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Institute
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 € 30M investment.
RWTH Campus pairs research and industry partners to pursue innovative ideas and yield cutting-edge products and services to face next-level digitization.
Digital, agile businesses outperform traditional businesses because of lower latencies in the entire reaction chain
acatech Industrie 4.0 Maturity Index:Helping established companies to build the development path to Industrie 4.0
The Maturity Index is offered to the Plattform Industrie 4.0 for transferring the approach to industry and defining Industrie 4.0 performance levels
Failure management in the production area has been intensely analyzed in the research community. Although several efficient methods have been developed and partially successfully implemented, producing companies still face a lot of challenges. The resulting main question is how manufacturers can be assisted by a sustainable approach enabling them to proactively detect and prevent failures before they occur. A high-resolution production system based on analyzed real-time data enables manufacturers to find an answer to the main question. In this context, Big Data technologies have gained importance since the critical success factor is not only to collect real-time data in the production but also to structure the data. Therefore, we present in this paper the implementation of Big Data technologies in the production area using the example of an actual research project. After the literature review, we describe a Big Data based approach to prevent failures in the production area. This approach mainly includes a real-time capable platform including complex event processing algorithms to define appropriate improvement measures.
Thanks to the challenges of the imminent energy turnaround, the power market faces a revolution regarding the energy distribution. In future, energy will not only be distributed from a limited number of large, centralized power plants but also from small, decentralized power generators, e.g. households. This also affects manufacturing companies, which are confronted with developing an energy management strategy. As those companies usually have not set high priorities on their energy management, there is a lack of a structured procedure to build an energy management strategy. Consequently, this creates the need for supporting methods to develop and implement an energy management strategy. This paper tackles the first step in the development of an energy management strategy. For this purpose, a target map is developed and possible use cases are systematized. The next steps of the implementation are presented using the example of load management.
Holistic PLM- Model
(2010)
Product Lifecycle Management (PLM) is a widely discussed topic concerning the increase of efficiency of product development in terms of time to market as well as customizing products to the different needs of customers worldwide adequately. Historically PLM focuses the early phases of the product’s lifecycle, namely the product development phase. Therein the roots of PLM are based in supporting the information logistics of product data: Consistent data sets should be available to all stakeholders in the different departments at all times. Due to the increasing product complexity PLM has to be extended in terms of the temporal dimension (not limited to product development phase) and systemic dimension (not limited to the information logistic aspect). In this paper the authors derive a holistic framework for Product Lifecycle Management by analysing existing integrated management approaches. The framework consists of four dimensions: PLM strategy, PLM process, Product structure and PLM IT-Architecture. The sustainability and benefits of the framework is demonstrated by applying the framework to the communication service provider industry (CSP).
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
Die Herausforderungen der Zukunft werden geprägt durch digital veredelte Produkte von höchster Qualität und hoher Variantenvielfalt bei gleichzeitig kleiner werdenden Losgrößen. Konventionelle Entwicklungsmethoden stoßen aufgrund zunehmender Komplexität und kürzer werdender Lebenszyklen auf Produktebene an ihre Grenzen. Dadurch werden bei kundenindividueller Produktion die Aufwände in der Arbeitsplanung und -vorbereitung überproportional größer. Eine mögliche Lösung stellt die generative Erstellung der Produktionsstückliste während der Montage dar. Durch das eventbasierte „Mitschreiben der Montage“ werden administrative und planungsintensive Prozesse in der Arbeitsvorbereitung überproportional reduziert und die Erstellung der Stückliste in die manuelle Montage transferiert.
With the development of publicly accessible broker systems within the last decade, the complexity of data-driven ecosystems is expected to become manageable for self-managed digitalisation. Having identified event-driven IT-architectures as a suitable solution for the architectural requirements of Industry 4.0, the producing industry is now offered a relevant alternative to prominent third-party ecosystems. Although the technical components are readily available, the realisation of an event-driven IT-architecture in production is often hindered by a lack of reference projects, and hence uncertainty about its success and risks. The research institute FIR and IT-expert synyx are thus developing an event-driven IT-architecture in the Center Smart Logistics' producing factory, which is designed to be a multi-agent testbed for members of the cluster. With the experience gained in industrial projects, a target IT-architecture was conceptualised that proposes a solution for a self-managed data-ecosystem based on open-source technologies. With the iterative integration of factory-relevant Industry 4.0 use cases, the target is continuously realised and validated. The paper presents the developed solution for a self-managed event-driven IT-architecture and presents the implications of the decisions made. Furthermore, the progress of two use cases, namely an IT-OT-integration and a smart product demonstrator for the research project BlueSAM, are presented to highlight the iterative technical implementability and merits, enabled by the architecture.
Manufacturing companies face the challenge of managing vast amounts of unstructured data generated by various sources such as social media, customer feedback, product reviews, and supplier data. Text-mining technology, a branch of data mining and natural language processing, provides a solution to extract valuable insights from unstructured data, enabling manufacturing companies to make informed decisions and improve their processes. Despite the potential benefits of text mining technology, many manufacturing companies struggle to implement use cases due to various reasons. Therefore, the project VoBAKI (IGF-Project No.: 22009 N) aims to enable manufacturing companies to identify and implement text mining use cases in their processes and decision-making processes. The paper presents an analysis of text mining use cases in manufacturing companies using Mayring's content analysis and case study research. The study aims to explore how text mining technology can be effectively used in improving production processes and decision-making in manufacturing companies.
The agricultural industry is facing unprecedented challenges in meeting the growing demand for food while minimizing its impact on the environment. To address these challenges, the industry is embracing technological advancements such as 5G networks to improve efficiency and productivity. However, the benefits of 5G technology must be weighed against the costs of implementing a suitable network. This paper presents cost-benefit dimensions that are needed to assess the economic feasibility of implementing 5G networks for several agricultural applications. The paper describes the costs of deploying and maintaining a 5G network and the benefits of several 5G-specific use cases, including precision agriculture, livestock monitoring, and swarm robotics. Using industry reports and case studies, the model quantifies the benefits of 5G networks, such as enabling new digital agricultural processes, increased productivity, and improved sustainability. It also considers the costs associated with equipment and infrastructure, as well as the challenges of deploying a network in rural areas. The results demonstrate that 5G networks can provide significant benefits to agricultural businesses and provide an overview about the cost factors. Both benefit and cost dimensions are analyzed for the 5G-specific agricultural use cases.
The adoption of artificial intelligence (AI) technologies in manufacturing companies is challenging, particularly for SMEs that lack the necessary skills to develop and integrate AI-based applications (AI applications) into their existing IT system landscape. To address this challenge, the research project VoBAKI (IGF-Project No.: 22009 N) aims to enable SMEs to identify and close skill gaps related to AI application development and implementation using proper sourcing strategies. This paper presents the interim results from the second phase of the project, which involves identifying the tasks in the lifecycle of AI applications and determining the specific skills required for executing these tasks. The presented results provide a detailed lifecycle including the phases for the development and usage of AI applications, as well as the specific tasks that SMEs must consider when implementing an AI application. These results serve as the foundation for future research regarding the required skills to execute the presented tasks and provide a roadmap for SMEs to close skill gaps and successfully implement AI applications.