TY - JOUR A1 - Roling, Wiebke M. A1 - Grum, Marcus A1 - Gronau, Norbert A1 - Kluge, Annette T1 - The roots of errors in adaptive performance BT - clustering behavioral patterns after the introduction of a change JF - Journal of workplace learning N2 - Purpose The purpose of this study was to investigate work-related adaptive performance from a longitudinal process perspective. This paper clustered specific behavioral patterns following the introduction of a change and related them to retentivity as an individual cognitive ability. In addition, this paper investigated whether the occurrence of adaptation errors varied depending on the type of change content. Design/methodology/approach Data from 35 participants collected in the simulated manufacturing environment of a Research and Application Center Industry 4.0 (RACI) were analyzed. The participants were required to learn and train a manufacturing process in the RACI and through an online training program. At a second measurement point in the RACI, specific manufacturing steps were subject to change and participants had to adapt their task execution. Adaptive performance was evaluated by counting the adaptation errors. Findings The participants showed one of the following behavioral patterns: (1) no adaptation errors, (2) few adaptation errors, (3) repeated adaptation errors regarding the same actions, or (4) many adaptation errors distributed over many different actions. The latter ones had a very low retentivity compared to the other groups. Most of the adaptation errors were made when new actions were added to the manufacturing process. Originality/value Our study adds empirical research on adaptive performance and its underlying processes. It contributes to a detailed understanding of different behaviors in change situations and derives implications for organizational change management. KW - adaptive performance KW - behavioral patterns KW - change KW - process perspective KW - quantitative KW - retentivity KW - rype of change content Y1 - 2024 U6 - https://doi.org/10.1108/JWL-10-2023-0168 SN - 1366-5626 VL - 36 IS - 4 SP - 267 EP - 281 PB - Emerald CY - Bradford ER - TY - JOUR A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - Neural agent-based production planning and control BT - an architectural review JF - Journal of Manufacturing Systems N2 - 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. KW - production planning and control KW - machine learning KW - neural networks KW - systematic literature review KW - taxonomy Y1 - 2022 U6 - https://doi.org/10.1016/j.jmsy.2022.10.019 SN - 0278-6125 SN - 1878-6642 VL - 65 SP - 743 EP - 766 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - A deep reinforcement learning based hyper-heuristic for modular production control JF - International journal of production research N2 - 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. KW - production control KW - modular production KW - multi-agent system KW - deep reinforcement learning KW - deep learning KW - multi-objective optimisation Y1 - 2023 U6 - https://doi.org/10.1080/00207543.2023.2233641 SN - 0020-7543 SN - 1366-588X SN - 0278-6125 SP - 1 EP - 22 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Ullrich, André A1 - Weber, Edzard A1 - Gronau, Norbert T1 - Regionale Refabrikationsnetzwerke BT - Potenziale und Herausforderungen der lokalen Wiederaufarbeitung von Produkten JF - Industrie 4.0 Management : Gegenwart und Zukunft industrieller Geschäftsprozesse N2 - 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 Aktivitäten, um zu einem nachhaltigen Umgang mit den zur Verfügung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es ermöglicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu fördern und effiziente Lösungen für die regionale Wieder- und Weiterverwendung von Gütern anzubieten. KW - Refabrikation KW - Regionale Ansätze KW - Remanufacturing Y1 - 2023 U6 - https://doi.org/10.30844/IM_23-2_11-14 SN - 2364-9208 VL - 39 IS - 2 SP - 11 EP - 14 PB - GITO mbH Verlag CY - Berlin ER - TY - JOUR A1 - Bender, Benedict A1 - Korjahn, Nicolas A1 - Gronau, Norbert T1 - Erfolgreich auf Handelsplattformen BT - Einblicke in die Marktstudie Handelsplattformen 2023/2024 JF - ERP-Management : Auswahl, Einführung und Betrieb von ERP-Systemen N2 - Obwohl Handelsplattformen zunehmend an Bedeutung gewinnen, besteht im deutschsprachigen Raum ein Mangel an umfassenden Marktübersichten. Dadurch fehlt es Verkäufern, potenziellen Plattformbetreibern und Kunden an einer soliden Grundlage für fundierte Entscheidungen. Das ändern wir mit folgendem Beitrag. Erfahren Sie hier das Wichtigste über den rasant wachsenden Markt der Handelsplattformen. Y1 - 2024 UR - https://www.wiso-net.de/document/ERP__714baef53e6bcc9690fd3c7c72605cc171f35f62 UR - https://erp-management.de/artikel/erfolgreich-auf-handelsplattformen/ SN - 1860-6725 VL - 20 IS - 1 SP - 76 EP - 82 PB - GITO mbH - Verlag für Industrielle Informationstechnik und Organisation CY - Berlin ER - TY - JOUR A1 - Grum, Marcus A1 - Hiessl, Werner A1 - Maresch, Karl A1 - Gronau, Norbert T1 - Design of a neuronal training modeling language BT - exemplified with the AI-based dynamic GUI adaption JF - AIS-Transactions on enterprise systems N2 - 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. KW - AI and business informatics KW - development of AI-based systems KW - AI-based decision support system KW - cooperative AI (human-in-the-loop) KW - process-oriented knowledge