Speaker: Alessandro Svensson, Head of Neo4j Innovation Lab, Neo4j

1. The Case for Graph in
Supply Chain
Alessandro Svensson
Head of Neo4j Innovation Lab
A look under the hood when
innovating with graphs
July 28, 2020

2. Neo4j Innovation Lab
Everything is Naturally Connected
Your Organization
Context of Behavior
Logistics
DNA-strings
Customers
Supply Chain
Health Causes
Insurance Fraud
Purchase Patterns
People
Events
Proteins
Traffic Light Patterns
Weather Conditions
Materials
Systems of Records
IT-infrastructure
Home appliances
Knowledge

3. Neo4j Innovation Lab
The organizations that understand and leverage
how everything is connected in the context of
their domain, enjoy tremendous opportunity

4. Neo4j Innovation Lab
Failure to see or shift to adapting your
organization to how everything is
connected, means your operating at a deficit
— and puts you at risk of disruption

5. Neo4j Innovation Lab
Case Study: The Consumer Web
C
34,3%B
38,4%A
3,3%
D
3,8%
1,8%
1,8%
1,8%
1,8%
1,8%
E
8,1%
F
3,9%

6. Neo4j Innovation Lab
The connected world requires a
new way of thinking

7. Neo4j Innovation Lab

8. Neo4j Innovation Lab

9. Neo4j Innovation Lab
The companies that succeed in the
connected world do so with graphs

10. Supply Chain
Management

11. Supply Chain is a Graph
Neo4j Innovation Lab

12. • Customers
• Employee
• Suppliers
• Materials
• Products
• Plants
• Distribution Centers
• Shipments
• Etc…
Supply Chain is a Graph
Neo4j Innovation Lab

13. Why Supply Chain Matters
Supply Chain Management with
Graphs
• In the global economy, companies must stabilize their supply chains
by working with multiple suppliers, boosting inventories, diversifying
customers, and investing in omni-channel distribution.
• Effective supply chain management is crucial to mitigate both
supply and demand side risk and…
• …ultimately as a strategy to optimize revenue.

14. Data represented as in a
relational database
Supply Chain Management with
Graphs
Traditional technology and optimization
models cannot account for chain-reactions
triggered by major disruptions, because of
its inability to handle connections between
entities sufficiently.
Why graphs?

15. Data represented as a graph
Supply Chain Management with
Graphs
Traditional technology and optimization
models cannot account for chain-reactions
triggered by major disruptions, because of
its inability to handle connections between
entities sufficiently.
Why graphs?

16. How to innovate successfully
in the in the age of connected
data?

17. 1. Data Capture
2. Data Modeling & Storage
3. Processing & Analytics
4. End user-applications & Insights
Consider these 4 steps:
(Collecting the most relevant data for the use case)
(Choosing the right technology for the right job)
(Queries and Algorithms)
(Tangible, end-results)

18. 1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights
Because of its connected
nature, supply chain is a
massive data challenge

19. What are the essential data-
points and behaviors relevant to
your use case to capture?
1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights
Demand
Supply
Manufacturing
Warehousing
Order
Fulfillment
Transportation
Weather
Geospatial
Third
Party
On Prem Data
Lakes
Data
Warehouse
Cloud
IT infrastructure

20. Disparate Silos
Cross-Silo Connections
Property Graph Model
1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights
Demand Supply Manufacturing Warehousing Order
Fulfillment
Transportation Weather Geospatial Third
Party

21. 1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights
Modeling a Supply Chain Graph
• Carrier
• Shipping Site
• Material
• Material Group
• Location
• Customer
• DC
• Plant
• Shipment
• Delivery
~16 in the model
• ON_DELIVERY
• SHIP_CARRIER
• SHIP_MODE
• CONTAINS
• IS_IN_GROUP
• SHIPPED_TO
• SHIPPED_FRO
• SOURCE
• HAS_LOCATION
Structural Elements
Behavioral Elements
Relationship Types
Temporal Elements
• TimeTree — Facilitates
point in time queries
and versioning

23. Query (e.g. Cypher/Python)
You know what you’re looking for and
make a decision in real-time
Local Patterns
Graph Algorithms
You’re learning the overall structure of a
network, updating data, and predicting
Global Computation
1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights
Input data

24. Learning through
prototyping

25. Supply Chain Software
Prototype
(Supply Chain Software)
Sign in
What’s going on
under the hood?

26. What’s going on under
the hood? (Conceptual)
Prototype
(Supply Chain Software)
Detecting the shortest, cheapest path
between a manufacturing plant (in green)
and a customer (in blue) in a supply chain.
Shortest Path Algorithms

27. Prototype
(Supply Chain Software)
Detecting the shortest, cheapest path
between a manufacturing plant (in green)
and a customer (in blue) in a supply chain.
Shortest Path Algorithms
What’s going on under
the hood? (Conceptual)

28. Detecting the shortest, cheapest path
between a manufacturing plant (in green)
and a customer (in blue) in a supply chain.
Shortest Path Algorithms
Weights to relationships that
indicates cheapest paths
Prototype
(Supply Chain Software)
What’s going on under
the hood? (Conceptual)

29. Detecting the shortest, cheapest path
between a manufacturing plant (in green)
and a customer (in blue) in a supply chain.
Shortest Path Algorithms
Prototype
(Supply Chain Software)
🔥
Weights to relationships that
indicates cheapest paths
What’s going on under
the hood? (Conceptual)

