@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{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}},
}

@article{30193,
  abstract     = {{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.}},
  author       = {{Meyer, Maurice and Fichtler, Timm and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{0890-0604}},
  journal      = {{Artificial Intelligence for Engineering Design, Analysis and Manufacturing}},
  keywords     = {{Artificial Intelligence, Industrial and Manufacturing Engineering}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Potentials and challenges of analyzing use phase data in product planning of manufacturing companies}}},
  doi          = {{10.1017/s0890060421000408}},
  volume       = {{36}},
  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}},
}

@inproceedings{26851,
  author       = {{Dumitrescu, Roman and Anacker, Harald and Grote, Eva-Maria and Rasor, Rik and Tekaat, Julian and Meyer, Maurice and Gausemeier, Jürgen and Steglich, Steffen}},
  booktitle    = {{Vorausschau und Technologieplanung - 16. Symposium Vorausschau und Technologieplanung}},
  editor       = {{Gausemeier, Jürgen and Bauer, Wilhelm and Dumitrescu, Roman}},
  location     = {{Berlin}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Erfolgspotentiale für die Zukunft des Engineeringstandorts Deutschland – Ein Beitrag zum Advanced Systems Engineering}}},
  year         = {{2021}},
}

@article{26855,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Cyber-physical systems (CPS) are able the collect huge amounts of data about themselves, their users, and their environment during their usage phase. By feeding these usage data back into product planning, manufacturers can optimize their engineering and decision-making processes. Despite promising potentials, most manufacturers still do not analyze usage data within product planning. Also, research on usage data-driven product planning is scarce. Therefore, this paper aims to identify the main concepts, advantages, success factors and challenges of usage data-driven product planning. To answer the corresponding research questions, a comprehensive systematic literature review is conducted. From its results, a detailed description of usage data-driven product planning consisting of six main concepts is derived. Furthermore, taxonomies for the advantages, success factors and challenges of usage data-driven product planning are presented. The six main concepts and the three taxonomies allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.</jats:p>}},
  author       = {{Meyer, Maurice and Wiederkehr, Ingrid and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{3289--3298}},
  title        = {{{UNDERSTANDING USAGE DATA-DRIVEN PRODUCT PLANNING: A SYSTEMATIC LITERATURE REVIEW}}},
  doi          = {{10.1017/pds.2021.590}},
  year         = {{2021}},
}

@inproceedings{26857,
  author       = {{Meyer, Maurice and Hemkentokrax, Jan-Philipp and Koldewey, Christian and Dumitrescu, Roman and Tröster, Peter M. and Schlegel, Michael and Kling, Christopher L. and Rapp, Simon and Albers, Albert}},
  booktitle    = {{Vorausschau und Technologieplanung - 16. Symposium Vorausschau und Technologieplanung}},
  editor       = {{Gausemeier, Jürgen and Bauer, Wilhelm and Dumitrescu, Roman}},
  location     = {{Berlin}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Zukunftsrobuste Weiterentwicklung von Produktportfolios: Erkenntnisse und Handlungsbedarfe aus der Praxis}}},
  volume       = {{400}},
  year         = {{2021}},
}

@article{26858,
  author       = {{Meyer, Maurice and Panzner, Melina and Koldewey, Christian and Dumitrescu, Roman}},
  journal      = {{Procedia CIRP}},
  pages        = {{1179--1184}},
  title        = {{{Towards Identifying Data Analytics Use Cases in Product Planning}}},
  volume       = {{104}},
  year         = {{2021}},
}

@article{19864,
  author       = {{Meyer, Maurice and Frank, Maximilian and Massmann, Melina and Dumitrescu, Roman}},
  journal      = {{Proceedings of The 11th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2020)}},
  title        = {{{Research and Consulting in Data-Driven Strategic Product Planning}}},
  year         = {{2020}},
}

