CN111126903A - Replenishment method, device and system - Google Patents
Replenishment method, device and system Download PDFInfo
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- CN111126903A CN111126903A CN201911289591.8A CN201911289591A CN111126903A CN 111126903 A CN111126903 A CN 111126903A CN 201911289591 A CN201911289591 A CN 201911289591A CN 111126903 A CN111126903 A CN 111126903A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
Abstract
The embodiment of the application discloses a replenishment method, a replenishment device and a replenishment system, wherein the method comprises the following steps: determining the safety stock days of a specific commodity in the sorting area and the near M-day average daily delivery quantity of the specific commodity; determining the safe inventory of the specific commodity in the sorting area, wherein the safe inventory is equal to the product of the safe inventory days of the specific commodity and the near M balance average daily delivery amount; and monitoring whether the actual inventory of the specific commodity in the sorting area is lower than the safe inventory of the specific commodity, and if so, performing replenishment operation. The problem that the replenishment is not timely that prior art scheme caused can be solved in this application.
Description
Technical Field
The invention relates to the field of logistics warehouse management, in particular to a replenishment method and a replenishment system.
Background
With the development of the e-commerce industry in China, the requirement for commodity storage is higher and higher. In order to improve the storage capacity and the picking efficiency of the warehouse, more and more warehouses select a storage mode of storage and picking separation, namely, a working area in the warehouse is divided into a storage area and a picking area. The storage area adopts the intensive storage mode to promote the storage capacity and the utilization efficiency of the warehouse. The picking zone uses either in-situ pallets or light shelves for picking. The picking operation of the commodities is performed in the picking area.
Although the storage mode of storage and picking separation has obvious advantages compared with the traditional storage mode of storage and picking integration, the operation personnel is required to replenish the commodities from the storage area to the picking area in time so as to meet the delivery demand of the picking area. If the goods are not supplemented timely, the normal operation of the picking operation is influenced.
The traditional replenishment operation mostly adopts a manual determination mode, and operators determine which commodities need replenishment and the replenishment amount according to experience, and pick and replenish goods from where and where. With the increasing of the types and the number of the stored goods in the warehouse, the operation mode is difficult to meet the operation requirements of modern warehousing.
For example, the problems of untimely replenishment and unreasonable replenishment quantity are easily caused by unreasonable conventional replenishment operation. Wherein the untimely replenishment can influence the normal operation of the picking operation. The improper replenishment quantity affects subsequent operations, for example, if the replenishment quantity is too large, the storage capacity of the picking area is difficult to meet the requirement, and if the replenishment quantity is too small, the replenishment frequency is increased.
In addition, the traditional replenishment operation depends on the experience of people, is not reasonable enough when selecting the destination bin, and easily causes the problem of low bin capacity utilization rate of the sorting area.
Disclosure of Invention
The invention aims to provide a replenishment method, a replenishment device and a replenishment system, which can solve the problem of untimely replenishment caused by the prior art.
The invention discloses a replenishment method in a first aspect, which comprises the following steps:
determining the safety stock days of a specific commodity in the sorting area and the near M-day average daily delivery quantity of the specific commodity;
determining the safe inventory of the specific commodity in the sorting area, wherein the safe inventory is equal to the product of the safe inventory days of the specific commodity and the near M balance average daily delivery amount;
and monitoring whether the actual inventory of the specific commodity in the sorting area is lower than the safe inventory of the specific commodity, and if so, performing replenishment operation.
Preferably, the number of safe stock days for a particular item in the pick zone is determined based on α and β, where α is the number of times the particular item is out of stock in the pick zone for the next N days and β is the number of times the particular item is out of stock minus the target number of times the particular item is filled for the next N days.
Preferably, the number of days of safety stock for the particular merchandise is determined by an iterative formula of initial number of days of safety stock + k1 × α -k2 × β.
Preferably, the approximately N days is approximately 30 days.
Preferably, the target replenishment frequency is 2.
