Application of Data Science in the Automotive Industry

Application of Data Science in
the Automotive Industry

Table Of
01
Introduction
02
Applications
of Data
Science
03
Product
Development
Content
04
Manufacturing
05
Autonomous
Vehicles
06
Sustainability
Initiatives
07
Conclusion

Introduction
With the automotive industry’s maturity and wide
reach, there are many opportunities for
companies to rebuild around data.
One application is working with data across
different data systems and data types. Many data
scientists are accustomed to using tabular data,
which means the data is in a table format, similar
to Excel. But automotive data scientists have a
much greater variety of data to work with.

Application of Data
Science
Data science drives product
development
Data science drives excellence in
manufacturing
Data science drives connected and
autonomous vehicles
Data science drives sustainability
initiatives

Data Science Drives Product
Development
Data science in automotive begins with product
development. Data science is used for tasks like
analyzing new model configurations and
modeling component part reliability. Instead of
building components and testing at each stage
as an isolated system, data science
supplements the process through simulation
and analysis at scale.

Data science drives excellence
in manufacturing
Automotive data scientists also ensure that
only high quality vehicles are sold. While
engineers are capable of testing the quality of
each vehicle, this has to be performed on an
individual basis for each vehicle. Data
scientists can analyze an entire population of
parts, suppliers, and test data. They closely
analyze the financial performance of suppliers,
predict their ability to deliver on time given
past performance, and use econometrics with
regressions to check the economic conditions
of supplier locations.

Data science drives connected
and autonomous vehicles
One of the hottest topics in futurism today is
connected and autonomous vehicles, which rely on
deep learning models and sensor fusion algorithms.
Data science is crucial to building these vehicles: It’s
used to translate IoT indicators like oil life monitors,
battery charge monitors, and full diagnostics
instrumentation into actionable insights. For example,
it’s not enough to simply detect a pedestrian—sensors
must be able to discern where they’re walking to. Also
important are safety systems, including driver
protection and environmental safety.

Data science drives sustainability
initiatives
Sustainability is very important to all automotive
manufacturers. Governments set targets for fuel
efficiency, but each auto company has its own goals.
Break Time: 01pm - 02pm
And each vehicle has a different fuel efficiency, so data
science is necessary to optimize the fuel efficiency of a
company’s entire line of vehicles. Optimization efforts
can allow auto manufacturers to claim government
credits for fuel efficiency. This has a threefold benefit
of being good for the environment, providing more
value for customers, and opening up a potential
source of income.

Conclusion
Data science impacts so many other
stages in the automotive lifecycle. In
marketing and sales, data science
predicts customer movement and
churn. In service and customer
analytics, data science improves the
customer post-purchase experience
and delivers improvements in product
quality.

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