@techreport{66548,
  abstract     = {{The Circular Economy is considered a key approach to addressing resource scarcity, rising raw material prices, and the need for more resilient value chains. However, its practical implementation is often hindered by the lack of suitable and standardized metrics, as well as by high requirements for horizontal and vertical data integration. This white paper illustrates, through selected Manufacturing-X projects, how federated data ecosystems can help establish Circular Economy metrics as an effective management instrument for growth, margin protection, and resilience.
Based on literature, industrial practice, and demonstrators, Circular Economy metrics are structured into four groups: overarching assessment approaches, disassembly-related indicators, lifetime-oriented metrics, and end-of-life and recycling-focused indicators. Use cases from Construct-X, Decide4ECO, Fluid 4.0, Chem-X, and other Manufacturing-X initiatives demonstrate the benefits of data-driven approaches for product, component, and material loops. At the same time, they reveal a current lack of interoperable, cross-industry standards for both metrics and data models.
The white paper concludes that data ecosystems such as Manufacturing-X are a key enabler for scaling circular business models. In particular, shared cross-sector frameworks for metrics, standardized information models based for example on the Asset Administration Shell, and the integration of circular metrics into existing systems and processes are essential. The work presented therefore marks an important step toward a data-driven and economically viable Circular Economy.}},
  author       = {{Pottebaum, Jens and Dietrich, Katrin and Schmidt, Lara and Biglari, Mostafa and Schmidt, Michael-Georg and Pistillo, Alessandro and Gravina, Nadja and Schmidt, Franziska}},
  keywords     = {{Circular Economy, Circular Economy Metrics, Manufacturing-X, Data Ecosystems}},
  publisher    = {{Manufacturing-X Guidance Board}},
  title        = {{{Understanding the Value of Data Ecosystems for Circular Economy Metrics}}},
  doi          = {{10.24406/PUBLICA-9382}},
  year         = {{2026}},
}

@inproceedings{66072,
  abstract     = {{Data Spaces are establishing themselves as a decentralized alternative for sovereign data sharing across organizations. However, their adoption faces significant challenges, particularly the lack of proven business models that encourage participation and value creation. A data space business model captures value differently than either traditional business models or platform business models. This paper addresses this challenge by developing the Data Space Canvas, a structured framework for designing and aligning business models in data spaces consisting of nine building blocks. The canvas was developed following the design science research methodology. The modular and role-specific structure of the Data Space Canvas facilitates shared understanding and supports business model development across participants. This research contributes a practical tool that enables organizations to conceptualize, communicate, and refine their role and business model in data spaces.}},
  author       = {{Zerbin, Julian and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{ECIS 2026 Proceedings}},
  keywords     = {{data ecosystems, business model, design science, resource orchestration theory, data sharing}},
  location     = {{Mailand}},
  title        = {{{The Data Space Canvas: A Framework For Structuring Business Models In Data Spaces}}},
  year         = {{2026}},
}