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