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SIMULATION-BASED PERFORMANCE ANALYSIS FOR FUTURE ROBUST MODULAR PRODUCT ARCHITECTURES

Published online by Cambridge University Press:  27 July 2021

Florian M. Dambietz,
Erik Greve and
Dieter Krause
Florian M. Dambietz*
Affiliation:
Hamburg University of Technology
Erik Greve
Affiliation:
Hamburg University of Technology
Dieter Krause
Affiliation:
Hamburg University of Technology
*
Seiler, Florian Michael, Hamburg University of Technology, PKT, Germany, florian.seiler@tuhh.de

Abstract

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The increased demand for customer-adapted product solutions shows an increasing trend of product variety, leading to an increased internal variety and therefore -costs. The concept of modularization provides apossible solution to this challenge by developing modular kits. Nevertheless, modularization methods to not lead to one individual modular kit, but to several alternatives. The decision of which alternative to implement can be crucial to the applying companys succes. During this decision-making both customer- and company perspectives need to be taken into account. This contribution is to present a simulation-based approach to support the decision making by using a model-based configuration system. Furthermore, as classical decision-making processes are based upon historical data, future aspects are usually not taken into account. In order to counteract this situation, this contribution intends to simulate as well future aspects impacting the modular product architecture. In this case, the simulation is used in order to evaluate the individual performances of a Design-for-Variety product architecture as opposed to a Design-for-Future-Robustness by applying this method to the example of customer-individual laser machines.

Keywords

SimulationDesign for Future-RobustnessProduct architectureConfigurationConstraint modelling

Information

Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021, pp. 2671 - 2680
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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