Belief rule-based methodology for mapping consumer preferences and setting product targets
Rapid and accurate identification of consumer demands and systematic assessment of product quality are essential to success for new product development, in particular for fast moving consumer goods such as food and drink products. This paper reports an investigation into a belief rule-based (BRB) methodology for quality assessment, target setting and consumer preference prediction in retro-fit design of food and drink products. The BRB methodology can be used to represent the relationships between consumer preferences and product attributes, which are complicated and nonlinear. A BRB system can initially be established using expert knowledge and then optimally trained and validated using data generated from consumer or expert panel assessments or from tests and experiments. The established BRBs can then be used to predict the consumer acceptance of new products or set product target values in retro-fit design. The proposed BRB methodology is applied to the design of a lemonade drink product using real data provided by a sensory product manufacturer in the UK. The results show that the BRB methodology can be used to predict consumer preferences with high accuracy and to set optimal target values for product quality improvement.
quality assessment target setting optimization consumer preference modeling preference mapping belief rule-based system product design