The high maturity of the merchandise and the openness of the marketplace have changed the types of consumptions. When enterprises have to face the market fluctuation more than they expect, some forecasting tools might be very useful for enterprises to strengthen their responsiveness. In light of this, enterprises have to provide a variety of products to satisfy different types of customer needs. Quality function deployment (QFD) is a customer-driven analytical tool, which can be applied to capture different types of dynamic customer needs based upon its structural deployment. Besides, with the uncertainties of the marketplace, the short-term information might be extremely useful to determine management policy for enterprises. In this case, grey prediction (GP) model can be applied with only four data sets to analyze dynamic trends in the near future as well as to strengthen the responsiveness of the enterprises. Based on the above discussions, this research has extended the concept of basic QFD along with GP model with few data sets to establish a dynamic forecasting model to analyze the future trends. Moreover, an example is illustrated in detail to discuss how this proposed model works under a variety of conditions. The results generated by GP model and other statistical methods are compared. The advantages of this proposed model include the high efficiency and structured deployments in customer needs and technical measures by reducing a lot of mathematical formulations to simplify the complexity and improve the forecasting quality. This proposed model has applied QFD as a research basis by integrating grey prediction model to analyze dynamic customer needs to replace the static customer information. Even when the deployment conditions have been changed, the feasibility of this proposed model is not going to be affected.