Preferably, the performing the restocking operation includes: determining the replenishment quantity of the specific commodity and replenishing the specific commodity in the picking area, wherein the replenishment quantity of the specific commodity is equal to the predicted delivery quantity of the specific commodity in the future L days minus the actual stock quantity of the specific commodity in the picking area.
Preferably, the predicted delivery volume of the specific commodity in the future L days is as follows:
and predicting the delivery volume of the specific commodity in the future L days by adopting a delivery volume prediction model based on the commodity description data, the user behavior data, the promotion data, the time data, the historical sales data, the weather data and/or the price data of the specific commodity.
Preferably, the performing replenishment operation further comprises: based on the bin utilization maximization objective, a restocking destination bin for the particular item at the picking zone is determined.
Preferably, said determining a restocking destination position of the particular item at the picking zone based on the position utilization maximization objective comprises:
and calculating the replenishment destination bin which meets the maximization of bin utilization rate by using the Hungarian algorithm.
The second aspect of the present invention also discloses a replenishment device, comprising:
the safety stock days determining unit is used for determining the safety stock days of the specific goods in the sorting area;
an average daily shipment amount determining unit for determining an average daily shipment amount of the specific commodity in the last M days;
the safety stock determining unit is used for determining the safety stock of the specific commodity in the sorting area, and the safety stock is equal to the product of the number of safety stock days of the specific commodity and the average daily delivery amount of the near M balance;
and the monitoring unit is used for monitoring whether the actual stock of the specific commodity in the sorting area is lower than the safe stock of the specific commodity, and if so, the replenishment operation is carried out.
The third aspect of the present invention also discloses a computer system, comprising:
one or more processors; and
a memory associated with the one or more processors for storing program instructions that, when read and executed by the one or more processors, perform the method as described above.
According to the specific embodiments provided herein, the present application discloses the following technical effects:
the method comprises the steps of determining the safety stock days of a specific commodity in a sorting area and the near M-day average daily delivery quantity of the specific commodity; determining the safe inventory of the specific commodity in the sorting area, wherein the safe inventory is equal to the product of the safe inventory days of the specific commodity and the near M balance average daily delivery amount; and monitoring whether the actual inventory of the specific commodity in the sorting area is lower than the safe inventory of the specific commodity, and if so, performing replenishment operation, thereby solving the problem of untimely replenishment caused by the prior technical scheme.
Of course, it is not necessary for any product to achieve all of the above-described advantages at the same time for the practice of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a block diagram of the system of the present application;
FIG. 2 is a flow chart of the method of the present application;
FIG. 3 is a view showing the structure of the apparatus of the present application;
FIG. 4 is a diagram of the computer system architecture of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived from the embodiments given herein by a person of ordinary skill in the art are intended to be within the scope of the present disclosure.
Fig. 1 shows a schematic diagram of a system architecture to which the present invention is applied. The system comprises a repository server 101, a Hive server cluster 102 and an algorithm engine server 103. The warehouse server 101 is used for recording the inventory distribution of the commodities in the warehouse area and the delivery information of each commodity, and transmitting the data to the Hive server cluster 102. The Hive server cluster 102 collects various data that the subsequent algorithm engine server 103 forecasts need, including various data that the warehouse server 101 transmits to it. And the algorithm engine server 103 performs analysis and calculation according to the data to judge whether to replenish goods. How to perform analysis calculation using the data to make a judgment of whether to replenish the stock will be described below.
Fig. 2 shows a flow chart of a replenishment method provided by the present invention. Referring to fig. 2, the method comprises the steps of:
step S201: the safe-stock days and the average daily shipment of the particular items in the picking zone for the next M days are determined.
The daily delivery task details of each warehouse can be extracted from the warehouse server, the daily delivery quantity of each commodity can be calculated, and finally the average daily delivery quantity of the specific commodity in the last M days can be calculated. And M can be 3, the average daily delivery volume in the near M days is as follows:
wherein M is1Yesterday shipment for the particular commodity, M2The amount of shipment of the specific commodity on the previous day, M3The shipment volume of the particular commodity on the third day before the particular commodity.
