@inproceedings{AbendrothBenderGronau2024, author = {Abendroth, Adrian and Bender, Benedict and Gronau, Norbert}, title = {The evolution of original ERP customization}, series = {Proceedings of the 26th International Conference on Enterprise Information Systems}, volume = {1}, booktitle = {Proceedings of the 26th International Conference on Enterprise Information Systems}, publisher = {SCITEPRESS - Science and Technology Publications}, address = {Set{\´u}bal}, isbn = {978-989-758-692-7}, issn = {2184-4992}, doi = {10.5220/0012305500003690}, pages = {17 -- 27}, year = {2024}, abstract = {Enterprise Resource Planning (ERP) system customization is often necessary because companies have unique processes that provide their competitive advantage. Despite new technological advances such as cloud computing or model-driven development, technical ERP customization options are either outdated or ambiguously formulated in the scientific literature. Using a systematic literature review (SLR) that analyzes 137 definitions from 26 papers, the result is an analysis and aggregation of technical customization types by providing clearance and aligning with future organizational needs. The results show a shift from ERP code modification in on-premises systems to interface and integration customization in cloud ERP systems, as well as emerging technological opportunities as a way for customers and key users to perform system customization. The study contributes by providing a clear understanding of given customization types and assisting ERP users and vendors in making customization decisions.}, 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{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} } @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} } @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} } @inproceedings{RojahnGronau2023, author = {Rojahn, Marcel and Gronau, Norbert}, title = {Digital platform concepts for manufacturing companies}, series = {10th International Conference on Future Internet of Things and Cloud (FiCloud)}, booktitle = {10th International Conference on Future Internet of Things and Cloud (FiCloud)}, publisher = {IEEE}, address = {[Erscheinungsort nicht ermittelbar]}, isbn = {979-8-3503-1635-3}, doi = {10.1109/FiCloud58648.2023.00030}, pages = {149 -- 158}, year = {2023}, abstract = {Digital Platforms (DPs) has established themself in recent years as a central concept of the Information Technology Science. Due to the great diversity of digital platform concepts, clear definitions are still required. Furthermore, DPs are subject to dynamic changes from internal and external factors, which pose challenges for digital platform operators, developers and customers. Which current digital platform research directions should be taken to address these challenges remains open so far. The following paper aims to contribute to this by outlining a systematic literature review (SLR) on digital platform concepts in the context of the Industrial Internet of Things (IIoT) for manufacturing companies and provides a basis for (1) a selection of definitions of current digital platform and ecosystem concepts and (2) a selection of current digital platform research directions. These directions are diverted into (a) occurrence of digital platforms, (b) emergence of digital platforms, (c) evaluation of digital platforms, (d) development of digital platforms, and (e) selection of digital platforms.}, language = {en} } @article{BenderKorjahnGronau2024, author = {Bender, Benedict and Korjahn, Nicolas and Gronau, Norbert}, title = {Erfolgreich auf Handelsplattformen}, series = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen}, volume = {20}, journal = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen}, number = {1}, publisher = {GITO mbH - Verlag f{\"u}r Industrielle Informationstechnik und Organisation}, address = {Berlin}, issn = {1860-6725}, pages = {76 -- 82}, year = {2024}, abstract = {Obwohl Handelsplattformen zunehmend an Bedeutung gewinnen, besteht im deutschsprachigen Raum ein Mangel an umfassenden Markt{\"u}bersichten. Dadurch fehlt es Verk{\"a}ufern, potenziellen Plattformbetreibern und Kunden an einer soliden Grundlage f{\"u}r fundierte Entscheidungen. Das {\"a}ndern wir mit folgendem Beitrag. Erfahren Sie hier das Wichtigste {\"u}ber den rasant wachsenden Markt der Handelsplattformen.}, language = {de} } @incollection{Gronau2022, author = {Gronau, Norbert}, title = {K{\"u}nstliche Intelligenz in der Produktionssteuerung}, series = {Handbuch Digitalisierung}, booktitle = {Handbuch Digitalisierung}, editor = {Roth, Stefan and Corsten, Hans}, publisher = {Verlag Franz Vahlen}, address = {M{\"u}nchen}, isbn = {978-3-8006-6562-4}, pages = {629 -- 650}, year = {2022}, language = {de} } @article{PanzerGronau2024, author = {Panzer, Marcel and Gronau, Norbert}, title = {Enhancing economic efficiency in modular production systems through deep reinforcement learning}, series = {Procedia CIRP}, volume = {121}, journal = {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{GrumHiesslMareschetal.2021, author = {Grum, Marcus and Hiessl, Werner and Maresch, Karl and Gronau, Norbert}, title = {Design of a neuronal training modeling language}, series = {AIS-Transactions on enterprise systems}, volume = {5}, journal = {AIS-Transactions on enterprise systems}, number = {1}, publisher = {GITO-Publ., Verl. f{\"u}r Industrielle Informationstechnik und Organisation}, address = {Berlin}, issn = {1867-7134}, doi = {10.30844/aistes.v5i1.20}, pages = {16}, year = {2021}, abstract = {As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.}, language = {en} }