@inproceedings{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}
}
@inproceedings{TeichmannUllrichKotarskietal.2021,
  author    = {Teichmann, Malte and Ullrich, Andr{\´e} and Kotarski, David and Gronau, Norbert},
  title     = {Facing the demographic change},
  series = {SSRN eLibrary / Social Science Research Network},
  booktitle = {SSRN eLibrary / Social Science Research Network},
  publisher = {Social Science Electronic Publ.},
  address   = {[Erscheinungsort nicht ermittelbar]},
  issn      = {1556-5068},
  doi       = {10.2139/ssrn.3858716},
  pages     = {6},
  year      = {2021},
  abstract  = {Digitization and demographic change are enormous challenges for companies. Learning factories as innovative learning places can help prepare older employees for the digital change but must be designed and configured based on their specific learning requirements. To date, however, there are no particular recommendations to ensure effective age-appropriate training of bluecollar workers in learning factories. Therefore, based on a literature review, design characteristics and attributes of learning factories and learning requirements of older employees are presented. Furthermore, didactical recommendations for realizing age-appropriate learning designs in learning factories and a conceptualized scenario are outlined by synthesizing the findings.},
  language  = {en}
}
@misc{PanzerBenderGronau2021,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Deep reinforcement learning in production planning and control},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  issn      = {2701-6277},
  doi       = {10.25932/publishup-60572},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605722},
  pages     = {13},
  year      = {2021},
  abstract  = {Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.},
  language  = {en}
}
@inproceedings{PanzerBenderGronau2021,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Deep reinforcement learning in production planning and control},
  series = {Proceedings of the Conference on Production Systems and Logistics},
  booktitle = {Proceedings of the Conference on Production Systems and Logistics},
  publisher = {Institutionelles Repositorium der Leibniz Universit{\"a}t Hannover},
  address   = {Hannover},
  issn      = {2701-6277},
  doi       = {10.15488/11238},
  pages     = {535 -- 545},
  year      = {2021},
  abstract  = {Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.},
  language  = {en}
}
@article{TeichmannUllrichWenzetal.2020,
  author    = {Teichmann, Malte and Ullrich, Andr{\´e} and Wenz, Julian and Gronau, Norbert},
  title     = {Herausforderungen und Handlungsempfehlungen betrieblicher Weiterbildungspraxis in Zeiten der Digitalisierung},
  series = {HMD Praxis der Wirtschaftsinformatik},
  volume    = {57},
  journal   = {HMD Praxis der Wirtschaftsinformatik},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  issn      = {1436-3011},
  doi       = {10.1365/s40702-020-00614-x},
  pages     = {512 -- 527},
  year      = {2020},
  abstract  = {Die Digitalisierung von Produktionsprozessen schreitet mit einer hohen Intensit{\"a}t voran. Weiterbildung hat eine hohe Relevanz f{\"u}r betriebliche Transformationsprozesse. Die betriebliche Weiterbildungspraxis ist den aktuellen Herausforderungen der Digitalisierung jedoch nicht gewachsen. Herausforderungen sind Kompetenzl{\"u}cken der Mitarbeiter, ungewisse Anforderungsprofile und T{\"a}tigkeitstypen, demographischer Wandel sowie veraltete didaktische Ans{\"a}tze. Zudem wird bestehender inhaltlicher und p{\"a}dagogischer Freiraum bei der Gestaltung von Weiterbildung oftmals nur unzureichend ausgenutzt. Die skizzierte Situation f{\"u}hrt dazu, dass der Mehrwert gegenw{\"a}rtiger Qualifizierungsangebote sowohl f{\"u}r Unternehmen als auch Besch{\"a}ftigte nicht ausgesch{\"o}pft wird. Ausgehend von Ver{\"a}nderungen durch Digitalisierung in der Produktion und deren Auswirkungen auf die Kompetenzentwicklung diskutiert dieser Beitrag Herausforderungen gegenw{\"a}rtiger betrieblicher Weiterbildung. Er leitet Handlungsempfehlungen ab, die mithilfe von Beispielen gewerkschaftlich unterst{\"u}tzter Weiterbildungspraxis illustriert werden. Im Ergebnis erhalten Interessierte einen {\"U}berblick {\"u}ber gegenw{\"a}rtige Herausforderungen und Handlungsempfehlungen f{\"u}r die Gestaltung und Durchf{\"u}hrung von Weiterbildung in Zeiten der Digitalisierung.},
  language  = {de}
}
@article{Gronau2020,
  author    = {Gronau, Norbert},
  title     = {Die Balanced ERP Scorecard (BESC)},
  series = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen},
  journal   = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen},
  number    = {2},
  publisher = {GITO mbH Verlag},
  address   = {Berlin},
  issn      = {1860-6725},
  doi       = {10.30844/ERP-II_20-2_6-9},
  pages     = {6 -- 9},
  year      = {2020},
