Established companies are increasingly opening their innovation systems to stay ahead of the competition. In this context, collaboration with innovative and agile start-ups are gaining continuously more importance. In the past established companies often used common corporate venturing methods such as incubators, accelerators, or corporate venture capital for collaboration. Compared to these methods the venture client model is relatively new and was invented by BMW employees in 2014. Using the venture client model, established companies become early clients of the start-ups even it is still a venture to do so. Together they carry out a pilot project to develop a proof of concept and validate it under real conditions. If successful, they enter a long-term collaboration to integrate the start-up’s solution into products, processes, or business models of the established company. The aim of this paper is to sum up the current literature and set a foundation for future research on this topic. Therefore, we conduct a systematic literature review. As a result, we provide a definition of the venture client model, describe characteristics, present advantages from the perspective of established companies and start-ups. In addition, we summarize the state of research regarding strategy, process, and organizational structure and for the venture client model. In terms of strategy, future research should elaborate defining elements of a strategy as well as ways to integrate the venture client model into the corporate innovation-ecosystem. Focusing on the process additional research is needed on a venture client reference process and its integration into the corporate’s core processes. Referring to the organizational structure a reference architecture for a venture client unit as well as its positioning within the organization (e.g., innovation management or corporate strategy) should be further investigated.

Automotive manufacturers and suppliers develop new vehicle technologies to increase traffic safety and transportation efficiency. Autonomous and cooperative vehicle systems are crucial examples of such advanced technologies. The hustle to deploy these fascinating systems onto public roads increases as customer’s expectations rise. Networked driving simulation represents an effective virtual prototyping tool that can support the development, and hence, accelerate system deployment. In networked driving simulation, two or more human-driven virtual vehicles share the same environment and form a very close approximation of real-world traffic interactions. This emerged multi-interactive virtual environment can serve various applications related to the new vehicle technologies and the ever increasing traffic complexity. This paper introduces the promising applications of networked driving simulation and outlines the necessary system design requirements. In addition, the work presents an extensive literature review and evaluation of utilizations of networked driving simulation. Furthermore, three compelling systems of networked driving simulation are analyzed regarding their technical specifications and application scopes. The systems are compared and evaluated using the derived requirements. Finally, potential future work is revealed regarding the design of resilient networked driving simulation systems that can be tailored for possible changes of application requirements.

The introduction of Systems Engineering is an approach for dealing with the increasing complexity of products and their associated product development. Several introduction strategies are available in the literature; nevertheless, the introduction of Systems Engineering into practice still poses a great challenge to companies. Many companies have already gained experience in the introduction of Systems Engineering. Therefore, as part of the SE4OWL research project, the need to conduct a study including expert interviews and to collect the experiences of experts was identified. A total of 78 hypotheses were identified from 13 expert interviews concerning the lessons learned. Using exclusion criteria, 52 hypotheses were validated in a subsequent quantitative survey with 112 participants. Of these 52 hypotheses, 40 could be confirmed based on the survey results. Only four hypotheses were rejected, and eight could neither be confirmed nor rejected. Through this research, guidance is provided to companies to leverage best practices for the introduction of their own Systems Engineering and to avoid the poor practices of other companies.

Mounting sensors in disk stack separators is often a major challenge due to the operating conditions. However, a process cannot be optimally monitored without sensors. Virtual sensors can be a solution to calculate the sought parameters from measurable values. We measured the vibrations of disk stack separators and applied machine learning (ML) to detect whether the separator contains only water or whether particles are also present. We combined seven ML classification algorithms with three feature engineering strategies and evaluated our model successfully on vibration data of an experimental disk stack separator. Our experimental results demonstrate that random forest in combination with manual feature engineering using domain specific knowledge about suitable features outperforms all other models with an accuracy of 91.27 %.

Automotive manufacturers and suppliers develop new vehicle systems, such as Advanced Driver Assistance Systems (ADAS), to increase traffic safety and driving comfort. ADAS are technologies that provide drivers with essential information or take over demanding driving tasks. More complex and intelligent vehicle systems are being developed toward fully autonomous and cooperative driving. Apart from the technical development challenges, training of drivers with these complex vehicle systems represents an important concern for automotive manufacturers. This paper highlights the new evolving requirements concerning the training of drivers with future complex vehicle systems. In accordance with these requirements, a new training concept is introduced, and a prototype of a training platform is implemented for utilization in future driving schools. The developed training platform has a scalable and modular architecture so that more than one driving simulator can be networked to a common driving instructor unit. The participating driving simulators provide fully immersive visualization to the drivers by utilizing head-mounted displays instead of conventional display screens and projectors. The driving instructor unit consists of a computer with a developed software tool for training session control, monitoring, and evaluation. Moreover, the driving instructor can use a head-mounted display to participate interactively within the same virtual environment of any selected driver. A simulation model of an autonomous driving system was implemented and integrated in the participating driving simulators. Using this simulation model, training sessions were conducted with the help of a group of test drivers and professional driving instructors to prove the validity of the developed concept and show the usability of the implemented training platform.

Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases are still being implemented as standalone versions; the existing IT-infrastructure is not fit for integrative bidirectional decision communication, and implementations only reach low technical readiness levels. We propose a reference architecture for the integration of prescriptive analytics use cases in smart factories. The method for the empirically grounded development of reference architectures by Galster and Avgeriou serves as a blueprint. Through the development and validation of a specific IoT-Factory use case, we demonstrate the efficacy of the proposed reference architecture. We expand the given reference architecture for one use case to the integration of a smart factory and its application to multiple use cases. Moreover, we identify the interdependency among multiple use cases within dynamic environments. Our prescriptive reference architecture provides a structured way to improve operational efficiency and optimize resource allocation.