원문정보
초록
영어
In order to meet the increasing daily demands of customers and reduce the unnecessary cost in retail stores as far as possible, the inventory management of retail stores becoming more and more important. However, because of various characteristics of demand in retail stores, the traditional demand forecasting technologies can’t work well. In this paper, we use the modified K-means clustering analysis to help determine the groups with different characteristics of demand. In addition, a demand forecasting model integrated BP neural networks and grey model is proposed to make the prediction more intelligent and general. The example illustrates that the proposed method for forecast is feasible by the comparative analysis between the predicted values and the actual values.
목차
1. Introduction
2. The Common Management Model for Commodities in Retail Stores
3. The Clustering Analysis of Commodities in Retail Stores
4. The Demand Forecasting Model Based on BP Neural Networks
5. Conclusion
Acknowledgements
References