@article{58425,
  abstract     = {{<jats:p>Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases are still being implemented as standalone versions; the existing IT-infrastructure is not fit for integrative bidirectional decision communication, and implementations only reach low technical readiness levels. We propose a reference architecture for the integration of prescriptive analytics use cases in smart factories. The method for the empirically grounded development of reference architectures by Galster and Avgeriou serves as a blueprint. Through the development and validation of a specific IoT-Factory use case, we demonstrate the efficacy of the proposed reference architecture. We expand the given reference architecture for one use case to the integration of a smart factory and its application to multiple use cases. Moreover, we identify the interdependency among multiple use cases within dynamic environments. Our prescriptive reference architecture provides a structured way to improve operational efficiency and optimize resource allocation.</jats:p>}},
  author       = {{Weller, Julian and Migenda, Nico and Naik, Yash and Heuwinkel, Tim and Kühn, Arno and Kohlhase, Martin and Schenck, Wolfram and Dumitrescu, Roman}},
  issn         = {{2227-7390}},
  journal      = {{Mathematics}},
  number       = {{17}},
  publisher    = {{MDPI AG}},
  title        = {{{Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories}}},
  doi          = {{10.3390/math12172663}},
  volume       = {{12}},
  year         = {{2024}},
}

@article{58421,
  author       = {{von Enzberg, Sebastian and Weller, Julian and Brock, Jonathan and Merkelbach, Silke and Panzner, Melina and Lick, Jonas and Kühn, Arno and Dumitrescu, Roman}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  pages        = {{1454--1461}},
  publisher    = {{Elsevier BV}},
  title        = {{{On the Current State of Industrial Data Science: Challenges, Best Practices, and Future Directions}}},
  doi          = {{10.1016/j.procir.2024.10.266}},
  volume       = {{130}},
  year         = {{2024}},
}

@article{58422,
  author       = {{Weller, Julian and Migenda, Nico and Enzberg, Sebastian von and Kohlhase, Martin and Schenck, Wolfram and Dumitrescu, Roman}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  pages        = {{424--429}},
  publisher    = {{Elsevier BV}},
  title        = {{{Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories}}},
  doi          = {{10.1016/j.procir.2024.03.022}},
  volume       = {{128}},
  year         = {{2024}},
}

@article{58420,
  author       = {{Hartmann, Stefan and Brock, Jonathan and Kühn, Arno and Dumitrescu, Roman}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  pages        = {{100--105}},
  publisher    = {{Elsevier BV}},
  title        = {{{Applying Artificial Intelligence in the Smart Factory: Lessons Learned from real-world use cases}}},
  doi          = {{10.1016/j.procir.2024.10.062}},
  volume       = {{130}},
  year         = {{2024}},
}

@inproceedings{58423,
  author       = {{Weller, Julian and Migenda, Nico and Kühn, Arno and Dumitrescu, Roman}},
  location     = {{Hawaii, Honululu}},
  publisher    = {{LibreCat University}},
  title        = {{{Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories}}},
  doi          = {{10.15488/17721}},
  year         = {{2024}},
}

@inproceedings{58431,
  author       = {{Merkelbach , Silke and Diedrich , Alexander  and Sztyber-Betley, Anna   and Travé-Massuyès, Louise  and Chanthery , Elodie  and Niggemann , Oliver  and Dumitrescu , Roman }},
  booktitle    = {{Open Access Series in Informatics (OASIcs)}},
  location     = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)}},
  pages        = {{31:1--31:15}},
  title        = {{{Using Multi-Modal LLMs to Create Models for Fault Diagnosis}}},
  volume       = {{125}},
  year         = {{2024}},
}

@inproceedings{58424,
  author       = {{Niederhaus, Marvin and Migenda, Nico and Weller, Julian and Schenck, Wolfram and Kohlhase, Martin}},
  booktitle    = {{2024 35th Conference of Open Innovations Association (FRUCT)}},
  publisher    = {{IEEE}},
  title        = {{{Technical Readiness of Prescriptive Analytics Platforms: A Survey}}},
  doi          = {{10.23919/fruct61870.2024.10516367}},
  year         = {{2024}},
}

@article{58435,
  author       = {{Wilke, Daria and Mansheim, Johanna and Dumitrescu, Roman}},
  issn         = {{0360-8581}},
  journal      = {{IEEE Engineering Management Review}},
  pages        = {{1--14}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Challenges and Research Needs in the Offer Phase of Special Purpose Machinery and Plant Engineering – A Qualitative Study with Industry Experts}}},
  doi          = {{10.1109/emr.2024.3503994}},
  year         = {{2024}},
}

