi-tier supplier discovery:
-- which Tier 3 suppliers feed into
-- our flagship product?
MATCH (p:Product {sku: 'WIDGET-PRO-X'})
      <-[:CONTAINS]-(c:Component)
      <-[:SUPPLIES*1..4]-(s:Supplier)
RETURN s.name, s.country, s.risk_score,
       c.name AS component,
       length(path) AS tier
ORDER BY tier, s.risk_score DESC

Your Supply Chain Is a Graph — Model It Like One

A supply chain is a network of suppliers, components, products, warehouses, shipping routes, and customers connected by typed relationships: who supplies what, what goes into which product, how it ships, where it's stored.

In a relational database, discovering your Tier 3 suppliers requires a chain of JOINs across multiple tables — slow, fragile, and often impossible beyond 2 levels. In ArcadeDB, multi-tier traversal is a single query that returns in milliseconds, regardless of depth.

ArcadeDB supports Cypher (OpenCypher), SQL, and Gremlin — your supply chain analysts can use SQL while your data engineers use Cypher, all against the same graph.

Real-Time Disruption Detection with Time Series

When a port closes, a supplier misses a delivery, or lead times start creeping up, you need to know immediately — not in next week's report. ArcadeDB's native time-series engine ingests delivery metrics, inventory levels, and shipping telemetry in real-time, with functions like rate(), delta(), and moving_avg() to detect trends before they become crises.

The key differentiator: time-series data is linked to graph entities. When a delivery rate drops, you don't just see a number — you instantly know which supplier, which components are affected, which products depend on those components, and which customers will be impacted. One query, full blast radius.

Ingest data via InfluxDB Line Protocol (compatible with Telegraf and Grafana Agent), REST API, SQL inserts, or Java API. Monitor everything in Grafana through ArcadeDB's native integration.

-- Track delivery performance over time
CREATE TIMESERIES TYPE DeliveryMetrics
  TIMESTAMP ts PRECISION MILLISECOND
  TAGS (supplier_id STRING, route STRING)
  FIELDS (lead_time_hrs DOUBLE,
          on_time LONG, delayed LONG,
          quantity LONG)
  RETENTION 730 DAYS

-- Detect suppliers with worsening lead times
SELECT s.name, s.country,
       ts.moving_avg(dm.lead_time_hrs, 7)
         AS avg_7d,
       ts.rate(dm.delayed, dm.ts) AS delay_trend,
       ts.delta(dm.lead_time_hrs, dm.ts)
         AS lead_time_change
FROM Supplier AS s
TIMESERIES s -> DeliveryMetrics AS dm
  FROM '2026-01-01' TO '2026-02-22'
WHERE ts.rate(dm.delayed, dm.ts) > 0
ORDER BY delay_trend DESC

-- Disruption blast radius: a supplier
-- goes down — what's the full impact?

MATCH (s:Supplier {name: 'Shenzhen Micro Ltd'})
      -[:SUPPLIES]->(c:Component)
      -[:CONTAINS]->(p:Product)
      -[:STORED_AT]->(w:Warehouse)
OPTIONAL MATCH
      (c)<-[:ALTERNATIVE_FOR]-(alt:Supplier)
RETURN c.name AS component,
       p.name AS product,
       p.revenue_annual AS revenue_at_risk,
       w.name AS warehouse,
       w.stock_weeks AS weeks_of_stock,
       collect(alt.name) AS alternatives,
       CASE WHEN alt IS NULL
         THEN 'CRITICAL - NO ALTERNATIVE'
         ELSE 'Switchable'
       END AS risk_level
ORDER BY revenue_at_risk DESC

-- Result: every product, warehouse,
-- revenue at risk, and whether we have
-- alternative suppliers — in one query

Blast Radius Analysis: From Disruption to Impact in Milliseconds

When a supplier fails, the first question is always: "What's the impact?" In a traditional system, answering this requires querying supplier tables, joining to component tables, joining to product tables, joining to inventory tables, and finally joining to customer order tables. Each JOIN adds latency and complexity.

In ArcadeDB, you traverse the graph from the disrupted supplier outward: which components are affected? Which products use those components? How much inventory remains in each warehouse? Are there alternative suppliers? What's the revenue at risk? A single query returns the complete blast radius with risk classification.

This isn't a report that takes hours to generate — it's a real-time query that returns in milliseconds, enabling supply chain managers to make decisions in minutes instead of days.

Logistics Optimization: Shortest Paths, Lowest Cost

Shipping routes are inherently graph problems: find the shortest path between a warehouse and a customer, considering cost, transit time, carbon footprint, and carrier reliability. Graph databases excel at pathfinding — the same algorithms that power navigation apps solve logistics optimization.

