This document discusses optimizing supply chains with Neo4j graph databases. It notes that supply chains have complex relationships that are naturally modeled as graphs. It promotes Neo4j's graph database, tools, and algorithms for building digital twins of supply chains for purposes like visibility, optimization, and calculating scope 3 carbon emissions. Examples are given of companies using Neo4j for supply chain applications like routing, asset management, and equipment maintenance.

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© 2022 Neo4j, Inc. All rights reserved.
1
Optimizing Your
Supply Chain
with Neo4j

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© 2022 Neo4j, Inc. All rights reserved.
Senior Director, Strategy & Innovation
michael.moore@neo4j.com
Michael Moore, Ph.D.

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Consider: What Drives Your Business?
It’s not the numbers, it’s the relationships behind them
Plants
Warehouses
Suppliers
Distributors
Competitors
Partners
Regulations
Employees
Citizens
Customers
Products
Parts
Services
Regions

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This is a Graph

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“By 2025, graph technologies will be
used in 80% of data and analytics
innovations...”
Top 10 Trends in Data and Analytics, 11 May 2020, Rita Sallam et al.

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6
Graphs have low complexity and high fidelity
SQL RDBMS ER Diagram Graph (“Whiteboard”)

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Depict the business
as a graph
Squash the graph
into tables
Jam in foreign keys
to relate the records,
populate global index
7
Cheap Memory makes Graphs Compelling
https://jcmit.net/memoryprice.htm
SQL RDBMS workarounds to conserve memory
1979: Oracle v2.0 Released (yes, 43 years ago!)
= hidden technical debt
per MB
per MB

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QUERY PERFORMANCE AS # OF JOINS INCREASE
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Connectedness and Size of Data Set
Response
Time
Relational and
other NoSQL
databases
Native Graph Database
1000x Advantage
Minutes to milliseconds
5+ hops
3+ degrees
Thousands of connections
0 to 2 hops
0 to 3 degrees
Few connections

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Graphs Naturally Handle Complex Data
A B C D E
A B C D E
One-to-Many
Relationships
Across Many
Entities
Wide Data Complex Data Hierarchical & Recursive Data
Many-to-Many
Relationships
Nested Tree
Structures
Recursion (Self-
Joins)
Deep
Hierarchies
Link Inference
(If C relates to A and A relates to E,
then C must relate to E)
Node Similarity
Hidden Data
Legacy Data Frozen Data
Legacy SQL Systems Data Lake Fact Tables Graph Data Science - Machine Reasoning
A
C
E

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Neo4j 5
Graph Data Platform
Neo4j Database
User Tools
• Developer Tools (Desktop, Browser, Data
Importer)
• Graph Visualization (Bloom)
• Administration (Neo4j Ops Manager)
Language Drivers & Connectors
• Language Drivers (Java, JavaScript, .NET,
Python, Go)
• Spring Data & GraphQL Frameworks
• Kafka (Streaming), Spark, BI Connectors
Neo4j Aura
• Cloud Database-as-a-Service
Graph Data Science
• Enhanced Analytics and Graph-Native ML
Language Standards
• GQL, openCypher

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Rich Tooling For Rapid Development
Local database for rapid dev Visualize and explore your data API-driven intelligent applications
Query editor and results visualizer
data
Importer
Code-free data loader
ops
manager
Centralized management
11

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Demand Shaping Inventory Positioning Perfect Fulfillment Sustainability
Consider a omni-channel, multi-
echelon supply chain and
demand & supply risk to
determine optimized inventory
levels
Combine distributed order
management with warehouse
and transportation optimization
and enable automation of
processes
Reduce energy consumption
across operations, measure
risks, measure gas emissions,
adopt a circular economy and
measure the social impact of
your supply chain
Understand customers and
market trends to dynamically
adjust segmentation, promotions
and generate highly accurate
forecasts
Demand Shaping Inventory Positioning Perfect Fulfillment Sustainability
Supply Chain Complexity Issues
End-to-End Supply Chain Visibility

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Supply Chains Are Graphs
• Global Supply Chain Visibility
• Routing, Logistics, Distribution
• BOM Management
• Supply Chain Resiliency
• Scope 3 Carbon Reporting
Supply chains have complex linkages
and deep hierarchies and are most
naturally modeled as a graph.

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© 2022 Neo4j, Inc. All rights reserved.
14
GHG Reporting Requirements
GHG
Reporting
Timelines
• March 21, 2022:
SEC released new
proposals for climate-
related risk disclosures.
• February 2024:
First disclosures on
Scope 1 and 2 for large
organizations.
• February 2025:
Disclosures on Scope 3
emissions and
emissions intensity
required for large
organizations.
Organizations' supply chains often account for more than
90% of their greenhouse gas (GHG) emissions, when taking
into account their overall climate impacts.

