automotive

In the last few years, quantum computing has achieved new successes, such as Google’s quantum supremacy experiment [1], and has been showing adoption by large industrial firms to tackle complex problems. But what has led up to these developments? What kinds of problems can we expect to be able to solve in the near term with quantum computing? What are the challenges that we encounter with this technology and deploying within industrial settings?

In hardly any other sector have the production conditions changed so rapidly in recent years as they have in the automotive industry: An increasing variety of models, greater variety, greater segmentation and the increasingly international nature of automobile production require efficient supplier plants with greater production depth. Suppliers who want to compete therefore have to continuously and flexibly adapt their processes to the dynamics of the major manufacturers. At the forefront of this is the company IT: From this it is expected that more and more formerly analogous processes will be reproduced digitally. But digitization is only possible with great knowledge of the industry and individually tailored solutions.

Automotive manufacturers and their suppliers need a high degree of process flexibility to respond quickly and efficiently to the market demand. Picture, however, production, purchasing and supply processes not interacting optimally, causing delays which inevitably pushes costs higher. The search for weak points in the supply chain is a major challenge, because common analysis tools cannot holistically capture the already complex processes. The new process mining technology, however, allows for the analysis and visualization of the entire process chain, a task which classic analysis tools fail to accomplish.