Warehouse replenishment prediction method and device, equipment and storage medium

Abstract

The invention discloses a warehouse replenishment prediction method and device, equipment and a storage medium, and the method comprises the steps: responding to a replenishment prediction request signal of a warehouse terminal of a supply warehouse, and querying shop groups of all logistics order related shops of the supply warehouse; inputting historical data of the purchase orders of the shop groups in the set observation period into an order prediction model to enable the order prediction model to output demand prediction data of the shop groups; calculating purchase weight coefficients of the supply warehouses relative to the shop groups according to historical data of logistics orders of the supply warehouses and purchase orders of the corresponding shop groups; and according to the purchase weight coefficient and the demand prediction data of the shop groups, calculating the replenishment prediction quantity of the supply warehouse. The method and device have the beneficial effects that the predicted quantity of replenishment in the future is generated by predicting the purchase orders of the shops corresponding to the supply warehouse, so that the supply warehouse is helped to replenish commodities more intelligently and accurately.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular, to a warehouse replenishment forecasting method, device, equipment and storage medium. Background technique [0002] Such as figure 1 As shown, in the related technology, the orders of multiple stores are collected through the Internet platform, and then unified procurement and logistics pick-up are carried out from the supplier, and then the carrier vehicle delivers to the corresponding store according to the purchase order. , in order to reduce the storage cost of stores such as convenience stores and improve the flexibility of procurement. [0003] Due to the relatively random purchase and storage behavior of stores, and after the e-commerce platform assembles orders according to the purchase orders, the logistics orders received by the supply warehouse are also relatively scattered, which makes it often impossible for the supply warehouse to make effective repl...

AI Technical Summary

Problems solved by technology

[0003] Due to the relatively random purchase and storage behavior of the store, and after the e-commerce platform assembles orders according to the purchase order, the logistics orders received by the supply warehouse are also relatively scattered, which makes it often impossible for the supply warehouse to make effective replenishment forecasts based on its own inventory situation.

Images