ArcadeDB's graph engine supports weighted shortest path, all paths, and constrained traversals that account for real-world logistics constraints: carrier capacity, customs requirements, hazardous materials restrictions, and preferred routes.

Combined with time-series data on historical transit times and real-time delay information, you can dynamically reroute shipments when disruptions occur — finding the next-best route in milliseconds.

-- Find optimal shipping routes
-- weighted by cost and transit time
SELECT expand(shortestPath(
  (SELECT FROM Warehouse
   WHERE name = 'EU-Central-1'),
  (SELECT FROM Customer
   WHERE id = 'CUST-4827'),
  'BOTH', 'SHIPS_VIA'
))

-- Dynamic rerouting: find alternatives
-- avoiding a disrupted port
MATCH (origin:Warehouse {name: 'EU-Central-1'})
      -[:SHIPS_VIA*1..6]->(dest:Customer
        {id: 'CUST-4827'})
WHERE NONE(n IN nodes(path)
       WHERE n.name = 'Port of Rotterdam')
RETURN [n IN nodes(path) | n.name]
       AS route,
       reduce(cost = 0, r IN relationships(path)
         | cost + r.cost_usd) AS total_cost
ORDER BY total_cost ASC
LIMIT 5

-- Store supplier capability embeddings
CREATE PROPERTY Supplier.capability_vec
  VECTOR(768)
CREATE INDEX ON Supplier (capability_vec)
  TYPE HNSW SIMILARITY cosine

-- Find alternative suppliers with similar
-- capabilities to a disrupted one
SELECT s.name, s.country, s.lead_time_avg,
       s.quality_score, s.certifications,
       vectorDistance(s.capability_vec,
         :disruptedSupplierVec) AS similarity
FROM Supplier AS s
WHERE s.capability_vec NEAR
  :disruptedSupplierVec
  AND s.status = 'active'
  AND s.name != 'Shenzhen Micro Ltd'
LIMIT 10

-- Full-text search on supplier contracts
-- for specific compliance clauses
SELECT s.name, s.contract_summary
FROM Supplier AS s
WHERE s.contract_text CONTAINSTEXT
  'force majeure penalty clause'

Alternative Supplier Discovery with Vectors

When a supplier goes down, finding a replacement isn't just about searching a directory — it's about finding a supplier with similar capabilities: manufacturing processes, quality certifications, lead times, geographic proximity, and component specifications.

ArcadeDB's native JVector engine stores supplier capability embeddings that encode these multidimensional attributes into a single vector. When a disruption hits, a nearest-neighbor search instantly finds the most capable alternatives — ranked by how closely they match the disrupted supplier's profile.

Combine vector similarity with full-text search on supplier contracts to verify compliance requirements, force majeure clauses, and regulatory certifications — all in the same database, without switching tools.

Inventory Intelligence: Graph Context + Time-Series Trends

Traditional inventory management treats each SKU in isolation. But inventory decisions depend on relationships: which products share components? If demand for Product A surges, which shared components will be depleted for Product B? Which warehouses serve overlapping regions?

ArcadeDB combines graph relationships (component-product-warehouse-customer dependencies) with time-series analytics (demand trends, consumption rates, reorder patterns) to provide inventory intelligence that accounts for the full network context.

Use time_bucket() for demand aggregation, rate() for consumption velocity, percentile() for safety stock calculations, and correlate() to detect demand patterns across related products — all joined with graph traversals that map component dependencies.

-- Track inventory levels over time
CREATE TIMESERIES TYPE InventoryLevels
  TIMESTAMP ts PRECISION MILLISECOND
  TAGS (sku STRING, warehouse STRING)
  FIELDS (quantity LONG, demand LONG,
          reorder_point LONG)
  RETENTION 365 DAYS

-- Components at risk: shared by multiple
-- products with high consumption velocity
MATCH (c:Component)<-[:CONTAINS]-(p:Product)
WITH c, count(p) AS product_count,
     collect(p.name) AS products
WHERE product_count > 3
RETURN c.name,
       products,
       ts.rate(c, 'InventoryLevels',
         'quantity', '2026-01-01',
         '2026-02-22') AS depletion_rate,
       ts.last(c, 'InventoryLevels',
         'quantity', '2026-01-01',
         '2026-02-22') AS current_stock
ORDER BY depletion_rate ASC

-- End-to-end traceability: trace a
-- product back to every raw material
MATCH (p:Product {batch: 'BATCH-2026-0218'})
      <-[:ASSEMBLED_FROM*1..8]-(material)
RETURN material.name, material.origin,
       material.certification,
       material.@type AS category,
       length(path) AS depth

-- Recall simulation: a raw material is
-- contaminated — find every product and
-- customer affected downstream
MATCH (m:RawMaterial
        {lot: 'LOT-TI-9942'})
      -[:ASSEMBLED_FROM*1..8]->(p:Product)
      -[:SHIPPED_TO]->(c:Customer)
RETURN p.name, p.batch, c.name,
       c.contact_email,
       count(*) AS units_affected
ORDER BY units_affected DESC

Compliance & End-to-End Traceability

Regulations like the EU Digital Product Passport, UFLPA (forced labor prevention), and conflict minerals reporting require companies to trace every component back to its raw material origin. This is fundamentally a graph traversal problem — follow the path from finished product to every upstream source.

