Artificial Intelligence Applications in the Automotive Industry Value Chain

Artificial intelligence (AI) is a field of human interest since the 1950s. In recent years, it has gained widespread attention due to the needed underlying technologies now available. AI technology is based on neuronal networks that require large amounts of data and fast computation to process learning algorithms, which help organizations greatly in their decision-making processes.

This paper explores AI applications in the automotive industry of the future with respect to the automotive value chain. The applications of AI are analyzed for all the subprocesses in the automotive value chain, which include: Research and Development, Procurement, Logistics, Operations, Marketing, Sales, and Connected Customer.

The AI applications for each sector are investigated in terms of risk and impact on businesses in the automotive sector. This helps to identify, from an organizational perspective, the most applicable areas of AI technology in the future automotive industry.

The recommendations made at the conclusion of this paper include explanations of how the AI application has to be made as part of the automotive value chain and the expected benefits of implementation. In addition, the findings are summarized in a table in the recommendations section, which shows the possible application areas of artificial intelligence in the automotive industry value chain along with the benefits and risks of the use-cases.

For a long time, the idea of replicating human cognitive processes through computer algorithms was primarily part of academic research laps and science-fiction movies. John McCarthy, one of the godfathers of artificial intelligence (AI) defines it as “The science and engineering of making intelligent machines, especially intelligent computer programs” (McCarthy, 2007).

AI can be described as a field of computer science which seeks to replicate human problem-solving approaches, incorporating the core skills of perception, understanding, and learning based on complex sets of data to perform human-like actions (Elements of AI, 2019) In recent years, AI and its applications have gained increasing attention, as some basis for using this technology more effectively is provided by cheaper memory space, faster data transmission, and the flood of data coming along with digitalization. Nowadays, AI is already implemented in a lot of our favorite smartphone apps and websites.

Large tech companies like Google and Amazon use it to analyze their customer data and offer personalized services. Additionally, there are numerous start-ups being built on ideas based on AI technology. How can this new technology be used in companies, which still have their main focus on the manufacturing of physical products?

This paper shows possible application areas of AI and machine learning (ML), a subset of AI, along the automotive value chain. In order to get a better picture of today’s automotive industry, the value chain and its structure are generally explained in the first place. Subsequently, the current trends in the automotive value chain are highlighted.

To view the possibilities, benefits, and risks of applying AI technology along the automotive value chain, the applications are then classified under the fields of R&D, Procurement, Logistics, Operations, Marketing, Sales and After Sales, and Connected Customer.