@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{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{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}},
}

@inproceedings{33706,
  author       = {{Panzner, Melina and Meyer, Maurice and Enzberg, Sebastian von and Dumitrescu, Roman}},
  booktitle    = {{Procedia CIRP}},
  issn         = {{2212-8271}},
  keywords     = {{General Medicine}},
  pages        = {{580--585}},
  publisher    = {{Elsevier BV}},
  title        = {{{Business-to-Analytics Canvas - Translation of Product Planning-Related Business Use Cases into Concrete Data Analytics Tasks}}},
  doi          = {{10.1016/j.procir.2022.05.298}},
  volume       = {{109}},
  year         = {{2022}},
}