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The paper describes a new stimulus using learning factories and an academic research programme - an M.Sc. in Digital Industrial Management and Engineering (DIME) comprising a double degree - to enhance international collaboration between four partner universities. The programme will be structured in such a way as to maintain or improve the level of innovation at the learning factories of each partner. The partners agreed to use Learning Factory focus areas along with DIME learning modules to stimulate international collaboration. Furthermore, they identified several research areas within the framework of the DIME program to encourage horizontal and vertical collaboration. Vertical collaboration connects faculty expertise across the Learning Factory network to advance knowledge in one of the focus areas, while Horizontal collaboration connects knowledge and expertise across multiple focus areas. Together they offer a platform for students to develop disciplinary and cross-disciplinary applied research skills necessary for addressing the complex challenges faced by industry. Hence, the university partners have the opportunity to develop the learning factory capabilities in alignment with the smart manufacturing concept. The learning factory is thus an important pillar in this venture. While postgraduate students/researchers in the DIME program are the enablers to ensure the success of entire projects, the learning factory provides a learning environment which is entirely conducive to fostering these successful collaborations. Ultimately, the partners are focussed on utilising smart technologies in line with the digitalization of the production process.
Computers are increasingly used in teams in various contexts, for example in negotiations. Especially when using computer-support for decision making processes, it is an important question whether active collaboration within the team - for example via audio-conference - has additional benefits beyond the supply of full task-relevant information via computer. In team negotiations, team representatives are only able to represent the whole team, if diverse preferences of the team members are aligned prior to the negotiation. In an experimental study with 150 participants, we provided team members with the complete information about each other's preferences during an either collaboratively (computer-mediated) or seperately conducted computer-supported negotiation preparation and subsequently asked them for their priorities as representatives of the team. Our results showed that providing complete task-relevant information via computer is insufficient to compensate for the absence of active collaboration within the team.
The persistent development towards decreasing batch sizes due to an ongoing product individualization, as well as increasingly dynamic market and competitive conditions lead to new changeability requirements in production environments. Since each of the individualized products mgith require different base materials or components and manufacturing resources, the paths of the products giong through the factory as well as the required internal transport and material supply processes are going to differ for every product. Conventional planning and control systems, which rely on predifined processes and central decision-making, are not capable to deal with the arising system's complexity along the dimensions of changing goods, layouts and throughput requirements. The concepts of "self-organization" in combination with "autonomous ocntrol" provide promising solutions to solve these new requirements by using among other things the potential of autonomous, decentralized and target-optimized logistical objects (e.g. smart products, bins and conveyor systems) wich are able to communicate and interact with each other as well as with human wokers. To investigate the potential of automation and human-robot collaboration for intralogistics, a research project for the development of a collaborative tugger train has been started at the ESB Logistics Learning Factory in lin with various student projects in neighboring research areas. This collaboraive tugger train system in combination with other manual (e.g. handcarts) and (semi-) automated conveyoer systems (e.g. automated guided forklift) will be integrated into a dynamic, self-organized scenario with varying production batch sizes to develop a method for target-oriented sefl-organization and autonomous control of intralogistics systems. For a structured investigation of self-organized scenarios a generic intralogistics model as well as a criteria cataloghe has been developed. The ESB Logistics Learning will serve as a practice-oriented research, validation and demonstration environment for these purposes.
This paper generalizes the theory of policy uncertainty with the new literature on rational inattention. First, the model demonstrates that inattention is dependent on the signal variance and the policy parameter. Second, I discover a novel trade-off showing that a policy instrument mitigates attention. Third, the policy instrument is non-linear and reciprocal to both the size and variance of the signal. The unifying theory creates new implications to economic theory and public policy alike.
The flexible and easy-to-use integration of production equipment and IT systems on the shop floor becomes more and more a success factor for manufacturing to adapt rapidly to changing situations. The approach of the Manufacturing Integration Assistant (MIALinx) is to simplify this challenge. The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. This paper presents the implementation of MIALinx to retrofit legacy machines for Industry 4.0 in a manufacturing environment and focus on the concept and implementation of the easy-to-use user interface as a key element.
