@inproceedings{67285,
  abstract     = {{This research provides a method for designing competenceoriented, modular workflows that support strategic decisionmaking in strategic product planning (SPP) under conditions of uncertainty and time pressure. Increasing digitalization and data availability require decision support systems that integrate analytical models while being tailored to the competencies of decision-makers. Based on a systematic literature review and stakeholder workshops, eight success factors for competency-oriented workflow integration are identified, including modularity, traceability, interoperability, adaptability, handling uncertainty, and user acceptance. Based on these factors, a five-step method is developed that links business processes, data sources, evaluation criteria, visualization formats, and graphical workflow implementation. Strategic and technical decision logic are integrated into a unified, modular framework using graphical modeling environments. The method is validated in workshops and demonstrates its applicability in time-critical, multi-criteria decision contexts. The results show improved transparency, adaptability, and practical usability of datadriven decision processes that support robust and traceable business decisions. }},
  author       = {{Gräßler, Iris and Özcan, Deniz}},
  booktitle    = {{24th INTERNATIONAL INDUSTRIAL SIMULATION CONFERENCE }},
  editor       = {{Gonzalez, Jose David Nunez and Romay, Manuel Grana}},
  isbn         = {{978-9-492859-40-2}},
  keywords     = {{Workflow, Decision Support, Strategic Product Planning, Decision-making}},
  location     = {{San Sebastian, Spain}},
  pages        = {{73--80}},
  title        = {{{Competence-Oriented Modular Workflow Design for Strategic Decisions}}},
  volume       = {{24}},
  year         = {{2026}},
}

@inproceedings{67284,
  abstract     = {{<jats:p>For foresightful strategic decisions in engineering and production, it is important to identify and classify operational engineering data inmanufacturing companies in terms of relevance and impact. Established methods like Scenario-Technique or roadmapping consider expertise of strategic decision-makers and central decision-making bodies by, for instance, workshops and interviews. There is no overarching approach for evaluating the variety of operational data sources and feed them into specific data processing pipelines targeting strategic decision support. Based on a systematic literature analysis and comprehensive industrial experience, requirements are derived. These requirements are used to develop a method to identify, classify and evaluate data for strategic decision-making. The method is validated in an industrial project. The aim is to exploit informal and fragmented data from operational levels into structured, transparent decision support for strategic players.</jats:p>}},
  author       = {{Gräßler, Iris and Özcan, Deniz}},
  booktitle    = {{AHFE International}},
  issn         = {{2771-0718}},
  location     = {{Paris}},
  publisher    = {{AHFE International}},
  title        = {{{Extended Exploitation of Data in Manufacturing Companies for Strategic Decision-Making}}},
  doi          = {{10.54941/ahfe1008175}},
  volume       = {{237}},
  year         = {{2026}},
}

@inproceedings{67286,
  abstract     = {{The effects of decisions in strategic product planning are usually far-reaching, but at the same time difficult to assess. Simulations help to make these effects more tangible. Until now, the use of simulation in strategic product planning has been limited to behavioral and innovation diffusion studies. This paper presents a process model for widening business models or planning upcoming product generations. It supports integrating the simulation of technical systems into decisionmaking processes to select the most suitable product for adaptation to new application scenarios. To this end, the characteristics of such new application scenarios are applied to existing product models in simulation, and the system behavior is evaluated based on key figures. This supports the comparison of different products and the selection of the product alternative requiring the least amount of modification. The process model enables decision-makers to systematically integrate simulation into the decision-making process and thus make more data-based decisions. }},
  author       = {{Gräßler, Iris and Özcan, Deniz and Döhner, Niklas}},
  booktitle    = {{24th INTERNATIONAL INDUSTRIAL SIMULATION CONFERENCE }},
  editor       = {{Gonzalez, Jose David Nunez and Romay, Manuel Grana}},
  isbn         = {{978-9-492859-40-2}},
  keywords     = {{Decision-making, Decision support systems, Mechanical engineering, System analysis, System engineering}},
  location     = {{San Sebastian, Spain}},
  pages        = {{65--72}},
  title        = {{{Leveraging System Simulation for Decision  Support in Strategic Product Planning}}},
  volume       = {{24}},
  year         = {{2026}},
}

