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DATA-DRIVEN DESIGN AUTOMATION FOR PRODUCT-SERVICE SYSTEMS DESIGN: FRAMEWORK AND LESSONS LEARNED FROM EMPIRICAL STUDIES

Published online by Cambridge University Press:  27 July 2021

Raj Jiten Machchhar  and
Alessandro Bertoni
Raj Jiten Machchhar*
Affiliation:
Blekinge Institute of Technology
Alessandro Bertoni
Affiliation:
Blekinge Institute of Technology
*
Machchhar, Raj JIten, Blekinge Institute of Technology, Mechanical Engineering, Sweden, raj.jiten.machchhar@bth.se

Abstract

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The digitalization era has brought about unprecedented challenges for the manufacturing industries, pushing them to deliver solutions that encompass both product and service-related dimensions, known as Product-service Systems. This paper presents a number of lessons learned in the process of integrating the analysis of operational data as decision support in engineering design based on the empirical studies from two Swedish manufacturing companies operating in the construction machinery sector. The paper highlights the need to consider a five-dimensional perspective when collecting and analyzing data, encompassing data from the product, the service, the environment, the infrastructure, and the humans involved. Finally, a conceptual framework for data-driven design automation of Product-service Systems is proposed by leveraging the use of these data, introducing the concept of a Product-Service System Configurator as an enabler of design automation. The implementation of the proposed framework on multiple case studies in different industrial contexts shall be considered as the next step of the research.

Keywords

Product-Service Systems (PSS)Data-driven DesignDesign AutomationSystems Engineering (SE)Decision making

Information

Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 841 - 850
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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