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An open infrastructure allows testing of new innovative possibilities for the manufacturing industry Industrie 4.0 is hitting the market. The e.GO Life - an electric car for the city - is developed in under three years and only € 50M investment.
Digital shadow enables fast adaption of products and production.
Digital, agile businesses outperform traditional businesses because of lower latencies in the entire reaction chain. The capability of using data and generate knowledge will different digital champions from losers.
The goal of Industrie 4.0 is a learning agile company;a mere technology driven approach is not sufficient.
A successful implementation of Industrie 4.0 in manufacturing companies requires a holistic transformation approach.
There are many reasons why the shift towards a learning, agile company fails.
For a successful implementation, the entire company structure has to be considered. A stepwise approach is required to build the agile enterprise - smart use of data is the critical success factor.
Company development within the structuring forcesis based on an Industrie 4.0 development path.
The four structuring forces illustrate the fundamental Industrie 4.0 development and are captured by key questions.
The Maturity Index is developed by renowned partners from industry and research Overview on strategic goals and derived projects.
Die Spielregeln der betrieblichen Praxis werden aufgrund ansteigender Dynamik der aktuellen Covid-19-Pandemie neu definiert, Erfolgsprinzipien verlieren über Nacht ihre Gültigkeit. Die einschränkenden Effekte der Krise führen zu einem Paradigmenwechsel, der bekannte und etablierte Formen der Zusammenarbeit sowie die Anforderungen an Führungsqualitäten verändert. Bis vor wenigen Wochen war die virtuelle Durchführung von Abstimmungsrunden, Steuerkreisen und Projekt-Workshops für viele Unternehmen nicht denkbar. Wer sich die Tragweite der Pandemie jedoch bewusst vor Augen führt, erkennt, dass die Auswirkungen aktuell und in Zukunft wie ein Katalysator für die digitale Transformation wirken. Handlungsoptionen, die vor drei Monaten als unmöglich galten, sind inzwischen etablierter Bestandteil des Arbeitsalltags.
It is crucial today that economies harness renewable energies and integrate them into the existing grid. Conventionally, energy has been generated based on forecasts of peak and low demands. Renewable energy can neither be produced on demand nor stored efficiently. Thus, the aim of this paper is to evaluate Deep Learning-based forecasts of energy consumption to align energy consumption with renewable energy production. Using a dataset from a use-case related to landfill leachate management, multiple prediction models were used to forecast energy demand.The results were validated based on the same dataset from the recycling industry. Shallow models showed the lowest Mean Absolute Percentage Error (MAPE), significantly outperforming a persistence baseline for both, long-term (30 days), mid-term (7 days) and short-term (1 day) forecasts. A potential decrease of up to 23% in peak energy demand was found that could lead to a reduction of 3,091 kg in CO2-emissions per year. Our approach requires low finanacial investments for energy-management hardware, making it suitable for usage in Small and Medium sized Enterprises (SMEs).
The digitalization of manufacturing processes is expected to lead to a growing interconnection of production sites, as well as machines, tools and work pieces. In the course of this development, new use-cases arise which have challenging requirements from a communication technology point of view. In this paper we propose a communication network architecture for Industry 4.0 applications, which combines new 5G and non-cellular wireless network technologies with existing (wired) fieldbus technologies on the shop floor. This architecture includes the possibility to use private and public mobile networks together with local networking technologies to achieve a flexible setup that addresses many different industrial use cases. It is embedded into the Industrial Internet Reference Architecture and the RAMI4.0 reference architecture. The paper shows how the advancements introduced around the new 5G mobile technology can fulfill a wide range of industry requirements and thus enable new Industry 4.0 applications. Since 5G standardization is still ongoing, the proposed architecture is in a first step mainly focusing on new advanced features in the core network, but will be developed further later.
Smartification and digital refinement of products to enable the design of smart ones is a pivotal challenge in the manufacturing industry. Companies fail to design smart products due to missing knowledge of digital technologies and their integral part in product development processes. This paper presents a methodology that enables the derivation of digital functions for smart products through selected cases in manufacturing usage. We develop a morphology that consists of digital functions for smartification. In this context, we explained and derived characteristics by a set of examples regarding smart products in the manufacturing industry. Our methodology reduces the time spent initiating a development project with the focus on smartification.
Im "Data Quality Center" widmen sich Experten und Forscher der Hochschule Heilbronn, des FIR und des Trovarit Competence Centers Datenmanagement gemeinsam der Frage, mit welchen Werkzeugen und Methoden Unternehmen effizient die Qualität ihrer Stammdaten messen und verbessern können. Erstes Ziel ist die Entwicklung einer Methodik und Toolchain für das betriebliche Stammdatenmanagement zur Evaluierung und Sicherung der Stammdatenqualität. Der Beitrag liefert erste Ergebnisse sowie eine Marktübersicht zu MDM-Lösungen. Außerdem wird die DQC-Methodik zur Bewertung der Stammdatenqualität im Unternehmen beschrieben.
This paper addresses the challenge of modelling individual cyber-physical systems (CPS) for small and medium-sized enterprises (SMEs) in manufacturing industries. CPS are key technology building blocks for the implementation of Industrie 4.0. Especially for SMEs the increase of production efficiency and reduction of manufacturing costs through CPS offer potential to maintain their competitiveness and innovation capacity. Although SMEs perceive the potential of CPS, they often lack financial and human resources to acquire the necessary CPS-competencies as well as an overview of all the currently available technological solutions. To overcome this issue a matching platform will offer SMEs support in finding suitable CPS-components by letting them express their functional and technical requirements. The matching logic is based on a set of morphologies that encompasses the functional and requirement spectrum of CPS-components. The matching algorithm analyses the input for congruence of requirements and available technologies and suggests suitable technology combinations. This paper describes the methodology of the matching platform, and introduces the research work to define and to develop the technology morphologies. The presented results facilitate the selection and configuration of CPS for SMEs.
Numerous traditional, agile and hybrid development approaches have been proposed for the development of CPS. As the choice of development process is crucial to the success of development projects, it has become a major challenge to identify the best-suited process. This paper introduces a methodology for identifying the best-suited CPS development process, based on the individual boundary conditions for a certain development project within a company. The authors used a set of eight indicators to assess a CPS-development project. The results of the assessment were matched with CPS-development approaches. Based on the matching results a best-suited development process was selected. The application is shown for a use case in the German manufacturing industry. The developed method aims to reduce the risk of project failure due to the wrong choice of development process.