Application of data science in the automotive industry

Application of Data Science in the Automotive Industry

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Introductio

Table Of Content

Manufacturing

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Conclusion

Application s of Data Science

Autonomou s Vehicles

Product Development

Sustainability Initiatives

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.

And each vehicle has a different fueBl reefafkicTiiemnec: y , s o d a t a science is necessary to optimize the

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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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