---
_id: '58474'
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>"
article_number: e18
author:
- first_name: Melina
  full_name: Panzner, Melina
  last_name: Panzner
- first_name: Sebastian
  full_name: von Enzberg, Sebastian
  last_name: von Enzberg
- first_name: Roman
  full_name: Dumitrescu, Roman
  id: '16190'
  last_name: Dumitrescu
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>'
  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>'
  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} }'
  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>.'
  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>.'
  short: M. Panzner, S. von Enzberg, R. Dumitrescu, Artificial Intelligence for Engineering
    Design, Analysis and Manufacturing 38 (2024).
date_created: 2025-01-31T14:24:26Z
date_updated: 2025-01-31T14:24:45Z
department:
- _id: '563'
doi: 10.1017/s0890060424000209
intvolume: '        38'
language:
- iso: eng
publication: Artificial Intelligence for Engineering Design, Analysis and Manufacturing
publication_identifier:
  issn:
  - 0890-0604
  - 1469-1760
publication_status: published
publisher: Cambridge University Press (CUP)
status: public
title: 'Developing a data analytics toolbox for data-driven product planning: a review
  and survey methodology'
type: journal_article
user_id: '15782'
volume: 38
year: '2024'
...
---
_id: '30193'
abstract:
- lang: eng
  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.
article_number: e14
author:
- first_name: Maurice
  full_name: Meyer, Maurice
  id: '77201'
  last_name: Meyer
  orcid: 0000-0003-0606-7321
- first_name: Timm
  full_name: Fichtler, Timm
  id: '66731'
  last_name: Fichtler
- first_name: Christian
  full_name: Koldewey, Christian
  id: '43136'
  last_name: Koldewey
  orcid: https://orcid.org/0000-0001-7992-6399
- first_name: Roman
  full_name: Dumitrescu, Roman
  id: '16190'
  last_name: Dumitrescu
citation:
  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>
  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>
  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}
    }'
  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>.
  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>.'
  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>.
  short: M. Meyer, T. Fichtler, C. Koldewey, R. Dumitrescu, Artificial Intelligence
    for Engineering Design, Analysis and Manufacturing 36 (2022).
date_created: 2022-03-02T19:23:18Z
date_updated: 2022-10-19T12:29:54Z
department:
- _id: '563'
doi: 10.1017/s0890060421000408
intvolume: '        36'
keyword:
- Artificial Intelligence
- Industrial and Manufacturing Engineering
language:
- iso: eng
publication: Artificial Intelligence for Engineering Design, Analysis and Manufacturing
publication_identifier:
  issn:
  - 0890-0604
  - 1469-1760
publication_status: published
publisher: Cambridge University Press (CUP)
status: public
title: Potentials and challenges of analyzing use phase data in product planning of
  manufacturing companies
type: journal_article
user_id: '15782'
volume: 36
year: '2022'
...