@incollection{UllrichGronau2020,
  author    = {Ullrich, Andr{\´e} and Gronau, Norbert},
  title     = {Time to change},
  series = {Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics},
  booktitle = {Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics},
  editor    = {Panetto, Herv{\´e} and Madani, Kurosh and Smirnov, Alexander},
  publisher = {SciTePress},
  address   = {[Erscheinungsort nicht ermittelbar]},
  isbn      = {978-989-758-476-3},
  doi       = {10.5220/0010148601090116},
  pages     = {109 -- 116},
  year      = {2020},
  abstract  = {Industry 4.0 leads to a radical change that is progressing incrementally. The new information and communication technologies provide many conceivable opportunities for their application in the context of sustainable corporate management. The combination of new digital technologies with the ecological and social goals of companies offers a multitude of unimagined potentials and challenges. Although companies already see the need for action, there was in the past and currently still is a lack of concrete measures that lever the potential of Industry 4.0 for sustainability management. During the course of this position paper we develop six theses (two from each sustainability perspective) against the background of the current situation in research and practice, and policy.},
  language  = {en}
}
@inproceedings{GrumThimRolingetal.2023,
  author    = {Grum, Marcus and Thim, Christof and Roling, Wiebke and Sch{\"u}ffler, Arnulf and Kluge, Annette and Gronau, Norbert},
  title     = {AI case-based reasoning for artificial neural networks},
  series = {Artificial intelligence and industrial applications},
  volume    = {771},
  booktitle = {Artificial intelligence and industrial applications},
  editor    = {Masrour, Tawfik and El Hassani, Ibtissam and Barka, Noureddine},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-43523-2},
  doi       = {10.1007/978-3-031-43524-9_2},
  pages     = {17 -- 35},
  year      = {2023},
  abstract  = {Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.},
  language  = {en}
}