An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: Case study of printed circuit board

Success in forecasting and analyzing sales for given goods or services can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore, reliable prediction of sales becomes a very important task. This article presents a novel sales forecasting approach by the integration of genetic fuzzy systems (GFS) and data clustering to construct a sales forecasting expert system. At first, all records of data are categorized into k clusters by using the K-means model. Then, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K-means genetic fuzzy system (KGFS) we apply it on a printed circuit board (PCB) sales forecasting problem which has been used as the case in different studies. We compare the performance of an extracted expert system with previous sales forecasting methods using mean absolute percentage error (MAPE) and root mean square error (RMSE). Experimental results show that the proposed approach outperforms the other previous approaches.

Research highlights
► We present a novel sales forecasting approach by integration of Genetic Fuzzy Systems (GFS) and Data Clustering.
► We use the K-means model to construct the K-means genetic fuzzy system (KGFS).
► We evaluate the performance of the developed expert system by Printed Circuit Board (PCB) sales forecasting problem.
► We compare the performance of presented KGFS against the previous sales forecasting methods and show the results.