Decision tree based demand forecasts for improving inventory performance

Demand forecasting with minimum error is the key to success in supply chain management. There is no dearth of techniques used for forecasting demand in retail sale. The advent of data mining systems gives rise to the use of business intelligence in various domains of retailing. The current paper makes an attempt to capture the knowledge of classification of the customers using decision tree as an input to the demand forecasting in retail sale. The paper suggests a model which has been used in retail sale for better forecasting of demands and improved performance of inventory in overall supply chain management. The proposed forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level.