As one of the most dynamic industries, the automotive industry is responsible for creating safer and more efficient vehicles. This means using a data-driven approach to find more effective mobility solutions and improve the automotive lifecycle.
Here’s how big data analytics contributes to the process.
Stage One: Product Development
Before a vehicle takes its final form and is ready for sale, it undergoes several model configurations and prototyping steps. Data science implementation begins at the very first stage of the automotive lifecycle, i.e., the product development stage.
Automotive data engineers use big data analytics tools to analyze new model components and configurations for each part. They can also use data science applications for conducting analysis and simulation collectively instead of testing products in an isolated system.
Stage Two: Manufacturing
Automotive data scientists also use big data analytics in the manufacturing process. To ensure that vehicles sold are high-quality and safe, data analytics tools can be used for bulk testing.
Testing each vehicle individually is a time-consuming task. To make the testing process more efficient, data scientists use advanced data analytics tools to test an entire population of vehicles and parts. They collect data for specific people to closely analyze each component’s current and future performance.
Additionally, the information gathered through data science can predict supplier interactions and their ability to meet deadlines based on previous performances. Data scientists may also use econometrics to predict and determine the economic aspects of vendor and supplier locations.
Stage Three: Connected and Autonomous Vehicles
The automotive industry is fast moving toward connected and autonomous vehicles. These vehicles use sensor fusion algorithms and deep learning models to perform various functions.
Data science can help produce connected and autonomous vehicles. Data scientists may use data analytics software, Google analytics, and other tools to interpret IoT indicators and gain actionable insights. For instance, data science can help develop sensors that detect pedestrians on roads and the direction of their movement. Similarly, it can be employed to develop advanced safety systems to enhance driver protection.
Stage Four: Sustainability Initiatives
Finally, automotive companies use data science for developing and driving sustainability initiatives. Auto companies focus heavily on optimizing fuel efficiency, keeping their goals aligned with government targets and requirements.
Data analytics tools allow manufacturers to improve fuel efficiency for each vehicle. They can implement fuel efficiency throughout their fleet, catering to the different automobile types and using customer analytics software to optimize models.
PREDIK Data-Driven is a big data analytics company that offers customer analytics and data analytics solutions to automotive businesses in the US. Contact our team today for more details or request a demo.
