Application of data science in the automotive industry

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Application of Data Science in the Automotive
Industry
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02
01 Introductio
03 Product Development
Applications of Data Science 05 Autonomous
Vehicles
Table Of Content
06 Sustainability Initiatives
04 Manufacturing
07 Conclusion
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Introduction
With the automotive industrys 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.
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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
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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.
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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.
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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 Its used to translate IoT
indicators like oil life monitors, battery
charge monitors, and full diagnostics
instrumentation into actionable insights. For
example, its not enough to simply detect a
pedestriansensors must be able to discern where
theyre walking to. Also important are safety
systems, including driver protection and
environmental safety.
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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. And each vehicle has
a different fueBl reefafkicTiiemnec
0y1,psmo-d02aptma science is necessary to
optimize the fuel efficiency of a companys
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.
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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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