@article{19866,
  author       = {{Meyer, Maurice and Frank, Maximilian and Massmann, Melina and Dumitrescu, Roman}},
  journal      = {{Journal of Systemics, Cybernetics and Informatics}},
  number       = {{2}},
  pages        = {{55--61}},
  title        = {{{Research and Consulting in Data-Driven Strategic Product Planning}}},
  volume       = {{18}},
  year         = {{2020}},
}

@article{27096,
  author       = {{Koldewey, Christian and Meyer, Maurice and Stockbruegger, Patrick and Dumitrescu, Roman and Gausemeier, Jürgen}},
  journal      = {{Procedia CIRP (91)}},
  pages        = {{851--857}},
  title        = {{{Framework and Functionality Patterns for Smart Service Innovation}}},
  year         = {{2020}},
}

@article{20447,
  author       = {{Massmann, Melina and Meyer, Maurice and Frank, Maximilian and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}},
  journal      = {{Procedia CIRP}},
  number       = {{93}},
  pages        = {{234--239}},
  title        = {{{Method for data inventory and classification}}},
  year         = {{2020}},
}

@article{20448,
  author       = {{Meyer, Maurice and Frank, Maximilian and Massmann, Melina and Wendt, Niklas and Dumitrescu, Roman}},
  journal      = {{Procedia CIRP}},
  number       = {{93}},
  pages        = {{965--970}},
  title        = {{{Data-Driven Product Generation and Retrofit Planning}}},
  year         = {{2020}},
}

@article{20449,
  author       = {{Uhlmann, Eckart and Dumitrescu, Roman and Polte, Julian and Meyer, Maurice and Simsek, Deniz}},
  journal      = {{ wt Werkstattstechnik online }},
  number       = {{07-08}},
  pages        = {{532--535}},
  title        = {{{Datengetriebene Steigerung der Verfügbarkeit}}},
  volume       = {{110}},
  year         = {{2020}},
}

@article{20450,
  author       = {{Massmann, Melina and Meyer, Maurice and Frank, Maximilian and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}},
  journal      = {{Procedia Manufacturing}},
  title        = {{{Framework for Data Analytics in Data-Driven Product Planning}}},
  year         = {{2020}},
}

@article{18350,
  author       = {{Koldewey, Christian and Meyer, Maurice and Stockbrügger, Patrick and Dumitrescu, Roman and Gausemeier, Jürgen}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  number       = {{91}},
  pages        = {{851--857}},
  title        = {{{Framework and Functionality Patterns for Smart Service Innovation}}},
  doi          = {{10.1016/j.procir.2020.02.244}},
  year         = {{2020}},
}

@inproceedings{27103,
  abstract     = {{One of the notable drivers of the fourth industrial revolution is the collection of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data in conjunction with product hypotheses leads to promising potentials in strategic product planning. In this thesis paper, we postulate the need for data-driven product generation and retrofit planning as an interdisciplinary field of research. We define and analyze the key concepts and derive requirements in a structured way. Based on an exhaustive research of existing approaches, we structure open research questions and propose a roadmap in order to shape future research efforts.
}},
  author       = {{Massmann, Melina and Meyer, Maurice and Dumitrescu, Roman and von Enzberg, Sebastian and Frank, Maximilian and Koldewey, Christian and Kühn, Arno and Reinhold, Jannik}},
  booktitle    = {{Proceedings of the CIRP DESIGN}},
  publisher    = {{Scientific Technical Committee Design of the International Academy for Production Engineering (CIRP)}},
  title        = {{{Significance and Challenges of Data-driven Product Generation and Retrofit Planning}}},
  year         = {{2019}},
}

@article{17392,
  author       = {{Massmann, Melina and Meyer, Maurice and Dumitrescu, Roman and Enzberg, Sebastian von and Frank, Maximilian and Koldewey, Christian and Kühn, Arno and Reinhold, Jannik}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  pages        = {{992--997}},
  title        = {{{Significance and Challenges of Data-driven Product Generation and Retrofit Planning}}},
  doi          = {{10.1016/j.procir.2019.04.226}},
  year         = {{2019}},
}