TY  - CHAP
A1  - Grum, Marcus
A1  - Klippert, Monika
A1  - Albers, Albert
A1  - Gronau, Norbert
A1  - Thim, Christof
T1  - Examining the quality of knowledge transfers
BT  - the draft of an empirical research
T2  - Proceedings of the Design Society
N2  - Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.
KW  - knowledge management
KW  - new product development
KW  - evaluation
Y1  - 2021
U6  - https://doi.org/10.1017/pds.2021.404
SN  - 2732-527X
VL  - 1
SP  - 1431
EP  - 1440
PB  - Cambridge University Press
CY  - Cambridge
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  - JOUR
A1  - Panzer, Marcel
A1  - Gronau, Norbert
T1  - Enhancing economic efficiency in modular production systems through deep reinforcement learning
JF  - Procedia CIRP
N2  - 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.
KW  - modular production
KW  - production control
KW  - multi-agent system
KW  - deep reinforcement learning
KW  - discrete event simulation
Y1  - 2024
U6  - https://doi.org/10.1016/j.procir.2023.09.229
SN  - 2212-8271
VL  - 121
SP  - 55
EP  - 60
PB  - Elsevier
CY  - Amsterdam
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  - BOOK
A1  - Gronau, Norbert
T1  - Knowledge Modeling and Description Language (KMDL) 3.0
BT  - an introduction into the creation of knowledge-intensive business processes
Y1  - 2024
SN  - 978-3-95545-416-6
PB  - GITO mbH Verlag
CY  - Berlin
ER  - 
TY  - JOUR
A1  - Adelhelm, Silvia
A1  - Braun, Andreas
A1  - Gronau, Norbert
A1  - Lürig, Detlef
A1  - Müller, Elisabeth
A1  - Vladova, Gergana
A1  - Wagner, Dieter
T1  - Mit Open Innovation zum Erfolg
BT  - So verbessern mittelständische Pharmaunternehmen ihre Innovationsprozesse
JF  - Handbuch prozessorientiertes Wissensmanagment
Y1  - 2014
SN  - 978-3-95545-026-7
SP  - 211
EP  - 226
PB  - GITO
CY  - Berlin
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  - JOUR
A1  - Ullrich, André
A1  - Vladova, Gergana
A1  - Marquart, Danny
A1  - Braun, Andreas
A1  - Gronau, Norbert
T1  - An overwiew of benefits and risks in open innovation projects and the influence of intermediary participation, decision-making authority, experience, and position on their perception
JF  - International journal of innovation management : IJIM
N2  - This paper presents an exploratory study investigating the influence of the factors (1) intermediary participation, (2) decision-making authority, (3) position in the enterprise, and (4) experience in open innovation on the perception and assessment of the benefits and risks expected from participating in open innovation projects. For this purpose, an online survey was conducted in Germany, Austria and Switzerland. The result of this paper is an empirical evidence showing whether and how these factors affect the perception of potential benefits and risks expected within the context of open innovation project participation. Furthermore, the identified effects are discussed against the theory. Existing theory regarding the benefits and risks of open innovation is expanded by (1) finding that they are perceived mostly independently of the factors, (2) confirming the practical relevance of benefits and risks, and (3) enabling a finer distinction between their degrees of relevance according to respective contextual specifics.
KW  - Open innovation
KW  - intermediaries
KW  - benefits
KW  - decision-making
KW  - experience;
KW  - risks
Y1  - 2022
U6  - https://doi.org/10.1142/S1363919622500128
SN  - 1363-9196
SN  - 1757-5877
VL  - 26
IS  - 02
PB  - World Scientific Publ.
CY  - Singapore
ER  - 
TY  - JOUR
A1  - Ullrich, André
A1  - Weber, Edzard
A1  - Gronau, Norbert
T1  - Regionale Refabrikationsnetzwerke
BT  - Potenziale und Herausforderungen der lokalen Wiederaufarbeitung von Produkten
JF  - Industrie 4.0 Management : Gegenwart und Zukunft industrieller Geschäftsprozesse
N2  - 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 Aktivitäten, um zu einem nachhaltigen Umgang mit den zur Verfügung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es ermöglicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu fördern und effiziente Lösungen für die regionale Wieder- und Weiterverwendung von Gütern anzubieten.
KW  - Refabrikation
KW  - Regionale Ansätze
KW  - Remanufacturing
Y1  - 2023
U6  - https://doi.org/10.30844/IM_23-2_11-14
SN  - 2364-9208
VL  - 39
IS  - 2
SP  - 11
EP  - 14
PB  - GITO mbH Verlag
CY  - Berlin
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  - Ullrich, André
A1  - Weber, Edzard
A1  - Gronau, Norbert
T1  - Regionale Refabrikationsnetzwerke
BT  - Potenziale und Herausforderungen der lokalen Wiederaufarbeitung von Produkten
T2  - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe
N2  - 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 Aktivitäten, um zu einem nachhaltigen Umgang mit den zur Verfügung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es ermöglicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu fördern und effiziente Lösungen für die regionale Wieder- und Weiterverwendung von Gütern anzubieten.
T3  - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 183 
KW  - Refabrikation
KW  - Regionale Ansätze
KW  - Remanufacturing
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-604510
SN  - 2364-9208
SN  - 1867-5808
IS  - 2
ER  - 
TY  - CHAP
A1  - Gonnermann, Jana
A1  - Brandenburger, Bonny
A1  - Vladova, Gergana
A1  - Gronau, Norbert
ED  - Bui, Tung X.
T1  - To what extent can individualisation in terms of different types of mode improve learning outcomes and learner satisfaction?
BT  - a pre-study
T2  - Proceedings of the 56th Annual Hawaii International Conference on System Sciences January 3-6, 2023
N2  - With the latest technological developments and associated new possibilities in teaching, the personalisation of learning is gaining more and more importance. It assumes that individual learning experiences and results could generally be improved when personal learning preferences are considered. To do justice to the complexity of the personalisation possibilities of teaching and learning processes, we illustrate the components of learning and teaching in the digital environment and their interdependencies in an initial model. Furthermore, in a pre-study, we investigate the relationships between the learner's ability to (digital) self-organise, the learner’s prior- knowledge learning in different variants of mode and learning outcomes as one part of this model. With this pre-study, we are taking the first step towards a holistic model of teaching and learning in digital environments.
KW  - advances in teaching and learning technologies
KW  - digital learning
KW  - digital teaching
KW  - experimental design
KW  - personalised learning
KW  - teaching and learning model
Y1  - 2023
SN  - 978-0-9981331-6-4
SP  - 123
EP  - 132
PB  - Department of IT Management Shidler College of Business University of Hawaii
CY  - Honolulu, HI
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  - 
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  - 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  -