An Intelligent Forecasting Model for Commodities in Retail Stores

Donghua Chen (), Xiaomin Zhu (), Runtong Zhang () and Shen Haikuo ()
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Donghua Chen: Beijing Jiaotong University
Xiaomin Zhu: Beijing Jiaotong University
Runtong Zhang: Beijing Jiaotong University
Shen Haikuo: Beijing Jiaotong University

A chapter in LISS 2014, 2015, pp 495-499 from Springer

Abstract: Abstract The inventory management is a key to meet the increasing daily demands of customers and reduce the unnecessary cost in retail stores. However, because of various characteristics of demands in retail stores, the traditional demand forecasting technologies don’t work well. In this paper, we use the modified K-means clustering analysis and a demand forecasting model with BP neural networks and grey model is proposed to make the prediction more intelligent and general. Making the comparative analysis between the predicted values and the actual values, the superiority of the proposed model is proved.

Keywords: Retail stores; Demand forecasting; Clustering analysis; BP neural network (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-43871-8_72

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DOI: 10.1007/978-3-662-43871-8_72

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