@article{47835, author = {{Ködding, Patrick and Ellermann, Kai and Koldewey, Christian and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{740--745}}, publisher = {{Elsevier BV}}, title = {{{Scenario-based Foresight in the Age of Digitalization and Artificial Intelligence – Identification and Analysis of Existing Use Cases}}}, doi = {{10.1016/j.procir.2023.01.015}}, volume = {{119}}, year = {{2023}}, } @inproceedings{54286, abstract = {{The integration of Artificial Intelligence (AI) techniques into various domains has revolutionized numerous industries, and Supply Chain Management (SCM) is no exception. This paper addresses the challenges encountered in SCM and the development of AI solutions within this context. Specifically, we focus on the application of AI in optimizing supply chain planning tasks. This includes forecasting demand, availability and feasibility checks for customer orders, supply chain network design and information flow inside the supply chain planning processes. However, the successful implementation of AI in SCM requires a deep understanding of both the domain-specific challenges and the capabilities and limitations of AI technologies. Thus, this paper proposes an overarching approach that facilitates collaboration between domain experts in SCM and AI experts, enabling them to jointly develop effective solutions.The paper begins by outlining the key challenges faced by SCM professionals, including demand volatility, complexities in inventory management, and dynamic market conditions. Subsequently, it delves into the challenges associated with developing AI solutions for SCM, including data quality, interpretability, and model transparency. To address these challenges, the proposed approach promotes close collaboration and knowledge exchange between SCM and AI experts. By leveraging the domain knowledge and experience of SCM experts, AI experts can better understand the special issues of SCM processes and tailor AI techniques to suit specific needs. In turn, SCM experts can gain insights into the capabilities and limitations of AI, allowing them to make informed decisions regarding the adoption and integration of AI in their supply chain planning operations. Furthermore, the paper discusses the importance of establishing a multidisciplinary team comprising experts from the fields of SCM, AI, and IT. This team-based approach fosters a holistic understanding of SCM challenges and ensures the development of AI solutions that align with business goals and practical constraints.In conclusion, this paper highlights the challenges in combining SCM and AI and proposes a collaborative approach to address these challenges effectively. By leveraging the expertise of both domain and AI experts, organizations can develop tailored AI solutions that enhance supply chain planning, improve decision-making processes, and drive competitive advantage. The proposed approach contributes to the successful integration of AI in SCM, ultimately leading to more efficient and resilient supply chains in the era of artificial intelligence.}}, author = {{Lick, Jonas and Wohlers, Benedict and Sahrhage, Philipp and Schreckenberg, Felix and Klöckner, Susanne and Von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Artificial Intelligence, Social Computing and Wearable Technologies}}, issn = {{2771-0718}}, publisher = {{AHFE International}}, title = {{{Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach}}}, doi = {{10.54941/ahfe1004185}}, year = {{2023}}, } @inproceedings{54506, author = {{Lick, Jonas and Schreckenberg, Felix and Sahrhage, Philipp and Wohlers, Benedict and Klöcker, Susanne and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Artificial Intelligence, Social Computing and Wearable Technologies, Vol. 113}}, editor = {{Karwowsk, Waldemar and Ahram, Tareq }}, location = {{Hawaii}}, title = {{{Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach}}}, year = {{2023}}, } @inproceedings{54503, author = {{Günther, Matthias and Göllner, Denis and Heihoff-Schwede, Jörg and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Tag des Systems Engineering 2023}}, editor = {{Wilke, Daria and Koch, Walter and Kaffenberger, Rüdiger and Dreiseitel, Stefan}}, location = {{Würzburg}}, title = {{{Engineering und Management von System of Systems–Gestaltungskonzepte im SoS-Engineering}}}, year = {{2023}}, } @inproceedings{54505, author = {{Wilke, Daria and Heitmann, Rebecca and Tekaat, Julian and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Tag des Systems Engineering 2023}}, editor = {{Wilke, Daria and Koch, Walter and Kaffenberger, Rüdiger and Dreiseitel, Stefan}}, location = {{Würzburg}}, title = {{{Reifegradmodell zur Einführung von Systems Engineering–Systemdenken als Handlungsfeld}}}, year = {{2023}}, } @inproceedings{54507, author = {{Mager, Thomas and Dumitrescu, Roman}}, location = {{Amberg}}, title = {{{Hybrid design approach for the design of high-frequency components in MID technology}}}, year = {{2023}}, } @inbook{54547, author = {{Eckertz, Daniel and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Open Science in Engineering}}, editor = {{Auer, Michael and Langmann, Reinhard and Tsiatsos, Thrasyvoulos}}, isbn = {{9783031424663}}, issn = {{2367-3370}}, location = {{Thessaloniki}}, publisher = {{Springer Nature Switzerland}}, title = {{{Modular Toolbox for Low-Code Development of Individual Augmented Reality Applications in Unity}}}, doi = {{10.1007/978-3-031-42467-0_40}}, year = {{2023}}, } @article{54567, author = {{Schlegel, Michael and Wiederkehr, Ingrid and Rapp, Simon and Koldewey, Christian and Albers, Albert and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, location = {{Kapstadt}}, pages = {{792--797}}, publisher = {{Elsevier BV}}, title = {{{Future-robust evolution of product portfolios: Need for action from theory and practice}}}, doi = {{10.1016/j.procir.2023.09.077}}, volume = {{120}}, year = {{2023}}, } @article{54565, author = {{Wiederkehr, Ingrid and Schlegel, Michael and Koldewey, Christian and Rapp, Simon and Dumitrescu, Roman and Albers, Albert}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, location = {{Kapstadt}}, pages = {{816--821}}, publisher = {{Elsevier BV}}, title = {{{Interacting Forces for a Resilient, Future-robust Evolution of Product Portfolios}}}, doi = {{10.1016/j.procir.2023.09.081}}, volume = {{120}}, year = {{2023}}, } @article{54566, author = {{Göllner, Denis and Dzienus, Sophie and Rasor, Rik and Anacker, Harald and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, location = {{Kapstadt}}, pages = {{1433--1438}}, publisher = {{Elsevier BV}}, title = {{{Guidelines for providing digital twins}}}, doi = {{10.1016/j.procir.2023.09.189}}, volume = {{120}}, year = {{2023}}, } @inproceedings{54568, author = {{Machon, Fabian and Haarmann, Lennard and Rabe, Martin and Dumitrescu, Roman and Bierbüsse, Marie and Tack, Mareen and Hanke, Rebecca and Kinder, Daniel}}, booktitle = {{Vorausschau und Technologieplanung}}, editor = {{Dumitrescu, Roman and Hölzle, Katharina}}, isbn = {{978-3-947647-32-3}}, location = {{Berlin}}, title = {{{Mehr Innovationen durch Venture Clienting – Fallstudie zur Initiative „Stratosfare“}}}, volume = {{413}}, year = {{2023}}, } @inproceedings{54569, author = {{Wiederkehr, Ingrid and Koldewey, Christian and Dumitrescu, Roman and Schlegel, Michael and Albers, Albert }}, location = {{Ljubljana}}, title = {{{Bridging the Gap between Product Innovation and Product Planning: A literature-based Conceptual Investigation}}}, year = {{2023}}, } @inproceedings{54571, author = {{Schlegel, Michael and Wiederkehr, Ingrid and Rapp, Simon and Koldewey, Christian and Albers, Albert and Dumitrescu, Roman}}, booktitle = {{Procedia CIRP}}, editor = {{Liu, Ang and Kara, Sami}}, issn = {{2212-8271}}, location = {{Sydney}}, pages = {{764--769}}, publisher = {{Elsevier BV}}, title = {{{Ontology for Future-robust Product Portfolio Evolution: A Basis for the Development of Models and Methods}}}, doi = {{10.1016/j.procir.2023.01.017}}, volume = {{119}}, year = {{2023}}, } @inproceedings{54574, author = {{Weller, Julian and Nico Migenda, Nico Migenda and Rui Liu, Rui Liu and Arthur Wegel, Arthur Wegel and Martin Kohlhase, Martin Kohlhase and Wolfram Schenck, Wolfram Schenck and Sebastian von Enzberg, Sebastian von Enzberg and Dumitrescu, Roman}}, location = {{Hamburg}}, title = {{{Towards a systematic approach for Prescriptive Analytics use cases in smart factories}}}, year = {{2023}}, } @inproceedings{54618, author = {{Wilke, Daria and Grewe, Carolin and Thavathilakarjah, Dhusjanth and Anacker, Harald and Dumitrescu, Roman}}, location = {{Kapstadt}}, title = {{{Method Engineering – a Systematic Literature Review on Scopus Base}}}, year = {{2023}}, } @inproceedings{54620, author = {{Seidenberg, Tobias and Ayoub, Joe and Figge, Mike and Anacker, Harald and Dumitrescu, Roman}}, location = {{Bangkok}}, title = {{{Method to support the selection of localization technologies for Industry 4.0}}}, year = {{2023}}, } @inproceedings{54621, author = {{Schreiner, Nick and Kürpick, Christian and Kühn, Arno and Dumitrescu, Roman}}, location = {{Buenos Aires}}, title = {{{Sustainability Data Map: Framework for Data-Based Product Carbon Footprinting of Technical Products}}}, year = {{2023}}, } @misc{54622, author = {{Gabriel, Stefan and Fechtelpeter,, Christian and Wulf, Jessica and Leßmann, Salome and Dumitrescu, Roman}}, title = {{{Transferkonzept eines Kompetenzzentrums der Arbeitsforschung in einer von mittelständischen Unternehmen geprägten Region}}}, year = {{2023}}, } @inproceedings{37553, author = {{Schrader, Elena and Bernijazov, Ruslan and Foullois, Marc and Hillebrand, Michael and Kaiser, Lydia and Dumitrescu, Roman}}, booktitle = {{2022 IEEE International Symposium on Systems Engineering (ISSE)}}, publisher = {{IEEE}}, title = {{{Examples of AI-based Assistance Systems in context of Model-Based Systems Engineering}}}, doi = {{10.1109/isse54508.2022.10005487}}, year = {{2023}}, } @inproceedings{45793, abstract = {{The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.}}, author = {{Grigoryan, Khoren and Fichtler, Timm and Schreiner, Nick and Rabe, Martin and Panzner, Melina and Kühn, Arno and Dumitrescu, Roman and Koldewey, Christian}}, booktitle = {{Procedia CIRP 33}}, keywords = {{Product Management, Data Analytics, Data-Driven Design, Product-related data, Lifecycle Data, Tool-support}}, location = {{Sydney}}, title = {{{Data-Driven Product Management: A Practitioner-Driven Research Agenda}}}, year = {{2023}}, }