@inproceedings{33503,
  author       = {{Gabriel, Stefan and Aring, Theresa and Hobscheidt, Daniela and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Tagungsband des 68. Frühjahrskongress der Gesellschaft für Arbeitswissenschaft}},
  location     = {{Magdeburg 02.03. - 04.03.2022}},
  publisher    = {{GfA-Press}},
  title        = {{{Handlungsfelder für die KI-Einführung in der Arbeitswelt produzierender Unternehmen}}},
  year         = {{2022}},
}

@inproceedings{33505,
  author       = {{Anacker, Harald and Günther, Matthias and Wyrwich, Fabian and Dumitrescu, Roman}},
  booktitle    = {{17th Annual System of Systems Engineering Conference (SOSE)}},
  location     = {{Rochester, NY, USA}},
  pages        = {{178--183}},
  title        = {{{Pattern based engineering of System of Systems - a systematic literature review}}},
  doi          = {{10.1109/SOSE55472.2022.9812697}},
  year         = {{2022}},
}

@inproceedings{31188,
  author       = {{Anacker, Harald and Dumitrescu, Roman and Könemann, Ulf and Wilke, Daria}},
  booktitle    = {{ Proceedings of the 16th Annual IEEE International Systems Conference}},
  location     = {{Montreal, Canada}},
  title        = {{{Identification of stakeholder-specific Systems Engineering competencies for industry}}},
  year         = {{2022}},
}

@inproceedings{33508,
  abstract     = {{In this work, methods will be evaluated to numerically calculate the passive electrical parameters of planar coils. These parameters can then be used to optimize inductive applications such as wireless power transmission. The focus here will be on inductive localization, which uses high-frequency magnetic fields and the resulting induced voltage to provide localization through the coupling parameter mutual inductance. To achieve localization with high accuracy and best possible operation (resonance, signal strength, etc.), the coil parameters need to be well known. For this reason, some numerical methods for the calculation of these quantities are presented and validated. In addition, the physical effects are thereby considered in more detail, allowing the localization procedure to be better optimized compared to simulative black-box methods. The goal should be a dedicated simulation platform for planar coils to be able to develop training data for neural networks and to test and optimize localization algorithms.}},
  author       = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  keywords     = {{Simulation Environment, Inductive Localization, Coil Parameters, Inductive Applications, Near-Field}},
  location     = {{Grenoble, France}},
  publisher    = {{IEEE}},
  title        = {{{Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings}}},
  doi          = {{10.1109/ssi56489.2022.9901416}},
  year         = {{2022}},
}

@inproceedings{33510,
  abstract     = {{In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised).}},
  author       = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  keywords     = {{Machine Learning, CNN, Hashing, semi-supervised learning}},
  location     = {{Grenoble, France}},
  publisher    = {{IEEE}},
  title        = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}},
  doi          = {{10.1109/ssi56489.2022.9901433}},
  year         = {{2022}},
}

@book{33516,
  author       = {{Fazal-Baqaie, Masud  and Linssen, Oliver and Volland, Alexander and Yigitbas, Enes and Engstler, Martin and Bertram, Martin and Kalenborn, Axel}},
  publisher    = {{Gesellschaft für Informatik e.V.}},
  title        = {{{Projektmanagement und Vorgehensmodelle 2022. Virtuelle Zusammenarbeit und verlorene Kulturen?}}},
  volume       = {{P 327}},
  year         = {{2022}},
}

@inproceedings{33553,
  author       = {{Pfeifer, Stefan and Akgül, Didem and Röbenack, Silke and Tihlarik, Amelie and Albert, Bruno and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{DS 118: Proceedings of NordDesign 2022}},
  editor       = {{Mortensen, N.H. and Hansen, C.T. and Deininger, M.}},
  isbn         = {{9781912254170}},
  location     = {{Copenhagen, Denmark}},
  publisher    = {{The Design Society}},
  title        = {{{Design Decisions in the Architecture Development of Advanced Systems: Towards traceable and sustainable Documentation and Communication}}},
  doi          = {{10.35199/norddesign2022}},
  year         = {{2022}},
}

