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In the past, plant layouts were regarded as highly static structures. With increasing internal and external factors causing turbulence in operations, it has become more necessary for companies to adapt to new conditions in order to maintain optimal performance. One possible way for such an adaptation is the adjustment of the plant layout by rearranging the individual facilities within the plant. Since the information about the plant layout is considered as master data and changes have a considerable impact on interconnected processes in production, it is essential that this data remains accurate and up-to-date. This paper presents a novel approach to create a digital shadow of the plant layout, which allows the actual state of the physical layout to be continuously represented in virtual space. To capture the spatial positions and orientations of the individual facilities, a pan-tilt-zoom camera in combination with fiducial markers is used. With the help of a prototypically implemented system, the real plant layout was captured and converted into different data formats for further use in exemplary external software systems. This enabled the automatic updating of the plant layout for simulation, analysis and routing tasks in a case study and showed the benefits of using the proposed system for layout capturing in terms of accuracy and effort reduction.
Since its first publication in 2015, the learning factory morphology has been frequently used to design new learning factories and to classify existing ones. The structuring supports the concretization of ideas and promotes exchange between stakeholders.
However, since the implementation of the first learning factories, the learning factory concept has constantly evolved.
Therefore, in the Working Group "Learning Factory Design" of the International Association of Learning Factories, the existing morphology has been revised and extended based on an analysis of the trends observed in the evolution of learning factory concepts. On the one hand, new design elements were complemented to the previous seven design dimensions, and on the other hand, new design dimensions were added. The revised version of the morphology thus provides even more targeted support in the design of new learning factories in the future.
The world is becoming increasingly digital. People have become used to learning and interacting with the world around them through technology, accelerated even further by the Covid-19 pandemic. This is especially relevant to the generation currently entering education systems and the workforce. Considering digital aids and methods of learning are important for future learning. The increasing online learning needs open the case for integrating digital learning aspects such as serious gaming within education and training systems. Learning factories fall amongst the education and training systems that can benefit from integration with digital learning extensions. Digital capabilities such as digital twins and models further enable the exploration of integrating digital serious games as an extension of learning factories. Since learning factories are meant for a range of different learning, training, and research purposes, such serious games need to be adaptable across stakeholder perspectives to maximize the value gained from the time and cost invested into such design and development. Research into the development of adaptive serious games for multiple stakeholder perspectives must first determine whether such development can be developed that reaches the objectives set for different included stakeholder perspectives. The purpose of this research is to investigate this at the hand of the practical development of a digital adaptive serious game for stakeholder perspectives.
Product engineering and subsequent phases of product lifecycles are predominantly managed in isolation. Companies therefore do not fully exploit potentials through using data from smart factories and product usage. The novel intelligent and integrated Product Lifecycle Management (i²PLM) describes an approach that uses these data for product engineering. This paper describes the i²PLM, shows the cause-and-effect relationships in this context and presents in detail the validation of the approach. The i²PLM is applied and validated on a smart product in an industrial research environment. Here, the subsequent generation of a smart lunchbox is developed based on production and sensor data. The results of the validation give indications for further improvements of the i²PLM. This paper describes how to integrate the i²PLM into a learning factory.
Development of an IoT-based inventory management solution and training module using smart bins
(2023)
Flexibility, transparency and changeability of warehouse environments are playing an increasingly important role to achieve a cost-efficient production of small batch sizes. This results in increasing requirements for warehouses in terms of flexibility, scalability, reconfigurability and transparency of material and information flows to deal with large number of different components and variable material and information flows due to small batch sizes. Therefore, an IoT-based inventory management solution and training module has been developed, implemented and validated at Werk150 – the Factory on campus of the ESB Business School. Key elements of the developed solution are smart bins using weight mats to track the bin’s content and additional sensors and buttons which are connected to an IoT – Hub to collect data of material consumption and manual handling operations. The use of weight mats for the smart bins offers the possibility to measure the container content independent of the specific component geometry and thus for a variety of components based on the specific component weights. The developed solution enables focusing on key for success elements of the system to provide synchronization of the flow of materials and information resulting an increase of flexibility and significantly higher transparency of the material flow. AIbased algorithms are applied to analyse the gathered data and to initiate process optimizations by providing the logistics decision makers a profound and transparent basis for decision making. In order to provide students and industry visitors of the learning factory with the necessary competences and to support the transfer into practice, a training module on IoT-based inventory management was developed and implemented.
Circular economy aims to support reuse and extends the product life cycles through repair, remanufacturing, upgrades and retrofits, as well as closing material cycles through recycling. To successfully manage the necessary transformation processes to circular economy, manufacturing enterprises rely on the competency of their employees. The definition of competency requirements for circular economy-oriented production networks will contribute to the operationalization of circular economy. The International Association of Learning Factories (IALF) statesin its mission the development of learning systems addressing these challenges for training of students and further education of industry employees. To identify the required competencies for circular economy, the major changes of the product life cycle phases have been investigated based on the state of the science and compared to the socio-technical infrastructure and thematic fields of the learning factories considered in this paper. To operationalize the circular economy approach in the product design and production phase in learning factories, an approach for a cross learning factory network (so called "Cross Learning Factory Product Production System (CLFPPS)") has been developed. The proposed CLFPPS represents a network on the design dimensions of learning factories. This approach contributes to the promotion of circular economy in learning factories as it makes use of and combines the focus areas of different learning factories. This enables the CLFPPS to offer a holistic view on the product life cycle in production networks.