@inproceedings{67295,
  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       = {{Gräßler, Iris and Özcan, Deniz}},
  booktitle    = {{1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}},
  editor       = {{Gräßler, Iris}},
  location     = {{Paderborn}},
  publisher    = {{LibreCat University}},
  title        = {{{Corporate resilience through generic AI-based workflows in strategic product planning}}},
  doi          = {{10.17619/UNIPB/1-2636}},
  volume       = {{1}},
  year         = {{2026}},
}

@inproceedings{61109,
  author       = {{Pottebaum, Jens and Gräßler, Iris and Ebel, Marcel and Özcan, Deniz and Döhner, Niklas and Pratzler-Wanczura, Sylvia and Derin, Enes and Krüger, Oliver and Kruijff-Korbayova, Ivana and Stampa, Merlin}},
  location     = {{Koblenz, Deutschland}},
  pages        = {{81--94}},
  publisher    = {{LibreCat University}},
  title        = {{{EU-Projekt CREXDATA: Erkenntnisse und Handlungsempfehlungen zum Einsatz KI-generierter Lageinformationen für die Lagebewertung und Maßnahmenplanung in Extremwetterlagen}}},
  doi          = {{10.5281/ZENODO.16740824}},
  year         = {{2025}},
}

@inproceedings{62181,
  abstract     = {{This research investigates how Artificial Intelligence (AI) can be systematically integrated into existing business processes by combining suitable competencies with graphical AI workflow modelling. While AI offers a high potential for automation and increased efficiency, its implementation often fails due to a lack of interdisciplinary competencies that bridge the gap between domain expertise and IT know-how. Low-code platforms and visual modelling tools are increasingly recognised as enablers, empowering non-programmers to intuitively create graphical AI- based workflows. Nevertheless, specific competencies are required to realise the full potential of AI, the domain specific knowledge and align technical understanding with AI capabilities. The paper reviews the state of the art in AI-driven business process automation and competencies for visual low-code approaches. It then presents a practical solution to identify and systematise essential competence areas. Based on this, a practical competence model is developed to support the design of user-friendly, AI-enabled workflows. This is tested in a practical application context — emergency management — where it supports critical decision-making processes and is validated through expert feedback. The study concludes by offering actionable recommendations to help organisations foster the necessary competencies and methods for competently integrating AI into their digital processes.</jats:p>}},
  author       = {{Gräßler, Iris and Özcan, Deniz}},
  booktitle    = {{AHFE International}},
  issn         = {{2771-0718}},
  location     = {{Split}},
  publisher    = {{AHFE International}},
  title        = {{{Graphical AI workflow modelling: Identifying relevant competencies in AI-based automation of business processes}}},
  doi          = {{10.54941/ahfe1006785}},
  volume       = {{198}},
  year         = {{2025}},
}

