@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}
}