Earticle

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

Abstract
 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

키워드

저자

• Xiaomin Zhu [ School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, PR China ]
• Xi Xiang [ School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, PR China ]
• Donghua Chen [ School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, PR China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605