Multiattribute decision support for eco-product form design

Eco-product design decision-making is often based on vaguely structured information because of the technical and natural attributes as well as inherently conflicting design objectives to be considered. To address this issue, this paper presents a design decision support system to translate consumers' perception or feeling of an eco-product into design elements, and help product designers understand consumers' perception. An experimental study on office chairs is conducted, which has identified 7 design elements and 27 representative office chairs as experimental samples for developing neural network (NN) models. NN models are applied to illustrate consumers' perception on designing eco-product with design element combinations. Fifteen visually perceivable eco-design value (EdV) attributes of eco-products are identified and categorized into aesthetic, functional, and environmental dimensions. A multiattribute decision making (MADM) model is used to generate an eco-design value (EdV) for each eco-product, on which eco-product form design preference ranking is based. It is demonstrated that the MADM model is applicable for environmental issues which are increasing in complexity. This study provides new insights in supporting eco-product form design decision making.