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Institut / FIR-Bereiche
The manufacturing industry has to exploit trends like “Industrie 4.0” and digitization not only to design production more efficiently, but also to create and develop new and innovative business models. New business models ensure that even SMEs are able to open up new markets and canvass new customers. This means that in order to stay competitive, SMEs must transform their existing business models.
The creation of new business models require smart products. The required data base for new business models cannot be provided by SMEs alone, whereas smart products are able to provide a foundation, given the creation of smart data and smart services they enable. These services then expand functions and functionality of smart products and define new business models.
However, the development of smart products by small and medium-sized enterprises is still lined with obstacles. Regarding the product development process the inclusion of smart products means that new and SME-unknown domains diffuse during the process. Although there are many models regarding this process there appears to be a substantial lack of taking into account the competencies enabled by the implementation of digital technologies. Hence, several SME-supporting approaches fail to address the two major challenges these enterprises are faced with. This paper generally describes valid objectives containing relevant stakeholders and their allocation to the phases of the product life cycle.
Within each objective the potential benefit for customers and producers is analyzed. The model given in this paper helps SMEs in defining the initiation of a product development project more precisely and hence also eases project scoping and targeting for the smartification of an already existing product.
Due to the drastically increasing amount of data, decision making in companies heavily relies on having the right data available. Also because of an increasing complexity of structures and processes, quick and precise flows of information become more important.
This paper introduces a new approach for modelling information flows, creating a basis for an efficient information management. It can be used to structure the information requirements and identify gaps within the information processing.
To display its benefits, the proposed Information Logistics Notation (ILN) is applied to the information logistics of todays and future energy market and grid stability management, both processes of increasing complexity.
"Digitale Transformation" und "Industrie 4.0" sind nur zwei Beispiele für gängige Termini, die sich mittlerweile in irgendeiner Form in den meisten Unternehmensstrategien wiederfinden. Und doch fehlt es häufig an der nötigen Vorstellungskraft, wie diese Themen im eigenen Unternehmen zukünftig auch umgesetzt werden können.
Die am 15. und 16. November 2017 im Cluster Smart Logistik auf dem RWTH Aachen Campus stattgefundene Aachener Informationsmanagement-Tagung hatte zum Ziel, genau an diesen Stellen Licht ins Dunkel zu bringen. Im Fokus stand dabei die Frage, wie das Informationsmanagement, also die Aufgabe, die Ressource "Information" bestmöglich zu nutzen, bei der Entwicklung neuer Geschäftsmodelle, die ein wesentliches Merkmal der digitalen Transformation von Unternehmen darstellen, eingesetzt werden kann.
In vielen Unternehmen ist das Thema „Digitalisierung“ direkt beim Top-Management aufgehangen. Während sich viele Führungskräfte bereits intensiv mit dem Thema auseinandergesetzt und richtungsweisende Entscheidungen über die zukünftigen Einsatzgebiete neuer Geschäftsmodelle und digitaler Technologien getroffen haben, kennen die vielen Mitarbeiter in den Fachbereichen das Thema jedoch häufig nur über neueste Meldungen aus dem Intranet oder aus unternehmensinternen Newslettern. Aber auch dort finden sich hauptsächlich interessante Videos von Führungskräften in Workshops und die Beschreibung einiger Leuchtturmprojekte. Die brennendsten Fragen der Mitarbeiter bleiben dort in den meisten Fällen jedoch unbeantwortet:
- Was bedeutet „Digitalisierung“ für mich?
- Sind meine Skills noch ausreichend?
- Welche Auswirkungen hat dieses Thema auf meine Arbeit?
- Was muss ich in Zukunft können?
The integration of renewable energies in a local industrial environment is an urgent task to reduce greenhouse gas emissions. Their energy intensive processes and local energy generation make waste management companies to optimal areas to analyze micro grids. The combination of the main task to process arriving waste and the reaction on micro grid needs without disregarding user preferences is the challenge that is focused with the following approach applying machine learning techniques.
First, the amount of waste is predicted with an artificial neural network. Then, the waste processing is optimized via an augmented Lagrangian algorithm regarding the energy costs that are based on volatile energy prices influenced from renewable energies. In addition, the optimization regards user preferences, which are learned from a user feedback with a support vector machine.
For the user interaction, an active learning paradigm is used. The approach is applied on biological waste treatment process in the waste management company of the district of Warendorf. The results show that the energy consumptions can be controlled in a micro grid context within the frame of user preference.
Manufacturing companies worldwide recognized the high potential of Industrie 4.0 in order to increasing production efficiency. Key benefits include creation of integrated systems, networked products and improvement of service portfolios. However, for many companies deriving and evaluating necessary measures to use Industrie 4.0 potentials represents a major challenge. This paper introduces the "acatech Industrie 4.0 Maturity Index" as an approach to meet this challenge. The development of multidimensional maturity model intents to provide companies an assessment methodology. The aim is to capture the status quo in companies in order to be able to develop individual roadmaps for the successful introduction of Industrie 4.0 and manage the transformation progressively.
Nowadays, the market for information and communication technologies used for IOT-applications grows daily. Since companies need technologies to transform their business processes corresponding to the digital revolution, they need to know which technologies are available, and fit the best for their use case. Their inertial issue is the lacking overview of technologies suitable to connect their production or logistics. Hence, this paper presents a methodology to select technologies (and combinations) based on their functions. It differentiates between information and communication technologies, digital technologies and connecting technologies by the physical function and its role in a cyber-physical system. Depending on the use case, the applicability of every technology varies. Due to that reason, the paper illustrates a ranked qualification of the technologies for typical use cases, focussing tracking and tracing issues in the intralogistics of producing companies. The evaluation is performed upon a literature research, a market study to identify suitable technologies, and various expert interviews to assess the applicability of the technologies.