Google provides an Automotive and manufacturing update

1. Proprietary + Confidential
Automotive Industry
Digital Twin Innovation
with Neo4j and
Google Cloud
Kevin Karagitz
Industry Manager, Automotive
Google Cloud

2. Proprietary + Confidential
Proprietary + Confidential
How Google Cloud drives transformation in automotive
Unlock the software-
defined vehicle
Deliver value-adding services to
your customers throughout the
vehicle lifecycle by leveraging
connected vehicle data and
activating your ecosystem
Deliver the Customer
Experience 360
Enable a personalized customer
journey across channels utilizing
advanced marketing analytics
and transformational “One
Google” tools
Power efficient operations
Increase visibility and optimize
manufacturing and supply chain
operations by connecting data
silos and rolling out leading-
edge AI/ML use cases
Accelerate R&D
Innovation
Supercharge R&D with leading
AI/ML and high performance
compute (HPC) technology for
simulation, ADAS development
and autonomous driving
Become a Digital Enterprise
Drive a culture of innovation with collaboration tools
and digital skills enablement. Modernize your
infrastructure, reduce cost and optimize processes
with hybrid/multi-cloud
Sustainability
Partner with Google Cloud to achieve your ESG
objectives and become a sustainable organization

3. Proprietary + Confidential
Google Cloud Automotive
17/20 top automotive manu-
facturers leverage Google Cloud
3/3 leading autonomous driving
companies leverage Google Cloud
35 cloud regions, 106 zones and 173
network edge locations, available in
more than 200 countries and
territories

4. Proprietary + Confidential
Changing global dynamics are disrupting manufacturing supply chains
New Business Models
Cybersecurity
Geopolitical Considerations
Health & Environmental Crisis
Sustainability
Critical Components
Shortages
COVID-19 health crisis Extreme weather

5. Proprietary + Confidential

6. Proprietary + Confidential
The digital supply chain twin represents the physical supply chain
Private Segment
“The enterprise data”, such as orders,
forecasts, inventory, pricing, cost, etc.
Community Segment
The “shared data”, trusted collaboration
of data sets with business partners
Public Segment
The “public data”, such as news,
weather, traffic, risk, sustainability, etc.
Digital Supply Chain Twin

7. Proprietary + Confidential
Proprietary + Confidential
Neo4j & Google: A powerful combination for automotive
● Purpose-built machine, PLC and manufacturing applications
data ingestion and normalization solution
● Planet-scale Infrastructure as a Service. Fully managed
Multi Cloud Data Warehouse
● Industry-leading and flexible ML/AI toolchain
● Market leader in graph technology
● Aligned with GCP Automotive solutions
● Complements GCP AI/ML and analytics technologies

8. Proprietary + Confidential
Proprietary + Confidential
Graph Technology Use Cases in the Automotive Industry
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.
Customer 360
Personalize experiences for the
complete customer life cycle

9. Proprietary + Confidential
8 of the Top 10 Automakers in the World
Rely on Neo4j for Innovative
Manufacturing Solutions
With the help of Neo4j and BRIX PVM,
we’ve built a knowledge graph that can
incorporate well-defined semantics for tests,
sub-tests, and measurements in a unified
manner. We’re capturing the knowledge of
an expert engineer.”
Manufacturing Engineering, Top Japanese Auto Manufacturer
Smart Manufacturing Vehicle Quality
The customer’s vehicle testing and validation process ran
smoother, testing data was reusable for future insights, and they
experienced a reduced time to market for their vehicles.