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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.
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.
Today’s mobile devices are part of powerful business ecosystems, which usually involve digital platforms. To better understand the complex phenomenon of coring and related dynamics, this paper presents a case study comparing iMessage as part of Apple’s iOS and WhatsApp. Specifically, it investigates activities regarding platform coring, as the integration of several functionalities provided by third-party applications in the platform core. The paper makes three contributions. First, a systematization of coring activities is developed. Coring modes are differentiated by the amount of coring and application maintenance. Second, the case study revealed that the phenomenon of platform coring is present on digital platforms for mobile devices. Third, the fundamentals of coring are discussed as a first step towards theoretical development. Even though coring constitutes a potential threat for third-party developers regarding their functional differentiation, an idea of what a beneficial partnership incorporating coring activities could look like is developed here.
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.
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.
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.