• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Scopus
  4. Developing a data analytics toolbox for data-driven product planning: A review and survey methodology
 
  • Details
  • Full
Options
2024
Journal Article
Title

Developing a data analytics toolbox for data-driven product planning: A review and survey methodology

Abstract
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.
Author(s)
Panzner, Melina
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Enzberg, Sebastian Von
Hochschule Magdeburg-Stendal
Dumitrescu, Roman
Paderborn University
Journal
Artificial Intelligence for Engineering Design Analysis and Manufacturing AIEDAM  
DOI
10.1017/S0890060424000209
Additional link
Full text
Language
English
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Keyword(s)
  • citizen data science

  • data analytics

  • data science

  • data-driven product planning

  • pipeline design

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024