@inproceedings{52369,
  abstract     = {{Megatrends, such as digitization or sustainability, are confronting the product management of manufacturing companies with a variety of challenges regarding the design of future products, but also the management of the actual products. To successfully position their products in the market, product managers need to gather and analyze comprehensive information about customers, developments in the products’ environment, product usage, and more. The digitization of all aspects of life is making data on these topics increasingly available – via social media, documents, or the internet of things from the products themselves. The systematic collection and analysis of these data enable the exploitation of new potentials for the adaption of existing products and the creation of the products of tomorrow. However, there are still no insights into the main concepts and cause-effect relationships in exploiting data-driven approaches for product management. Therefore, this paper aims to identify the main concepts and advantages of data-driven product management. To answer the corresponding research questions a comprehensive systematic literature review is conducted. From its results, a detailed description of the main concepts of data-driven product management is derived. Furthermore, a taxonomy for the advantages of data-driven product management is presented. The main concepts and the taxonomy allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.}},
  author       = {{Fichtler, Timm and Grigoryan, Khoren and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}},
  keywords     = {{Product Lifecyle Management (PLM), Data Analytics, Data-driven Design, Engineering Management, Lifecycle Data}},
  location     = {{Rabat, Morocco}},
  publisher    = {{IEEE}},
  title        = {{{Towards a Data-Driven Product Management – Concepts, Advantages, and Future Research}}},
  doi          = {{10.1109/ictmod59086.2023.10438135}},
  year         = {{2023}},
}

@inbook{53553,
  author       = {{Özcan, Leon and Drewel, Marvin and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{Digitalisierung: Fallstudien, Tools und Erkenntnisse für das digitale Zeitalter}},
  editor       = {{Schallmo, Daniel and Lang, Klaus and Werani, Thomas and Krumay, Barbara}},
  isbn         = {{9783658366339}},
  issn         = {{2569-2348}},
  publisher    = {{Springer Fachmedien Wiesbaden}},
  title        = {{{Strategische Planung des Einstiegs in die Plattformökonomie}}},
  doi          = {{10.1007/978-3-658-36634-6_7}},
  year         = {{2023}},
}

@techreport{53554,
  author       = {{Dumitrescu, Roman and Riemensperger, Frank and Schuh, Günther and Biehler, Jan and Frey, Anna and Hocken, Christian and Koldewey, Christian and Kühn, Arno and Rabe, Martin and Schacht, Maximilian and Comans, Sebastian and Fichtler, Timm and Govioni, Alina and Harland, Tobias and Kaufmann, Jonas and Optehostert, Felix and Rieger, Marcel and Scholtysik, Michel and Schreiner, Nick and Sedlmeir, Joachim and Sommer, Franziska}},
  title        = {{{acatech Maturity Index Smart Services}}},
  year         = {{2023}},
}

@inproceedings{49363,
  author       = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}},
  title        = {{{Circular Product-Service-System Ideation Canvas – A Framework for the Design of circular Product-Service-System Ideas}}},
  volume       = {{120}},
  year         = {{2023}},
}

@inbook{47834,
  author       = {{Ködding, Patrick and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{New Digital Work}},
  editor       = {{Shajek, Alexandra and Hartmann, Ernst A.}},
  pages        = {{51--67}},
  title        = {{{Scenario-Based Foresight in the Age of Digital Technologies and AI}}},
  year         = {{2023}},
}

@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}},
}

@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}},
}

@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{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}},
}

@inproceedings{45812,
  author       = {{Özcan, Leon and Fichtler, Timm and Kasten, Benjamin and Koldewey, Christian and Dumitrescu, Roman}},
  keywords     = {{Digital Platform, Platform Strategy, Strategic Management, Platform Life Cycle, Interview Study, Business Model, Business-to-Business, Two-sided Market, Multi-sided Market}},
  location     = {{Ljubljana}},
  title        = {{{Interview Study on Strategy Options for Platform Operation in B2B Markets}}},
  year         = {{2023}},
}

@article{47420,
  author       = {{Kürpick, Christian and Rasor, Anja and Scholtysik, Michel and Kühn, Arno and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  keywords     = {{General Medicine}},
  pages        = {{614--619}},
  publisher    = {{Elsevier BV}},
  title        = {{{An Integrative View of the Transformations towards Sustainability and Digitalization: The Case for a Dual Transformation}}},
  doi          = {{10.1016/j.procir.2023.02.155}},
  volume       = {{119}},
  year         = {{2023}},
}

