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A DATA-DRIVEN APPROACH TO USER-EXPERIENCE-FOCUSED MODEL-BASED ROADMAPPING FOR NEW PRODUCT PLANNING

Published online by Cambridge University Press:  27 July 2021

Ilia Iuskevich ,
Andreas-Makoto Hein ,
Kahina Amokrane-Ferka ,
Abdelkrim Doufene  and
Marija Jankovic
Ilia Iuskevich*
Affiliation:
Université Paris-Saclay, CentraleSupélec, Laboratoire Genie Industriel IRT SystemX
Andreas-Makoto Hein
Affiliation:
IRT SystemX
Kahina Amokrane-Ferka
Affiliation:
IRT SystemX
Abdelkrim Doufene
Affiliation:
IRT SystemX
Marija Jankovic
Affiliation:
Université Paris-Saclay, CentraleSupélec, Laboratoire Genie Industriel
*
Iuskevich, Ilia, Université Paris-Saclay, Le Laboratoire Génie Industriel CentraleSupélec, France, ilya.yuskevich@gmail.com

Abstract

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User experience (UX) focused business needs to survive and plan its new product development (NPD) activities in a highly turbulent environment. The latter is a function of volatile UX and technology trends, competition, unpredictable events, and user needs uncertainty. To address this problem, the concept of design roadmapping has been proposed in the literature. It was argued that tools built on the idea of design roadmapping have to be very flexible and data-driven (i.e., be able to receive feedback from users in an iterative manner). At the same time, a model-based approach to roadmapping has emerged, promising to achieve such flexibility. In this work, we propose to incorporate design roadmapping to model-based roadmapping and integrate it with various user testing approaches into a single tool to support a flexible data-driven NPD planning process.

Keywords

Organisation of product developmentProduct modelling / modelsNew product developmentTechnology RoadmappingStrategic management
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 61 - 70
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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