Development of novel design methodology for product mass customization based on human attributes and cognitive behaviours
The competition in the global market is accelerating rapidly because of less technological gap, matured manufacturing level, and various changing customer needs. Increasingly customers choose products in terms of experience desires, psychological desires and whether the products can reflect their values, in addition to the main product functions. Moreover, there are a large number of small and medium sized manufacturing companies in the developing countries. OEM (Original Equipment Manufacturer) and simple mass production cannot generate good value for these manufacture companies, and they have been seeking new opportunities to create higher value for their products/services and satisfy different needs of customers. Mass customization is one of the main business forms in the future, which can best meet the needs of individual customer, especially psychological needs. The key to mass customization is to provide enough modules to meet individual needs with a limited cost increase. The problem has been how to identify the real user needs and individual differences. The purpose of this research is to develop a sound design methodology based upon the current product design theories and practices for future product innovation and sustainable growth of small and medium sized manufacturing enterprises. The research focuses on the user-product cognitive behaviours and the relationship between human attributes and product features. Orthogonal experiment, eye tracking technology and artificial neural network have been successfully applied in this research. The research has developed a user needs hierarchy model and added value hierarchy model, and a robust theoretical basis to predict and evaluate (individual) user needs for product design. The research has further made the following contributions: 1) The relationship between human attributes and product features has been established, which can help designers understand the differences of various customer groups; 2) The different effects of various influence factors on people’s cognition and preference choice based on vision have been analysed and discussed; 3) A new method to identify, cluster, and combine common needs and personalized needs in early design stage for mass customization has been developed; 4) The research results can be reused in the future design of the same or similar kind of products.
Emotional design, Personalized design, Eye tracking, Neural network
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Brunel University School of Engineering and Design PhD Theses