Smart factories, driven by the integration of automation and digital technologies, have revolutionized industrial production by enhancing efficiency, productivity, and flexibility. However, the optimization and continuous improvement of these complex systems present numerous challenges, especially when real-world data collection is time-consuming, expensive, or limited. In this paper, we propose a novel method for semi-automated improvement of smart factories using synthetic data and cause-effect-relations, while incorporating the aspect of self-organization. The method leverages the power of synthetic data generation techniques to create representative datasets that mimic the behaviour of real-world manufacturing systems. These synthetic datasets serve together with the cause-and-effect relationships as a valuable resource for factory optimization, as they enable extensive experimentation and analysis without the constraints of limited or costly real-world data. Furthermore, the method embraces the concept of self organization within smart factories. By allowing the system to adapt and optimize itself based on feedback from the synthetic data, cause-effect-relationships, the factory can dynamically reconfigure and adjust its processes. To facilitate the improvement process, the method integrates the synthetic data with advanced analytics and machine learning algorithms as well as and the cause-and-effect relationships. This synergy between human expertise and technological advancements represents a compelling path towards a truly optimized smart factory of the future.
The early involvement of experiences gained through intelligence and data analysis is becoming increasingly important in order to develop new products, leading to a completely different conception of product creation, development and engineering processes using the advantages that the dedication of the digital twin entails. Introducing a novel stage gate process in order to be holistically anchored in learning factories adopting idea generation and idea screening in an early stage, beta testing of first prototypes, technical implementation in real production scenarios, business analysis, market evaluation, pricing, service models as well as innovative social media portals. Corresponding product modelling in the sense of sustainability, circular economy, and data analytics forecasts the product on the market both before and after market launch with the interlinking of data interpretation nearby in real-time. The digital twin represents the link between the digital model and the digital shadow. Additionally, the connection of the digital twin with the product provides constantly updated operating status and process data as well as mapping of technical properties and real-world behaviours. A future-networking product, by embedded information technology with the ability to initiate and carry out one's own further development, is able to interact with people and environments and thus is relevant to the way of life of future generations. In today's development work for this new product creation approach, on one hand, "Werk150" is the object of the development itself and on the other hand the validation environment. In the next step, new learning modules and scenarios for trainings at master level will be derived from these findings.
Industrial practice is characterized by random events, also referred to as internal and external turbulences, which disturb the target-oriented planning and execution of production and logistics processes. Methods of probabilistic forecasting, in contrast to single value predictions, allow an estimation of the probability of various future outcomes of a random variable in the form of a probability density function instead of predicting the probability of a specific single outcome. Probabilistic forecasting methods, which are embedded into the analytics process to gain insights for the future based on historical data, therefore offer great potential for incorporating uncertainty into planning and control in industrial environments. In order to familiarize students with these potentials, a training module on the application of probabilistic forecasting methods in production and intralogistics was developed in the learning factory 'Werk150' of the ESB Business School (Reutlingen University). The theoretical introduction to the topic of analytics, probabilistic forecasting methods and the transition to the application domain of intralogistics is done based on examples from other disciplines such as weather forecasting and energy consumption forecasting. In addition, data sets of the learning factory are used to familiarize the students with the steps of the analytics process in a practice-oriented manner. After this, the students are given the task of identifying the influencing factors and required information to capture intralogistics turbulences based on defined turbulence scenarios (e.g. failure of a logistical resource) in the learning factory. Within practical production scenario runs, the students apply probabilistic forecasting using and comparing different probabilistic forecasting methods. The graduate training module allows the students to experience the potentials of using probabilistic forecasting methods to improve production and intralogistics processes in context with turbulences and to build up corresponding professional and methodological competencies.
Especially, if the potential of technical and organizational measures for ergonomic workplace design is limited, exoskeletons can be considered as innovative ergonomic aids to reduce the physical workload of workers. Recent scientific findings from ergonomic analyses with and without exoskeletons are indicating that strain reduction can be achieved, particularly at workplaces with lifting, holding, and carrying processes. Currently, a work system design method is under development incorporating criteria and characteristics for the design of work systems in which a human worker is supported by an exoskeleton. Based on the properties of common passive and active exoskeletons, factors influencing the human on which an exoskeleton can have a positive or negative effect (e.g. additional weight) were derived. The method will be validated by the conceptualization and setup of several work system demonstrators at Werk150, the factory of ESB Business School on campus of Reutlingen University, to prove the positive ergonomic effect on humans and the supporting process to choose the suitable exoskeleton. The developed method and demonstrators enable the user to experience the positive ergonomic effects of exoskeletal support in lifting, holding and carrying processes in logistics and production. The new work system design method will contribute to the fact that employees can pursue their professional activity longer without substantial injuries or can be used more flexibly at different work stations. Also new work concepts, strategies and scenarios are opened up to reduce the risk of occupational accidents and to promote the compatibility of work for employees. A training module is being developed and evaluated with participants from industry and master students to build up competence.