@inproceedings{58432,
  author       = {{Merkelbach, Silke and Heuwinkel, Tim and Dumitrescu, Roman}},
  booktitle    = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  publisher    = {{IEEE}},
  title        = {{{PID-Gen: Towards an Algorithm for the Generation of Random P&amp;IDs}}},
  doi          = {{10.1109/etfa61755.2024.10710643}},
  year         = {{2024}},
}

@inproceedings{58436,
  author       = {{Humpert, Lynn and Wilke, Daria and Tissen, Denis and Rummney, Johannes and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{2024 IEEE International Systems Conference (SysCon)}},
  publisher    = {{IEEE}},
  title        = {{{Investigation of validation methods for system design in the B2B sector}}},
  doi          = {{10.1109/syscon61195.2024.10553571}},
  year         = {{2024}},
}

@inproceedings{58434,
  author       = {{Wilke, Daria and Humpert, Lynn and Suwal, Pragita and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of NordDesign 2024}},
  publisher    = {{The Design Society}},
  title        = {{{Framework for the Selection of Systems Engineering Modelling Languages and Methods for Special Purpose Machinery}}},
  doi          = {{10.35199/norddesign2024.59}},
  year         = {{2024}},
}

@inproceedings{58433,
  author       = {{Hanke, Fabian and Moallim, Karan and Bernijazov, Ruslan and Demir, Riza and Brunnhausser, Jorg and Dumitrescu, Roman and Lindow, Kai}},
  booktitle    = {{Proceedings of NordDesign 2024}},
  publisher    = {{The Design Society}},
  title        = {{{Intelligent Part Comparison in Computer Aided Design}}},
  doi          = {{10.35199/norddesign2024.16}},
  year         = {{2024}},
}

@inproceedings{58445,
  author       = {{Wyrwich, Fabian  and Tschirner, Christian  and Bohnenkamp, Tinus  and Hovemann, Aschot  and Dumitrescu, Roman}},
  booktitle    = {{Tag des Systems Engineering 2024}},
  location     = {{Leipzig}},
  title        = {{{Anwendung und Integration des Application Lifecycle Managements im Produktlebenszyklus-Ergebnisse einer Industrie-Studie}}},
  year         = {{2024}},
}

@inproceedings{58443,
  author       = {{Wilke, Daria  and Grote, Eva-Maria and Schierbaum, Anja  and Dumitrescu, Roman}},
  booktitle    = {{Tag des Systems Engineering 2024}},
  location     = {{Leipzig}},
  title        = {{{Messbare Erfolgskriterien von Systems Engineering}}},
  year         = {{2024}},
}

@article{58467,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Traditional work models often need more flexibility and time autonomy for employees, especially in manufacturing. Quantitative approaches and Artificial Intelligence (AI) applications offer the potential to improve work design. However, current research does not entirely focus on human-centric criteria that enable time autonomy. This paper addresses this gap by developing a set of criteria to evaluate intelligent personnel planning approaches based on their ability to enhance time autonomy for employees. Existing quantitative approaches are not sufficient to fully integrate the developed criteria.</jats:p><jats:p>Consequently, a novel model approach is proposed in an attempt to bridge the gap between current practices and the newly developed criteria. This two-stage planning approach fosters democratization of time autonomy on the shopfloor, moving beyond traditional top-down scheduling. The paper concludes by outlining the implementation process and discusses future developments with respect to AI for this model approach.</jats:p><jats:p><jats:italic>Practical Relevance</jats:italic>: In order to make working conditions on the shopfloor in high-wage countries more attractive, an alternative organization of shift work is needed. Intelligent planning approaches that combine traditional operations research methods with artificial intelligence approaches can democratize shift organization regarding time autonomy. Planning that takes both employee and employer preferences into account in a balanced way will strengthen the long-term competitiveness of manufacturing companies in high-wage countries and counteract the shortage of skilled labor.</jats:p>}},
  author       = {{Latos, Benedikt and Buckhorst, Armin and Kalantar, Peyman and Bentler, Dominik and Gabriel, Stefan and Dumitrescu, Roman and Minge, Michael and Steinmann, Barbara and Guhr, Nadine}},
  issn         = {{0340-2444}},
  journal      = {{Zeitschrift für Arbeitswissenschaft}},
  number       = {{3}},
  pages        = {{277--298}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Time autonomy in personnel planning: Requirements and solution approaches in the context of intelligent scheduling from a holistic organizational perspective Zeitautonomie in der Personaleinsatzplanung: Anforderungen und Lösungsansätze im Rahmen einer intelligenten Planung aus ganzheitlicher organisationaler Perspektive}}},
  doi          = {{10.1007/s41449-024-00432-7}},
  volume       = {{78}},
  year         = {{2024}},
}

