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SUPPORTING CHANGEABILITY QUANTIFICATION IN PRODUCT-SERVICE SYSTEMS VIA CLUSTERING ALGORITHM

Published online by Cambridge University Press:  19 June 2023

Raj Jiten Machchhar ,
Omsri Kumar Aeddula ,
Alessandro Bertoni ,
Johan Wall  and
Tobias Larsson
Raj Jiten Machchhar*
Affiliation:
Blekinge Institute of Technology
Omsri Kumar Aeddula
Affiliation:
Blekinge Institute of Technology
Alessandro Bertoni
Affiliation:
Blekinge Institute of Technology
Johan Wall
Affiliation:
Blekinge Institute of Technology
Tobias Larsson
Affiliation:
Blekinge Institute of Technology
*
Machchhar, Raj Jiten, Blekinge Institute of Technology, Sweden, raj.jiten.machchhar@bth.se

Abstract

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The design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry.

Keywords

Product-Service Systems (PSS)Systems Engineering (SE)Decision makingChangeability quantificationEarly design phases
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3225 - 3234
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), 2023. Published by Cambridge University Press

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