@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}
}
@incollection{TeichmannBusseGonnermannetal.2023,
  author    = {Teichmann, Malte and Busse, Jana and Gonnermann, Jana and Reimann, Daniela and Ritterbusch, Georg David and Langemeyer, Ines and Gronau, Norbert},
  title     = {Konzeption, Erstellung und Evaluation von VR-R{\"a}umen f{\"u}r die betriebliche Weiterbildung in KMU},
  series = {Digitalisierung der Arbeitswelt im Mittelstand 3},
  booktitle = {Digitalisierung der Arbeitswelt im Mittelstand 3},
  editor    = {Nitsch, Verena and Brandl, Christopher and H{\"a}ußling, Roger and Roth, Philip and Gries, Thomas and Schmenk, Bernhard},
  publisher = {Springer Vieweg},
  address   = {Berlin},
  isbn      = {978-3-662-67023-1},
  doi       = {10.1007/978-3-662-67024-8_5},
  pages     = {155 -- 204},
  year      = {2023},
  abstract  = {Der Beitrag adressiert die Erstellung von Virtual-Reality gest{\"u}tzten (Lehr- und Lern-) R{\"a}umen f{\"u}r die betriebliche Weiterbildung im Rahmen eines Forschungsprojektes. Der damit verbundene Konzeptions- und Umsetzungsprozess ist mit verschiedenen Herausforderungen verbunden: einerseits ist Virtual-Reality ein vergleichsweise neues Lehr- und Lernmedium, womit wenig praktische Handreichungen zur praktischen Umsetzung existieren. Andererseits existieren theoretisch-konzeptionelle Ans{\"a}tze zur Gestaltung digitaler Lehr- und Lernarrangements, die jedoch 1) oft Gefahr laufen, an den realen Bed{\"u}rfnissen der Praxis „vorbei" zu gehen und 2) zumeist nicht konkret Virtual-Reality bzw. damit verbundene Lehr- und Lernumgebungen adressieren. In dieser Folge sind Best-Practice Beispiele basierend auf erfolgreichen Umsetzungsvorhaben, die nachfolgenden Projekten als „Wegweiser" dienen k{\"o}nnten, {\"a}ußerst rar. Der Beitrag setzt an dieser Stelle an: basierend auf zwei real existierenden betrieblichen Anwendungsf{\"a}llen aus den Bereichen Natursteinbearbeitung sowie Einzel- und Sondermaschinenbau werden Herausforderungen und L{\"o}sungswege des Erstellungsprozesses von Virtual-Reality gest{\"u}tzten (Lehr- und Lern-)R{\"a}umen beschrieben. Ebenfalls werden basierend auf den gemachten Projekterfahrungen Handlungsempfehlungen f{\"u}r die gelingende Konzeption, Umsetzung und Evaluation dieser R{\"a}ume formuliert. Betriebliche Besch{\"a}ftigte aus den Bereichen Aus- und Weiterbildung, Management oder Human Ressources, die in eigenen Projekten im Bereich Virtual Reality aktiv werden wollen, profitieren von den herausgestellten praktischen Handreichungen. Forschende Personen sollen Anregungen f{\"u}r weiterf{\"u}hrende Forschungsvorhaben erhalten.},
  language  = {de}
}
@inproceedings{GrumRappGronauetal.2019,
  author    = {Grum, Marcus and Rapp, Simon and Gronau, Norbert and Albers, Albert},
  title     = {Accelerating knowledge},
  series = {Business modeling and software design},
  volume    = {356},
  booktitle = {Business modeling and software design},
  editor    = {Shishkov, Boris},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-24853-6},
  doi       = {10.1007/978-3-030-24854-3_7},
  pages     = {95 -- 113},
  year      = {2019},
  abstract  = {As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation.},
  language  = {en}
}
@inproceedings{GrumKlippertAlbersetal.2021,
  author    = {Grum, Marcus and Klippert, Monika and Albers, Albert and Gronau, Norbert and Thim, Christof},
  title     = {Examining the quality of knowledge transfers},
  series = {Proceedings of the Design Society},
  volume    = {1},
  booktitle = {Proceedings of the Design Society},
  publisher = {Cambridge University Press},
  address   = {Cambridge},
  issn      = {2732-527X},
  doi       = {10.1017/pds.2021.404},
  pages     = {1431 -- 1440},
  year      = {2021},
  abstract  = {Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.},
  language  = {en}
}
@misc{PanzerBenderGronau2023,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {A deep reinforcement learning based hyper-heuristic for modular production 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      = {1867-5808},
  doi       = {10.25932/publishup-60564},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605642},
  pages     = {24},
  year      = {2023},
  abstract  = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.},
  language  = {en}
}
