Automotive industry

How machine learning is taking the automotive industry to a new level

Machine learning is taking the automotive industry to a new level by improving user experience and leveraging the power of big data.

Most manufacturing operations in automotive industries still largely depend on human experience-based decisions. The emergence of Big Data, in conjunction with machine learning in automotive companies, has opened a pathway that helps bring about operational and business transformations, thereby leading to an increased level of precision in decision-making and improved performs.

The automotive industry continues to face a dynamic set of challenges. Changing market conditions, increased competition, globalization, cost pressures and volatility are causing the market landscape to change. Self-driving cars and changing usage patterns have increased customer expectations. Needless to say, the auto industry is on the verge of a revolution. Analytics is an area that has demonstrated an opportunity to deliver significant competitive advantage. The automobile is transformed by technology. AI and machine learning algorithms have found an increasing level of applicability in this industry. The collaboration of big data analytics and machine learning has enhanced the ability to process large volumes of data, accelerating the growth of AI systems. Machine learning in the automotive industry has a remarkable ability to bring out hidden relationships between data sets and make predictions.


Source: McKinsey

1. Improve vehicle performance through the integration of big data analytics


Machine learning algorithms can accurately integrate customer feedback analysis results into social media, for example, text and tweet analysis. This helps develop vehicle and subsystem performance to guide future product design. It also helps to detect failure patterns to establish a relationship between the failure and the causes of the failure. Take the example of a car company that has discovered that the cause of failure of several operations in the car is associated with region-specific problems such as poor fuel quality, weather conditions, road infrastructure, etc This company may use machine learning systems to develop region-specific customizations that can improve product reliability.

2. Take advantage of preventive and predictive maintenance


Source: Prometheus Group

Machine learning algorithms can help plan and execute predictive maintenance efficiently. Predictive maintenance uses monitoring and predictive modeling to determine machine condition and to predict what is likely to fail and when. Machine learning systems can help adjust the maintenance interval, where the same maintenance is performed but shifted backwards or forwards in time or mileage. Thus, machine learning systems can enhance predictive maintenance capabilities and help accurately predict future failures instead of diagnosing existing ones.

3. Improve the overall user experience in the vehicle


Machine learning facilitates personalization and intelligent personal assistance. It integrates scan results and learns user personality traits, creating user-specific profiles, which can then be leveraged to provide personalization and support.

Machine learning algorithms can be very useful in solving automotive problems, but organizations that implement big data analytics and machine learning systems need to know how to select the algorithm and the vectors of appropriate entry/feature for a specific problem domain. Selecting the correct feature vectors requires domain experts, and selecting the correct algorithms requires experienced data scientists. Once they know how to define the problem domain and business goals, and validate the selected algorithm in terms of functionality and performance metrics, machine learning systems can accurately demonstrate tangible business benefits.