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Formidable Data Collection Requirements
Upstream Value Chain Data
x Emission Factors
+
Downstream Value Chain Data
x Emission Factors
+
Existing Scope 1 and Scope
Estimates
= Total Carbon Estimate

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© 2022 Neo4j, Inc. All rights reserved.
Value Chain Complexity
Calculation Complexity

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Supply Chain Digital Twins
Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits
Benno Gerlach, Simon Zarnitz, Benjamin Nitsche and Frank Straube
Logistics 2021, 5, 86. https://doi.org/10.3390/logistics5040086
Network Level
Site Level

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18
OrbitMI
Maritime Routing
• Digital twin PLM system with full BoM
for all Army equipment, including costs,
armaments, force posture and readiness.
• Complex analysis is 7.5 X faster
• Rapid “What-If” analysis enables more
agile response to global scenarios
U.S. Army
Force Readiness
• Knowledge graph of 27 Million warranty
& service documents
• Graph AI learns failure mode “prime
examples” to anticipate maintenance
• Improves equipment lifespan and
customer satisfaction
Caterpillar
AI for Maintenance
Customer Examples of Supply Chain Twins
• Digital twin of global maritime routes
• Subsecond route planning
• Global carbon emissions reduced by
60,000 tons annually
• $12-16M ROI for OrbitMI customers

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Supply Chain Optimization using Neo4j
Neo4j Graph Data
Science Library
Neo4j
Database
Neo4j
Bloom
Inference & Predictions Graph Digital Twin Visualization & Investigation

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Graph Algorithms in Supply Chains
Graph algorithms enable reasoning
about network structure
K-Shortest Paths to identify the best
alternative routes

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21
Graph Algorithms in Supply Chain
Graph algorithms enable reasoning
about network structure
K-Shortest Paths to identify the best
alternative routes
Betweenness Centrality to find
critical bottlenecks or risk points
Degree Centrality to see distribution
centers with high use

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Graph Algorithms in Supply Chain
Graph algorithms enable reasoning
about network structure
K-Shortest Paths to identify the best
alternative routes
Similarity to find providers that can
step in during a disruption
Betweenness Centrality to find
critical bottlenecks or risk points
Degree Centrality to see distribution
centers with high use

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© 2022 Neo4j, Inc. All rights reserved.
Demo: Logistics Pathfinding with GDS
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24
North American Rail Network (NARN) Digital Twin
North American Rail Network
Total 527K Miles of Track
USDOT NARN GEOJSON datasets - nodes and lines
253K nodes, 715K relationships, 1GB
https://github.com/graphadvantage/neo4j-na-rail-network
Norfolk Southern rail network graph plotted using NeoMap

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Neo4j Graph Database
Yards connected by track type
Graph Data Science - Projecting Network Views
main-lines-network
Has low bridges & tunnels
double-stack-network
No low bridges & tunnels
Neo4j Graph Data Science
Projected virtual graph views
standard rail car
double-stack
intermodal rail car

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26
Neo4j Pathfinding
//Project main lines
CALL gds.graph.project(
'main-lines-network', 'Node', {relType: {type:
'CONNECTS_MIO', orientation: 'UNDIRECTED',
properties: {miles: {property: 'miles',
defaultValue: 1}}}}, {})
//Project double stack lines
CALL gds.graph.project(
'double-stack-network', 'Node', {relType: {type:
'CONNECTS_DS', orientation: 'UNDIRECTED',
properties: {miles: {property: 'miles',
defaultValue: 1}}}}, {})
//Find a route
CALL gds.shortestPath.dijkstra.stream(
'main-lines-network',
{relationshipWeightProperty: 'MILES',
sourceNode: id(start), targetNode: id(end)})
What is the lowest cost path between two points?
“Cost” could be physical distance, fuel consumption, trackage fees, carbon emissions, etc
Dijkstra shortest path solution visualized in NeoDash
Minimize
this cost

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© 2022 Neo4j, Inc. All rights reserved.
Demo: Manufacturing Supply Chain Analytics
Google SC Twin + Neo4j
27

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● Purpose-built supply chain twin solution
● Planet-scale Infrastructure as a Service. Fully
managed Multi Cloud Data Warehouse
● Industry-leading and flexible ML/AI toolchain
● Global market leader in graph technology
● Aligned with Google Cloud Supply Chain Twin
solution
● Compliments Google Cloud AI/ML and analytics
technologies
Neo4j & Google Cloud:
A powerful combination for supply chain
transformation

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Supply
Chain Twin
BigQuery
Event
processing
Cloud
functions
PubSub
Use cases
User
Engagement
Neo4j
Connector for BI
Neo4j Bloom
Monitoring and analysis /
Custom applications
Analytics
Graph queries
Looker
Graph store
Supply
Chain AI
Demand
Shaping
Inventory
Positioning
Perfect
Fulfillment
Sustainability
Vertex AI
Dataflow
Data
Engineering
Feature
Engineering
Graph
Data
Science
Data Sources
Private
ERP
WMS
TMS
Telemetry
IoT
Partner (ISV & Tech)
Community
Suppliers
Logistics Providers
Customers
Transportation Visibility
Public
Weather
Risk
Sustainability
Healthcare
Climate
Social
Maps
Events
Canonical
Data Model
Supply Chain
Pulse
Google Workspace

