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Institute
Die Wettbewerbsposition von Unternehmen zukunftsorientiert ausbauenPlattformen als strategisches Geschäftsmodell können die unternehmerische Wettbewerbsfähigkeit verbessern. Mittels eigener Plattformen koordinieren Unternehmen externe Wertschöpfung, setzen strategische Impulse am Markt und reduzieren gleichzeitig die Risiken von Innovationen.Die Stellung am Markt festigen, absichern oder ausbauenDer Autor vermittelt einen Eindruck von den Potenzialen plattformbasierter Geschäftsmodelle, wobei er auf mögliche Stolpersteine hinweist. Dieses Buch zeigt anhand von Beispielen erfolgreicher Pioniere, welche unternehmerischen Chancen die Integration externer Partner in die Wertschöpfungskette mit sich bringt. Zudem bietet es folgende Inhalte:Praxistauglicher Zugang zu den Grundlagen der Plattformökonomie durch sorgfältig zusammengestellte ChecklistenHandlungsempfehlungen für kleine und mittelständische Unternehmen für die Realisierung plattformbasierter GeschäftsmodelleEntscheider werden durch das wissenschaftlich-fundierte Vorgehensmodell von der Konzeption bis zur nachhaltigen Umsetzung begleitetPlattformbasierte Geschäftsmodelle - Neue Ansätze zur Erweiterung bestehender Businesskonzepte gewährt einen Blick hinter die Kulissen von Plattformen für Industrie und Handel und hilft, mögliche Synergien für Unternehmen und Geschäfte zu identifizieren und von den Möglichkeiten der Plattformökonomie zu profitieren.
Enterprise Resource Planning (ERP) systems are critical to the success of enterprises, facilitating business operations through standardized digital processes. However, existing ERP systems are unsuitable for startups and small and medium-sized enterprises that grow quickly and require adaptable solutions with low barriers to entry. Drawing upon 15 explorative interviews with industry experts, we examine the challenges of current ERP systems using the task technology fit theory across companies of varying sizes. We describe high entry barriers, high costs of implementing implicit processes, and insufficient interoperability of already employed tools. We present a vision of a future business process platform based on three enablers: Business processes as first-class entities, semantic data and processes, and cloud-native elasticity and high availability. We discuss how these enablers address current ERP systems' challenges and how they may be used for research on the next generation of business software for tomorrow's enterprises.
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.