@misc{VladovaUllrichBenderetal.2021, author = {Vladova, Gergana and Ullrich, Andr{\´e} and Bender, Benedict and Gronau, Norbert}, title = {Students' Acceptance of Technology-Mediated Teaching - How It Was Influenced During the COVID-19 Pandemic in 2020: A Study From Germany}, series = {Postprints der Universit{\"a}t Potsdam Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {1867-5808}, doi = {10.25932/publishup-52161}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-521615}, pages = {17}, year = {2021}, abstract = {In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring-summer 2020 semester. Our study focused on (1) the students' acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study's results and derive short- and long-term implications for science and practice.}, language = {en} } @misc{ScheelBender2021, author = {Scheel, Laura and Bender, Benedict}, title = {Industrial Internet of Things(IIoT)-Plattformtypen im Maschinen- und Anlagenbau}, 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 = {1867-5808}, doi = {10.25932/publishup-60571}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605717}, pages = {20}, year = {2021}, abstract = {Das Angebot digitaler Plattformen ist mittlerweile auch im Maschinen- und Anlagenbau weit verbreitet. Dabei konnte in den letzten Jahren der Trend verzeichnet werden, dass die Herstellerunternehmen von Maschinen und Anlagen nicht mehr ausschließlich physische Produkte ver{\"a}ußern, sondern zus{\"a}tzliche auf das Produkt abgestimmte Dienstleistungen, wie bspw. digitale Services. Dieser Wandel kann einen großen Einfluss auf die Ver{\"a}nderung des Gesch{\"a}ftsmodells haben und je nach Komplexit{\"a}t der digitalen Plattformen unterschiedliche Ausmaße annehmen, die auch strategische Entscheidungen bestimmen k{\"o}nnen. In diesem Beitrag wird eine Klassifizierung der digitalen Plattformen im deutschen Maschinen- und Anlagenbau vorgenommen, mithilfe derer unterschiedliche Plattformtypen auf Grundlage ihrer Funktionszusammensetzung identifiziert werden. Demnach k{\"o}nnen bspw. Plattformen, {\"u}ber die lediglich grundlegende Funktionen wie die Verwaltung von Maschinen angeboten werden, von umfangreicheren Plattformen unterschieden werden, die eine h{\"o}here Komplexit{\"a}t aufweisen und somit einen gr{\"o}ßeren Einfluss auf die Ver{\"a}nderung des Gesch{\"a}ftsmodells haben. Diese Einteilung unterschiedlicher Plattformtypen kann Unternehmen im Maschinen- und Anlagenbau dabei unterst{\"u}tzen, strategische Entscheidungen bez{\"u}glich der Entwicklung und des Angebots digitaler Plattformen zu treffen und eine Einordnung ihrer digitalen Plattform im Wettbewerb vorzunehmen.}, language = {de} } @misc{PanzerBenderGronau2022, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Neural agent-based 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 = {1867-5808}, doi = {10.25932/publishup-60477}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604777}, pages = {26}, year = {2022}, abstract = {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.}, 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} } @misc{PanzerBenderGronau2023, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {A deep reinforcement learning based hyper-heuristic for modular production 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 = {1867-5808}, doi = {10.25932/publishup-60564}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605642}, pages = {24}, year = {2023}, abstract = {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.}, language = {en} } @article{GrumBenderAlfaetal.2018, author = {Grum, Marcus and Bender, Benedict and Alfa, A. S. and Gronau, Norbert}, title = {A decision maxim for efficient task realization within analytical network infrastructures}, series = {Decision support systems : DSS ; the international journal}, volume = {112}, journal = {Decision support systems : DSS ; the international journal}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0167-9236}, doi = {10.1016/j.dss.2018.06.005}, pages = {48 -- 59}, year = {2018}, abstract = {Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation.}, language = {en} } @misc{BenderHabibGronau2020, author = {Bender, Benedict and Habib, Natalie and Gronau, Norbert}, title = {Digitale Plattformen}, 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}, number = {1}, issn = {1867-5808}, doi = {10.25932/publishup-60541}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605419}, pages = {11}, 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} } @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} }