Digital Shadows for Cross-Organizational Data Exchange

Abstract

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.