TY - GEN A1 - Bender, Benedict A1 - Habib, Natalie A1 - Gronau, Norbert T1 - Digitale Plattformen BT - Strategien für KMU T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - Obwohl digitale Plattformen vornehmlich von Großunternehmen betrieben werden, bieten sie klein- und mittelständischen Unternehmen (KMU) Potenziale zur Verbreitung innovativer Technologien und für den Ausbau ihres Geschäftsmodells. Für die Umsetzung digitaler Plattformen stehen Unternehmen mehrere Strategien zur Verfügung. Der Beitrag vergleicht und bewertet grundlegende Strategien am Beispiel eines Maschinenbauunternehmens. Die Ergebnisse dienen als Grundlage für die Entscheidungsfindung von KMU. KW - Digitale Plattformen KW - KMU KW - Strategie KW - Geschäftsmodell KW - Industrie 4.0 KW - Maschinen- und Anlagenbau Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-605419 SN - 1867-5808 IS - 1 ER - TY - GEN A1 - Ullrich, André A1 - Weber, Edzard A1 - Gronau, Norbert T1 - Regionale Refabrikationsnetzwerke BT - Potenziale und Herausforderungen der lokalen Wiederaufarbeitung von Produkten T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 183 KW - Refabrikation KW - Regionale Ansätze KW - Remanufacturing Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-604510 SN - 2364-9208 SN - 1867-5808 IS - 2 ER - TY - CHAP A1 - Gonnermann, Jana A1 - Brandenburger, Bonny A1 - Vladova, Gergana A1 - Gronau, Norbert ED - Bui, Tung X. T1 - To what extent can individualisation in terms of different types of mode improve learning outcomes and learner satisfaction? BT - a pre-study T2 - Proceedings of the 56th Annual Hawaii International Conference on System Sciences January 3-6, 2023 N2 - 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. KW - advances in teaching and learning technologies KW - digital learning KW - digital teaching KW - experimental design KW - personalised learning KW - teaching and learning model Y1 - 2023 SN - 978-0-9981331-6-4 SP - 123 EP - 132 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - GEN A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - Neural agent-based production planning and control BT - an architectural review T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 172 KW - production planning and control KW - machine learning KW - neural networks KW - systematic literature review KW - taxonomy Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-604777 SN - 1867-5808 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 - GEN A1 - Bender, Benedict A1 - Gronau, Norbert ED - Bui, Tung T1 - Introduction to the Minitrack on towards the future of enterprise systems T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - Enterprise systems have long played an important role in businesses of various sizes. With the increasing complexity of today’s business relationships, pecialized application systems are being used more and more. Moreover, emerging technologies such as artificial intelligence are becoming accessible for enterprise systems. This raises the question of the future role of enterprise systems. This minitrack covers novel ideas that contribute to and shape the future role of enterprise systems with five contributions. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 188 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-605406 SN - 978-0-9981331-5-7 SN - 1867-5808 ER - TY - JOUR A1 - Bender, Benedict A1 - Habib, Natalie A1 - Gronau, Norbert T1 - Digitale Plattformen BT - Strategien für KMU JF - Wirtschaftsinformatik und Management N2 - Obwohl digitale Plattformen vornehmlich von Großunternehmen betrieben werden, bieten sie klein- und mittelständischen Unternehmen (KMU) Potenziale zur Verbreitung innovativer Technologien und für den Ausbau ihres Geschäftsmodells. Für die Umsetzung digitaler Plattformen stehen Unternehmen mehrere Strategien zur Verfügung. Der Beitrag vergleicht und bewertet grundlegende Strategien am Beispiel eines Maschinenbauunternehmens. Die Ergebnisse dienen als Grundlage für die Entscheidungsfindung von KMU. KW - Digitale Plattformen KW - KMU KW - Strategie KW - Geschäftsmodell KW - Industrie 4.0 KW - Maschinen- und Anlagenbau Y1 - 2020 U6 - https://doi.org/10.1365/s35764-020-00292-w SN - 1867-5913 SN - 1867-5905 VL - 13 IS - 1 SP - 68 EP - 76 PB - Gabler CY - Wiesbaden 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 - CHAP A1 - Rojahn, Marcel A1 - Gronau, Norbert ED - Bui, Tung X. T1 - Openness indicators for the evaluation of digital platforms between the launch and maturity phase T2 - Proceedings of the 57th Annual Hawaii International Conference on System Sciences N2 - In recent years, the evaluation of digital platforms has become an important focus in the field of information systems science. The identification of influential indicators that drive changes in digital platforms, specifically those related to openness, is still an unresolved issue. This paper addresses the challenge of identifying measurable indicators and characterizing the transition from launch to maturity in digital platforms. It proposes a systematic analytical approach to identify relevant openness indicators for evaluation purposes. The