[{"abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title>\r\n\t  <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>"}],"status":"public","type":"journal_article","publication":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing","article_number":"e18","language":[{"iso":"eng"}],"_id":"58474","user_id":"15782","department":[{"_id":"563"}],"year":"2024","citation":{"ama":"Panzner M, von Enzberg S, Dumitrescu R. Developing a data analytics toolbox for data-driven product planning: a review and survey methodology. <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>. 2024;38. doi:<a href=\"https://doi.org/10.1017/s0890060424000209\">10.1017/s0890060424000209</a>","chicago":"Panzner, Melina, Sebastian von Enzberg, and Roman Dumitrescu. “Developing a Data Analytics Toolbox for Data-Driven Product Planning: A Review and Survey Methodology.” <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i> 38 (2024). <a href=\"https://doi.org/10.1017/s0890060424000209\">https://doi.org/10.1017/s0890060424000209</a>.","ieee":"M. Panzner, S. von Enzberg, and R. Dumitrescu, “Developing a data analytics toolbox for data-driven product planning: a review and survey methodology,” <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>, vol. 38, Art. no. e18, 2024, doi: <a href=\"https://doi.org/10.1017/s0890060424000209\">10.1017/s0890060424000209</a>.","apa":"Panzner, M., von Enzberg, S., &#38; Dumitrescu, R. (2024). Developing a data analytics toolbox for data-driven product planning: a review and survey methodology. <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>, <i>38</i>, Article e18. <a href=\"https://doi.org/10.1017/s0890060424000209\">https://doi.org/10.1017/s0890060424000209</a>","short":"M. Panzner, S. von Enzberg, R. Dumitrescu, Artificial Intelligence for Engineering Design, Analysis and Manufacturing 38 (2024).","mla":"Panzner, Melina, et al. “Developing a Data Analytics Toolbox for Data-Driven Product Planning: A Review and Survey Methodology.” <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>, vol. 38, e18, Cambridge University Press (CUP), 2024, doi:<a href=\"https://doi.org/10.1017/s0890060424000209\">10.1017/s0890060424000209</a>.","bibtex":"@article{Panzner_von Enzberg_Dumitrescu_2024, title={Developing a data analytics toolbox for data-driven product planning: a review and survey methodology}, volume={38}, DOI={<a href=\"https://doi.org/10.1017/s0890060424000209\">10.1017/s0890060424000209</a>}, number={e18}, journal={Artificial Intelligence for Engineering Design, Analysis and Manufacturing}, publisher={Cambridge University Press (CUP)}, author={Panzner, Melina and von Enzberg, Sebastian and Dumitrescu, Roman}, year={2024} }"},"intvolume":"        38","publication_status":"published","publication_identifier":{"issn":["0890-0604","1469-1760"]},"title":"Developing a data analytics toolbox for data-driven product planning: a review and survey methodology","doi":"10.1017/s0890060424000209","date_updated":"2025-01-31T14:24:45Z","publisher":"Cambridge University Press (CUP)","date_created":"2025-01-31T14:24:26Z","author":[{"first_name":"Melina","last_name":"Panzner","full_name":"Panzner, Melina"},{"last_name":"von Enzberg","full_name":"von Enzberg, Sebastian","first_name":"Sebastian"},{"last_name":"Dumitrescu","full_name":"Dumitrescu, Roman","id":"16190","first_name":"Roman"}],"volume":38},{"keyword":["Artificial Intelligence","Industrial and Manufacturing Engineering"],"article_number":"e14","language":[{"iso":"eng"}],"_id":"30193","department":[{"_id":"563"}],"user_id":"15782","abstract":[{"text":"The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.","lang":"eng"}],"status":"public","publication":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing","type":"journal_article","title":"Potentials and challenges of analyzing use phase data in product planning of manufacturing companies","doi":"10.1017/s0890060421000408","date_updated":"2022-10-19T12:29:54Z","publisher":"Cambridge University Press (CUP)","volume":36,"author":[{"full_name":"Meyer, Maurice","id":"77201","last_name":"Meyer","orcid":"0000-0003-0606-7321","first_name":"Maurice"},{"first_name":"Timm","id":"66731","full_name":"Fichtler, Timm","last_name":"Fichtler"},{"first_name":"Christian","id":"43136","full_name":"Koldewey, Christian","orcid":"https://orcid.org/0000-0001-7992-6399","last_name":"Koldewey"},{"last_name":"Dumitrescu","id":"16190","full_name":"Dumitrescu, Roman","first_name":"Roman"}],"date_created":"2022-03-02T19:23:18Z","year":"2022","intvolume":"        36","citation":{"ieee":"M. Meyer, T. Fichtler, C. Koldewey, and R. Dumitrescu, “Potentials and challenges of analyzing use phase data in product planning of manufacturing companies,” <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>, vol. 36, Art. no. e14, 2022, doi: <a href=\"https://doi.org/10.1017/s0890060421000408\">10.1017/s0890060421000408</a>.","chicago":"Meyer, Maurice, Timm Fichtler, Christian Koldewey, and Roman Dumitrescu. “Potentials and Challenges of Analyzing Use Phase Data in Product Planning of Manufacturing Companies.” <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i> 36 (2022). <a href=\"https://doi.org/10.1017/s0890060421000408\">https://doi.org/10.1017/s0890060421000408</a>.","ama":"Meyer M, Fichtler T, Koldewey C, Dumitrescu R. Potentials and challenges of analyzing use phase data in product planning of manufacturing companies. <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>. 2022;36. doi:<a href=\"https://doi.org/10.1017/s0890060421000408\">10.1017/s0890060421000408</a>","short":"M. Meyer, T. Fichtler, C. Koldewey, R. Dumitrescu, Artificial Intelligence for Engineering Design, Analysis and Manufacturing 36 (2022).","bibtex":"@article{Meyer_Fichtler_Koldewey_Dumitrescu_2022, title={Potentials and challenges of analyzing use phase data in product planning of manufacturing companies}, volume={36}, DOI={<a href=\"https://doi.org/10.1017/s0890060421000408\">10.1017/s0890060421000408</a>}, number={e14}, journal={Artificial Intelligence for Engineering Design, Analysis and Manufacturing}, publisher={Cambridge University Press (CUP)}, author={Meyer, Maurice and Fichtler, Timm and Koldewey, Christian and Dumitrescu, Roman}, year={2022} }","mla":"Meyer, Maurice, et al. “Potentials and Challenges of Analyzing Use Phase Data in Product Planning of Manufacturing Companies.” <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>, vol. 36, e14, Cambridge University Press (CUP), 2022, doi:<a href=\"https://doi.org/10.1017/s0890060421000408\">10.1017/s0890060421000408</a>.","apa":"Meyer, M., Fichtler, T., Koldewey, C., &#38; Dumitrescu, R. (2022). Potentials and challenges of analyzing use phase data in product planning of manufacturing companies. <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>, <i>36</i>, Article e14. <a href=\"https://doi.org/10.1017/s0890060421000408\">https://doi.org/10.1017/s0890060421000408</a>"},"publication_identifier":{"issn":["0890-0604","1469-1760"]},"publication_status":"published"}]