Abstract
The aim of this paper is to provide strong support for superstores through a data-driven approach. By analyzing and fitting the sales history data of each category and individual product, this paper uses BP-neural network model to predict the sales volume and transforms the replenishment problem into a knapsack problem, so as to plan the future pricing strategy and replenishment volume of the superstore. This helps to optimize the category structure of the superstore, increase the profit margin, reduce the wastage rate, and improve the service quality.
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