Integrated Model for Product Quality Forecasting System Using Grey Theory and Neural Network

High-quality products are considered as one of the most important practices for achieving success. However, it is really hard to predict and forecast the product quality due to some undetermined parameters. In order to forecast product quality from various aspects, we propose an integrated model of utilizing grey theory and neural network. In this paper, the grey forecasting model for product quality is established by applying grey theory. Grey model is applied to compute an aggregated efficiency score based on the input and output data. Since quantitative factors are difficult to mathematically manipulate when forecasting the efficiency in neural network, a forecasting model is developed for product quality based on grey neural network model. In addition, analytical capability of the proposed method can reduce the number of training samples. In our case, this approach is demonstrated on a real and complete dataset of 36 samples for product quality. Finally, an example is given to explain the use and effectiveness of the proposed computational approach. As a result of this research, grey neural network can now be adequately applied to forecast the product quality.