@article{PanzerBenderGronau2023,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {A deep reinforcement learning based hyper-heuristic for modular production control},
  series = {International journal of production research},
  journal   = {International journal of production research},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {0020-7543},
  doi       = {10.1080/00207543.2023.2233641},
  pages     = {1 -- 22},
  year      = {2023},
  abstract  = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.},
  language  = {en}
}
@book{Gronau2024,
  author    = {Gronau, Norbert},
  title     = {Knowledge Modeling and Description Language (KMDL) 3.0},
  publisher = {GITO mbH Verlag},
  address   = {Berlin},
  isbn      = {978-3-95545-416-6},
  pages     = {135},
  year      = {2024},
  language  = {en}
}
@article{AdelhelmBraunGronauetal.2014,
  author    = {Adelhelm, Silvia and Braun, Andreas and Gronau, Norbert and L{\"u}rig, Detlef and M{\"u}ller, Elisabeth and Vladova, Gergana and Wagner, Dieter},
  title     = {Mit Open Innovation zum Erfolg},
  series = {Handbuch prozessorientiertes Wissensmanagment},
  journal   = {Handbuch prozessorientiertes Wissensmanagment},
  publisher = {GITO},
  address   = {Berlin},
  isbn      = {978-3-95545-026-7},
  pages     = {211 -- 226},
  year      = {2014},
  language  = {de}
}
@article{UllrichVladovaMarquartetal.2022,
  author    = {Ullrich, Andr{\´e} and Vladova, Gergana and Marquart, Danny and Braun, Andreas and Gronau, Norbert},
  title     = {An overwiew of benefits and risks in open innovation projects and the influence of intermediary participation, decision-making authority, experience, and position on their perception},
  series = {International journal of innovation management : IJIM},
  volume    = {26},
  journal   = {International journal of innovation management : IJIM},
  number    = {02},
  publisher = {World Scientific Publ.},
  address   = {Singapore},
  issn      = {1363-9196},
  doi       = {10.1142/S1363919622500128},
  pages     = {41},
  year      = {2022},
  abstract  = {This paper presents an exploratory study investigating the influence of the factors (1) intermediary participation, (2) decision-making authority, (3) position in the enterprise, and (4) experience in open innovation on the perception and assessment of the benefits and risks expected from participating in open innovation projects. For this purpose, an online survey was conducted in Germany, Austria and Switzerland. The result of this paper is an empirical evidence showing whether and how these factors affect the perception of potential benefits and risks expected within the context of open innovation project participation. Furthermore, the identified effects are discussed against the theory. Existing theory regarding the benefits and risks of open innovation is expanded by (1) finding that they are perceived mostly independently of the factors, (2) confirming the practical relevance of benefits and risks, and (3) enabling a finer distinction between their degrees of relevance according to respective contextual specifics.},
  language  = {en}
}
@article{UllrichWeberGronau2023,
  author    = {Ullrich, Andr{\´e} and Weber, Edzard and Gronau, Norbert},
  title     = {Regionale Refabrikationsnetzwerke},
  series = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse},
  volume    = {39},
  journal   = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse},
  number    = {2},
  publisher = {GITO mbH Verlag},
  address   = {Berlin},
  issn      = {2364-9208},
  doi       = {10.30844/IM_23-2_11-14},
  pages     = {11 -- 14},
  year      = {2023},
  abstract  = {Die Herstellung von Produkten bindet Energie sowie auch materielle Ressourcen. Viel zu langsam entwickeln sich sowohl das Bewusstsein der Konsumenten sowie der Produzenten als auch gesetzgebende Aktivit{\"a}ten, um zu einem nachhaltigen Umgang mit den zur Verf{\"u}gung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es erm{\"o}glicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu f{\"o}rdern und effiziente L{\"o}sungen f{\"u}r die regionale Wieder- und Weiterverwendung von G{\"u}tern anzubieten.},
  language  = {de}
}
@misc{BenderHabibGronau2020,
  author    = {Bender, Benedict and Habib, Natalie and Gronau, Norbert},
  title     = {Digitale Plattformen},
