Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry

In order to obtain a better control of market trend and profit for the company, timely identification of sales is very important for businesses. Upward and downward trends in sales signify new market trends and understanding of sales trends is important for marketing as well as for customer retention. This research develops a hybrid model by integrating K-mean cluster and fuzzy neural network (KFNN) to forecast the future sales of a printed circuit board factory. Based on the K-mean clustering technique, the historical data can be classified into different clusters. The accuracy of the forecasted model can be further improved by referring the new data to be forecasted from a more focused region, i.e., a smaller region after clustering. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and input into the hybrid model for future monthly sales forecasted. The experimental results derived from the proposed model show the effectiveness of the hybrid model when compared with other approaches. (C) 2009 Elsevier B.V. All rights reserved.