The determination of the number of days for safe storage may be based on α and β, with α being the number of times the particular item is out of stock in the picking area on a near N day, β being the number of times the particular item is out of stock minus the target number of times the particular item is filled on a near N day, N may be 30, and the target number of times the particular item is filled on a near N day may be 2.
The optimal number of safe inventory days for each commodity can be obtained by self-evolution through the algorithm continually iterating during the application process. The optimal number of days of safe stock for a particular good may be continually evolved, for example, by the following iterative formula:
the best safety stock days is the initial safety stock days + k1×α-k2×β
When N is 30 and the number of times of the specific replenishment within N days is 2, α is the number of times of the specific commodity in the picking area in the last 30 days, β is the number of times of the specific commodity in the last 30 days minus 2, k1 and k2 are preset coefficients larger than zero, the value of k1 can be set to 0.1, and the value of k2 can be set to 0.1.
In practical application, initial step sizes of iteration of k1 and k2 can be set, and values of k1 and k2 are adjusted according to the convergence result of the number of days of the best safety stock. If the convergence effect fluctuates greatly, the value can be reduced, and if the convergence is slow, the value can be increased. Through simulation of historical data, the effect is better when k1 and k2 both take 0.1.
It should be noted that, when the optimal number of days of safety stock is optimized by using the iterative formula, if the situation that the frequency of replenishment of the near N days minus the target frequency of replenishment of the near N days is less than zero occurs, β is made zero, so that the design is such that the frequency of replenishment of the near N days is better than the target frequency of replenishment in consideration of the situationOf course, there is no need to introduce a product term associated with β for the time being (i.e., -k)2X β ") is corrected.
Step S202: and determining the safety stock of the specific commodity in the sorting area, wherein the safety stock is equal to the product of the safety stock days of the specific commodity and the average daily delivery amount of the near M days.
The ideal safe inventory can satisfy: the inventory level can basically achieve two aims that the specific commodity is not subjected to emergency replenishment and the replenishment frequency is reasonable. The number of days of safety stock has already been determined for the target in step S201, and therefore the average daily shipment of the specific commodity multiplied by the number of days of safety stock for nearly M days largely coincides with the ideal safety stock amount, for which the product of the number of days of safety stock of the specific commodity and the average daily shipment for nearly M days is adopted as a standard in determining the safety stock amount of the specific commodity in step S202.
When M is taken to be 3, the calculation formula of the safety stock quantity may be as follows:
wherein M is1Yesterday shipment for the particular commodity, M2The amount of shipment of the specific commodity on the previous day, M3The shipment volume of the particular commodity on the third day before the particular commodity.
Step S203: and monitoring whether the actual inventory of the specific commodity in the sorting area is lower than the safe inventory of the specific commodity, and if so, performing replenishment operation.
As described in step S202, the safe stock can achieve two objectives that the specific commodity is not subject to emergency replenishment and the replenishment frequency is reasonable, and when the actual stock of the specific commodity is lower than the safe stock of the specific commodity, the specific commodity in the picking area needs to be replenished.
Therefore, the starting of the replenishment operation is not judged manually at will, but is compared by using a more reasonable safe stock as a standard, and the replenishment time setting can ensure that the commodity stock in the sorting area can meet the delivery requirement of the sorting area and control the replenishment frequency of the sorting area within a reasonable range.
One problem faced by the process of performing the replenishment operation in step S203 is how to determine the replenishment quantity. The restocking operation may thus include determining the restocking amount for the particular item and restocking the particular item at the picking zone.
The replenishment quantity of the specific commodity is equal to the predicted delivery quantity of the specific commodity in the future L days minus the actual stock quantity of the specific commodity in the sorting area.
The value range of L in the application can be set to 1-30.
The process of predicting the delivery volume of the specific commodity in the future L days can be based on a delivery volume prediction model, and the prediction process can use commodity description data, user behavior data, promotion data, time data, historical sales data, weather data, price data and the like of the specific commodity.
