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Fundamentale Veränderungen der heutigen Arbeitswelt stellen Menschen, Systeme, Prozesse und ganze Organisationen vor erhebliche Herausforderungen. Der Faktor Mensch leistet in allen Bereichen dieses Wirkgefüges einen essentiellen Beitrag zum Wettbewerbsvorteil vieler produzierender Unternehmen am Standort Deutschland. Der Wandel von Automatisierung zu selbststeuernden Unternehmen geht dabei nicht spurlos an dem wandlungsfähigsten Glied dieses Gefüges, dem Menschen, vorüber. Belastungsarten verändern sich, singuläre Bewältigungsstrategien genügen nicht mehr, um einen optimalen Beanspruchungszustand jedes einzelnen Individuums zu erreichen und gleichzeitig das höchstmögliche Potenzial zu schöpfen. Das Belastungs- und Beanspruchungscockpit bildet einen Lösungsansatz zur systematischen und durchgängigen Bewertung von Belastungszuständen und der individuellen Beanspruchung von Beschäftigten an Montagearbeitsplätzen. Es liefert in Echtzeit Informationen zum Belastungs- und Beanspruchungszustand des Mitarbeiters und kann mit Ergonomiebewertungsverfahren verknüpft werden. Der Aspekt der Multidimensionalität umfasst die Bewertung verschiedener Indikatoren unter Betrachtung ihrer Wirkzusammenhänge.
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.
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.
Increasingly volatile market conditions and manufacturing environments combined with a rising demand for highly personalized products, the emergence of new technologies like cyber-physical systems and additive manufacturing as well as an increasing cross-linking of different entities (Industrie 4.0) will result in fundamental changes of future work and logistics systems. The place of production, the logistical network and the respective production system will underlie the requirements of constant changes and therefore sources and sinks of logistical networks have to obey the versatility of (cyber-physical) production systems. To cope with the arising complexity to control and monitor changeable production and logistics systems, decentralized control systems are the mean of choice since centralized systems are pushed to their limits in this regard. This paradigm shift will affect the overall concept under which production and logistics is planned, managed and controlled and how companies interact and collaborate within the emerging value chains by using dynamic methods to generate and execute the created network and to allocate available resources to fulfill the demand for customized products. In this field of research learning factories, like the ESB Logistics Learning Factory at ESB Business School (Reutlingen University), provide a great potential as a risk free test bed to develop new methods and technical solutions, to investigate new technologies regarding their practical use and to transfer the latest state of knowledge and specific competences into the training of students and professionals. Keeping with these guiding principles ESB Business School is transferring its existing production system into a cyber-physical production system to investigate innovative solutions for the design of human-machine collaboration and technical assistance systems as wells as to develop decentralized control methods for intralogistics systems following the requirements of changeable work systems including the respective design of dynamic inbound and outbound logistic networks.
During the first years of their employment, the graduates are a liability to industry. The employer goes an extra mile to bridge the gap between university-exiting and profitable employment of engineering graduates. Unfortunately some cannot take this risk. Given this scenario, this paper presents a learning factory approach as a platform for the application of knowledge so as to develop the required engineering competences in South African engineering graduates before they enter the labour market. It spells out the components of a Stellenbosch University Learning Factory geared towards production of engineering graduates with the required industrial skills. It elaborates on the didactics embedded in the learning factory environment, tailor-made to produce engineers who can productively contribute to the growth of the industry upon exiting the university.
Gamification, the use of game elements for non-gaming purposes, may just make a huge impact on education, a contribution the world in general and South Africa in particular, desperately needs. In today’s fast-paced work environment, there is not only a severe skills shortage, but also a great need for graduates with practical knowledge - students that are not purely “book smart”. Didactic teaching habits have created an education realm in which reciting facts is more often than not what gets students to pass. Learning factories are physical, operational factories that serve as exemplary and realistic hands-on learning environments and provide an important step towards more industry-prepared graduates. Top universities around the world are establishing such environments and are showing superb results. This paper explores the potential benefit of applying gamification in such a setting to enhance the learning environment even further, and provide opportunities for training otherwise difficult to teach topics, such as shop floor management.
Die zunehmende Durchdringung von cyber-physischen Systemen und deren Vernetzung zu cyberphysischen Produktionssystemen (CPPS) führt zu fundamentalen Veränderungen von zukünftigen Montage-, Fertigungs- und Logistiksystemen, welche innovative Methoden zur Planung, Steuerung und Kontrolle von wandlungsfähigen Produktionssystemen erfordern. Zukünftige logistische Systeme werden dabei den Anforderungen einer hochfrequenten Veränderung und Re-Konfiguration ausgelöst durch wandlungsfähige Produktionssysteme für individualisierte Produkte und kleinen Losgrößen unterliegen. Der Einsatz dezentraler Steuerungssysteme, bei denen die komplexen Planungs-, Steuerungs- und Kontrollprozesse auf zahlreiche Knoten und Entitäten des entstehenden Steuerungssystems verteilt werden, bietet ein großes Potential, den Anforderungen in cyber-physischen Logistiksystemen gerecht zu werden. Eine zentrale Herausforderung ist dabei die echtzeitfähige Steuerung und Re-Konfiguration von sogenannten hybriden Logistiksystemen, welche u.a. durch die Kollaboration von Mensch und Maschine, der Kombination verschiedenartiger Fördermittel sowie verschiedenartiger Steuerungsarchitekturen geprägt sind und darüber hinaus auf hybriden Entscheidungsfindungsprozessen beruhen, welche die Fähigkeiten von Menschen und (cyber-physischen) Systemen synergetisch nutzen.
