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UNDERSTANDING USAGE DATA-DRIVEN PRODUCT PLANNING: A SYSTEMATIC LITERATURE REVIEW

Published online by Cambridge University Press:  27 July 2021

Maurice Meyer ,
Ingrid Wiederkehr ,
Christian Koldewey  and
Roman Dumitrescu
Maurice Meyer*
Affiliation:
Heinz Nixdorf Institute, University of Paderborn
Ingrid Wiederkehr
Affiliation:
Heinz Nixdorf Institute, University of Paderborn
Christian Koldewey
Affiliation:
Heinz Nixdorf Institute, University of Paderborn
Roman Dumitrescu
Affiliation:
Heinz Nixdorf Institute, University of Paderborn Fraunhofer Institute for Mechatronic Systems Design IEM
*
Meyer, Maurice, Heinz Nixdorf Institute, University of Paderborn, Advanced Systems Engineering, Germany, [email protected]

Abstract

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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.

Keywords

Data-Driven Product PlanningBig dataMachine learningEarly design phasesProduct Lifecycle Management (PLM)
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3289 - 3298
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), 2021. Published by Cambridge University Press

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