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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.
System- und Schnittstellenbeherrschung, Ideen- und Innovationsmanagement sowie die virtuell integrierte Produkt- und Prozessplanung sind zu entwickelnde Kompetenzen, die der veränderten Rolle des Menschen in der Industrie 4.0 Rechnung tragen. Dezidiert adressiert werden können diese in zukunftsweisend ausgerüsteten Lernfabriken.
While academia and industry see large potential for human-robot collaboration (HRC), only a small number of realized HRC application is currently found in industry. To gather more data about current hindrances to wider implementation of collaborative robots, a study among 15 robot manufactureres and 14 system integrators of collaborative robot technology has been conducted through a predesigned questionnaire procedure. Additionally, five industrial users of human-robot collaboration have been interviewed on the main challenges they experienced during the initial implementation process. The quantitative data has been analyzed using the Wilcoxon-Signed-Rank-Test. Accoring to the study participants, the main challenges within the implementation currently are the identification of HRC-suitable processes, the application of relevant safety norms (such as ISO 10218, ISO/TS 15066) and the application-individual risk assessment.
After the initiator of the ESB Logistics Learning Factory, Prof. Vera Hummel had made experience in developing and implementing a concept for a Learning Factory for Advanced Industrial Engineering (aIE) at the University of Stuttgart, Institute IFF between 2005 and 2008, she was appointed as a full professor at ESB Business School, a faculty of Reutlingen University in March 2010. Lacking a realistic, hands on learning and teaching environment of industrial scale for its industrial engineering students, first ideas for a Learning Factory that would strongly focus on all aspects of production logistics were drafted in 2012. Already back then, a strong integration of virtual and physical factory was desired: While the Learning Factory itself would be physical, the neighboring partners along the supply chain, such as suppliers or distribution warehouses, could be added in a fully virtual way. Considering implementation of the ESB Logistics Learning Factory a strategic initiative of the university, initial funding was provided by the faculty ESB Business School itself. Following its own creed, to provide future-oriented training for the region, also primarily local suppliers and manufacturers were selected as equipment providers to the new Learning Factory. During the initialization phase, 2014, a total of three researchers and nine students worked approximately four months to set up a first assembly line, storage racks, AGVs, or pick-by-light systems in conjunction with the underlying didactical concept. Since then, several hundred of students have participated in trainings and lectures held in the ESB Logistics Learning Factory, several research projects were carried out, and multiple high-level politicians and industry executives have been touring the shop floor. Also, more than EUR 2 million in research and infrastructure funds could be secured for expansion and upgrade — allowing the ESB Logistics Learning Factory today to represent many core aspects of an Industrie 4.0 production environment.
Mastering of complex systems and interfaces, idea and innovation management as well as virtually integrated product and process planning are essential competences to be developed and fostered to cope with the changing role of the workforce in a future industry 4.0 work system. Learning factories, like the Logistics Learning Factory at Reutlingen University, which are equipped with state-of-the-art infrastructure, offer a high potential to decidedly address these competences.
Die steigende Personalisierbarkeit von Produkten fuhrt zu einem wachsenden Variantenspektrum in der Fertigung. Nicht zuletzt aufgrund der damit einhergehenden Produktionskomplexität und den hohen Wandlungsanforderungen an die Montage werden viele komplexe Stückgüter weiterhin überwiegend manuell montiert. Visuelle Assistenzsysteme geben den Mitarbeitern die nötige Handlungsunterstützung, wenn kein Produkt dem anderen gleicht und damit das Fehlerpotenzial steigt.
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.
Shorter product life cycles and emerging technologies in the field of industrial equipment are changing the prerequisites and circumstances under which the design of assembly and logistics systems take place. Planners have to adapt the production in accordance with the underlying product at a higher pace, oversee a more complex system and - most importantly - find the ideal solution for functional as well as social interaction between humans and machines in a cyber-physical system. 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 towards more productive, less burdening work. To be able to design work systems which act on that maxim, specific competences such as the ability of integrated process and product planning as well as systems and interface competence are required. The ESB Logistics Learning Factory trains students as well as professionals to gain such qualification 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 composed of a manual assembly system, service robots, visual assistance systems, sensor-based work load monitoring and logistical resources. Group-based, activity oriented scenarios enable the participants to put the learnings into practice within their professional environments. By this, learning factories have an indirect impact on the transfer of proven best practices to the industry and thereby on the diffusion of the idea of human-centric working environment.
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.