  abstract  = {Wodurch zeichnen sich die besten ERP-Systeme aus? Diese Frage wird dem Autor dieses Beitrags immer wieder gestellt. Mit der Vorstellung der Balanced ERP Scorecard (BESC) besteht nun eine M{\"o}glichkeit, ein System aus verschiedenen Perspektiven zu bewerten und so individuell zu einer Einsch{\"a}tzung des f{\"u}r eine konkrete Unternehmenssituation besten Systems zu gelangen. Daher beschreibt dieser Beitrag zun{\"a}chst die Perspektiven der BESC und dann m{\"o}gliche Kriterien f{\"u}r eine Ausgestaltung dieser Scorecard.},
  language  = {de}
}
@article{GronauTeichmann2020,
  author    = {Gronau, Norbert and Teichmann, Malte},
  title     = {ERP-Auswahl f{\"u}r ein Professional Services Unternehmen},
  series = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen},
  volume    = {16},
  journal   = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen},
  number    = {2},
  publisher = {Gito mbh Verlag f{\"u}r Industrielle Informationstechnik und Organisation},
  address   = {Berlin},
  issn      = {1860-6725},
  doi       = {10.30844/ERP_20-2_31-35},
  pages     = {31 -- 35},
  year      = {2020},
  abstract  = {Die Branche der Dienstleistungsunternehmen (Professional Services) hat einige Anforderungen, die sie von den „klassischen" ERP-Branchen Industrie und Handel unterscheidet. Dieser Beitrag beschreibt einige der aktuellen Herausforderungen dieses immer wichtiger werdenden Wirtschaftszweigs und geht dann am Beispiel eines mittelst{\"a}ndischen Ingenieurdienstleisters auf typische Anforderungen dieser Branche, infrage kommende Systeme und das Vorgehen zur Auswahl ein.},
  language  = {de}
}
@book{Gronau2020,
  author    = {Gronau, Norbert},
  title     = {Wiederanlauf nach der Krise},
  publisher = {GITO mbH Verlag},
  address   = {Berlin},
  isbn      = {978-3-95545-343-5},
  pages     = {145},
  year      = {2020},
  abstract  = {Jetzt intelligent aus der Krise heraus starten! Der Anlauf von Neuprodukten und der jetzt notwendige Wiederanlauf nach der Corona-Krise haben einige Gemeinsamkeiten: In beiden F{\"a}llen muss vorsichtig vorgegangen werden, es muss eine hohe Wandlungsf{\"a}higkeit und Anpassungsf{\"a}higkeit aufrechterhalten werden, um schnell auf eine Ver{\"a}nderung der Situation reagieren zu k{\"o}nnen und es werden geeignete Werkzeuge und Prognoseverfahren ben{\"o}tigt, um sich die Auswirkungen von geplanten Schritten vorher verdeutlichen zu k{\"o}nnen. Das vorliegende Herausgeberwerk wurde zusammengestellt, um Expertenwissen f{\"u}r die Praxis nutzbar zu machen. Die {\"u}ber 30 Aufs{\"a}tze in diesem Werk dienen dem Ziel, das Thema „Wiederanlauf nach der Krise" aus allen wichtigen Blickwinkeln zu beleuchten und alle relevanten Branchen zu ber{\"u}cksichtigen. Nach der Darstellung allgemeiner Prinzipien des Wiederanlaufs werden Methoden und Werkzeuge vorgestellt, bevor dann Spezika einzelner Branchen diskutiert werden. Im Vordergrund steht dabei die international vernetzte Automobilindustrie sowie die globalen Lieferketten. Weitere Branchen wie die Elektronikfertigung und die Herstellung von Konsumg{\"u}tern werden ebenfalls besprochen. Zielgruppe dieses Werkes sind vor allem diejenigen, die jetzt den Wiederanlauf von Produktionssystemen nach der drastischen Reduzierung durch das neuartige Corona-Virus planen, vorbereiten und umsetzen.},
  language  = {de}
}
@article{TeichmannUllrichKnostetal.2020,
  author    = {Teichmann, Malte and Ullrich, Andr{\´e} and Knost, Dennis and Gronau, Norbert},
  title     = {Serious games in learning factories},
  series = {Procedia manufacturing},
  volume    = {45},
  journal   = {Procedia manufacturing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2351-9789},
  doi       = {10.1016/j.promfg.2020.04.104},
  pages     = {259 -- 264},
  year      = {2020},
  abstract  = {The usage of gamification in the contexts of commerce, consumption, innovation or eLearning in schools and universities has been extensively researched. However, the potentials of serious games to transfer and perpetuate knowledge and action patterns in learning factories have not been levered so far. The goal of this paper is to introduce a serious game as an instrument for knowledge transfer and perpetuation. Therefore, reqirements towards serious games in the context of learning factories are pointed out. As a result, that builds on these requirements, a serious learning game for the topic of Industry 4.0 is practically designed and evaluated.},
  language  = {en}
}
@incollection{Gronau2020,
  author    = {Gronau, Norbert},
  title     = {Modellieren des Umgangs mit Wissen f{\"u}r Industrie 4.0},
  series = {Mensch-Technik-Interaktion in der digitalisierten Arbeitswelt},
  booktitle = {Mensch-Technik-Interaktion in der digitalisierten Arbeitswelt},
  editor    = {Freitag, Michael},
  publisher = {GITO mbH Verlag},
  address   = {Berlin},
  isbn      = {978-3-95545-353-4},
  doi       = {10.30844/wgab_2020},
  pages     = {79 -- 101},
  year      = {2020},
  language  = {de}
}