Machine learning-based predictive analytics and big data in the automotive sector
Melanie Lourens, S. Sharma, Revathy Pulugu, Anita Gehlot, Geetha Manoharan, Dhiraj Kapila
Abstract
Data science and machine learning's inherent capacities to automatically learn and optimize processes and products are making them more indispensable to the automotive industry of the future. This essay will define data science and machine learning and draw parallels between the two fields. A definition of automatic optimization is provided, and its value as a tool when paired with data analytics is shown. It illustrates how these technologies are currently being applied in the industry and presents examples using the important subprocesses in the automotive value chain. The sector is only beginning to explore the many of applications for these innovations, therefore we utilize futuristic use cases to illustrate their transformative potential. The article concludes by demonstrating how these technological advancements may increase productivity in the automotive industry and fortify the sector's customer focus across the board, from product and creation process to customers and their interaction with the product.