@book{62182,
  abstract     = {{Die vorliegende Zukunftsstudie „Automation 2035“ gibt einen Ausblick auf die Entwicklung der Automatisierungstechnik in den nächsten 10 Jahren. Neben der Beschreibung von Trends wie Kreislaufwirtschaft, Automatisierung der Märkte, Biologisierung, autonome Systeme und Robotik sowie IT-Sicherheit wird die zu erwartende Veränderung in der Bildung beschrieben. Dazu verwenden wir Methoden der Zukunftsforschung und arbeiten mit der Szenario-Technik, um Zukunftsperspektiven der Automation aufzuzeigen. Personas werden eingesetzt, um die zukünftigen Entwicklungen plastisch aus den Augen der Personen im Jahr 2025 und in der Zukunft im Jahr 2035 zu beschreiben. Schlüsselthemen und Trends: ... ... Inhalt Executive summary 1 1 Einführung 3 2 Zukunftsfelder für die Automatisierungstechnik 2035 4 2.1 Kreislaufwirtschaft 4 2.2 Automatisierung der Märkte 7 2.3 Biologisierung 8 2.4 Autonome Systeme und Robotik 10 2.5 Security 12 2.6 Veränderung der Ausbildung 13 3 Szenario der Automation 2035 16 4 Personas 18 4.1 Unternehmer 18 4.2 Ingenieurin 19 4.3 Schüler 20 5 Thesen und Ausblick 22 Methodik 24 Autorenteam 25 Schrifttum 26... </p>}},
  author       = {{Gräßler, Iris and Özcan, Deniz and Tusek, Alena Marie and Bilgic, Attila and Lange, Christian and Stich, Christian and Maul, Christine and Heizmann, Michael and Weyrich, Michael and Dessel,, Sascha and Miny, Torben and Jumar, Ulrich}},
  isbn         = {{9783911670180}},
  publisher    = {{VDI Verlag}},
  title        = {{{Automation 2035}}},
  doi          = {{10.51202/9783911670180}},
  year         = {{2025}},
}

@inproceedings{56346,
  author       = {{Gräßler, Iris and Özcan, Deniz}},
  booktitle    = {{AHFE International}},
  location     = {{Split}},
  publisher    = {{AHFE International}},
  title        = {{{Quality Key Figures for Developing Future Scenarios}}},
  doi          = {{10.54941/ahfe1005553}},
  volume       = {{158}},
  year         = {{2024}},
}

@inproceedings{55431,
  author       = {{Gräßler, Iris and Özcan, Deniz}},
  booktitle    = {{Automation 2024}},
  isbn         = {{9783181024379}},
  pages        = {{363–374}},
  publisher    = {{VDI Verlag}},
  title        = {{{Anwendungspotenziale von Künstlicher Intelligenz im Model-Based Systems Engineering für automatisierte Systeme}}},
  doi          = {{10.51202/9783181024379-363}},
  volume       = {{2437}},
  year         = {{2024}},
}

@inproceedings{54540,
  abstract     = {{System models are used to merge relevant engineering artefacts and relationships. Therefore, a high model quality must be ensured. Currently, there is no method for defining company-specific metrics to assess system model quality. In a six-step research approach, a method is defined: (1) literature analysis on quality assessment approaches, (2) derivation of success factors, (3) evaluation of approaches, (4) development of a method, (5) application in automotive industry, and (6) evaluation. The method supports system engineers to derive tailored metrics to objectively assess the model quality.</jats:p>}},
  author       = {{Gräßler, Iris and Wiechel, Dominik and Özcan, Deniz and Taplick, Patrick}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{2545--2554}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Tailored metrics for assessing the quality of MBSE models}}},
  doi          = {{10.1017/pds.2024.257}},
  volume       = {{4}},
  year         = {{2024}},
}

@inproceedings{47108,
  author       = {{Gräßler, Iris and Özcan, Deniz and Preuß, Daniel}},
  booktitle    = {{Design fox X - Beiträge zum 34. DfX-Symposium}},
  editor       = {{Krause, Dieter and Paetzold-Byhain, Kristin and Wartzack, Sandro}},
  location     = {{Radebeul}},
  pages        = {{163 -- 172}},
  title        = {{{KI-basierte Extrahierung von Anforderungen aus Regularien für die Automobilentwicklung}}},
  doi          = {{10.35199/dfx2023.17}},
  volume       = {{34}},
  year         = {{2023}},
}

@inproceedings{46490,
  author       = {{Gräßler, Iris and Tusek, Alena Marie and Özcan, Deniz}},
  booktitle    = {{Automation 2023}},
  isbn         = {{9783181024195}},
  location     = {{Baden-Baden}},
  pages        = {{863 -- 874}},
  publisher    = {{VDI Verlag}},
  title        = {{{Entwicklung zukunftsfähiger Geschäftsmodelle in der Automatisierungstechnik anhand von Megatrends}}},
  doi          = {{10.51202/9783181024195-863}},
  volume       = {{2419}},
  year         = {{2023}},
}