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Success Factors for the Validation of Requirements for New Product Generations – A Case Study on Using Field Gathered Data

Published online by Cambridge University Press:  26 May 2022

S. Wagenmann ,
N. Bursac ,
S. Rapp  and
A. Albers
S. Wagenmann*
Affiliation:
Karlsruhe Institute of Technology, Germany
N. Bursac
Affiliation:
Karlsruhe Institute of Technology, Germany
S. Rapp
Affiliation:
Karlsruhe Institute of Technology, Germany
A. Albers
Affiliation:
Karlsruhe Institute of Technology, Germany
*
steffen.wagenmann@trumpf.com

Abstract

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This paper investigates which activities and success factors can be identified for the data-driven validation of functional requirements. For this purpose, a case study is conducted at a machine tool manufacturer. To validate functional requirements by analyzing data of reference products, these activities must be performed iteratively: basic work, interdisciplinary work, programming and check results. For the successful execution of data-driven validation, the success factors: data origin, acceptance, data quality, knowledge about data and combination of domain knowledge must be considered.

Keywords

industry 4.0digital twinproduct generation engineering (PGE)decision makinginternet of things (IoT)
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1805 - 1814
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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