Refine
Year of publication
Document Type
- Article (9)
- Conference Proceeding (8)
- Postprint (6)
- Contribution to a Periodical (5)
- Part of a Book (1)
- Other (1)
Keywords
- deep reinforcement learning (4)
- machine learning (4)
- production control (4)
- systematic literature review (4)
- COVID-19 (3)
- Digitale Plattformen (3)
- ERP (3)
- digital learning (3)
- Geschäftsmodell (2)
- Industrie 4.0 (2)
- Industry 4.0 (2)
- Internet of Things (2)
- KMU (2)
- Maschinen- und Anlagenbau (2)
- Strategie (2)
- TAM (2)
- deep learning (2)
- discipline differences (2)
- e-learning (2)
- modular production (2)
- multi-agent system (2)
- multi-objective optimisation (2)
- neural networks (2)
- production planning (2)
- production planning and control (2)
- taxonomy (2)
- technology acceptance (2)
- technology-mediated teaching (2)
- university teaching (2)
- Adaptivität (1)
- Altsyteme (1)
- Analytics (1)
- Anwendungszentrum Industrie 4.0 (1)
- Architecture concepts (1)
- Automatisierung (1)
- CPS (1)
- Case Study (1)
- Coring (1)
- Cross-System (1)
- Cyber-physical systems (1)
- Decentral Decision Making (1)
- Digital Marketplaces (1)
- Digital Platforms (1)
- ERP system (1)
- Enterprise Resource Planning (1)
- Enterprise System (1)
- Geschäftsmodelle (1)
- Industrial Analytics (1)
- Internet of things (1)
- Invidiuallösungen (1)
- KVP (1)
- Kaizen (1)
- Lernfabrik (1)
- Lernszenario (1)
- Mobile IIoT-Technologie (1)
- Mobile Software Ecosystems (1)
- Problems (1)
- Process Mining (1)
- Production (1)
- Prozessintegration (1)
- Qualität (1)
- RFID (1)
- RPA (1)
- Research Agenda (1)
- Robotic Process Automation (1)
- Simulation (1)
- Spielifizierung (1)
- Systematisches Vorgehen (1)
- Systemauswahl (1)
- Task realization strategies (1)
- Three-tier Architecture (1)
- Verbesserungen (1)
- Verbesserungsprozess (1)
- application center Industrie 4.0 (1)
- automation (1)
- coring (1)
- cyber-physical production systems (1)
- cyber-physical systems (1)
- database (1)
- digital learning factory (1)
- digital marketplaces (1)
- digital platforms (1)
- eference Architecture Model (1)
- enteprise-level (1)
- enterprise system (1)
- enterprise systems (1)
- evaluation (1)
- future (1)
- geographical distribution (1)
- higher education (1)
- hybrid simulation (1)
- information systems research (1)
- learning scenario (1)
- manufacturing systems (1)
- mobile IIoT-technologies (1)
- mobile software ecosystems (1)
- problems (1)
- process integration (1)
- production engineering computing (1)
- production networks (1)
- requirements (1)
- simulation (1)
- subject differences (1)
- task realization strategies (1)
- virtual learning (1)
Obwohl Handelsplattformen zunehmend an Bedeutung gewinnen, besteht im deutschsprachigen Raum ein Mangel an umfassenden Marktübersichten. Dadurch fehlt es Verkäufern, potenziellen Plattformbetreibern und Kunden an einer soliden Grundlage für fundierte Entscheidungen. Das ändern wir mit folgendem Beitrag. Erfahren Sie hier das Wichtigste über den rasant wachsenden Markt der Handelsplattformen.
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.
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.
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.
The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.
Enterprise systems have long played an important role in businesses of various sizes. With the increasing complexity of today’s business relationships, specialized 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.
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.
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.
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.
Robotic Process Automation (RPA) steht für die softwareunterstützte Bedienung von Softwarelösungen über deren Benutzeroberfläche. Das primäre Ziel, das mit RPA erreicht werden soll, ist die automatisierte Ausführung von Routineaufgaben, die bisher einen menschlichen Eingriff erforderten. Das Potenzial von RPA, Prozesse langfristig zu verbessern, ist allerdings stark begrenzt. Die Automatisierung von Prozessen und die Überbrückung von Medienbrüchen auf der Front-End-Ebene führt zu einer Vielzahl von Abhängigkeiten und Bedingungen, die in diesem Beitrag zusammengefasst werden. Der Weg zu einer nachhaltigen Unternehmensarchitektur (bestehend aus Prozessen und Systemen) erfordert offene, adaptive Systeme mit moderner Architektur, die sich durch ein hohes Maß an Interoperabilität auf verschiedenen Ebenen auszeichnen.