How Data Science Is Used Throughout The Automotive Lifecycle A data-driven strategy is necessary for creating better, safer vehicles. With connected and autonomous vehicles, data science unlocks better mobility solutions for all. The Ford Model T was introduced in 1908 and quickly became popular due to its low cost, durability, versatility, and ease of maintenance. It is credited with "putting the world on wheels," increasing global mobility through manufacturing efficiencies at a cost the average consumer could afford. Today, the automotive industry is still on the cutting edge of technology, changing the way people get from point A to point B. Michael Crabtree, Lead Data Scientist at Ford Motor Company and instructor of our course Credit Risk Modeling in Python, stated in a recent webinar that the key difference is that its innovation is now driven by data science rather than manufacturing. Join the popular data analytics course in Mumbai, to gain profound knowledge on big data tools.
In the automotive industry, smart cities necessitate data science. Data science is scaling mobility for low-income communities in the same way that the manufacturing scalability of the Model T did over 100 years ago. It facilitates this change for everyone, regardless of class, gender, or ability, by making transportation easily accessible without the high cost of ownership. Optimization algorithms, for example, can provide businesses with energy-efficient vehicles to service rural communities for services ranging from Amazon deliveries to plumbing and food delivery. Data scientists also collaborate with reliability engineers to develop vehicles that help differently-abled communities. These are just a few examples, but Michael claims that there are almost limitless applications for data science, with many more yet to be discovered.
Working with data Because of the maturity and breadth of the automotive industry, there are numerous opportunities for companies to rebuild around data. One application interacts with data from various data systems and data types. Many data scientists are used to working with tabular data, which is data in a table format similar to Excel. However, automotive data scientists have access to a much broader range of data. In the automotive industry, for example, raw instrumentation data is commonly stored as a stream of hexadecimal digits. They may also come across data from intelligence systems, such as images and sensor point clouds. An automotive data scientist may be needed to understand why an autonomous vehicle behaves in a certain way and how this varies across vehicle models. Another opportunity is volume: Michael's largest database at Ford has 80 billion rows and queries in less than 10 seconds! Some of the automotive industry's real-time and
transactional systems process over 150 million records per day. Large data clusters are required because so much automotive data is generated. Many companies in the automotive industry have petabyte (million gigabytes) data clusters.
Every stage of the automotive product lifecycle involves data science. ● Product development is fueled by data science. Before a vehicle can be sold to a consumer, several steps must be completed. Product development is where data science in automotive begins. Data science is used to analyze new model configurations and model component part reliability, among other things. Data science supplements the process through simulation and analysis at scale rather than building components and testing at each stage as an isolated system.
● Manufacturing excellence is driven by data science. In addition, automotive data scientists ensure that only high-quality vehicles are sold. While engineers can test each vehicle's quality, each vehicle must be tested individually. Data scientists can analyze a large population of parts, suppliers, and test data. They closely examine suppliers' financial performance, forecast their ability to deliver on time based on previous performance, and use econometrics with regressions to assess the economic conditions of supplier locations.
● Data science propels connected and self-driving vehicles. Connected and autonomous vehicles, which rely on deep learning models and sensor fusion algorithms, are one of the hottest topics in futurology today. Data science is essential in developing these vehicles because it converts IoT indicators such as oil life monitors, battery charge monitors, and full diagnostics instrumentation into actionable insights. It's not enough to simply detect a pedestrian; sensors must also be able to determine where they're walking. Safety systems, such as driver protection and environmental safety, are also essential.
● Sustainability initiatives are driven by data science. All automotive manufacturers place a high value on sustainability. Governments set targets for fuel efficiency, but each automaker has its own set of objectives. And because each vehicle has a different fuel efficiency, data science is required to optimize the fuel efficiency of a company's entire vehicle line. So, if a company wants to sell both a large gas-guzzling pickup truck and an electric car, automotive data scientists can perform an optimization to reduce the overall fleet's fuel consumption while meeting the company's global sales targets. Automobile manufacturers may be able to claim government credits for fuel efficiency as a result of optimization efforts. This has three advantages: ● It is good for the environment. ● It provides more value to customers. ● It opens up a new market.
Other data science applications in automotive
Aside from what we've already mentioned, data science impacts many other stages of the automotive lifecycle. Data science predicts customer movement and churns in marketing and sales. Data science improves the customer post-purchase experience and product quality in service and customer analytics. To delve deeper into how data science is influencing the future of automotive, check out the data science course in Mumbai, and become a certified data scientist in automotive industry.