  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},
  number    = {1},
  issn      = {1867-5808},
  doi       = {10.25932/publishup-60541},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605419},
  pages     = {11},
  year      = {2020},
  abstract  = {Obwohl digitale Plattformen vornehmlich von Großunternehmen betrieben werden, bieten sie klein- und mittelst{\"a}ndischen Unternehmen (KMU) Potenziale zur Verbreitung innovativer Technologien und f{\"u}r den Ausbau ihres Gesch{\"a}ftsmodells. F{\"u}r die Umsetzung digitaler Plattformen stehen Unternehmen mehrere Strategien zur Verf{\"u}gung. Der Beitrag vergleicht und bewertet grundlegende Strategien am Beispiel eines Maschinenbauunternehmens. Die Ergebnisse dienen als Grundlage f{\"u}r die Entscheidungsfindung von KMU.},
  language  = {de}
}
@misc{UllrichWeberGronau2023,
  author    = {Ullrich, Andr{\´e} and Weber, Edzard and Gronau, Norbert},
  title     = {Regionale Refabrikationsnetzwerke},
  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},
  number    = {2},
  issn      = {2364-9208},
  doi       = {10.25932/publishup-60451},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604510},
  pages     = {6},
  year      = {2023},
  abstract  = {Die Herstellung von Produkten bindet Energie sowie auch materielle Ressourcen. Viel zu langsam entwickeln sich sowohl das Bewusstsein der Konsumenten sowie der Produzenten als auch gesetzgebende Aktivit{\"a}ten, um zu einem nachhaltigen Umgang mit den zur Verf{\"u}gung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es erm{\"o}glicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu f{\"o}rdern und effiziente L{\"o}sungen f{\"u}r die regionale Wieder- und Weiterverwendung von G{\"u}tern anzubieten.},
  language  = {de}
}
@incollection{GonnermannBrandenburgerVladovaetal.2023,
  author    = {Gonnermann, Jana and Brandenburger, Bonny and Vladova, Gergana and Gronau, Norbert},
  title     = {To what extent can individualisation in terms of different types of mode improve learning outcomes and learner satisfaction?},
  series = {Proceedings of the 56th Annual Hawaii International Conference on System Sciences January 3-6, 2023},
  booktitle = {Proceedings of the 56th Annual Hawaii International Conference on System Sciences January 3-6, 2023},
  editor    = {Bui, Tung X.},
  publisher = {Department of IT Management Shidler College of Business University of Hawaii},
  address   = {Honolulu, HI},
  isbn      = {978-0-9981331-6-4},
  pages     = {123 -- 132},
  year      = {2023},
  abstract  = {With the latest technological developments and associated new possibilities in teaching, the personalisation of learning is gaining more and more importance. It assumes that individual learning experiences and results could generally be improved when personal learning preferences are considered. To do justice to the complexity of the personalisation possibilities of teaching and learning processes, we illustrate the components of learning and teaching in the digital environment and their interdependencies in an initial model. Furthermore, in a pre-study, we investigate the relationships between the learner's ability to (digital) self-organise, the learner's prior- knowledge learning in different variants of mode and learning outcomes as one part of this model. With this pre-study, we are taking the first step towards a holistic model of teaching and learning in digital environments.},
  language  = {en}
}
@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{KlippertStolpmannGrumetal.2023,
  author    = {Klippert, Monika and Stolpmann, Robert and Grum, Marcus and Thim, Christof and Gronau, Norbert and Albers, Albert},
  title     = {Knowledge transfer quality improvement},
  series = {Procedia CIRP},
  volume    = {119},
  booktitle = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2023.02.171},
  pages     = {919 -- 925},
  year      = {2023},
  abstract  = {Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect.},
  language  = {en}
}
@inproceedings{PanzerGronau2024,
  author    = {Panzer, Marcel and Gronau, Norbert},
  title     = {Enhancing economic efficiency in modular production systems through deep reinforcement learning},
  series = {Procedia CIRP},
  volume    = {121},
  booktitle = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2023.09.229},
