Diffusion Forecasting of Innovative Products Using an Improved Grey Model

Abstract

As market competition intensifies, most companies realize that they have to constantly develop new products to survive the competition. Though there is always a great risk involved with product development. The accurate anticipation of product diffusion will help reduce the risk of blind investment. In this study a comprehensive procedure for analyzing the diffusion of new product launching is proposed. The new procedure is comprised of two stages: (a) first the major factors that influence the diffusion of products most are identified using the grey relational analysis and (b) secondly an improved grey prediction model is then used to predict the product diffusion based on the selected factors. The improved grey prediction model, called the GMC model, uses convolution integration to promote the forecasting ability of the traditional GM model. The diffusion data of several product categories are examined. The results show that different major macroeconomic indices need to be used in the prediction model according to whether the goods are durable or non-durable. The inclusion of these macroeconomic indices in the GMC model can significantly improve the prediction accuracy. The proposed procedure can help companies improve their prediction ability and provide managers with more marketing information.