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A Back Propagation Neural Network-Based Method for Intelligent Decision-Making

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  • Hao Zhang
  • Jia-Hui Mu
  • Abd E.I.-Baset Hassanien

Abstract

A shortage or backlog of inventory can easily occur due to the backward forecasting method typically used, which will affect the normal flow of funds in pharmacies. This paper proposes a replenishment decision model with back propagation neural network multivariate regression analysis methods. With the regular pattern between sales and individual variables, supplemented with the safety stock empirical formula, an accurate replenishment quantity can be obtained. In the case analysis, this paper takes the sales situation of a pharmacy as an example and tests the accuracy and stability of the model. The results show that the model has good prediction accuracy which can be introduced into the intelligent pharmacy system and used in the replenishment of the intelligent pharmacy to prevent overstocking or a shortage of stock, thus improving the financial situation, reducing the manpower burden of typical retail pharmacy, and helping residents buy medicines.

Suggested Citation

  • Hao Zhang & Jia-Hui Mu & Abd E.I.-Baset Hassanien, 2021. "A Back Propagation Neural Network-Based Method for Intelligent Decision-Making," Complexity, Hindawi, vol. 2021, pages 1-11, February.
    • Handle: RePEc:hin:complx:6610797
    DOI: 10.1155/2021/6610797
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    Cited by:

    1. Shahzad Zaheer & Nadeem Anjum & Saddam Hussain & Abeer D. Algarni & Jawaid Iqbal & Sami Bourouis & Syed Sajid Ullah, 2023. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model," Mathematics, MDPI, vol. 11(3), pages 1-24, January.

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