@inproceedings{33552,
  author       = {{Disselkamp, Jan-Philipp and Seidenberg, Tobias and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the IEEE}},
  location     = {{Nancy, France}},
  title        = {{{Design of an optimised value creation network for zero emission ferries}}},
  year         = {{2022}},
}

@inproceedings{33556,
  author       = {{Eckertz, Daniel and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 5th International Conference on Information and Computer Technologies (ICICT)}},
  location     = {{New York City, NY, United States}},
  publisher    = {{IEEE}},
  title        = {{{Knowledge-based Interactive Configuration Tool for Industrial Augmented Reality Systems}}},
  doi          = {{10.1109/icict55905.2022.00021}},
  year         = {{2022}},
}

@inproceedings{33554,
  author       = {{Merkelbach, Silke and von Enzberg, Sebastian and Kuhn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the IEEE}},
  publisher    = {{IEEE}},
  title        = {{{Towards a Process Model to Enable Domain Experts to Become Citizen Data Scientists for Industrial Applications}}},
  doi          = {{10.1109/icps51978.2022.9816871}},
  year         = {{2022}},
}

@inproceedings{33557,
  author       = {{Eckertz, Daniel and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of NordDesign 2022}},
  publisher    = {{The Design Society}},
  title        = {{{Systematics for the individual assessment of augmented reality potentials to support product validation}}},
  doi          = {{10.35199/norddesign2022.12}},
  year         = {{2022}},
}

@inproceedings{33558,
  author       = {{Wilke, Daria and Pfeifer, Stefan and Heitmann, Rebecca  and Anacker, Harald  and Dumitrescu, Roman and Franke, Volker }},
  booktitle    = {{Proceedings of the IEEE ISSE }},
  location     = {{Wien, Österreich}},
  title        = {{{Implementation of Systems Engineering: A maturity-based approach}}},
  year         = {{2022}},
}

