@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}},
}

@inproceedings{64602,
  author       = {{Gräßler, Iris and Hesse, Philipp and Jahnke, Ulrich and Habdank, Matthias}},
  booktitle    = {{Procedia CIRP}},
  issn         = {{2212-8271}},
  pages        = {{915--920}},
  publisher    = {{Elsevier BV}},
  title        = {{{Verification of CO2 emissions for the generative design of lightweight mobility systems using digital product passport}}},
  doi          = {{10.1016/j.procir.2026.01.158}},
  volume       = {{138}},
  year         = {{2026}},
}

@inproceedings{64603,
  author       = {{Gräßler, Iris and Tusek, Alena Marie}},
  booktitle    = {{Procedia CIRP}},
  issn         = {{2212-8271}},
  pages        = {{945--950}},
  publisher    = {{Elsevier BV}},
  title        = {{{Sustainability criteria for future foresight in manufacturing companies}}},
  doi          = {{10.1016/j.procir.2026.01.163}},
  volume       = {{138}},
  year         = {{2026}},
}

@article{65821,
  author       = {{Gräßler, Iris and Rarbach, Sven and Pottebaum, Jens and Luessen, Florian and Hoffmann, Joerg}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  pages        = {{587--592}},
  publisher    = {{Elsevier BV}},
  title        = {{{Metadata Model for Engineering of Sustainable Products in Value Creation Networks}}},
  doi          = {{10.1016/j.procir.2026.05.099}},
  volume       = {{140}},
  year         = {{2026}},
}

@article{65820,
  author       = {{Gräßler, Iris and Rarbach, Sven and Pottebaum, Jens and Hoffmann, Joerg}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  pages        = {{245--250}},
  publisher    = {{Elsevier BV}},
  title        = {{{Metadata-based assessment of Data Quality for Engineering of Sustainable Products}}},
  doi          = {{10.1016/j.procir.2026.05.255}},
  volume       = {{142}},
  year         = {{2026}},
}

@inproceedings{66288,
  abstract     = {{<jats:title>ABSTRACT:</jats:title>
                  <jats:p>Engineers simulate system behavior to support decisions in product engineering. Leveraging such engineering simulation data in strategic product planning can support idea generation and early evaluation of design alternatives and limitations. However, limited resources and expertise hinder broader uptake in strategic product planning. This paper investigates simulator integration into automated workflows and key processing components to enable simulation without in-depth expertise. This approach improves strategic product planning by creating data-based decision support.</jats:p>}},
  author       = {{Gräßler, Iris and Döhner, Niklas}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  keywords     = {{simulation-based design, design tools, multi-/cross-/trans-disciplinary approaches, simulation data reuse}},
  pages        = {{357--366}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Leveraging extreme-scale simulation data: a workflow framework for multidisciplinary simulator integration}}},
  doi          = {{10.1017/pds.2026.10394}},
  volume       = {{6}},
  year         = {{2026}},
}

@inbook{66332,
  author       = {{Gräßler, Iris and Rarbach, Sven and Pottebaum, Jens}},
  booktitle    = {{Nachhaltigkeit in der Produktentwicklung}},
  isbn         = {{9783658521165}},
  publisher    = {{Springer Fachmedien Wiesbaden}},
  title        = {{{PLM und Datenökosysteme für eine MBSE-basierte zirkuläre Wertschöpfung}}},
  doi          = {{10.1007/978-3-658-52117-2_17}},
  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}},
}

@inproceedings{66517,
  abstract     = {{Models of Circular Economy introduce circularity strategies like reuse, repair or remanufacture. A significant level of complexity is added when these strategies are applied not only on integrated product level, but on lower levels of assemblies and parts. Therefore, approaches, like Design for Assembly, Disassembly and Reassembly (DfADR) become increasingly important including the consideration of ADR capabilities. To put DfADR into practice, it is necessary to structure ADR-related product data and make it usable for product engineers. For instance, assemblies resulting from disassembly might not be consistent with original assembly structures. Therefore, a Product Meta Data Model (MDM) is required to handle assembly-related data even when it has extreme characteristics. A literature review on related MDMs reveals a range of models for general product data and identifies a gap in consideration of assembly-related data. In an expert workshop, requirements are raised to evaluate existing models and develop ADR-related extensions. The MDM is conceptually modelled and validated by competence questions. The paper presents an approach that makes assembly-related data usable for hybrid decision support and enables engineers to think ahead of ADR processes.}},
  author       = {{Gräßler, Iris and Vollenkemper, Felix and Pottebaum, Jens}},
  booktitle    = {{1st International Symposium on Hybrid Intelligence in Product and Production Engineering}},
  editor       = {{Graessler, Iris}},
  location     = {{Paderborn}},
  publisher    = {{LibreCat University}},
  title        = {{{A product Meta Data Model of assembly-related extreme data to enable hybrid decision support}}},
  doi          = {{10.17619/UNIPB/1-2642}},
  year         = {{2026}},
}