Lernfabriken, wie die ESB Logistik-Lernfabrik an der ESB Business School (Hochschule Reutlingen), bieten dabei weitreichende Möglichkeiten, diese innovativen Methoden, Systeme und technischen Lösungen in einer industrienahen und risikofreien Fabrikumgebung zu entwickeln sowie in die Ausbildung von Studierenden und Weiterbildung von Teilnehmern aus der Industrie zu transferieren. Um die Forschung, Lehre sowie Aus- und Weiterbildung im Bereich zukünftiger Montage-, Fertigungs- und Logistiksysteme auszuweiten, wird das bestehende Produktionssystem der ESB Logistik-Lernfabrik im Rahmen verschiedenster Forschungs- und Studentenprojekte schrittweise in ein dezentral gesteuertes cyber-physisches Produktionssystem, basierend auf einer ereignisorientierten, cloud-basierten und dezentralen Steuerungsarchitektur, überführt.
Global, competitive markets which are characterised by mass customisation and rapidly changing customer requirements force major changes in production styles and the configuration of manufacturing systems. As a result, factories may need to be regularly adapted and optimised to meet short-term requirements. One way to optimise the production process is the adaptation of the plant layout to the current or expected order situation. To determine whether a layout change is reasonable, a model of the current layout is needed. It is used to perform simulations and in the case of a layout change it serves as a basis for the reconfiguration process. To aid the selection of possible measurement systems, a requirements analysis was done to identify the important parameters for the creation of a digital shadow of a plant layout. Based on these parameters, a method is proposed for defining limit values and specifying exclusion criteria. The paper thus contributes to the development and application of systems that enable an automatic synchronisation of the real layout with the digital layout.
Teaching at assembly workstations in production in SMEs (small and medium sized companies) often does not take place at all or only insufficiently. In addition to the lack of technical content, there are also aggravatingly incorrect movement sequences from an ergonomic point of view, which "untrained" people usually automatically acquire. An AI based approach is used to analyze a definite workflow for a specific assembly scope regarding the behavior of several employees. Based on these different behaviors, the AI gives feedback at which points in time, work steps and movement’s particularly dangerous incorrect postures occur. Motion capturing and digital human model simulation in combination with the results of the AI define the optimized workflow. Individual employees can be trained directly due to the fact that AI identifies their most serious incorrect postures and provide them with a direct analogy of their “wrong” posture and “easy on the joints posture”. With the assistance of various test persons, the AI can conduct a study in which the most frequently occurring incorrect postures can be identified. This could be realized in general or tailored to specific groups of people (e.g. "People over 1.90m tall must be particularly careful not to make the following mistake...). The approach will be tested and validated at the Werk150, the factory of the ESB Business School, on the campus of the Reutlingen University. The new gained knowledge will be used subsequently for training in SMEs.
Manufacturing companies are confronted with external (e.g. short-term change of product configuration by the customer) and internal (e.g. production process deviations) turbulences which are affecting the performance of production. Predefined, centrally controlled logistics processes are limiting the possibilities of production to initiate countermeasures to react in an optimized way to these turbulences. The autonomous control of intralogistics offers a great potential to cope with these turbulences by using the respective flexibility corridors of production systems and applying intelligent logistic objects with decentralized decision and process execution capabilities to maintain a target-optimized production. A method for AI-based storage-location- and material-handling-optimization to achieve performance-optimized intralogistics system through continuous monitoring of performance-relevant parameters and influencing factors by using AI (e.g. for pattern recognition) has been developed. To provide the basis to investigate and demonstrate the potentials of autonomously controlled intralogistics in connection with turbulences of production and in combination with AI, an intelligent warehouse involving an indoor localization system, smart bins, manual, semi-automated/collaborative and autonomous transport systems has been developed and implemented at Werk150, the factory on campus of ESB Business School (Reutlingen University). This scenario, which has been integrated into graduate training modules, allows the analysis and demonstration of different measures of intralogistics to cope with turbulences in production involving amongst others storage and material provision processes. The target fulfilment of the applied intralogistics measures to master arising turbulences is assessed based on the overall performance of production considering lead times and adherence to delivery dates. By applying artificial intelligence (AI) algorithms the intelligent logistical objects (smart bin, transport systems, etc.) as well as the entire logistics system should be enabled to improve their decision and process execution capabilities to master short-term turbulences in the production system autonomously.