Data Clustering and Fuzzy Neural Network for Sales Forecasting in Printed Circuit Board Industry

Reliable prediction of sales can improve the quality of business strategy. This research develops a hybrid model by integrating K-mean cluster and fuzzy back propagation network (KFBPN) to forecast the future sales of a printed circuit board factory. Based on the K-mean clustering technique, the historic data can be classified into different clusters, thus the noise of the original data can be reduced and a more homogeneous region can be established for a more accurate prediction. 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 forecasting. Experimental results show the effectiveness of the hybrid model when compared with other approaches