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Auflistung nach Schlagwort "Digital Shadows"

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  • Konferenzbeitrag
    Digital Shadows for Cross-Organizational Data Exchange
    (Modellierung 2022 Satellite Events, 2022) Koren, István
    Production settings typically involve heterogeneous systems that create a challenging environment for collecting data in light of digital transformation. Once overcoming these difficulties, data-driven opportunities for manufacturing companies include increasing efficiency and productivity, reducing costs, and improving quality control. On the shop floor, digital shadows and digital twins are elements of these modernization strategies, e.g., to leverage machine learning methods for decision support. Recently, some approaches have transferred these concepts to the organizational level, like digital twins of organizations. In this paper, we envision how we can use data collections from the shop floor, captured as digital shadows, to share data across organizational boundaries to create new business models and ultimately enter new markets. We discuss the necessary enhancements of our conceptual model for digital shadows presented in previous work. We are convinced that digital shadows can help companies embrace innovative, data-driven business models to face challenges like sustainability.
  • Konferenzbeitrag
    A Model-Assisted and Data-Driven Ecosystem Based on Digital Shadows
    (40 Years EMISA 2019, 2020) Jarke, Matthias
    Data-driven machine learning methods are typically most successful when they can rely on very large and in some sense, homogeneous training sets in areas where little prior scientific knowledge exists. Production engineering, management, and usage satisfy few of these criteria and therefore do not show very many success stories, beyond narrowly defined specific issues in specific contexts. While, in contrast, the last years have seen impressive successes in model-driven materials and production engineering methods, these methods lack context and real-time adaptivity. Our vision of an Internet of Production, pursued in an interdisciplinary DFG Excellence Cluster at RWTH Aachen University, addresses these shortcomings: Through sophisticated heterogeneous data integration and controlled data sharing approaches, it broadens the experience base of cross organizational product and process data. At the method level, it interleaves fast “reduced models from different engineering fields, with enhanced explainable machine learning techniques and model-driven re-engineering during operations. As a common conceptual modeling abstraction, we investigate Digital Shadows, a strongly empowered variant of the well-known view concept from data management. Several initial experiments indicate the power of this approach but also highlight many further research challenges.