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Google Supply Chain Twin L2 Graph Data Model

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31
The Modern Supply Chain is a Knowledge Graph
https://www2.deloitte.com/content/dam/insights/us/articles/3465_Digital-supply-network/DUP_Digital-supply-network.pdf

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32
Knowledge Graphs
• Ontologies
• Taxonomies
• Friendly Naming
• Schema/Structure
• Master Data
• Slowly Changing Dims
• Hierarchies
• Mappings
• Business Processes
• Signal Events
• Granular Detail
• Real Time Context
• Communities
• Dependencies
• Isomorphic Subgraphs
• ML Predictions
A knowledge graph combines consistent business semantics, entities extracted
and unified from source data, detailed transactional flows, and in-graph
analytics/inference for decision support.
Semantic
Conventions
Resolved
Entities
Operational
Transactions
Graph
Inference

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33
Advantages of Graphs
FAST ELEGANT EFFICIENT UNIFYING INSIGHTFUL
Relationships
(and nodes)
are stored in
memory for
real-time
access
Complex
business
processes are
simply and
faithfully
represented
Queries
traverse
locally-linked
objects with
consistent
performance
Creates a
flexible,
connected
view across
disparate data
domains
Builds up
context,
enabling
reasoning,
inference and
predictions

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© 2022 Neo4j, Inc. All rights reserved.
34
Thank you!
Contact us at
sales@neo4j.com

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© 2022 Neo4j, Inc. All rights reserved.
DEMO

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Scope 3 Upstream Data Types
Scope 3 Category Primary Data Source Secondary Data Source
1. Purchased goods and
services
• Product-level cradle-to-gate GHG data from suppliers calculated
using site-specific data
• Site-specific energy use or emissions data from suppliers
• Industry average emission factors per material consumed from life
cycle inventory databases
2. Capital goods • Product-level cradle-to-gate GHG data from suppliers calculated
using site-specific data
• Site-specific energy use or emissions data from capital goods
suppliers
• Industry average emission factors per material consumed from life
cycle inventory databases
3. Fuel- and energy-
related activities
(not incl in scope 1
or scope 2)
• Company-specific data on upstream emissions (extraction of fuels)
• Grid-specific T&D loss rate
• Company-specific power purchase
data and generator-specific emission rate for purchased power
• National average data on upstream emissions (e.g. from life cycle
inventory database)
• National average T&D loss rate • National average power purchase
data
4. Upstream
transportation and
distribution
• Activity-specific energy use or emissions data from third-party
transportation and distribution suppliers
• Actual distance traveled
• Carrier-specific emission factors
• Estimated distance traveled by mode based on industry-average data
5. Waste generated in
operations
• Site-specific emissions data from waste management companies
• Company-specific metric tons of waste generated
• Company-specific emission factors
• Estimated metric tons of waste generated based on industry-avg data
• Industry average emission factors
6. Business travel • Activity-specific data from transportation suppliers (e.g., airlines)
• Carrier-specific emission factors
• Estimated distance traveled based
on industry-average data
7. Employee commuting • Specific distance traveled and
mode of transport collected from employees
• Estimated distance traveled based on industry-average data
8. Upstream leased
assets
• Site-specific energy use data collected by utility bills or meters • Estimated emissions based on industry-average data (e.g. energy use
per floor space by building type)

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Category Primary Data Examples Secondary Data Examples
9. Transportation and
distribution of sold
products
• Activity-specific energy use or emissions data from third-party
transportation and distribution partners
• Activity-specific distance traveled
• Company-specific emission factors (e.g., per metric ton-km)
• Estimated distance traveled based on industry-average data
• National average emission factors
10. Processing of sold
products
• Site-specific energy use or emissions from downstream value chain
partners
• Estimated energy use based on industry-average data
11. Use of sold products • Specific data collected from consumers • Estimated energy used based on national average statistics on product
use
12. End-of-life treatment
of sold products
• Specific data collected from consumers on disposal rates
• Specific data collected from waste management providers on
emissions rates or energy use
• Estimated disposal rates based on national average statistics
• Estimated emissions or energy use based on national average statistics
13. Downstream leased
assets
• Site-specific energy use data collected by utility bills or meters • Estimated emissions based on industry-average data (e.g., energy use
per floor space by building type)
14. Franchises • Site-specific energy use data collected by utility bills or meters • Estimated emissions based on industry-average data (e.g., energy use
per floor space by building type)
15. Investments • Site-specific energy use or emissions data • Estimated emissions based on industry-average data
Scope 3 Downstream Data Types

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Scope 3 Requires Upstream and Downstream Reporting
https://www.epa.gov/climateleadership/scope-3-inventory-guidance

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Granular Emission Factors for all GHG sources
4700+ Scope 3
Emission Factors
● Upstream (WTT)
● Downstream
● Freight Modality
● Carrier Type & Size
● Fuel Type
● UoM
● GHG Unit
https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2022
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