@inproceedings{66547,
  abstract     = {{Tacit knowledge is particularly valuable in product engineering. Experiences reside in minds of employees and are not systematically documented but could have a significant influence on sustainable product engineering. Existing approaches do not offer sufficient support for harnessing technical tacit knowledge regarding sustainable product engineering. In this paper a three-step method is presented: The method contains knowledge acquisition, requirements extraction und requirements validation for quality assurance. Acquisition is performed by support artifacts like interview guidelines. Extraction is supported by Artificial Intelligence, converting statements into requirements. These requirements are validated using a specific questionnaire. The developed approach is applied in a funded project of the European Union (EU) with automotive industry partners. The method supports product engineers to collect tacit knowledge from experienced employees, transform unsystematic knowledge into standardized technical requirements and validate them. Application of this method enables creation of validated requirements that can be applied throughout the company and are not exclusively dependent on a single employee.}},
  author       = {{Mansheim, Johanna and Gräßler, Iris and Pfeifer, Jan Niklas}},
  booktitle    = {{1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}},
  editor       = {{Graessler, Iris}},
  keywords     = {{Tacit knowledge, Sustainability, Product Engineering, Artificial Intelligence}},
  location     = {{Paderborn}},
  pages        = {{229--238}},
  publisher    = {{Universitätsbibliothek}},
  title        = {{{Harnessing tacit Knowledge from Sustainable Product Engineering through Artificial Intelligence}}},
  doi          = {{10.17619/UNIPB/1-2639}},
  volume       = {{1}},
  year         = {{2026}},
}

@inproceedings{66514,
  abstract     = {{Manufacturing companies increasingly integrate the strive towards sustainability into product engineering to reduce resource consumption and environmental impacts. A substantial share of a product’s environmental performance is determined during engineering. Early assessments rely on generic data, which is gradually replaced by more concrete simulation and primary data as product maturity increases. The effective use of engineering simulation models for Life Cycle Assessment (LCA) therefore remains a central challenge under heterogeneous, distributed and rapidly evolving data. This article presents a systematic review of product engineering approaches for integrating simulation models into LCA. A seven-step research approach is applied to identify, categorize and evaluate existing approaches across CAx tools. Relevant simulation parameters for sustainability assessment, including material properties, process descriptors, consumables and waste streams, are identified and analyzed. Based on identified data characteristics, the potential of Data Science and Artificial Intelligence methods for LCA data preparation is assessed. A conceptual knowledge-graph-based decision support approach is derived to enable structured integration, traceability and reuse of simulation data for sustainability assessment. The approach is evaluated using criteria from literature. Structured integration of simulation data improves early-stage data quality and supports decision-making in sustainable product engineering.}},
  author       = {{Gräßler, Iris and Aydin, Simon and Rarbach, Sven}},
  booktitle    = {{1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}},
  editor       = {{Gräßler, Iris}},
  location     = {{Paderborn}},
  publisher    = {{Universitätsbibliothek}},
  title        = {{{Systematic review of engineering simulation models for life cycle assessment}}},
  doi          = {{10.17619/UNIPB/1-2637}},
  year         = {{2026}},
}

@inproceedings{66683,
  abstract     = {{Design for Assembly, Disassembly and Reassembly (DfADR) prepares for high levels of material circularity in terms of repair and remanufacturing. The intention of reassembling products and their part simplies that inspection and sorting need to be integrated with value-conserving objectives. Further, disassembly and reassembly require higher competence levels of workforce than assembly. To provide the required information during engineering, a metadata model (MDM) is required that links established product models to capability models. Since processes to be carried out for realizing a product largely determine required employees’ capabilities, the proposed approach uses the ADR process domain to connect product and capability domains. The research is based on a systematic literature analysis to identify existing approaches regarding MDMs of ADR processes. Based on the results, an integrative MDM is developed that answers defined competence questions. The model enables queries which support engineers by feedback on which skills are required for a product. This builds up a basis to check whether required skills are available in the manufacturing company, either early in product engineering or later in ADR planning.}},
  author       = {{Gräßler, I. and Hesse, Thomas and Pottebaum, Jens}},
  booktitle    = {{1st International Symposium on Hybrid Intelligence in Product and Production Engineering}},
  editor       = {{Graessler, Iris}},
  location     = {{Paderborn}},
  publisher    = {{Universitätsbibliothek}},
  title        = {{{Enabling extreme data by using DS/AI approaches}}},
  doi          = {{10.17619/UNIPB/1-2640}},
  year         = {{2026}},
}

