Data-driven ordering and preventive transfer optimization method and system

Abstract

The invention relates to a data-driven ordering and preventive transfer optimization method and system. The method comprises the following steps: S1, fitting a random distribution function of consumer demands of a product in different regions and different sales stages according to historical orders and distribution data, and obtaining the expected demands of a first sales stage and a second sales stage; s2, based on the stock state of each distribution center at the end of the first sales stage and the expected demand of the second sales stage, establishing a preventive transfer strategy optimization model, and obtaining an optimal preventive transfer strategy at the end of the first sales stage; and S3, under different order levels, according to the final optimal preventive transfer strategy of the first sales stage, establishing an initial order quantity optimization model, and obtaining the initial optimal order quantity of each distribution center. According to the method provided by the invention, from the perspective of logistics service providers, inventory resource distribution of each region is optimized, accurate supply and demand matching is realized, high-quality logistics services are provided for upstream retailers, consumer demands are quickly responded, and the logistics service quality is improved.

Description

technical field [0001] The invention relates to the field of inventory management and logistics distribution of intelligent logistics supply chain, in particular to a data-driven ordering and preventive transshipment optimization method and system. Background technique [0002] With the vigorous development of e-commerce, consumers have unlimited purchasing options, but the enterprise's learning of product demand curve is incomplete. Therefore, when enterprises determine the inventory allocation strategy of each distribution center, they are faced with incomplete information. Usually, when they allocate limited inventory, they need to learn and mine to achieve the optimal goal. At present, the operation process of the e-commerce logistics supply chain is that the upstream seller outsources the logistics (including warehousing and distribution) to the logistics service provider. Requests and other information are sent to the logistics service provider, and then the logistics...

AI Technical Summary

Problems solved by technology

[0004] 1. The inventory strategy formulated by upstream sellers is unreasonable, and the supply and demand in various regions do not match

[0005] In the context of e-commerce, consumers can choose a variety of products online, and it is difficult for retailers to learn the consumption preferences of consumers in various regions and fully grasp the demand of consumers in various regions, so it is difficult to formulate reasonable inventory replenishment strategies. Create a supply-demand mismatch

[0006] 2. Upstream sellers do not know the real-time inventory situation of each distribution center, and preventive transshipment is difficult to implement

[0007] Since each seller has sales all over the country and stocks in multiple regions, after the seller outsources logistics services to logistics service providers, he rarely pays attention to the real-time inventory of distribution centers in various regions, and there will be inventory in distribution centers in some regions. Insufficient inventory and excess inventory in distribution centers in other regions

However, the seller does not have real-time inventory information, and cannot carry out scientific preventive inventory transfer

[0008] 3. The supply chain composed of upstream sellers and logistics service providers has low synergy efficiency

[0009] In terms of traditional inventory management and logistics distribution, the upstream seller decides the inventory strategy, and the logistics service provider completes the order distribution. Low

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