@inproceedings{55308,
  abstract     = {{Established companies are undertaking major transformation initiatives of their corporate structures and organisational forms to cope with the complexity during the engineering of cyber-physical production systems (CPPS). A frequently discussed issue is the measurability of this transformation progress. This paper conducts a systematic literature analysis of approaches regarding measurability of transformation and evaluates their application in the context of a systems engineering transformation. Measure-ment criteria are derived from the identified approaches, categorised, and finally evaluated by industry experts regarding their applicability. The categorised measurement criteria can be used to accurately measure the progress of a transformation process.}},
  author       = {{Gräßler, Iris and Grewe, Benedikt}},
  keywords     = {{Organizational Transformation, Systems Engineering, Meausrement, Metrics, Organizational Change}},
  location     = {{Ischia, Italy}},
  title        = {{{Measuring Systems Engineering Transformation: A systematic literature review}}},
  doi          = {{https://doi.org/10.1016/j.procir.2026.01.202}},
  year         = {{2026}},
}

@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}},
}

@article{17358,
  abstract     = {{Approximate circuits trade-off computational accuracy against improvements in hardware area, delay, or energy consumption. IP core vendors who wish to create such circuits need to convince consumers of the resulting approximation quality. As a solution we propose proof-carrying approximate circuits: The vendor creates an approximate IP core together with a certificate that proves the approximation quality. The proof certificate is bundled with the approximate IP core and sent off to the consumer. The consumer can formally verify the approximation quality of the IP core at a fraction of the typical computational cost for formal verification. In this paper, we first make the case for proof-carrying approximate circuits and then demonstrate the feasibility of the approach by a set of synthesis experiments using an exemplary approximation framework.}},
  author       = {{Witschen, Linus Matthias and Wiersema, Tobias and Platzner, Marco}},
  issn         = {{1557-9999}},
  journal      = {{IEEE Transactions On Very Large Scale Integration Systems}},
  keywords     = {{Approximate circuit synthesis, approximate computing, error metrics, formal verification, proof-carrying hardware}},
  number       = {{9}},
  pages        = {{2084 -- 2088}},
  publisher    = {{IEEE}},
  title        = {{{Proof-carrying Approximate Circuits}}},
  doi          = {{10.1109/TVLSI.2020.3008061}},
  volume       = {{28}},
  year         = {{2020}},
}

@inproceedings{5625,
  abstract     = {{The increasing availability and deployment of open source software in personal and commercial environments makes open source software highly appealing for hackers, and others who are interested in exploiting software vulnerabilities. This deployment has resulted in a debate ?full of religion? on the security of open source software compared to that of closed source software. However, beyond such arguments, only little quantitative analysis on this research issue has taken place. We discuss the state-of-the-art of the security debate and identify shortcomings. Based on these, we propose new metrics, which allows to answer the question to what extent the review process of open source and closed source development has helped to fix vulnerabilities. We illustrate the application of some of these metrics in a case study on OpenOffice (open source software) vs. Microsoft Office (closed source software).}},
  author       = {{Schryen, Guido and Kadura, Rouven}},
  booktitle    = {{24th Annual ACM Symposium on Applied Computing}},
  keywords     = {{Open source software, Closed source software, Security, Metrics}},
  title        = {{{Open Source vs. Closed Source Software: Towards Measuring Security}}},
  year         = {{2009}},
}