@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}
}
@book{Gronau2021,
  author    = {Gronau, Norbert},
  title     = {ERP-Systeme},
  series = {De Gruyter Studium},
  journal   = {De Gruyter Studium},
  edition   = {4. Auflage},
  publisher = {De Gruyter Oldenbourg},
  address   = {Berlin ; Boston},
  isbn      = {978-3-11-066339-6},
  issn      = {2365-7197},
  doi       = {10.1515/9783110663396},
  pages     = {XIII, 372},
  year      = {2021},
  language  = {de}
}
@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}
}