30. Prototype
(Supply Chain Software)
Calculate all possible routes
PATH_0 (green) is the shortest path through the supply chain nodes. PATH_1
(orange) and PATH_2 (blue) are the next most cost-effective paths.
What’s going on under
the hood? (Conceptual)

31. Prototype
(Supply Chain Software)
Calculate all possible routes
PATH_0 (green) is the shortest path through the supply chain nodes. PATH_1
(orange) and PATH_2 (blue) are the next most cost-effective paths.
“Best” paths based on relevant criteria
What’s going on under
the hood? (Conceptual)

32. Prototype
(Supply Chain Software)
Centrality Algorithm
Graph depicting the centrality scores of nodes based on incoming and
outgoing shipments.
What’s going on under
the hood? (Conceptual)

33. Prototype
(Supply Chain Software)
Centrality Algorithm
Graph depicting the centrality scores of nodes based on incoming and
outgoing shipments.
What’s going on under
the hood? (Conceptual)

34. Prototype
(Supply Chain Software)
Node Similarity
I.e. Jaccard similarity, overlap
similarity, cosine distance,
euclidean distance etc.
Centrality Algorithm
Calculating centrality
scores
What’s going on under
the hood? (Conceptual)

35. Under the
hood
Applications
INPUT DATA
+
Analytics Pipeline
+
AI/ML-pipeline
1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights
Dashboards Route-planningExploration

36. Under the
hood
Applications
INPUT DATA
+ +
Analytics Pipeline
(AI/ML-pipelines)
It’s what happens under the hood that determine
the potential of your applications.
If you have a connected data problem
— make sure to solve it with graphs!
1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights

37. If you want to learn more…

38. The Applied Use Case
Training Program
Learning graphs in the context
of a use case
Brought to you by:
Neo4j Innovation Lab & Neo4j Graph Academy

39. 1. Data Capture 2. Data Model & Storage 3. Processing & Analytics 4. Applications & Insights
Micro
Batches
Micro
Batches
Real-Time
Transactions
Graph
Algorithm
Feedback
Cypher
Workbench
Modeling & Code Gen
Hop
ETL
Apache Kafka
Event Streams
GRAND Stack
Dashboard
Bloom &
Linkurious
Graph Visualizations
Neo4j BI
Connector
SQL BI Tools
Neo4j Browser
SQL BI Tools
DBC HDFS
ETL CSV JSON
Neo4j Graph
Database
Graph Data
Science Library
Cypher
Keymaker
Analytics Pipelines
Micro
Batches
Micro
Batches
Real-Time
Transactions
Graph
Algorithm
Feedback
Cypher
Workbench
Modeling & Code Gen
Hop
ETL
Apache Kafka
Event Streams
GRAND Stack
Dashboard
Bloom &
Linkurious
Graph Visualizations
Neo4j BI
Connector
SQL BI Tools
Neo4j Browser
SQL BI Tools
DBC HDFS
ETL CSV JSON
Neo4j Graph
Database
Graph Data
Science Library
Cypher
Keymaker
Analytics Pipelines
Neo4j Supply Chain
Solutions Framework

40. 1. Data Ingestion
2. Data Model & Storage
3. Processing & Analytics
4. Applications & Insights
Micro
Batches
Micro
Batches
Real-Time
Transactions
Graph
Algorithm
Feedback
Cypher
Workbench
Modeling & Code Gen
Hop
ETL
Apache Kafka
Event Streams
GRAND Stack
Dashboard
Bloom &
Linkurious
Graph Visualizations
Neo4j BI
Connector
SQL BI Tools
Neo4j Browser
SQL BI Tools
DBC HDFS
ETL CSV JSON
Neo4j Graph
Database
Graph Data
Science Library
Cypher
Keymaker
Analytics Pipelines
Neo4j AML Solutions
Framework

41. Keymaker
Analytics Pipelines
Cypher
Workbench
Modeling & Code Gen
Hop
ETL
Apache Kafka
Event Streams
Neo4j Graph
Database
Graph Data
Science Library
Cypher
GRAND Stack
Dashboard
Bloom &
Linkurious
Graph Visualizations
Neo4j BI
Connector
SQL BI Tools
Neo4j Browser
SQL BI Tools
The Property
Graph Model
The Case for
Graphs
Curriculum
Module
Graphs in Supply Chain
Module
Graph Queries & Algorithms
Module
Building Applications

42. Curriculum
Module
The Case for Graphs
Module
Graphs in Practice
Module
Solutions Framework
• The Case for Graphs in
Supply Chain
• Property Graph Model
• Money Queries
• Modeling
• Data Loading
Techniques
• Cypher
• Graph Algorithms
• Supply Chain
Solutions Framework
• Keymaker Analytics
Pipeline
• Application building
examples

43. About the program
A training program that teaches graphs in the context of a use case
What?
Enterprise developer & architect teams at large/midsize organizations
Who should participate?
12 hour instructor lead curriculum combined with self-
paced assessments and exercises.
How?
Fast, and inexpensive, way to learn how to apply graphs
from best practice in preparation for a more robust POC.
Why?
Use Case
Training
POC

44. Please feel free to reach out to me
for more information!
alessandro.svensson@neo4j.com
Alessandro Svensson, Neo4j

45. Thank you for listening! "
alessandro.svensson@neo4j.com
Alessandro Svensson, Neo4j