TY - JOUR A1 - Weber, Edzard A1 - Stein, Maureen A1 - Gronau, Norbert T1 - Demokratieorientierte eGovernment-Systeme Y1 - 2009 SN - 1436 - 3011 ER - TY - JOUR A1 - Gronau, Norbert A1 - Weber, Edzard T1 - Defining an infrastructure for knowledge intensive business processes N2 - Business processes can be modelled and analysed extensively with well known and established methods. The simple signs of static knowledge do not fulfil the requirements of a comprehensive and integrated approach of process-oriented knowledge management. The Knowledge Modelling Description Language KMDL is able to represent the creation, use and necessity of knowledge along common business processes. Therefore KMDL can be used to formalise knowledge-intensive processes with a focus on certain knowledge-specific characteristics and to identify weak points in these processes. The tool K-Modeller is introduced for a computer-aided modelling and analysing. Y1 - 2004 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 - CHAP A1 - Gronau, Norbert A1 - Rojahn, Marcel ED - Leupold, Andreas ED - Wiebe, Andreas ED - Glossner, Silke T1 - Das Industrial Internet of Things (IIOT) T2 - IT-Recht : Recht, Wirtschaft und Technik der digitalen Transformation Y1 - 2021 UR - https://beck-online.beck.de/Bcid/Y-400-W-LeupoldGlossnerHdbITR-GL-Teil10-1 SN - 978-3-406-74458-7 SP - 1115 EP - 1124 PB - C.H. Beck CY - München ET - 4., überarbeitet und erweitert ER - TY - CHAP A1 - Gronau, Norbert A1 - Weber, Edzard A1 - Heinze, Priscilla T1 - Cyclic process model transformation T2 - Proceedings of the 12th European Conference on Knowledge Management N2 - Process analysis usually focuses only on single and selected processes. It is either existent processes that are recorded and analysed or reference processes that are implemented. So far no evident effort has been put into generalising specific process aspects into patterns and comparing those patterns with regard to their efficiency and effectiveness. This article focuses on the combination of dynamic and holistic analytical elements in enterprise architectures. Our goal is to outline an approach to analyse the development of business processes in a cyclical matter and demonstrate this approach based on an existent modelling language. We want to show that organisational learning can derive from the systematic analysis of past and existent processes from which patterns of successful problem solving can be deducted. Y1 - 2011 SN - 978-1-908272-09-6 IS - 2 SP - 349 EP - 359 PB - Academic Conferences Ltd. CY - Reading 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 - CHAP A1 - Glaschke, Christian A1 - Gronau, Norbert A1 - Bender, Benedict T1 - Cross-System Process Mining using RFID Technology T2 - Proceedings of the Sixth International Symposium on Business Modeling and Software Design - BMSD N2 - In times of digitalization, the collection and modeling of business processes is still a challenge for companies. The demand for trustworthy process models that reflect the actual execution steps therefore increases. The respective kinds of processes significantly determine both, business process analysis and the conception of future target processes and they are the starting point for any kind of change initiatives. Existing approaches to model as-is processes, like process mining, are exclusively focused on reconstruction. Therefore, transactional protocols and limited data from a single application system are used. Heterogeneous application landscapes and business processes that are executed across multiple application systems, on the contrary, are one of the main challenges in process mining research. Using RFID technology is hence one approach to close the existing gap between different application systems. This paper focuses on methods for data collection from real world objects via RFID technology and possible combinations with application data (process mining) in order to realize a cross system mining approach. KW - Process Mining KW - RFID KW - Production KW - Cross-System Y1 - 2016 SN - 978-989-758-190-8 U6 - https://doi.org/10.5220/0006223501790186 SP - 179 EP - 186 PB - SCITEPRESS - Science and Technology Publications CY - Setúbal ER - TY - JOUR A1 - Haase, Jennifer A1 - Hanel, Paul H. P. A1 - Gronau, Norbert T1 - Creativity enhancement methods for adults BT - a meta-analysis JF - Psychology of aesthetics, creativity, and the arts N2 - This meta-analysis synthesizes 332 effect sizes of various methods to enhance creativity. We clustered all studies into 12 methods to identify the most effective creativity enhancement methods. We found that, on average, creativity can be enhanced, Hedges’ g = 0.53, 95% CI [0.44, 0.61], with 70.09% of the participants in the enhancement conditions being more creative than the average person in the control conditions. Complex training courses, meditation, and cultural exposure were the most effective (gs = 0.66) while the use of cognitive manipulation drugs was the least and also noneffective, g = 0.10. The type of training material was also important. For instance, figural methods were more effective in enhancing creativity, and enhancing converging thinking was more effective than enhancing divergent thinking. Study effect sizes varied considerably across all studies and for many subgroup analyses, suggesting that researchers can plausibly expect to find reversed effects occasionally. We found no evidence of publication bias. We discuss theoretical implications and suggest future directions for best practices in enhancing creativity. (PsycInfo Database Record (c) 2023 APA, all rights reserved) KW - manipulation KW - enhancement KW - assessment KW - effectiveness KW - creativity training Y1 - 2023 U6 - https://doi.org/10.1037/aca0000557 SN - 1931-3896 SN - 1931-390X PB - American Psychological Association CY - Washington, DC ER - TY - JOUR A1 - Gronau, Norbert A1 - Uslar, Mathias T1 - Creating Skill Catalogues for Competency Management Systems with KMDL N2 - The efficient use of human capital is one of the most important factors in todays' business competition. Competition is strongly influenced by qualified staff. In order to aid the human resources department to keep up with strategic decisions various competency management systems have been created that make the development of human resources easier and more precise. Competency management systems are only as good as the information that they are based on. The mostly used basic information is the skill catalogue. But there are nearly no applicable methods yet to create such a catalogue thoroughly. This paper introduces a reasonable approach to create such a catalogue with the description language for knowledge-intensive processes KMDL. Y1 - 2004 UR - http://wi.uni-potsdam.de ER -