@book{48666,
  abstract     = {{Unter dem Einfluss der Digitalisierung wandeln sich mechatronische Produkte zunehmend in cyber-physische Systeme (CPS). Diese sind in der Lage, umfangreiche Daten während ihres Betriebs zu sammeln und über digitale Netzinfrastrukturen zur Verfügung zu stellen. Gemeinsam mit weiteren Daten aus der Betriebsphase versprechen sie wertvolle Erkenntnisse über das Produkt und dessen Nutzer, welche für die Hersteller der CPS insbesondere für die Planung zukünftiger Produktgenerationenrelevant sind. Die zielgerichtete Nutzung von Betriebsdaten in der strategischen Produktplanung stellt produzierende Unternehmen jedoch noch vor zahlreiche Herausforderungen, z. B. hinsichtlich der Identifizierung Erfolg versprechender Use Cases. Das vorliegende Buch greift diese Herausforderungen auf und stellt ein Instrumentarium vor, das produzierende Unternehmen zur datengestützten Produktplanung befähigt. Neben der Vorstellung praxiserprobter Methoden und Werkzeuge werden Einblicke in vier Pilotprojekte gegeben. Das Instrumentarium entstand im Forschungsprojekt „DizRuPt“, das vom Bundesministerium für Bildung und Forschung (BMBF) gefördert wurde.}},
  editor       = {{Dumitrescu, Roman and Koldewey, Christian}},
  publisher    = {{Heinz Nixdorf Institut}},
  title        = {{{Datengestützte Produktplanung}}},
  doi          = {{10.17619/UNIPB/1-1667}},
  volume       = {{408}},
  year         = {{2023}},
}

@inproceedings{49318,
  author       = {{Tissen, Denis and Koldewey, Christian and Dumitrescu, Roman}},
  location     = {{Ljubljana, Slovenia}},
  title        = {{{A process-model for tailoring prototyping of cyber-physical systems}}},
  year         = {{2023}},
}

@inproceedings{29149,
  author       = {{Koldewey, Christian and Dumitrescu, Roman and Rabe, Martin }},
  booktitle    = {{Proceedings of the 55th Hawaii International Conference on System Sciences}},
  location     = {{Hawaii, USA}},
  title        = {{{Introduction to the Data-driven Services in Manufacturing Minitrack - Exploring Management, Engineering, and Organizational Transformation}}},
  year         = {{2022}},
}

@inproceedings{29380,
  abstract     = {{Cyber-physical systems generate and collect huge amounts of usage data during operation. Analyzing these data may enable manufacturing companies to identify weaknesses and learn about the users of their products. Such insights are valuable in the early phases of product development like product planning, as they facilitate decision-making for product improvement. The analysis and exploitation of usage data in product planning, however, is a new task for manufacturing companies. To reduce mistakes and improve the results, companies should build upon a suitable reference process model. Unfortunately, established models for analyzing data cannot be easily applied for product planning. In this paper, we propose a reference process model for usage data-driven product planning. It builds on three well-established models for analyzing data and addresses the unique characteristics of usage data-driven product planning. Finally, we customize the model for a manufacturing company and demonstrate how it could be implemented in practice.}},
  author       = {{Meyer, Maurice and Wiederkehr, Ingrid and Panzner, Melina and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 55th Hawaii International Conference on System Sciences}},
  pages        = {{6105--6114}},
  title        = {{{A Reference Process Model for Usage Data-Driven Product Planning}}},
  year         = {{2022}},
}

@article{27776,
  author       = {{Koldewey, Christian and Rasor, Anja and Reinhold, Jannik and Gausemeier, Jürgen and Dumitrescu, Roman and Chohan, Nadia and Frank, Maximilian}},
  issn         = {{0040-1625}},
  journal      = {{Technological Forecasting and Social Change}},
  publisher    = {{Elsevier}},
  title        = {{{Aligning strategic position, behavior, and structure for smart service businesses in manufacturing}}},
  doi          = {{10.1016/j.techfore.2021.121329}},
  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}},
}