DOI: 10.14704/nq.2018.16.6.1608

Inventory Prediction Based on Backpropagation Neural Network

Lichuan Gu, Yueyue Han, Chengji Wang, Guiyang Shu, Juanjuan Feng, Chao Wang

Abstract


This paper aims to develop an effective way to predict the inventory demand of agricultural materials. Focusing on the demand of agricultural pesticide, the author introduced the backpropagation neural network (BPNN) and optimized the BPNN inventory prediction model by multiple interpolation method. In this way, a novel inventory prediction strategy was created, with the national macro policy, the pest and disease resistance, the market role and other factors as part of the BPNN. For the lack of input samples, the multiple interpolation method was adopted to restore the missing data. Then, the replacement values were combined in different ways to reveal the variation pattern of prediction error, making it possible to predict the exact pesticide demand. The research provides the decision support for inventory management in agricultural materials enterprises.

Keywords


Agricultural Materials, Inventory Prediction, Backpropagation Neural Network (BPNN), Multiple Interpolation

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