@techreport{33702,
  abstract     = {{<jats:p>Im Rahmen dieser Studie wird der Status Quo des KI-Einsatzes in der industriellen Arbeitswelt in der Region OstWestfalenLippe erfasst und beschrieben. Dadurch wird eine Grundlage geschaffen, um eine zielführende Unterstützung der Gestaltung von durch Künstliche Intelligenz (KI) gestützter Arbeitsprozesse in Unternehmen zu ermöglichen, indem beispielsweise bedarfsbezogene Maßnahmen entwickelt und durchgeführt sowie weiterer Forschungsbedarf aufgezeigt wird.  Die Befragung wurde im Jahr 2021 von dem Kompetenzzentrum Arbeitswelt.Plus sowie dem Spitzencluster it’s OWL initiiert. Dabei sind drei Zielgruppen – Unternehmensleitung, Personalabteilung (HR) sowie Arbeitnehmer*innen – adressiert worden. Insgesamt nahmen 317 Personen aus 89 verschiedenen Unternehmen bzw. Organisationen an der Befragung teil – zu 38 % Unternehmer*innen, zu 13 % Personaler*innen und zu 49 % Arbeitnehmer*innen. Die meisten der Teilnehmenden stammten aus der Elektroindustrie, dem Maschinenbau sowie dem Informations- und Kommunikationstechnologie (IKT)-Sektor.  Die Befragungsergebnisse zeigen, dass sich die meisten Unternehmen in der Anfangsphase der KI-Nutzung befinden. Zwischen einzelnen Unternehmensbereichen und verschiedenen Branchen zeigen sich gewisse Unterschiede in der Nutzungsphase. Die Befragten stehen aktuell vor der Nutzung von vor allem teilautonomen KI-Systemen, die ausführende und analytische menschliche Tätigkeitenbeispielsweise durch Informationsbereitstellungen unterstützen. Wesentliche Ziele der KI-Nutzung sind die Effizienzsteigerung, Qualitätsverbesserung, Entscheidungsoptimierung sowie Unterstützung der Arbeitnehmer*innen. Allerdings werden in allen Unternehmen die fehlende Expertise sowie insgesamt die Komplexität des Themenfelds als Hinderungsgründe identifiziert.  In allen Unternehmen und allen Unternehmensbereichen werden hohe Auswirkungen durch KI erwartet. Auf die Arbeitsgestaltung werden insgesamt eher positive Auswirkungen erwartet. Die Befragten schätzen die Bedeutung von KI, ihre Aufgeschlossenheit sowie ihr Vertrauen gegenüber KI als insgesamt hoch ein, ihr Verständnis von KI dagegen eher als gering. Tendenziell zeigt sich eine große Diskrepanz zwischen Selbst- und Fremdbild mit einer teils deutlich negativeren Wahrnehmung anderer. Die Befragten erwarten außerdem steigende Kompetenzanforderungen sowie einen hohen Weiterbildungsbedarf, insbesondere bezüglich des grundlegenden Verständnisses über KI. In den wenigsten Unternehmen existiert jedoch ein gezieltes Weiterbildungsangebot.  Die Erkenntnisse aus der Befragung fließen im Rahmen des Kompetenzzentrums Arbeitswelt.Plus in die gezielte Gestaltung und Einführung KI-gestützter Arbeitsformen sowie bedarfsgerechter Unterstützungsangebote ein. Die hohe Komplexität der KI-Einführung sowie die sowohl technischen als auch mitarbeiterbezogenen Herausforderungen verdeutlichen den Bedarf für eine soziotechnische Perspektive und ein systematisches Vorgehen bei der Gestaltung dieses vielschichtigen Themenfelds.</jats:p>}},
  author       = {{Papenkordt, Jörg and Gabriel, Stefan and Thommes, Kirsten and Dumitrescu, Roman}},
  publisher    = {{Kompetenzzentrum Arbeitswelt.Plus}},
  title        = {{{Künstliche Intelligenz in der industriellen Arbeitswelt - Studie zum Status Quo in der Region OstWestfalenLippe}}},
  doi          = {{10.55594/tmao3234}},
  year         = {{2022}},
}

@article{33701,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>Künstliche Intelligenz bietet großes Potenzial im Engineering. Der Einsatz gestattet insbesondere für Wissensarbeiter eine effiziente Arbeitsteilung, in der beispielsweise fehleranfällige und repetitive Aktivitäten unterstützt werden. Eine erfolgreiche Einführung bedarf einer vorangehenden Analyse von nutzenstiftenden Einsatzpotenzialen, bei der alle Anwendenden frühzeitig einbezogen werden. Der folgende Beitrag verdeutlicht dieses Vorgehen anhand eines realen Beispiels im Sondermaschinenbau.</jats:p>}},
  author       = {{Kharatyan, Aschot and Humpert, Lynn and Anacker, Harald and Dumitrescu, Roman and Wäschle, Moritz and Albers, Albert and Horstmeyer, Sarah}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  keywords     = {{Management Science and Operations Research, Strategy and Management, General Engineering}},
  number       = {{6}},
  pages        = {{427--431}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Künstliche Intelligenz im Engineering}}},
  doi          = {{10.1515/zwf-2022-1074}},
  volume       = {{117}},
  year         = {{2022}},
}

@article{33705,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>The ongoing digitalization of products offers product managers new potentials to plan future product generations based on data from the use phase instead of assumptions. However, product managers often face difficulties in identifying promising opportunities for analyzing use phase data. In this paper, we propose a method for planning the analysis of use phase data in product planning. It leads product managers from the identification of promising investigation needs to the derivation of specific use cases. The application of the method is shown using the example of a manufacturing company.</jats:p>}},
  author       = {{Meyer, Maurice and Wiederkehr, Ingrid and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{753--762}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Planning the Analysis of Use Phase Data in Product Planning}}},
  doi          = {{10.1017/pds.2022.77}},
  volume       = {{2}},
  year         = {{2022}},
}