acquisition KW - modeling language Y1 - 2021 UR - https://www.aes-journal.com/index.php/ais-tes/article/view/20/18 U6 - https://doi.org/10.30844/aistes.v5i1.20 SN - 1867-7134 VL - 5 IS - 1 PB - GITO-Publ., Verl. für Industrielle Informationstechnik und Organisation CY - Berlin ER - TY - JOUR A1 - Vladova, Gergana A1 - Gronau, Norbert T1 - KI-basierte Assistenzsysteme in betrieblichen Lernprozessen JF - Industrie 4.0 Management : Gegenwart und Zukunft industrieller Geschäftsprozesse N2 - Assistenzsysteme finden im Kontext der digitalen Transformation immer mehr Einsatz. Sie können Beschäftigte in industriellen Produktionsprozessen sowohl in der Anlern- als auch in der aktiven Arbeitsphase unterstützen. Kompetenzen können so arbeitsplatz- und prozessnah sowie bedarfsorientiert aufgebaut werden. In diesem Beitrag wird der aktuelle Forschungsstand zu den Einsatzmöglichkeiten dieser Assistenzsysteme diskutiert und mit Beispielen illustriert. Es werden unter anderem auch Herausforderungen für den Einsatz aufgezeigt. Am Ende des Beitrags werden Potenziale für die zukünftige Nutzung von AS in industriellen Lernprozessen und für die Forschung identifiziert. KW - KI KW - kognitive Assistenzsysteme KW - betriebliche Lernprozesse KW - Weiterbildung Y1 - 2022 U6 - https://doi.org/10.30844/I40M_22-2_11-14 SN - 2364-9216 SN - 2364-9208 VL - 38 IS - 2 SP - 11 EP - 14 PB - GITO mbH Verlag für Industrielle Informationstechnik und Organisation CY - Berlin ER - TY - JOUR A1 - Ullrich, André A1 - Teichmann, Malte A1 - Gronau, Norbert T1 - Fast trainable capabilities in software engineering-skill development in learning factories JF - Ji suan ji jiao yu = Computer Education / Qing hua da xue N2 - The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities. Accordingly, an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods. We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities. This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach, which enables non-computer scientists to code. Therein, the learning content is provided in small tasks and embedded in learning factory scenarios. Therefore, different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated, yet condensed skills catalogue. This enables the development of training and education units that focus on the most important skills demanded on the market. To achieve this objective, individual learning scenarios are developed. Of course, proper basic skills in coding cannot be learned over night but software programming is also no sorcery. KW - learning factory KW - programming skills KW - software engineering KW - training Y1 - 2021 U6 - https://doi.org/10.16512/j.cnki.jsjjy.2020.12.002 SN - 1672-5913 IS - 12 SP - 2 EP - 10 PB - [Verlag nicht ermittelbar] CY - Bei jing shi ER - TY - JOUR A1 - Thim, Christof A1 - Ullrich, André A1 - Eigelshoven, Felix A1 - Gronau, Norbert A1 - Ritter, Ann-Carolin T1 - Crowdsourcing bei industriellen Innovationen T1 - Crowdsourcing for industrial innovations BT - Lösungsansätze und Herausforderung für KMU BT - solutions and challenges for SMEs JF - Industrie 4.0 Management : Gegenwart und Zukunft industrieller Geschäftsprozesse N2 - Die Innovationstätigkeit im industriellen Umfeld verlagert sich durch die Digitalisierung hin zu Produkt-Service-Systemen. Kleine und mittlere Unternehmen haben sich in ihrer Entwicklungstätigkeit bisher stark auf die Produktentwicklung bezogen. Der Umstieg auf „smarte“ Produkte und die Kopplung an Dienstleistungen erfordert häufig personelle und finanzielle Ressourcen, welche KMU nicht aufbringen können. Crowdsourcing stellt eine Möglichkeit dar, den Innovationsprozess für externe Akteure zu öffnen und Kosten- sowie Geschwindigkeitsvorteile zu realisieren. Bei der Integration von Crowdsourcing-Elementen ist jedoch einigen Herausforderungen zu begegnen. Dieser Beitrag zeigt sowohl die Potenziale als auch die Barrieren einer Crowdsourcing-Nutzung im industriellen Umfeld auf. N2 - Innovation activity in the industrial environment is shifting towards product-service systems as a result of digitalisation. Small and medium-sized enterprises have so far focused their development activities strongly on product development. The switch to “smart” products and the coupling to services often requires personnel and financial resources that SMEs cannot provide. Crowdsourcing is one way of opening up the innovation process to external actors and realising cost and speed advantages. However, the integration of crowdsourcing elements faces several challenges. This article shows both the potentials and the barriers of crowdsourcing in the industrial environment. KW - Crowdsourcing KW - industrielle Innovationen KW - KMU KW - industrial innovation KW - SMEs Y1 - 2020 U6 - https://doi.org/10.30844/I40M_20-6_S9-13 SN - 2364-9208 VL - 36 IS - 6 SP - 9 EP - 13 PB - GITO mbH Verlag CY - Berlin ER - TY - JOUR A1 - Bender, Benedict A1 - Bertheau, Clementine A1 - Körppen, Tim A1 - Lauppe, Hannah A1 - Gronau, Norbert T1 - A proposal for future data organization in enterprise systems BT - an analysis of established database approaches JF - Information systems and e-business management N2 - The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future. KW - database KW - enterprise system KW - ERP system KW - requirements KW - problems KW - future Y1 - 2022 U6 - https://doi.org/10.1007/s10257-022-00555-6 SN - 1617-9846 SN - 1617-9854 VL - 20 SP - 441 EP - 494 PB - Springer CY - Heidelberg ER -