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ARTIFICIAL NEURAL NETWORKS SUPPORTING CAUSE AND EFFECT STUDIES IN PRODUCT-SERVICE SYSTEM DEVELOPMENT

Abstract A data analysis method based on artificial neural networks aiming to support cause-and-effect analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability […]

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The Model-Driven Decision Arena: Augmented Decision-Making for Product-Service Systems Design

Abstract The shift towards Product-Service Systems (PSS) stresses the need to embed new and unique capabilities in Decision Support Systems, with the aim of helping the engineering team in handling the pool of information and knowledge available during decision events. Emerging from a multiple case study in the Swedish manufacturing industry, this paper describes the […]

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A model-driven decision arena: Augmenting decision making in early design

ABSTRACT A wide variety of expert competencies, transcending traditional disciplines, are needed to foresee and evaluate the impact of decisions in the conceptual phase of engineering design. Where this previously was a trade-off regarding design and development of the pure physical artefact it is now a complex ambiguity game involving all disciplines touching a solution […]

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