A Retail Demand Forecasting Model Based on Data Mining Techniques


Islek I., Öğüdücü Ş.

24th IEEE International Symposium on Industrial Electronics (ISIE), Rio-De-Janeiro, Brezilya, 3 - 05 Haziran 2015, ss.55-60 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Rio-De-Janeiro
  • Basıldığı Ülke: Brezilya
  • Sayfa Sayıları: ss.55-60

Özet

This paper addresses the problem of forecasting various product demands of main distribution warehouses. Demand forecasting is the activity of building forecasting models to estimate the quantity of a product that customers will purchase. It is affected from numerously different factors such as warehouse region size, customer count, product type etc. When the number of the distribution warehouses and products increases, it becomes considerably hard to estimate the demand of customers. In this study, we provide an appropriate methodology for demand forecasting which is capable of overcoming the aforementioned limitations while providing a high estimation accuracy. The proposed methodology clusters similar warehouses according to their sale behavior using bipartite graph clustering. After that, hybrid forecasting phase which combines moving average model and Bayesian Network machine learning algorithm is applied. Our experimental results on real data set show that this approach considerably improves the forecasting performance.