Mass customization and mass personalization meet at the crossroads of Industry 4.0: A case of augmented digital engineering.

  • Published In: Systems Engineering, 2023, v. 26, n. 6. P. 715 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Barata, João; Cardoso, Jorge C. S.; Cunha, Paulo Rupino 3 of 3

Abstract

After mass production and then mass customization, the time is almost ripe for mass personalization. The goal is to offer unique products designed for the needs of each customer. However, production in larger series of products also has its advantages, and the promise of "lot size one" is still far from being the norm in several sectors of the economy. As a result of an action research project in a small household ceramic producer, this paper explores the potential of a hybrid strategy. Augmented digital engineering is adopted to (1) ensure customer participation along the entire product design lifecycle, (2) maintain the benefits of modularization and low cost, (3) minimize the waste of time and materials during product design, and (4) seek a minimum trade‐off between customer desires and engineering strategy. For theory, our work describes Industry 4.0 technology's role in achieving individual customer interaction and value co‐creation in hybrid strategies of mass customization and mass personalization. For practice, we present an example of technological architecture to implement augmented digital engineering in Industry 4.0, accessible to scenarios of hand‐intensive work and creative design processes. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Systems Engineering. 2023/11, Vol. 26, Issue 6, p715
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:1098-1241
  • DOI:10.1002/sys.21682
  • Accession Number:173438167
  • Copyright Statement:Copyright of Systems Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.