@article{58468,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Systems Engineering is developing differently in each sector and region. In German industry, especially in mechanical and plant engineering, Systems Engineering is of major importance. The introduction of Systems Engineering raises the question of which roles and competencies are required. This article examines the evolution of roles in Systems Engineering from a German perspective. Based on a literature review, the evolution of the identified Systems Engineering roles over time is shown, starting with the seminal publication by Sheard in 1996. It points out that only minimal adjustments and occasional role renaming have occurred. However, the review shows a common understanding of essential areas of responsibility within the SE and changes over time. The next step is to examine the current understanding of Systems Engineering roles in the industry. A quantitative analysis of job postings in Germany reveals a diverse interpretation of the term 'Systems Engineer; more than half of the positions cannot be categorized according to the INCOSE definitions. The job postings are used to determine which tasks are associated with the job, how often they occur, and in what combination. The primary responsibilities of systems engineers include creating and managing requirements, architecture processes, validation, and verification processes, and coordinating with customers and stakeholders. Finally, three representative companies from the mechanical and plant engineering sector were selected to analyze existing roles and tasks. From this, a common understanding of tasks and responsibilities is combined and organized into clusters. These are used by the companies to locate and thus derive their roles. The result is in an integrative approach that enables companies, especially in the midsize and medium sectors, to design the introduction in line with stakeholder demands. In summary, the industry's ongoing adaptation necessitates the evolution of Systems Engineering roles and competencies for successful and sustainable development and implementation of systems.</jats:p>}},
  author       = {{Kaiser, Lydia and Wilke, Daria and Förster, Felix and Köhler, Ingmarie and Dumitrescu, Roman}},
  issn         = {{2334-5837}},
  journal      = {{INCOSE International Symposium}},
  number       = {{1}},
  pages        = {{1413--1428}},
  publisher    = {{Wiley}},
  title        = {{{Evolving Roles in Systems Engineering —‐ Insights from Germany's Mechanical and Plant Engineering Sector}}},
  doi          = {{10.1002/iis2.13216}},
  volume       = {{34}},
  year         = {{2024}},
}

@article{58466,
  author       = {{Carayannis, Elias G. and Dumitrescu, Roman and Falkowski, Tommy and Zota, Nikos-Rigert}},
  issn         = {{0018-9391}},
  journal      = {{IEEE Transactions on Engineering Management}},
  pages        = {{14754--14774}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Empowering SMEs “Harnessing the Potential of Gen AI for Resilience and Competitiveness”}}},
  doi          = {{10.1109/tem.2024.3456820}},
  volume       = {{71}},
  year         = {{2024}},
}

@article{58474,
  abstract     = {{<jats:title>Abstract</jats:title>
	  <jats:p>The application of data analytics to product usage data has the potential to enhance engineering and decision-making in product planning. To achieve this effectively for cyber-physical systems (CPS), it is necessary to possess specialized expertise in technical products, innovation processes, and data analytics. An understanding of the process from domain knowledge to data analysis is of critical importance for the successful completion of projects, even for those without expertise in these areas. In this paper, we set out the foundation for a toolbox for data analytics, which will enable the creation of domain-specific pipelines for product planning. The toolbox includes a morphological box that covers the necessary pipeline components, based on a thorough analysis of literature and practitioner surveys. This comprehensive overview is unique. The toolbox based on it promises to support and enable domain experts and citizen data scientists, enhancing efficiency in product design, speeding up time to market, and shortening innovation cycles.</jats:p>}},
  author       = {{Panzner, Melina and von Enzberg, Sebastian and Dumitrescu, Roman}},
  issn         = {{0890-0604}},
  journal      = {{Artificial Intelligence for Engineering Design, Analysis and Manufacturing}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Developing a data analytics toolbox for data-driven product planning: a review and survey methodology}}},
  doi          = {{10.1017/s0890060424000209}},
  volume       = {{38}},
  year         = {{2024}},
}

@article{58475,
  author       = {{Schlegel, Michael and Just, Markus and Pfaff, Felix and Wiederkehr, Ingrid and Koldewey, Christian and Kempf, Christoph and Dumitrescu, Roman and Albers, Albert}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  pages        = {{555--560}},
  publisher    = {{Elsevier BV}},
  title        = {{{Future Robust Product Portfolio Development: Modelling Innovation Potentials in Product Portfolios}}},
  doi          = {{10.1016/j.procir.2024.03.036}},
  volume       = {{128}},
  year         = {{2024}},
}

@inproceedings{58469,
  author       = {{Schreiner, Nick and de Oliveira, FM and Trienens, Malte and Kürpick, Christian and Asmar, Laban and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Proceedings R&D Management Conference}},
  publisher    = {{LibreCat University}},
  title        = {{{Corporate Emission Profiles: Analyzing the 160 largest German Companies}}},
  doi          = {{10.24406/PUBLICA-3609}},
  year         = {{2024}},
}