TY - CHAP A1 - Gronau, Norbert ED - Shishkov, Boris T1 - Modeling the handling of knowledge for Industry 4.0 T2 - Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5–7, 2021, Proceedings N2 - 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. KW - 4th industrial revolution KW - knowledge management KW - business process management Y1 - 2021 SN - 978-3-030-79975-5 SN - 978-3-030-79976-2 U6 - https://doi.org/10.1007/978-3-030-79976-2_12 VL - 422 SP - 207 EP - 223 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Grum, Marcus ED - Shishkov, Boris T1 - Managing human and artificial knowledge bearers BT - the creation of a symbiotic knowledge management approach T2 - Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings N2 - As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences. KW - knowledge management KW - artificial intelligence KW - neuronal systems KW - design of knowledge-driven systems KW - symbiotic system design Y1 - 2020 SN - 978-3-030-52305-3 SN - 978-3-030-52306-0 U6 - https://doi.org/10.1007/978-3-030-52306-0_12 SP - 182 EP - 201 PB - Springer International Publishing AG CY - Cham ER - TY - CHAP A1 - Grum, Marcus A1 - Gronau, Norbert ED - Shishkov, Boris T1 - Quantification of knowledge transfers BT - the design of an experiment setting for the examination of knowledge transfers T2 - Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5–7, 2021, Proceedings N2 - 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. KW - knowledge management KW - knowledge transfer KW - conversion KW - empirical examination KW - experiment Y1 - 2021 SN - 978-3-030-79975-5 SN - 978-3-030-79976-2 U6 - https://doi.org/10.1007/978-3-030-79976-2_13 VL - 422 SP - 224 EP - 242 PB - Springer International Publishing CY - Cham ER -