![]() ![]() Of manufacturing systems is often an experience-based art. The manufacturing workforce is not ready for such disruptions due to the lack of vertical skills on digital technologies. ![]() Manufacturing industries are facing radical changes under the technological acceleration of Industry 4.0. The paper also presents the results of the prototypical implementation of the project. The courses are designed in a modular way, and aim to teach manufacturing concepts in three main topics: (1) the physical system and its dynamics, (2) the physical-digital data connections for smart online analytics, and (3) the exploitation of digital models for production. Beside digital learning contents, the learners are offered an interaction with lab-scale models of production systems built with modular components such as LEGO ®. ![]() The project delivers effective training courses to enable the uptake of industrial technologies and smart manufacturing systems for professionals, either executives or technicians. This paper presents how the FactoryBricks project aims at overcoming the aforementioned issues. Further, the companies need of offering training paths for long-life learning of their employees finds several obstacles in the availability of skilled trainers and the trainee's low engagement with traditional learning models. ![]() Production planning and control of manufacturing systems is often an experience-based art. It is applied to a learning factory for a company in the mobility industry to evaluate its superiority to an intuitive approach. This procedure extends the design approach of Abele et al. Subsequently, the best possible combination of configuration alternatives is determined algorithmically by solving an optimisation problem (step IV). The utility values of the potential configuration alternatives are determined based on an evaluation method (step III). Then, factory areas and configuration alternatives containing a combination of factory elements are derived from the products and processes (step II). For this purpose, four steps are distinguished: First, requirements for the technical configuration are derived from the primary goals of the learning factory (step I). In this paper, a method is presented that is based on solving an optimisation problem, which ensures the best possible selection of factory elements considering a target function with restrictions like the budget or the usable area. Intuitive selection has the disadvantage that the best possible selection is only achieved by chance. The selection of factory elements for learning factories usually takes place based on intuition. In addition to this, the learning factory concept is also regarded as an innovative learning concept in the field of didactics.ĭuring the design of learning factories, the configuration of the technical system plays an important role. The book addresses therefore both researchers in production-related areas, that want to conduct industry-relevant research and education, as well as managers and engineers in industry, who are searching for an effective way to train their employees. While today numerous learning factories have been built in industry (big automotive companies, pharma companies, etc.) and academia in the last decades, a comprehensive handbook for the scientific community and practitioners alike is still missing. Further research priorities and innovative learning factory concepts to overcome current barriers are offered. The state of the art of learning factories curricula design and their use to enhance learning and research as well as potentials and limitations are presented. Learning factory best-practice examples are presented in detailed and structured manner. Definitions of the term learning factory and a corresponding morphological model are provided as well as a detailed overview of existing learning factory approaches in industry and academia, showing the broad range of different applications and varying contents. It outlines the motivations, historic background, and the didactic foundations of learning factories. This book presents the state of the art of learning factories. ![]()
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