@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} } @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{TeichmannUllrichKotarskietal.2021, author = {Teichmann, Malte and Ullrich, Andr{\´e} and Kotarski, David and Gronau, Norbert}, title = {Facing the demographic change}, series = {SSRN eLibrary / Social Science Research Network}, booktitle = {SSRN eLibrary / Social Science Research Network}, publisher = {Social Science Electronic Publ.}, address = {[Erscheinungsort nicht ermittelbar]}, issn = {1556-5068}, doi = {10.2139/ssrn.3858716}, pages = {6}, year = {2021}, abstract = {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.}, language = {en} } @misc{PanzerBenderGronau2021, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Deep reinforcement learning in 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 = {2701-6277}, doi = {10.25932/publishup-60572}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605722}, pages = {13}, year = {2021}, abstract = {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.}, language = {en} } @inproceedings{PanzerBenderGronau2021, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Deep reinforcement learning in production planning and control}, series = {Proceedings of the Conference on Production Systems and Logistics}, booktitle = {Proceedings of the Conference on Production Systems and Logistics}, publisher = {Institutionelles Repositorium der Leibniz Universit{\"a}t Hannover}, address = {Hannover}, issn = {2701-6277}, doi = {10.15488/11238}, pages = {535 -- 545}, year = {2021}, abstract = {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.}, 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{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} }