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The industrial food production is currently caught between the increas-ing demands of numerous stakeholders, economic profitability and the challenges of digitization. A solution to face these various challenges can be seen in the aggregation of data into higher-value, independent data products that can be of-fered and sold on a buyer's market. Large amounts of heterogeneous data are already available in the value chain of the industrial food production, e.g. throughout the data-driven harvesting of primary products, further processing by interconnected production facilities and the information-intensive product distri-bution to end consumers. However, the data is usually only evaluated and used locally for the optimization of internal processes or, at the most, within compre-hensive partnerships. The purpose of this paper is to identify new revenue oppor-tunities for current and future players in the industrial food production by using data as an independent economic good (data products). For this purpose, scenar-ios for the development and use of data products via Industrial Internet of Things platforms are developed for a food technical reference process, the industrial chocolate production and its value chain. On this basis, examples for different types of data products and their value propositions are derived. The results can not only serve food producers and relevant stakeholders but all industrial produc-ers as an input for the future, yield-increasing orientation of their business models.
Die Variantenfließfertigung ermöglicht die Herstellung konfigurierbarer Produkte bei kurzen Durchlaufzeiten und geringen Beständen. Im Vergleich zu anderen Organisationsformen der Produktion gestaltet sich die Produktionsplanung und -steuerung aufgrund der Variantenvielfalt als anspruchsvoll. Im vorliegenden Beitrag wird der erste Schritt einer Methodik vorgestellt, welche für die Konfiguration der Reihenfolgeplanung entwickelt wurde.
Progress in the development of small electric and hybrid aircraft promises business opportunities for thin-haul air mobility services. In order to develop demand-oriented flight plan scenarios for Germany, this paper presents a model to estimate the marked volume of thin-haul air mobility. To quantify the potential demand, our model includes the steps of trip generation, trip distribution and mode choice. Trip generation and distribution takes place between 412 geographic subdivisions of Germany and is based on calibrated traffic forecast data for the year 2030. For the first time the five relevant modes of transport, namely: car, intercity train, intercity bus, commercial aircraft and thin-haul air mobility services, have been included in one model. The step of choosing the transport mode is implemented via a generalized cost approach, taking into account travel costs and travel time. Additionally, route modeling of all transport modes is enhanced by real market data using large-scale data readouts of web interfaces. As primary result we predict a market share of 6 % or 81 million trips per year for thin-haul air mobility services. The demand concentrates on a small number of airports: 30 % of the trips are estimated to be between only 20 airports. Hubs and main routes are identified to offer the potential for scheduled air services.
Auf Basis einer systematischen Literaturanalyse wurden insgesamt 11 Kennzahlen identifiziert, welche die Grundlage zur Beschreibung der operativen Leistungsfähigkeit von Unternehmen bilden. Die Kennzahlen wurden in die vier Leistungsdimensionen Effizienz, Qualität, Zeit und Flexibilität eingeteilt.
Es geht um die Entwicklung eines Software-Tools zur Unterstützung bei der Auswahl von geeigneten 3D-Druckdienstleistern im Kontext der additiven Ersatzteillogistik. Im Fokus steht der Logistikdienstleister als potentieller Nutzer des Softwaretools. Das Softwaretool erfüllt zwei zentrale Funktionen: Überprüfung ob ein Ersatzteil additiv gefertigt werden soll und Auswahl eines konkreten Produzenten durch Matchingalgorithmus.
Immer noch ist es um die Zufriedenheit der Kunden mit der Qualität von Diestleistungen hierzulande nicht gut bestellt. An der Verbesserung dieses Zustands müssen wir arbeiten, in den Unternehmen, in den staatlichen Organisationen und in den Bildungseinrichtungen, denn die Frage, ob und wie wir den Wandel zur uneingeschränkten Servicegesellschaft bewältigen - der ohne eine konsequente Digitalisierung der Wertschöpfung unmöglich ist -, hat auch entscheidenden Einfluss auf die Zukunft des Wirtschaftsstandorts Deutschland.
Influenced by the high dynamic of the markets and the steadily increasing demand for short delivery times the importance of supply chain optimization is growing. In particular, the order process plays a central role in achieving short delivery times and constantly needs to evaluate the trade-off between high inventory and the risk of stock-outs. However, analyzing different order strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve processes in the whole value chain. The objective of this research is to evaluate two different order strategies (t, q, t, S) in a four-stage supply chain. In order to measure the performance of the supply chain the quantity of the backlog will be considered. A Design of Experiments approach is supposed to enhance the significance of the simulation results.
The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity.
"New Service-Work"
(2021)
Durch die Corona-Krise befinden wir uns auf einmal alle in einer völlig neuen Arbeitswelt. Innerhalb kürzester Zeit waren Unternehmen im Service gezwungen, Antworten für – vielleicht auch manchmal schon länger bestehende Fragen – rund um das Thema „New Work“ zu finden. Daher hat sich der KVD in den vergangenen Monaten im Rahmen einer ausgewiesenen Expertenrunde aus Praxis und Forschung mit dem Thema „New Service-Work“ auseinandergesetzt. Vor dem Hintergrund der aktuellen Herausforderungen haben wir die zentralen Handlungsfelder für die Umsetzung dieser neuen Arbeitswelt einmal intensiver beleuchtet. In über 25 digitalen Treffen wurden die Chancen, Risiken oder auch Hemmnisse zu den jeweiligen Handlungsfeldern intensiv diskutiert und aufbereitet. Mit einigen aktuellen, innovativen Lösungsansätzen aus unseren Mitgliedsunternehmen hoffen wir, auch Ihnen erste Hinweise oder Impulse geben zu können, wie auch bald in Ihrem Unternehmen „New Service-Work“ Einzug halten könnte.
In manufacturing, adherence to delivery dates is one of the main logistic goals. The production control department has to cope with short-term deviations from the planned route sheets. Because of unforeseen disruptions, e.g. machine breakdowns or shortage of material or personnel, in some situations, the promised delivery date to the customer is at stake. In practice, a fast and reasonable decision on how to deal with the delayed order is required. This decision process is often based on a qualitative analysis relying on the planner’s subjective assessment of a complex situation. To improve the quality of possible countermeasures this paper presents an application, which supports the decision process through a quantified analysis using real-time data from business application systems in combination with a simulation of the value stream. The developed app is part of the decision process and estimates the effect of selected countermeasures to accelerate a delayed order. Performance indicators illustrate the effect of the countermeasures on the specific order as well as the whole system. This approach empowers the planner to assess unforeseen situations and aims to improve the quality of the decision-making process. This paper describes the architecture of the application, its simulation ecosystem, the relevant data and the decision process to select the most effective countermeasures.