@incollection{ThimGrumSchueffleretal.2021, author = {Thim, Christof and Grum, Marcus and Sch{\"u}ffler, Arnulf and Roling, Wiebke and Kluge, Annette and Gronau, Norbert}, title = {A concept for a distributed Interchangeable knowledge base in CPPS}, series = {Towards sustainable customization: cridging smart products and manufacturing systems}, booktitle = {Towards sustainable customization: cridging smart products and manufacturing systems}, editor = {Andersen, Ann-Louise and Andersen, Rasmus and Brunoe, Thomas Ditlev and Larsen, Maria Stoettrup Schioenning and Nielsen, Kjeld and Napoleone, Alessia and Kjeldgaard, Stefan}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-90699-3}, doi = {10.1007/978-3-030-90700-6_35}, pages = {314 -- 321}, year = {2021}, abstract = {As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can "forget" their present specialization.}, language = {en} } @incollection{GrumThimGronau2021, author = {Grum, Marcus and Thim, Christof and Gronau, Norbert}, title = {Aiming for knowledge-transfer-optimizing intelligent cyber-physical systems}, series = {Towards sustainable customization : cridging smart products and manufacturing systems}, booktitle = {Towards sustainable customization : cridging smart products and manufacturing systems}, editor = {Andersen, Ann-Louise and Andersen, Rasmus and Brunoe, Thomas Ditlev and Larsen, Maria Stoettrup Schioenning and Nielsen, Kjeld and Napoleone, Alessia and Kjeldgaard, Stefan}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-90699-3}, doi = {10.1007/978-3-030-90700-6_16}, pages = {149 -- 157}, year = {2021}, abstract = {Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.}, language = {en} } @incollection{GronauRojahn2021, author = {Gronau, Norbert and Rojahn, Marcel}, title = {Das Industrial Internet of Things (IIOT)}, series = {IT-Recht : Recht, Wirtschaft und Technik der digitalen Transformation}, booktitle = {IT-Recht : Recht, Wirtschaft und Technik der digitalen Transformation}, editor = {Leupold, Andreas and Wiebe, Andreas and Glossner, Silke}, edition = {4., {\"u}berarbeitet und erweitert}, publisher = {C.H. Beck}, address = {M{\"u}nchen}, isbn = {978-3-406-74458-7}, pages = {1115 -- 1124}, year = {2021}, language = {de} } @incollection{LangemeyerGronauSchmidWalzetal.2021, author = {Langemeyer, Ines and Gronau, Norbert and Schmid-Walz, Sabrina and Kotarski, David and Reimann, Daniela and Teichmann, Malte}, title = {From employee to expert}, series = {2021 Crossing Boundaries Muttenz/Basel and Bern : 4th International VET Conference Crossing Boundaries 8 to 9 April 2021, online, Muttenz and Bern, Switzerland}, booktitle = {2021 Crossing Boundaries Muttenz/Basel and Bern : 4th International VET Conference Crossing Boundaries 8 to 9 April 2021, online, Muttenz and Bern, Switzerland}, doi = {10.5281/zenodo.4590196}, pages = {226 -- 231}, year = {2021}, abstract = {In the context of the collaborative project Ageing-appropriate, process-oriented and interactive further training in SME (API-KMU), innovative solutions for the challenges of demographic change and digitalisation are being developed for SMEs. To this end, an approach to age-appropriate training will be designed with the help of AR technology. In times of the corona pandemic, a special research design is necessary for the initial survey of the current state in the companies, which will be systematically elaborated in this paper. The results of the previous methodological considerations illustrate the necessity of a mix of methods to generate a deeper insight into the work processes. Video-based retrospective interviews seem to be a suitable instrument to adequately capture the employees' interpretative perspectives on their work activities. In conclusion, the paper identifies specific challenges, such as creating acceptance among employees, open questions, e.g., how a transfer or generalization of the results can succeed, and hypotheses that will have to be tested in the further course of the research process.}, language = {en} } @incollection{GrumKotarskiAmbrosetal.2021, author = {Grum, Marcus and Kotarski, David and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert}, title = {Managing knowledge of intelligent systems}, series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, volume = {422}, booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79975-5}, doi = {10.1007/978-3-030-79976-2_5}, pages = {78 -- 96}, year = {2021}, abstract = {Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.}, language = {en} } @incollection{Gronau2021, author = {Gronau, Norbert}, title = {Modeling the handling of knowledge for Industry 4.0}, series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, volume = {422}, booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79975-5}, doi = {10.1007/978-3-030-79976-2_12}, pages = {207 -- 223}, year = {2021}, abstract = {Industry 4.0, i.e. the connection of cyber-physical systems via the Internet in production and logistics, leads to considerable changes in the socio-technical system of the factory. The effects range from a considerable need for further training, which is exacerbated by the current shortage of skilled workers, to an opening of the previously inaccessible boundaries of the factory to third-party access, an increasing merging of office IT and manufacturing IT, and a new understanding of what machines can do with their data. This results in new requirements for the modeling, analysis and design of information processing and performance mapping business processes. In the past, procedures were developed under the name of "process-oriented knowledge management" with which the exchange and use of knowledge in business processes could be represented, analyzed and improved. However, these approaches were limited to the office environment. A method that makes it possible to document, analyze and jointly optimize the new possibilities of knowledge processing by using artificial intelligence and machine learning in production and logistics in the same way and in a manner compatible with the approach in the office environment does not exist so far. The extension of the modeling language KMDL, which is described in this paper, will contribute to close this research gap. This paper describes first approaches for an analysis and design method for a knowledge management integrating man and machine in the age of Industry 4.0.}, language = {en} } @incollection{GrumGronau2021, author = {Grum, Marcus and Gronau, Norbert}, title = {Quantification of knowledge transfers}, series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, volume = {422}, booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79975-5}, doi = {10.1007/978-3-030-79976-2_13}, pages = {224 -- 242}, year = {2021}, abstract = {Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.}, language = {en} } @incollection{VladovaUllrichBenderetal.2021, author = {Vladova, Gergana and Ullrich, Andr{\´e} and Bender, Benedict and Gronau, Norbert}, title = {Yes, we can (?)}, series = {Technology and innovation in learning, teaching and education : second international conference, TECH-EDU 2020, Vila Real, Portugal, December 2-4, 2020 : proceedings}, booktitle = {Technology and innovation in learning, teaching and education : second international conference, TECH-EDU 2020, Vila Real, Portugal, December 2-4, 2020 : proceedings}, editor = {Reis, Ars{\´e}nio and Barroso, Jo{\~a}o and Lopes, J. Bernardino and Mikropoulos, Tassos and Fan, Chih-Wen}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-73987-4}, doi = {10.1007/978-3-030-73988-1_17}, pages = {225 -- 235}, year = {2021}, abstract = {The COVID-19 crisis has caused an extreme situation for higher education institutions around the world, where exclusively virtual teaching and learning has become obligatory rather than an additional supporting feature. This has created opportunities to explore the potential and limitations of virtual learning formats. This paper presents four theses on virtual classroom teaching and learning that are discussed critically. We use existing theoretical insights extended by empirical evidence from a survey of more than 850 students on acceptance, expectations, and attitudes regarding the positive and negative aspects of virtual teaching. The survey responses were gathered from students at different universities during the first completely digital semester (Spring-Summer 2020) in Germany. We discuss similarities and differences between the subjects being studied and highlight the advantages and disadvantages of virtual teaching and learning. Against the background of existing theory and the gathered data, we emphasize the importance of social interaction, the combination of different learning formats, and thus context-sensitive hybrid learning as the learning form of the future.}, language = {en} }