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

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.

Automated summary

This study proposes a replenishment decision model based on back propagation neural network multivariate regression analysis, which can accurately predict the replenishment quantity in pharmacies. By applying this model in intelligent pharmacies, it can effectively prevent overstocking or shortage of inventory, thereby improving financial situation.