@inproceedings{33708,
  abstract     = {{The megatrend digitalization turns mechatronic products into continuous collectors and generators of use phase data. By analyzing this data, manufacturers can uncover valuable insights about the products and the users. Especially in product planning, these insights could be used to plan promising future product generations. The systematic exploitation of data analytics results, however, represents a serious challenge, as research on the topic is still scarce. In this paper, we present 13 design principles for exploiting data analytics results in product planning. The results are based on a systematic literature review and a workshop with a research consortium. The evaluation of the design principles is demonstrated with a real case of a manufacturing company. The identified design principles represent a first contribution to a still scarcely explored research field.}},
  author       = {{Meyer, Maurice and Fichtler, Timm and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{ AMCIS 2022 Proceedings}},
  location     = {{Minneapolis}},
  title        = {{{How can Data Analytics Results be Exploited in the Early Phase of Product Development? 13 Design Principles for Data-Driven Product Planning}}},
  year         = {{2022}},
}

@article{33707,
  author       = {{Meyer, Maurice and Panzner, Melina and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  keywords     = {{General Medicine}},
  pages        = {{1053--1058}},
  publisher    = {{Elsevier BV}},
  title        = {{{17 Use Cases for Analyzing Use Phase Data in Product Planning of Manufacturing Companies}}},
  doi          = {{10.1016/j.procir.2022.05.107}},
  volume       = {{107}},
  year         = {{2022}},
}

@misc{33709,
  author       = {{Wiecher, Carsten  and Mandel, Constantin  and Günther, Matthias  and Fischbach, Jannik  and Greenyer, Joel  and Greinert, Matthias  and Wolff, Carsten  and Dumitrescu, Roman and Mendez, Daniel  and Albers, Albert}},
  booktitle    = {{arXiv preprint}},
  title        = {{{Model-based Analysis and Specification of Functional Requirements and Tests for Complex Automotive Systems}}},
  year         = {{2022}},
}

@article{33714,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Industry 4.0 promises many potentials in production. Examples are a data-driven optimization of production processes of individual machines, driverless transport systems, and assistance systems. Nevertheless, companies are still hesitant to invest in Industry 4.0 applications. Studies show that one of the main reasons for that is the unclear economic benefit. In this work, we present a systematic approach for the evaluation of Industry 4.0 applications in production. The main goal of the systematic is to create transparency over the evaluation process of an investment in an Industry 4.0 application in production. The evaluation of a concrete technical solution in an existing production system is supported. As a theoretical foundation, a characterization of investments in Industry 4.0 applications is given. From that, a procedure model is derived. It puts the activities to be carried out, the tools to be used and results in a temporal context. The application of the systematic is shown on the basis of an application example.</jats:p>}},
  author       = {{Joppen, Robert and Kühn, Arno and Förster, Magdalena and Dumitrescu, Roman}},
  issn         = {{1868-7865}},
  journal      = {{Journal of the Knowledge Economy}},
  keywords     = {{Economics and Econometrics}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Evaluation of Industry 4.0 Applications in Production}}},
  doi          = {{10.1007/s13132-022-00959-2}},
  year         = {{2022}},
}

@article{33713,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>The development of technical systems requires close cooperation of stakeholders from different disciplines. This collaboration takes place in workshops. Driven by digitalization and by the current pandemic such workshops take place primarily online. Suitable collaboration tools and methods are crucial to success. At the beginning of such workshops, use and damage scenarios are identified. In this paper, we presented a method and tool for identifying and modeling use and damage scenarios, which we evaluated in 14 online workshops with a total of 118 participants over a period of almost 3 years.</jats:p>}},
  author       = {{Japs, Sergej and Schmidt, Sebastian and Kargl, Frank and Kaiser, Lydia and Kharatyan, Aschot and Dumitrescu, Roman}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{1599--1608}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Collaborative Modeling of Use Case &amp; Damage Scenarios in Online Workshops Using a 3D Environment}}},
  doi          = {{10.1017/pds.2022.162}},
  volume       = {{2}},
  year         = {{2022}},
}