main contributions of this study are the following (1) the development of a comprehensive procedure for analyzing indicators, (2) the categorization of indicators as evaluation metrics within a multidimensional grid-box model, (3) the selection and evaluation of relevant indicators, (4) the identification and assessment of digital platform architectures during the launch-to-maturity transition, and (5) the evaluation of the applicability of the conceptualization and design process for digital platform evaluation. KW - federated industrial platform ecosystems KW - technologies KW - business models KW - data-driven artifacts KW - design-science research KW - digital platform openness KW - evaluation KW - morphological analysis Y1 - 2024 SN - 978-0-99813-317-1 SP - 4516 EP - 4525 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - CHAP A1 - Abendroth, Adrian A1 - Bender, Benedict A1 - Gronau, Norbert T1 - The evolution of original ERP customization BT - a systematic literature review of technical possibilities T2 - Proceedings of the 26th International Conference on Enterprise Information Systems N2 - 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. KW - Enterprise Resource Planning (ERP) System KW - Customization KW - Modification KW - Tailoring KW - Literature Review Y1 - 2024 SN - 978-989-758-692-7 U6 - https://doi.org/10.5220/0012305500003690 SN - 2184-4992 VL - 1 SP - 17 EP - 27 PB - SCITEPRESS - Science and Technology Publications CY - Setúbal ER - TY - CHAP A1 - Klippert, Monika A1 - Stolpmann, Robert A1 - Grum, Marcus A1 - Thim, Christof A1 - Gronau, Norbert A1 - Albers, Albert T1 - Knowledge transfer quality improvement BT - the quality enhancement of knowledge transfers in product engineering T2 - Procedia CIRP N2 - 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. KW - knowledge transfer KW - product generation engineering KW - improvement KW - quality KW - intervention Y1 - 2023 U6 - https://doi.org/10.1016/j.procir.2023.02.171 SN - 2212-8271 VL - 119 SP - 919 EP - 925 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Panzer, Marcel A1 - Gronau, Norbert T1 - Enhancing economic efficiency in modular production systems through deep reinforcement learning T2 - Procedia CIRP N2 - 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. KW - modular production KW - production control KW - multi-agent system KW - deep reinforcement learning KW - discrete event simulation Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2023.09.229 SN - 2212-8271 VL - 121 SP - 55 EP - 60 PB - Elsevier CY - Amsterdam 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 - CHAP A1 - Teichmann, Malte A1 - Ullrich, André A1 - Kotarski, David A1 - Gronau, Norbert T1 - Facing the demographic change BT - recommendations for designing learning factories as age-appropriate teaching-learning environments for older blue-collar workers T2 - SSRN eLibrary / Social Science Research Network N2 - 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. KW - learning factory KW - vocational training KW - learning environment KW - age-appropriate competence development KW - demographic change Y1 - 2021 U6 - https://doi.org/10.2139/ssrn.3858716 SN - 1556-5068 PB - Social Science Electronic Publ. CY - [Erscheinungsort nicht ermittelbar] ER - TY - CHAP A1 - Vladova, Gergana A1 - Ullrich, André A1 - Bender, Benedict A1 - Gronau, Norbert ED - Reis, Arsénio ED - Barroso, João ED - Lopes, J. Bernardino ED - Mikropoulos, Tassos ED - Fan, Chih-Wen T1 - Yes, we can (?) BT - a critical review of the COVID-19 semester T2 - Technology and innovation in learning, teaching and education : second international conference, TECH-EDU 2020, Vila Real, Portugal, December 2-4, 2020 : proceedings N2 - 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. KW - COVID-19 KW - higher education KW - virtual learning KW - digital learning KW - subject differences Y1 - 2021 SN - 978-3-030-73987-4 SN - 978-3-030-73988-1 U6 - https://doi.org/10.1007/978-3-030-73988-1_17 SP - 225 EP - 235 PB - Springer CY - Cham ER - TY - GEN A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - Deep reinforcement learning in production planning and control BT - A systematic literature review T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 198 KW - deep reinforcement learning KW - machine learning KW - production planning KW - production control KW - systematic literature review Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-605722 SN - 2701-6277 SN - 1867-5808 ER - TY - CHAP A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - Deep reinforcement learning in production planning and control BT - A systematic literature review T2 - Proceedings of the Conference on Production Systems and Logistics N2 - 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. KW - deep reinforcement learning KW - machine learning KW - production planning KW - production control KW - systematic literature review Y1 - 2021 U6 - https://doi.org/10.15488/11238 SN - 2701-6277 SP - 535 EP - 545 PB - Institutionelles Repositorium der Leibniz Universität Hannover CY - Hannover ER - 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 -