Inventory replenishment method and device and electronic equipment

Abstract

The embodiment of the invention relates to the field of e-commerce logistics, and discloses an inventory replenishment method and device and electronic equipment. The invention relates to an inventory replenishment method. The method comprises the following steps: obtaining a predicted sales volume in a preset time period; determining an inventory upper limit and an inventory lower limit according to the historical sales volume, the current inventory minimum available duration and the predicted sales volume; determining a replenishment quantity according to the historical sales volume, the current inventory, the upper limit of the inventory and the lower limit of the inventory in response to the current inventory being smaller than the upper limit of the inventory; and generating a replenishment order in response to the fact that the replenishment quantity exceeds a preset value. The invention further relates to an inventory replenishment device and electronic equipment. According to some embodiments, the replenishment amount is accurately determined, the replenishment order is automatically generated, and the economic benefit is effectively improved.

Description

technical field [0001] Embodiments of the present invention relate to the field of e-commerce logistics, and in particular, to a method, device and electronic equipment for inventory replenishment. Background technique [0002] The traditional inventory management model is a push-type replenishment system, based on a single enterprise's local optimization and implementation control decentralized management system, which lacks cross-departmental cooperation and coordination. Traditional replenishment decisions are based on demand forecasts rather than sales implementation demand data. Due to the "bullwhip" effect of the supply chain and forecast errors, corporate inventory costs increase and cost efficiency decreases. Cost changes make inventory optimization more difficult, and because replenishment decisions are made based on some cost parameters and through mathematical models, if the cost parameters are uncertain, it is difficult for the decision-making algorithm to obtain...

AI Technical Summary

Problems solved by technology

Traditional replenishment decisions are based on demand forecasts rather than sales implementation demand data. Due to the "bullwhip" effect of the supply chain and forecast errors, corporate inventory costs increase and cost efficiency decreases

Cost changes make inventory optimization more difficult, and because replenishment decisions are made based on some cost parameters and through mathematical models, if the cost parameters are uncertain, it is difficult for the decision-making algorithm to obtain accurate data

In addition, the inventors of the present application also found that although insufficient replenishment will limit the sales volume, frequent replenishment will also increase the logistics cost of replenishment and the cost of storage

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