@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} } @article{GrumGronau2024, author = {Grum, Marcus and Gronau, Norbert}, title = {The impact of knowledge characteristics on process performance}, series = {Business process management journal}, volume = {30}, journal = {Business process management journal}, number = {4}, publisher = {Emerald}, address = {Bingley}, issn = {1463-7154}, doi = {10.1108/BPMJ-10-2023-0853}, pages = {1088 -- 1110}, year = {2024}, abstract = {Purpose With shorter product cycles and a growing number of knowledge-intensive business processes, time consumption is a highly relevant target factor in measuring the performance of contemporary business processes. This research aims to extend prior research on the effects of knowledge transfer velocity at the individual level by considering the effect of complexity, stickiness, competencies, and further demographic factors on knowledge-intensive business processes at the conversion-specific levels. Design/methodology/approach We empirically assess the impact of situation-dependent knowledge transfer velocities on time consumption in teams and individuals. Further, we issue the demographic effect on this relationship. We study a sample of 178 experiments of project teams and individuals applying ordinary least squares (OLS) for regression analysis-based modeling. Findings The authors find that time consumed at knowledge transfers is negatively associated with the complexity of tasks. Moreover, competence among team members has a complementary effect on this relationship and stickiness retards knowledge transfers. Thus, while demographic factors urgently need to be considered for effective and speedy knowledge transfers, these influencing factors should be addressed on a conversion-specific basis so that some tasks are realized in teams best while others are not. Guidelines and interventions are derived to identify best task realization variants, so that process performance is improved by a new kind of process improvement method. Research limitations/implications This study establishes empirically the importance of conversion-specific influence factors and demographic factors as drivers of high knowledge transfer velocities in teams and among individuals. The contribution connects the field of knowledge management to important streams in the wider business literature: process improvement, management of knowledge resources, design of information systems, etc. Whereas the model is highly bound to the experiment tasks, it has high explanatory power and high generalizability to other contexts. Practical implications Team managers should take care to allow the optimal knowledge transfer situation within the team. This is particularly important when knowledge sharing is central, e.g. in product development and consulting processes. If this is not possible, interventions should be applied to the individual knowledge transfer situation to improve knowledge transfers among team members. Social implications Faster and more effective knowledge transfers improve the performance of both commercial and non-commercial organizations. As nowadays, the individual is faced with time pressure to finalize tasks, the deliberated increase of knowledge transfer velocity is a core capability to realize this goal. Quantitative knowledge transfer models result in more reliable predictions about the duration of knowledge transfers. These allow the target-oriented modification of knowledge transfer situations so that processes speed up, private firms are more competitive and public services are faster to citizens. Originality/value Time consumption is an increasingly relevant factor in contemporary business but so far not been explored in experiments at all. This study extends current knowledge by considering quantitative effects on knowledge velocity and improved knowledge transfers.}, language = {en} } @article{RolingGrumGronauetal.2024, author = {Roling, Wiebke M. and Grum, Marcus and Gronau, Norbert and Kluge, Annette}, title = {The roots of errors in adaptive performance}, series = {Journal of workplace learning}, volume = {36}, journal = {Journal of workplace learning}, number = {4}, publisher = {Emerald}, address = {Bradford}, issn = {1366-5626}, doi = {10.1108/JWL-10-2023-0168}, pages = {267 -- 281}, year = {2024}, abstract = {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.}, 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} } @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} } @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} } @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{UllrichTeichmannGronau2021, author = {Ullrich, Andr{\´e} and Teichmann, Malte and Gronau, Norbert}, title = {Fast trainable capabilities in software engineering-skill development in learning factories}, series = {Ji suan ji jiao yu = Computer Education / Qing hua da xue}, journal = {Ji suan ji jiao yu = Computer Education / Qing hua da xue}, number = {12}, publisher = {[Verlag nicht ermittelbar]}, address = {Bei jing shi}, issn = {1672-5913}, doi = {10.16512/j.cnki.jsjjy.2020.12.002}, pages = {2 -- 10}, year = {2021}, abstract = {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.}, language = {en} }