Manufacturing has to adapt to changing situations in order to stay competitive.It demands a flexible and easy-to-use integration of production equipment and ICT systems. The contribution of this paper is the presentation of the implementation of the Manufacturing Integration Assistant (MIALinx). The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. Furthermore, we describe the implementation of MIALinx by commissioning it in a manufacturing environment to retrofit legacy machines for Industrie 4.0. Finally, we validate the suitability of our approach by applying our solution in a medium-size company.
Haptic softness is a central product attribute for many fabric-related retailers. Can those retailers use music - an easy to implement in-store atmospheric cue - to influence consumers' perception of this central product attribute? Across four studies, this research shows that high (vs. low) music softness enhances consumers' haptic softness perceptions. We argue that this cross-modal effect occurs owing to a transfer of softness-related associations from the auditory to the haptic modality. To better inform retail practice, we examine three managerially relevant boundary conditions at the product and store levels.
Mystery shopping (MS) is a widely used tool to monitor the quality of service and personal selling. In consultative retail settings, assessments of mystery shoppers are supposed to capture the most relevant aspects of sales people’s service and sales behavior. Given the important conclusions drawn by managers from MS results, the standard assumption seems to be that assessments of mystery shoppers are strongly related to customer satisfaction and sales performance. However, surprisingly scant empirical evidence supports this assumption. We test the relationship between MS assessments and customer evaluations and sales performance with large-scale data from three service retail chains. Surprisingly, we do not find asubstantial correlation. The results show that mystery shoppers are not good proxies for real customers. While MS assessments are not related to sales, our findings confirm the established correlation between customer satisfaction measurements and sales results.
Development of an easy teaching and simulation solution for an autonomous mobile robot system
(2019)
With mass customized production becoming the mainstream, industries are shifting from large-scale manufacturing to flexible and customized production of small batch sizes. Agile manufacturing strategies adopted by SMEs are driving the usage of collaborative robots in today's factories. Major challenges in the adoption of cobots in the industry are the lack of a highly trained workforce to program the robot to perform complex tasks and integration of robot systems to other smart devices in the factory. In addition, the teaching and simulation by non-robotics experts of many industrial collaborative robot systems like the KUKA LBR iiwa is a major challenge, since these systems are designed to be programmed by robot experts and not by shop floor workers or other non-experts. This paper describes the research and development activities done for reducing the barriers in operation and ensure holistic integration of LBR iiwa cobot in the assembly on the example of the ESB Logistics Learning Factory. These include a visual programming solution for the easy teaching of various tasks. Robotic tasts are classified based on common robotics applications and application-specific blocks abstracting specific actions are implemented. A factory worker with no programming competency cour create robot programs by combining these blocks using a Graphical User Interface. In addition, a simulation solution was developed to visualized, analyse, and optimize robotic workflow before deployment. an autonomous mobile robot is integrated with the LBR iiw to improve reconfigurability and thus also the productivity. The system as a whole is controlled using an event-driven distributed control system. Finally, the capabilities of the system are analysed based on the design principles of Industrie 4.0 and potential future research ideas are discussed to further improve the system.
Digitalization changes the manufacturing dramatically. In regard of employees’ demands, global trends and the technological vision of future factories, automotive manufacturing faces a huge number of diverse challenges. Currently, research focuses on technological aspects of future factories in terms of digitalization. New ways of work and new organizational models for future factories have not been described yet. There are assumptions on how to develop the organization of work in a future factory but up to now, literature shows deficits in scientifically substantiated answers in this research area. Consequently, the objective of this paper is to present an approach on a work organization design for automotive Industry 4.0 manufacturing. Future requirements were analyzed and deducted to criteria that determine future agile organization design. These criteria were then transformed into functional mechanisms, which define the approach for shopfloor organization design