  pages     = {55 -- 60},
  year      = {2024},
  abstract  = {In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios.},
  language  = {en}
}
@article{VladovaGronau2022,
  author    = {Vladova, Gergana and Gronau, Norbert},
  title     = {KI-basierte Assistenzsysteme in betrieblichen Lernprozessen},
  series = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse},
  volume    = {38},
  journal   = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse},
  number    = {2},
  publisher = {GITO mbH Verlag f{\"u}r Industrielle Informationstechnik und Organisation},
  address   = {Berlin},
  issn      = {2364-9216},
  doi       = {10.30844/I40M_22-2_11-14},
  pages     = {11 -- 14},
  year      = {2022},
  abstract  = {Assistenzsysteme finden im Kontext der digitalen Transformation immer mehr Einsatz. Sie k{\"o}nnen Besch{\"a}ftigte in industriellen Produktionsprozessen sowohl in der Anlern- als auch in der aktiven Arbeitsphase unterst{\"u}tzen. Kompetenzen k{\"o}nnen so arbeitsplatz- und prozessnah sowie bedarfsorientiert aufgebaut werden. In diesem Beitrag wird der aktuelle Forschungsstand zu den Einsatzm{\"o}glichkeiten dieser Assistenzsysteme diskutiert und mit Beispielen illustriert. Es werden unter anderem auch Herausforderungen f{\"u}r den Einsatz aufgezeigt. Am Ende des Beitrags werden Potenziale f{\"u}r die zuk{\"u}nftige Nutzung von AS in industriellen Lernprozessen und f{\"u}r die Forschung identifiziert.},
  language  = {de}
}
@article{KlugeSchuefflerThimetal.2024,
  author    = {Kluge, Annette and Sch{\"u}ffler, Arnulf S. and Thim, Christof and Gronau, Norbert},
  title     = {Facilitating and hindering factors for routine adaptations in manufacturing and effects on human performance- unexpected insights from three experimental studies in a special purpose setting},
  series = {Ergonomics : an international journal of research and practice in human factors and ergonomics},
  journal   = {Ergonomics : an international journal of research and practice in human factors and ergonomics},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {1366-5847},
  doi       = {10.1080/00140139.2024.2369706},
  pages     = {1 -- 19},
  year      = {2024},
  abstract  = {Consumer behaviour changes and strategic management decisions are driving adaptations in manufacturing routines. Based on the theory of situational strength, we investigated how contextual and person-related factors influence workers' adaptation in a two-worker position routine. Contextual factors, like retrieval cues (Study 1), time pressure (Study 2), and convenience (Study 3), were varied. Person-related factors included retentivity, general and routine-specific self-efficacy, and perceived adaptation costs. Dependent variables included various error types and production time before and after adaptation. In each study, 148 participants were trained in a production routine at t1 and executed an adapted routine at t2, one week later. Repeated measures ANOVA for performance at t1 and t2, and MANOVA for performance at t2, revealed that time increased for all groups at t2. For participants in Studies 1 \& 2, error rates remained consistent. Retentivity significantly impacted errors at both t1 and t2, emphasising that routine changes in a 'running business' take time, regardless of contextual factors. Workers with lower retentivity may require additional support.},
  language  = {en}
}
@misc{PanzerBenderGronau2022,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Neural agent-based 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      = {1867-5808},
  doi       = {10.25932/publishup-60477},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604777},
  pages     = {26},
  year      = {2022},
  abstract  = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.},
  language  = {en}
}
@article{PanzerBenderGronau2022,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Neural agent-based production planning and control},
  series = {Journal of Manufacturing Systems},
  volume    = {65},
  journal   = {Journal of Manufacturing Systems},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0278-6125},
  doi       = {10.1016/j.jmsy.2022.10.019},
  pages     = {743 -- 766},
  year      = {2022},
  abstract  = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.},
  language  = {en}
}