automotive

Machine learning takes automation to a new level. But what does this imply for the role of humans, who seem to remain essential for the effective control of AI systems. The development of energy-efficient and fair algorithms and the optimization of data quality are crucial for the future viability of machine learning and artificial intelligence. The articles in this issue examine the technology's key potential and areas of application.

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?

Due to COVID-19 the production lines of the German automotive manufacturers were stopped for an average of 30 days. Even allegations of exhaust manipulation, the required structural change in the direction of electro mobility as well as stricter CO2 regulations are representatives for risks that impact automotive value chains. In order to encounter such risks in an entrepreneurial manner, cooperative, integrative and analytical skills are required, which are brought together in a mature risk management model. While automotive manufacturers and suppliers are in the scientific and public focus of such risks less attention is paid to the risk management of directly affected contract logistics service providers in this industry. In this article a risk management model for contract logistics service providers in the automotive sector is developed that leads to elaborated risk mitigation strategies. A differentiation of risks should take place on three levels by not only company-internal ...

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.

In recent decades the maintenance has experienced a continuous change in the industry by the requirements and thus their influence has risen to the production system. But every industry and every production process is affected differently from it. In this paper, therefore, the most recent developments in the maintenance be excerpted from current studies and transferred into the automotive industry, specific into the supplier industry with screen printing products. Previously we created from current studies for the changing meaning of maintenance a cause-effect diagram. In order to raise the perception of the maintenance change in the industry, we conducted subsequently by an expert survey. From this we were able to describe deriving effects on the production system and the formulation of future research needs.

In the automotive industry, picking areas - so called supermarkets - are used to supply the production with material. By supplying the material in small quantities and with high frequency supermarkets are an important enabler of just in time production. A new method is presented, which allows a quantitative evaluation of order processing time and physical workload. Using the method, design alternatives as well as existing supermarkets can be assessed quantitatively at a favorable cost-benefit ratio.

Due to a difficult market situation represented by the world economic and related market crisis and a steady growth of model-variety the automotive industry is facing major challenges: short innovation cycles, increased complexity of production and cost pressures. To respond to these challenges advanced approaches for long-term planning of production systems have to be implemented. This paper presents approaches of plant design utilizing digital factory and shows applications of operations research (OR) for the planning of production systems. The application of OR methods holds significant potential not only for the network planning level but also for supporting strategic decisions on the production system level.

The automotive industry faces substantial challenges: a difficult market environment, increasingly complex technologies and shorter product life cycles characterize the industry. Modularization has been considered a promising remedy to help solving some of these demanding problems. Rewards of the application of modular systems are expected from various different sides. So far, however, its implications for production have rarely been discussed. This paper presents three production concepts for modular pro-ducts and their respective advantages and disadvantages. The integration of the production perspective allows more prudent strategic decisions regarding modularization.