automotive industry

Artificial intelligence is increasingly being integrated into vehicles, but conventional product development processes often do not fully capture the specific requirements of AI projects. In order to meet these requirements, a process reference model (PRM) has been developed specifically for the development of AI functionalities in the automotive industry. This model is intended to support companies in adapting their conventional software development processes more easily to the special features of AI projects.

Transformative forces present companies with enormous challenges. At the same time, new forms of collaboration and new roles and responsibilities are emerging. Due to the risks associated with change, many employees hold on to old habits and work processes, which can slow down positive developments. The challenge for managers is to recognize this resistance, prevent it and turn it into acceptance or even proactive support.

The decentralized data ecosystem Gaia-X, which is currently under development, supports the future viability of the digital data economy in Europe. But how can relevant use cases be realized in Gaia-X from a service-oriented perspective? To answer this question, this article presents a methodology that describes a structured and interdisciplinary approach to service development in the ongoing Gaia-X 4 ROMS consortium research project [1]. In this project, federated services are realized in five processing steps on the basis of use cases. IT experts, software developers and industry users can leverage the model to efficiently coordinate the joint realization of use cases with Gaia-X and the goal of sovereign data exchange.

Finished vehicle logistics is of particular importance for the automotive industry, because it is the link between manufacturer and customers. If problems occur during the distribution of a car, it becomes visible to the customer, unless the problems can be solved during the following processes. Process interfaces between companies are frequently causing issues. That is why a survey was executed in order to identify those issues at the interfaces between car manufacturers and logistics service providers.

The technological and demographic change in the automotive industry leads to challenges for competence management. Increasing requirements with regard to professional and methodological competencies, combined with shortened innovation cycles, demand an increasing flexibility in employee trainings. The research project PLUG+LEARN aims at the development of reconfigurable solutions in competence development. Competencies along the automotive value chain are pooled in order to achieve a wide application of the results. This enables an effective cooperation between the actors of the PLUG+LEARN market place.

Over the last two decades, themes such as resource scarcity, environmental pollution and rising commodity prices have gained increasing importance in the design of automotive industry production processes. As a result, the pressure on car manufacturers to plan their production facilities according to energy efficiency criteria is constantly rising. At present, machine tool suppliers still provide insufficient data to support purchasing decisions based on energy efficiency aspects. A case study conducted with Daimler AG explores a car manufacturer’s specific information requirements with respect to energy efficiency. Furthermore, an approach for the integration of energy data in the product data sheets of machine tool suppliers is presented.

Quality assurances in the automotive industry pose additional processing times which are highly fluctuating and difficult to calculate. Within the rework processes the registration of vehicle positions is done manually. As a result the available information is not continuous and sufficient for an efficient control of these processes. Radio frequency identification (RFID) based positioning systems are one possibility to track the vehicles in the rework process automatically and to provide the required information. This paper presents the potential of a RFID-based positioning system in the rework area as well as examples for technical realization.

During the last decades, automotive manufacturers have outsourced many processes. Hence, complex networks consisting of automotive manufacturers, suppliers and logistics service providers have grown in the automotive industry. The resulting transports of components in these networks often require special load carriers. Consequently, companies nowadays face the challenge to handle a growing number of load carriers. For improving the load carrier management a higher transparency is required regarding the load carriers in the whole network. Transparency can be assured by the application of radio frequency identification technology. However, especially logistics service providers have to invest in this technology, which can be implemented in different ways. In this paper, some application possibilities are described and analyzed regarding economic and energy efficiency.

Brazil offers great potential, e.g. for the automotive industry. However procurement, production and distribution structures often lead to specific challenges. To cope with these challenges tested logistic planning approaches can be transferred. This article describes the cooperation between the Fraunhofer IML, Germany, and various Brazilian partners to initiate such a knowledge transfer and to adopt supply chain planning methods for the Brazilian automotive industry.

Nowadays, every partner in a global supply chain storages the logged information central. Due to the increasing complexity in supply chains, the central storage of information doesn’t fulfill the high requirements. Therefore it is essential to get a more transparent supply chain by exchanging important information. On the one hand, a technical part for exchanging information is needed and on the other hand, a standardization of which information should be exchanged is necessary. This paper presents a framework for the information management of global supply chains at the example of automotive logistics. The framework describes which information and where this information should be exchanged in a supply chain.