https://www.slideshare.net/neo4j/a-fusion-of-machine-learning-and-graph-analysis-for-freeform-data-entry-clustering-252223817

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Building Digital Twins with
Google Cloud and Neo4j
Christopher Upkes, Neo4j
Simon Floyd, Industry Director, Manufacturing, Google Cloud
June 7, 2022. GRAPHCONNECT 2022
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Table of Contents
Trends and opportunities
The Digital Thread
Top use cases
Google Cloud approach
Our solutions with Neo4j
Building the Graph for the Digital Thread
01
02
03
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05
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Trends & Opportunities
Electrifying everything for sustainability,
compliance, and new levels of reliability.
Activating wearable computing
with private 5G networks
Using AI when human cognition is
insufficient or low-value add
Creating revenue streams over the
lifetime of product ownership
Building digital resilience with
a manufacturing ontology
Connecting everything
with the digital thread

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The Digital Thread: connecting digital twins
and data throughout the product lifecycle
Manufacturing
Operations
Supply Chain &
Logistics
Products &
Customers

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OWNERSHIP
IoT
PPM
PROTOTYPING
PLM
ENGINEERING
DMS
SALES
CRM
OMNICHANNEL
FSM
MAINTENANCE
ERP
SOURCING
MES
PRODUCTION
Data Cloud for Manufacturers
AI
Analytics
Predictions
Visualization
Collaboration
REMANUFACTURING
MES
Optimal inventory
placement and
promotions.
Customer
personalization and
upsell selling.
Product
enhancement and
digital services.
Intelligence for
design and
innovation.
Supply chain risk
mitigation and
optimization.
Manufacturing
and quality
improvement.
Intelligent Products
Manufacturing Data Engine
Supply Chain Twin/Pulse
…
Digital Thread can solve for complex, data
intensive use cases across the value chain

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Simulation
NPI and PPM
So ware requirements
Product
de nition
Use cases diagrams
Product structure
Suppliers / AML / AVL
So ware development
Design definition
EXAMPLE CHALLENGES
We’re solving for context and meaning in the
connection between data types.
Features: relationship between the market
requirements, and product requirements.
Manufacturability: relationship between design,
procurement and supply chain constraints.
Functionality: relationship between use cases,
hardware capabilities, and software operation.

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Our solutions connect key data from the field to the enterprise and
enable advanced analytics and AI
Products
Connected, personalized digital
experiences with AI powered features,
analytics and commerce.
Intelligent Products
Essentials
Create new or update capabilities
with AI or ML to enhance the
ownership experience and enable
monetization.
Customer
360
Create customer-centric
omnichannel and personalized
product experiences backed with
advanced analytics.
Interaction Insights
Manufacturing
End-to-end connectivity from shop floor to
cloud with AI to reduce waste, increase
quality and increase productivity.
Visual
Inspection AI
Use AI to identify cosmetic defects
and assembly conformance with
high accuracy.
Manufacturing Data
Engine
Connect and analyze factory
equipment and processes at scale,
optimize operations, e.g.,
predictive maintenance.
Supply Chain
Build a digital representations of physical
supply chains by organizing and
orchestrating critical data.
Supply Chain
Pulse
Empowers business users to
manage end to end supply chains
in real time via visibility, alerting,
collaboration and using AI.
Supply Chain
Twin
Digital representation of the
physical supply chain that can be
used for planning and operations
decisions.
Shipment
+48 hours
vs. plan
Firebase
Cloud for
Marketing
Vertex AI BigQuery Looker
Cloud
Bigtable
Apigee API
Platform
Cloud
Storage
Visual
Inspection AI
Dataflow
Pub/Sub
Graph
Database
Management
System

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The top use cases that can be
solved with a digital thread
Full Context Decision
Making
Knowledge Transfer Design, Supply or
Manufacturing Variance
Impact and influence of
people, parts, process
Warranty Claim Validity Product Liability Authenticity Verification Recall Resolution
Analyze information with context,
relationships, and impact
Gain an understanding of a
product through the data
Quickly determine the difference
between products; parts, suppliers
Quantify and visualize the impact
of people, parts or processes
Determine if a warranty claim is
valid or fraudulent
Trace the root cause of a liability
issue
Identify if a product is authentic
or if the parts are OEM
Perform rapid what-if scenarios
with data context and access

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Neo4j graph database applications in Discrete Manufacturing
Manufacturing Digital Twin
Analyze Bill of Materials for
compliance, supplier management,
counterfeit parts, lead times, parts
life cycles, etc.
Supply Chain Twin
Optimize the flow of goods, uncover
vulnerabilities and boost overall
supply chain resilience.
Product 360
Personalize experiences for the
complete customer life cycle

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Building the
Graph for the
Digital Thread

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Graph Model Comparison
Master Graph ID Management Context Graph
● Untrusted or
incomplete sources
● All attribution included
in the graph
● Source of record
● Trusted and complete
sources
● Bipartite graph (source
IDs and clues)
● Whole graph process
● Trusted and complete
sources
● Attribution required for
traversal in graph
● Traversal entrypoint

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The Context Graph
In a Context Graph, unlike a Master Data Graph, key data
connects disparate trusted sources, ensuring context is
maintained as we traverse the graph.
Often, common business tasks involve retrieval of information from
numerous business applications. This requires management of
multiple accounts and logins and more importantly, understanding of
multiple application interfaces.
Trusted Source
Trusted Source
Trusted Source
Key
Data

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Discrete Manufacturing Context
In At the center of the Manufacturing Digital Twin is the part
community. This key structure connects trusted sources and
ensures context as we traverse the digital thread.
As an example, EBOMs, MBOMs and Batch Trees are all connected to
the part community. Other business entities, such as engineers,
vendors, and machinists and artifacts such as POs and QA
documents are connected to the BOM structures.
Engineering
Manufacturing
Procurement
Part
Community

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Place Image Here
Graph Communities
● Consist of connected trees
● Center of community generates influence
● Community outskirts are influenced
● Traverse community to understand
influence

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Place Image Here
Part Communities

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Part Community Model

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The Flow of Influence on the Thread
As we follow the thread from our anchor point, we travel through
the community structures where we realize the influence as
“pull” on the thread..

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Use Case: Knowledge Transfer
Imagine you are brand new to the engineering team at that builds
sophisticated technology for your consumers. The assemblies
you work on have been in production for years and some of the
engineers that worked on previous versions of the product have
retired or moved on.

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Use Case: Knowledge Transfer
With your Manufacturing Digital Twin, you can build your new
EBOM, submit it to the graph and follow then follow the digital
thread.
As we traverse the thread, we can see that we are able to reach a
recall through our connectivity to a particular MBOM Version and it’s
associated Quality Control Artifacts. Because there is a path between
the new EBOM and a recall, we can analyze the individual nodes that
the BOMs are composed of and determine the repeated design flaw.

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Place Image Here
And Suddenly…Centrality

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And Suddenly…Centrality

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Centrality is a Spotlight
● People
● Vendors
● Artifacts
● Assemblies
● Parts

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Path Analysis: An Examination of the
Thread
The length of our thread can help us to better understand,
compare and contrast the complexity and efficiency of individual
processes.
Some traversals return paths of unexpected length, usually longer
than expected.

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Path Analysis: An Examination of the
Thread
Understanding and analyzing the expansion of our traversal
provides invaluable insight.
By understanding the number of unique paths available for a
particular thread we can better understand complexity and the
associated risk.

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Things to Remember
● Try and solve problems with every source addition
● Architecture must support ad-hoc queries
● Data should be loaded in near-real time
● Embrace “schema-less”
● The Context Graph is typically a cache

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Thank you.
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