@proceedings{66704,
  editor       = {{Gräßler, Iris}},
  location     = {{Paderborn}},
  publisher    = {{Universitätsbibliothek}},
  title        = {{{1st International Symposium : March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}}},
  doi          = {{10.17619/UNIPB/1-2663}},
  year         = {{2026}},
}

@inproceedings{66709,
  abstract     = {{In increasingly volatile and uncertain markets, corporate resilience has become a critical capability in strategic product planning. Companies face significant challenges in systematically monitoring and interpreting heterogeneous environmental data originating from diverse sources, formats, and temporal contexts. While predefined workflows and decision trees can support strategic analysis, they often lack the flexibility required to cope with dynamic market conditions and foresightrelated data from extreme dispersed and heterogeneous sources. This paper proposes a method to enhance corporate resilience through the application of generic, reusable AI-based workflows in strategic product planning. The approach integrates Data Science and Artificial Intelligence methods into modular, visually modelled workflows that enable hybrid human-AI decision-making. Based on a systematic literature review and an analysis of industrial challenges, key success factors and resilience criteria are identified. These insights are used to develop a method that supports internal and external analyses, scenario-based strategy development, and adaptive implementation monitoring within a generic workflow structure. The method leverages techniques such as machine learning and generative AI to process structured and unstructured data, identify patterns, and support real-time strategic assessments. Validation with decision-makers from medium-sized companies demonstrates improved transparency, repeatability, and cross-functional collaboration compared to predefined workflows.}},
  author       = {{Özcan, Deniz and Gräßler, Iris}},
  booktitle    = {{1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}},
  editor       = {{Graessler, Iris}},
  location     = {{Paderborn}},
  publisher    = {{Universitätsbibliothek}},
  title        = {{{Corporate resilience through generic AI-based workflows in strategic product planning}}},
  doi          = {{10.17619/UNIPB/1-2636}},
  year         = {{2026}},
}

@inproceedings{66702,
  abstract     = {{Strategic product planning defines foresighted directions for product engineering by determining which ideas, technologies and concepts are pursued further. Decisions made in this phase strongly influence subsequent life cycle phases but are characterized by high uncertainty, fragmented information, and predominantly qualitative assessment practices. At the same time, increasing digitalization generates large, heterogeneous, and dynamic data from markets, engineering, usage, and sustainability domains. In this work, such information is understood as extreme data, as its heterogeneity, variety, and dynamics exceed the capabilities of traditional planning approaches. This paper investigates the potential of leveraging extreme data to strengthen hybrid decision support in strategic product planning. A structured literature review is conducted to identify key challenges. These challenges are analysed along the generic product life cycle to capture decision points, information flows, and information circularity. Relevant data sources are identified with respect to their relevance for strategic decisions. Based on this analysis, seventeen potentials for hybrid decision support are derived and clustered into five fields of action. The results indicate that extreme data can enhance strategic foresight, idea evaluation and portfolio decisions, lifecycle-spanning knowledge integration, engineering support for strategic decisions, and the adoption of AI-based methods.}},
  author       = {{Gräßler, Iris and Goldstein, Julius Hendrik}},
  booktitle    = {{1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}},
  editor       = {{Gräßler, Iris}},
  location     = {{Paderborn}},
  publisher    = {{Universitätsbibliothek}},
  title        = {{{Hybrid decision support in strategic product planning}}},
  doi          = {{10.17619/UNIPB/1-2644}},
  year         = {{2026}},
}

@article{59478,
  author       = {{Gräßler, Iris and Rarbach, Sven and Pottebaum, Jens}},
  issn         = {{2942-6170}},
  journal      = {{Industry 4.0 Science}},
  number       = {{2}},
  publisher    = {{GITO mbH Verlag}},
  title        = {{{Data Quality in the Engineering of Circular Products - Decision support for circular value creation through data ecosystems}}},
  doi          = {{10.30844/i4se.25.2.12}},
  volume       = {{2025}},
  year         = {{2025}},
}

