@misc{BenderGronau2022, author = {Bender, Benedict and Gronau, Norbert}, title = {Introduction to the Minitrack on towards the future of enterprise systems}, 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}, editor = {Bui, Tung}, isbn = {978-0-9981331-5-7}, issn = {1867-5808}, doi = {10.25932/publishup-60540}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605406}, pages = {4}, year = {2022}, abstract = {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.}, language = {en} } @article{BenderHabibGronau2020, author = {Bender, Benedict and Habib, Natalie and Gronau, Norbert}, title = {Digitale Plattformen}, series = {Wirtschaftsinformatik und Management}, volume = {13}, journal = {Wirtschaftsinformatik und Management}, number = {1}, publisher = {Gabler}, address = {Wiesbaden}, issn = {1867-5913}, doi = {10.1365/s35764-020-00292-w}, pages = {68 -- 76}, year = {2020}, abstract = {Obwohl digitale Plattformen vornehmlich von Großunternehmen betrieben werden, bieten sie klein- und mittelst{\"a}ndischen Unternehmen (KMU) Potenziale zur Verbreitung innovativer Technologien und f{\"u}r den Ausbau ihres Gesch{\"a}ftsmodells. F{\"u}r die Umsetzung digitaler Plattformen stehen Unternehmen mehrere Strategien zur Verf{\"u}gung. Der Beitrag vergleicht und bewertet grundlegende Strategien am Beispiel eines Maschinenbauunternehmens. Die Ergebnisse dienen als Grundlage f{\"u}r die Entscheidungsfindung von KMU.}, language = {de} } @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} } @inproceedings{RojahnGronau2024, author = {Rojahn, Marcel and Gronau, Norbert}, title = {Openness indicators for the evaluation of digital platforms between the launch and maturity phase}, series = {Proceedings of the 57th Annual Hawaii International Conference on System Sciences}, booktitle = {Proceedings of the 57th Annual Hawaii International Conference on System Sciences}, editor = {Bui, Tung X.}, publisher = {Department of IT Management Shidler College of Business University of Hawaii}, address = {Honolulu, HI}, isbn = {978-0-99813-317-1}, pages = {4516 -- 4525}, year = {2024}, abstract = {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.}, 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} } @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} } @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{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} }