ArcadeDB handles arbitrary-depth traversals efficiently. Trace a product batch through 8+ tiers of assembly back to raw material origins, with certification and compliance data at every node. When a recall happens, instantly identify every downstream product and customer affected.

Store audit trails, certifications, and inspection reports as document properties on graph nodes — no separate document database needed. Full-text search finds specific regulatory clauses across millions of supplier contracts.

Supply Chain Applications by Model

Supply chains generate data in every model: relationships between entities (graph), metrics over time (time series), capability profiles (vectors), contracts and certifications (documents + full-text). ArcadeDB handles all of them natively.

Application	Graph	Time Series	Vectors	Full-Text
Multi-Tier Visibility	Supplier → component → product	—	—	—
Disruption Detection	Blast radius traversal	Lead time & delay trends	—	—
Alternative Sourcing	Existing supplier links	Historical performance	Capability matching	Contract search
Route Optimization	Weighted shortest path	Transit time history	—	—
Inventory Intelligence	Component dependencies	Demand & depletion rate	Demand pattern matching	—
Compliance & Recall	Upstream/downstream trace	Audit trail timeline	—	Certification search
Demand Forecasting	Cross-product correlation	Seasonal patterns	Similar product matching	—
Sustainability Tracking	Origin → transport → product	Emissions over time	—	ESG report search

Why ArcadeDB for Supply Chain

Supply chain visibility requires mapping relationships (graph), monitoring metrics over time (time series), finding similar suppliers (vectors), and searching contracts (full-text). Most solutions require 3–4 separate systems. ArcadeDB is one.

Capability	ArcadeDB	Neo4j	TigerGraph	ArangoDB
Native Graph	✓ SQL + Cypher + Gremlin	✓ Cypher	✓ GSQL	✓ AQL
Native Time Series	✓ Full engine + Grafana	✗	✗	✗
Native Vector Search	✓ JVector (HNSW + DiskANN)	✓ Vector index	✓ TigerVector	○ Experimental
Native Full-Text Search	✓ Built-in	✓ Lucene-based	✗	✓ ArangoSearch
Document Model	✓ Native JSON documents	○ Properties only	○ Properties only	✓ Native JSON
IoT Data Ingestion	✓ InfluxDB Line Protocol	✗	✗	✗
Deployment	Anywhere (self-hosted)	AuraDB or self-hosted	Savanna or self-hosted	Oasis or self-hosted
License	Apache 2.0 (forever)	GPL / Commercial	Community / Enterprise	Apache 2.0 / Enterprise

Open Source. Apache 2.0. Forever.

Your supply chain data is mission-critical. You should own your infrastructure completely — the data, the database, and the freedom to deploy it on-premises, in your cloud, or at the edge. ArcadeDB is licensed under Apache 2.0, and we've made a public commitment: we will never change it.

No surprise license switches. No cloud-only features. No vendor lock-in. Read our commitment →

Enterprise Deployment Success

"We modeled our entire global supply network in ArcadeDB — 12,000 suppliers across 47 countries, with 85,000 component relationships. When the Red Sea disruptions hit, we ran a blast radius query in under 200ms that identified every affected product line, alternative suppliers, and warehouse stock levels. The same analysis used to take our team two days with spreadsheets."

— VP of Supply Chain Operations, Global Manufacturing Company
(Details limited by confidentiality agreement)

Impact Metrics:

75% faster supplier discovery and qualification
Disruption response time reduced from 2 days to 15 minutes
12,000 suppliers with 85,000 component relationships mapped
Sub-200ms blast radius analysis across 8 tiers
3 separate systems (graph DB + time-series DB + search engine) replaced by 1

Industries Using Supply Chain Graphs

Automotive: Multi-tier BOM tracking, just-in-time delivery monitoring
Aerospace & Defense: Compliance tracing, conflict minerals reporting
Pharmaceuticals: Cold chain monitoring, batch traceability
Electronics: Component sourcing, semiconductor supply mapping
Retail & CPG: Demand forecasting, last-mile optimization
Food & Beverage: Farm-to-fork traceability, recall management
Energy: Equipment sourcing, maintenance part tracking
Logistics: Route optimization, carrier performance analytics

Supply Chain Management