TY - CHAP A1 - Bender, Benedict A1 - Gronau, Norbert A1 - Winter, Robert ED - Bui, Tung X. T1 - Minitrack introduction enterprise-level information systems BT - research minitrack T2 - Proceedings of the 56th Hawaii International Conference on System Sciences N2 - While Information Systems (IS) Research on the individual and workgroup level of analysis is omnipresent, research on the enterprise-level IS is less frequent. Even though research on Enterprise Systems and their management is established in academic associations and conference programs, enterprise-level phenomena are underrepresented. This minitrack provides a forum to integrate existing research streams that traditionally needed to be attached to other topics (such as IS management or IS governance). The minitrack received broad attention. The three selected papers address different facets of the future role of enterprise-wide IS including aspects such as carbonization, ecosystem integration, and technology-organization fit. KW - information systems research KW - enteprise-level KW - enterprise systems Y1 - 2023 SN - 978-0-9981331-6-4 SN - 2572-6862 SP - 5809 EP - 5810 PB - Hawaii International Conference on System Sciences CY - Honolulu, HI 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 - publish-Ing. CY - Hannover 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 - GEN A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - A deep reinforcement learning based hyper-heuristic for modular production control T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 173 KW - production control KW - modular production KW - multi-agent system KW - deep reinforcement learning KW - deep learning KW - multi-objective optimisation Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-605642 SN - 1867-5808 ER - TY - JOUR A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - A deep reinforcement learning based hyper-heuristic for modular production control JF - International journal of production research N2 - 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. KW - production control KW - modular production KW - multi-agent system KW - deep reinforcement learning KW - deep learning KW - multi-objective optimisation Y1 - 2023 U6 - https://doi.org/10.1080/00207543.2023.2233641 SN - 0020-7543 SN - 1366-588X SN - 0278-6125 SP - 1 EP - 22 PB - Taylor & Francis CY - London 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 - 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 - 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 -