Open Access

Industry 4.0 predicts that industrial processes, technological infrastructure and all corresponding Business processes, with the help of information and communication technology (ICT), will advance to integrated, ad-hoc interconnected and decentralized Cyber-Physical Production Systems (CPPS) with real-time capabilities of selfoptimization and adaptability. Considering this change, the human being will remain in a dominant role, because it is not expected that the human factor with its characteristics and capabilities will be substituted entirely by autonomously acting technology in the foreseeable future. The mechanical intelligence, for instance, is limited to the selection of predefined options, while human creativity, flexibility, the ability to learn and to improve are required to design and configure systems, processes and products. Humans have the expertise and experience to analyze, assess and solve - even in exceptional situations. However, the amount of purely manual tasks for shop floor workers will decrease. Their role will change from a manually executing to a proactive preconceiving worker with increased responsibility. Due to the growing degree of digitalization and interconnectedness, also the tasks and responsibilities for planning and design personnel will continuously expand and become more complex. The work in versatile ad-hoc networks with advanced ICT-Tools and assistance systems will lead to increased requirements regarding the knowledge, capability and capacity of the respective employees. The on-going pervasion of IT and emergence of systems with unprecedented complexity specifically require significantly improved capabilities in analysis, abstraction, problem solving and decision making from future labour. Accordingly, the industry is asking for graduates that are educated interdisciplinary and practice-oriented. Some universities already meet these expectations, using learning factories for realistic, action-oriented classes and trainings. Lecturers are confronted with the challenge to identify future job profiles and correlated qualification requirements, especially regarding the conceptualization and implementation of CPPS, and to adapt and enhance their education concepts and methods adequately and consequently. For the new, virtual world of manufacturing a proper understanding of engineering as well as Computer sciences is essential. Industry 4.0 implies this interdisciplinary split. Integrated competencies for product and process planning and design, methodological competencies for systematical idea and innovation management as well as a holistic system and Interface competence will be crucial to achieve interconnection of physical and digital processes and machines. The Vienna University of Technology and the ESB Reutlingen committed to integrate key aspects of Industry 4.0 into their respective learning factories successively. Thus, the students will act as the coordinators of the CPPS and thereby remain in the center of all learning and implementation activities.

In the last decade, numerous learning factories for education, training, and research have been built up in industry and academia. In recent years learning factory initiatives were elevated from a local to a European and then to a worldwide level. In 2014 the CIRP Collaborative Working Group (CWG) on Learning Factories enables a lively exchange on the topic "Learning Factories for future oriented research and education in manufacturing". In this paper results of discussions inside the CWG are presented. First, what is meant by the term Learning Factory is outlined. Second, based on the definition a description model (morphology) for learning factories is presented. The morphology covers the most relevant characteristics and features of learning factories in seven dimensions. Third, following the morphology the actual variance of learning factory manifestations is shown in six learning factory application scenarios from industrial training over education to research. Finally, future prospects of the learning factory concept are presented.

In academia and industry learning factories are established as close-to-reality learning environments for education and training in the manufacturing domain. Although the approach and concept of existing learning factories is often similar, orientation and design of individual facilities are diverse. So far, there is no structured framework to describe learning factory approaches. In the paper a multidimensional description model is presented in form of a morphology which can be used as a starting point for the structuring and classification of existing learning factory application scenarios as well as a support for the development and improvement of learning factory approaches.

Shorter product life cycles and emerging technologies are changing the circumstances under which the design of assembly and logistics systems has to be carried out. Engineers are in charge of adapting the production in accordance with the underlying product at a higher pace, oversee a more complex system and find the ideal solution for a functional work system design as well as social interactions between humans and machines in cyber-physical systems. Such collaborative work systems consider the individual capabilities and potentials of humans and machines to combine them in a manner that assists the operator during his daily work routine. To be able to design such work systems, specific competences such as the ability of integrated process and product planning as well as systems and interface competence are required. Learning factories train students as well as professionals to gain such qualifications by providing a close-to-reality learning environment based on a didactical concept which covers all relevant methods for ergonomic work system design and a state-of-the-art infrastructure. Group-based, activity oriented scenarios enable the participants to put the learnings into their everyday work life. Thereby, learning factories have an indirect impact on the transfer of proven best practices to the industry.

In recent years, the numer of hybrid work systems using human robot collaboration (HRC) increased in industrial production environments - enhancing productivity while reducing work-related burden. Despite growing availability of HRC-suitable manipulation and safety technology, tools and techniques facilitating the design, planning and implementation process are still lacking. System engineers who strive to implement technically feasible, ergonomically meaningful and economically beneficial HRC application need to make design and technology decisions in various subject areas, whereas the design alternatives per morphological analysis is applied to establish a description model that can serve as both a supporting design guideline for future HRC application of value-adding, industrial quality as well as a tool to characterize and compare existing applications. It focuses on HRC within assembly processes, and illustrates the complexity of HRC applications in a comprehensible manner through its multi-dimensional structure. The morphology has been validated through its application on various existing industrial HRC applications, research demonstrators and interviews of experts from academia.