@inproceedings{61043,
  abstract     = {{<jats:p>Dynamic market conditions, technological disruption and social change require organizations to continuously adapt and evolve. However, studies on organizational change show that the majority of transformations undertaken fail because they are characterized by a lack of clarity, overload and ineffective measures. This paper shows how a clear structure as a critical success factor can make the chaos and challenges of a transformation manageable.  The focus here is on a practice-oriented framework that divides a transformation into nine essential building blocks with activities that are critical to success. The structure of the framework is understood as a flexible organizing principle for a transformation without hindering creativity and dynamics. Case studies show the adaptability and applicability of the framework to different characteristics and dimensions of transformation. The transformation framework provides an operative structure and enables transformation managers for transparent orchestration and implementation of transformation.</jats:p>}},
  author       = {{Gräßler, Iris and Grewe, Benedikt and Fritzen, Marc}},
  booktitle    = {{AHFE International}},
  issn         = {{2771-0718}},
  location     = {{Pula, Croatia}},
  publisher    = {{AHFE International}},
  title        = {{{The importance of structure in transformation chaos: A Transformation Framework}}},
  doi          = {{10.54941/ahfe1006790}},
  volume       = {{198}},
  year         = {{2025}},
}

@inproceedings{61055,
  abstract     = {{<jats:title>ABSTRACT:</jats:title><jats:p>Challenges of increasing system complexity and the need for interdisciplinary collaboration are prompting companies to reorganize towards Systems Engineering (SE). As part of the implementation of large-scale transformation programs, transformation progress is of great interest to management and employees involved. Existing maturity models lack measurable variables and reliable forecast. For this reason, a maturity model for evaluating SE Transformation is developed, that builds on quantitative metrics and enables an overarching view on transformation considering cultural aspects. Literature-based criteria for evaluating SE Transformation lay the foundation for measures and referenced metrics and indicators. Due to its data-centricity, the model presented enables a more comprehensive, fact-based decision-making basis for the design and steering of SE Transformation programs.</jats:p>}},
  author       = {{Graessler, Iris and Grewe, Benedikt and Felgen, Luc}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  location     = {{Dallas, USA}},
  pages        = {{1081--1090}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Data-driven decision support in the design and controlling of systems engineering transformation: a maturity model}}},
  doi          = {{10.1017/pds.2025.10122}},
  volume       = {{5}},
  year         = {{2025}},
}

@article{60140,
  abstract     = {{<jats:title>Abstract</jats:title>
	  <jats:p>The increasing prevalence of embedded software in today’s vehicles is leading to growing complexity, which can only be managed effectively through the use of reliable interdisciplinary engineering processes. With this in mind, systems engineering (SE) is currently being introduced on a large scale into the automotive industry. Pilot projects have demonstrated the potential for implementing changes, but these have not yet been accompanied by viable implementation concepts for SE. In the context of the proposed application-based research, the SETup automotive method (<jats:bold>S</jats:bold>ystems <jats:bold>E</jats:bold>ngineering <jats:bold>T</jats:bold>ransformation <jats:bold>u</jats:bold>nder <jats:bold>p</jats:bold>iloting in the <jats:bold>automotive</jats:bold> industry) is presented, which comprises a step-by-step procedure of introducing SE into large automotive companies. By introducing SE by pilot projects first, both an in-process tailoring of all processes, methods, tools and structures (PMTS) required for the introduction and an in-process validation of the pilot scheme elaborated by the pilot projects are achieved. The presented method builds upon fundamental approaches to change management, which have been developed over many years in both research and practice. It has been validated by the industrial practice of SE transformation at German car manufacturers and suppliers. As a result, decision-makers, transformation managers and systems engineers are provided with a scientifically based and field-tested set of steps for the introduction of SE in their own company.</jats:p>}},
  author       = {{Graessler, Iris and Grewe, Benedikt}},
  issn         = {{2053-4701}},
  journal      = {{Design Science}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{SETup automotive: a Method for Systems Engineering Transformation in automotive industry}}},
  doi          = {{10.1017/dsj.2025.10}},
  volume       = {{11}},
  year         = {{2025}},
}

@article{60940,
  author       = {{Gräßler, Iris and Rarbach, Sven and Grewe, Benedikt}},
  issn         = {{2942-6170}},
  journal      = {{Industry 4.0 Science}},
  number       = {{3}},
  publisher    = {{GITO mbH Verlag}},
  title        = {{{Strategic Product Planning Model – Digital twins for circular products and production processes}}},
  doi          = {{10.30844/i4se.25.3.24}},
  volume       = {{2025}},
  year         = {{2025}},
}