Title :
A retail demand forecasting model based on data mining techniques
Author :
İrem İşlek;Şule Gündüz Öğüdücü
Author_Institution :
Idea Teknoloji Ç
fDate :
6/1/2015 12:00:00 AM
Abstract :
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.
Keywords :
"Bipartite graph","Demand forecasting","Bayes methods","Supply chains","Data mining","Clustering algorithms"
Conference_Titel :
Industrial Electronics (ISIE), 2015 IEEE 24th International Symposium on
Electronic_ISBN :
2163-5145
DOI :
10.1109/ISIE.2015.7281443