Study on the Inventory Forecasting in Supply Chains Based on Rough Set Theory and Improved BP Neural Network

  • Xuping Wang
  • Yan Shi
  • Junhu Ruan
  • Hongyan Shang
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 4)

Abstract

It has never stopped to study inventory management problems, and a variety of inventory control models have been proposed, but the existing models have their shortcomings and aren’t suitable to the inventory forecasting in supply chains. According to those shortcomings and the actual situation in supply chains, the paper combined rough set theory and BP neural network to analyze the inventory forecasting in supply chains. The introduction of rough sets cut down the input dimensions of BP neural network, and the neural network algorithm was improved by adding the momentum factor and applying adaptive learning rate. And, according to the inventory data of a manufacturing enterprise in Handan city, the paper proved the validity of the proposed model.

Keywords

supply chains inventory forecasting rough set theory BP neural network improved algorithm 

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Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Xuping Wang
    • 1
  • Yan Shi
    • 1
  • Junhu Ruan
    • 1
  • Hongyan Shang
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Huachiew Chalermprakiet UniversityBnagkokThailand

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