The article description data may include: the first-level classification, the second-level classification and the third-level classification of the commodities. The user behavior data may include: the daily browsing amount, browsing number and purchasing number of the commodities. The promotional data may include: number of participating promotional events, amount of tickets sent, amount of tickets used. The time data may include: whether to rest a day, whether to be holiday, whether to promote a day greatly, and whether to promote a grade greatly. The historical sales data may include: yesterday sales, antecedent sales, big antecedent sales, sales of the last week, sales of the last two weeks, and sales of the last month. The weather data may include: temperature, weather category. The price data may include: yesterday price, this week average price. Meanwhile, according to the characteristics of warehouse operation, specific characteristic data in the logistics field can be introduced, wherein the characteristic data comprises yesterday delivery volume, previous-day delivery volume, last-week delivery volume, last-two-week delivery volume, last-month delivery volume, current stock and thirty-day average stock of commodities.
The shipment volume prediction model may employ a machine learning model RF model.
In the training process of the shipment quantity prediction model, the problem of insufficient training data can be met in some cases, and the training data can be segmented and recombined in a window-cutting rolling mode, so that more historical data are constructed, and the problem of insufficient model training is solved.
When the replenishment quantity of a specific commodity is determined, the replenished commodity needs to be put into a destination bin during replenishment operation. The method for determining the destination bin is determined by adopting human experience in the prior art, and the method is easy to cause waste of bin space. To address this problem, in a preferred embodiment of the present invention, a restocking destination bin for the particular item at the picking zone is determined based on a bin utilization maximization objective. In the concrete implementation, a Hungarian algorithm can be utilized to calculate the replenishment destination position meeting the maximization of the position utilization rate. Namely:
Max∑Cij*xij
xij＝0or1
wherein: cijAnd representing the bin capacity utilization rate of the goods i to be restocked after being matched with the available bin j. x is the number ofijIs the result of the matching of the ith commodity with the jth bin, xijA 1 indicates a successful match.
Therefore, the selection of the freight destination position is based on the principle of maximizing the utilization rate of the bin capacity of the sorting area after the freight is replenished, and the storage capacity of the sorting area can be fully utilized.
It should be noted that the candidate object of the destination bin may be defined as a bin whose bin capacity utilization rate is lower than a certain threshold (e.g. 50%), which may ensure that the bin with lower bin capacity utilization rate participates in the allocation of the destination bin, and may maximally ensure that the utilization rates of the bins are balanced.
As shown in fig. 3, another aspect of the present invention further provides a replenishment device, including:
a number of safety stock days determination unit 31 for determining the number of safety stock days for a specific product in the picking zone.
Preferably, the number-of-days-for-safety-stock determination unit 31 is configured to determine the number of days-for-safety-stock of the specific item in the picking zone based on α and β, where α is the number of times that the specific item is out of stock in the picking zone for the last N days, and β is the number of times that the specific item is out of stock minus the target number of times that the specific item is filled for the last N days.
The method can be determined by an iterative formula, wherein the iterative formula is initial safety stock days + K1 × α -K2 × β, the last N days are nearly 30 days, and the target replenishment times are 2.
An average daily shipment determination unit 32 for determining the average daily shipment for the specific commodity for the last M days.
A safety stock determining unit 33 for determining the safety stock of the specific commodity in the sorting area, wherein the safety stock is equal to the product of the number of safety stock days of the specific commodity and the recent average daily shipment.
And the monitoring unit 34 is used for monitoring whether the actual stock of the specific commodity in the sorting area is lower than the safe stock of the specific commodity, and if so, the replenishment operation is carried out.
The monitoring unit 34 is specifically configured to determine a replenishment quantity of the specific commodity and replenish the specific commodity in the picking area, where the replenishment quantity of the specific commodity is equal to the predicted delivery quantity of the specific commodity in the future L days minus the actual inventory quantity of the specific commodity in the picking area.
And predicting the delivery volume of the specific commodity in the future L days by adopting a delivery volume prediction model based on commodity description data, user behavior data, promotion data, time data, historical sales data, weather data and/or price data of the specific commodity.
The monitoring unit 34 is further configured to determine a replenishment destination bin for the specific item in the picking zone based on the bin utilization maximization objective. Specifically, a replenishment destination position which meets the maximization of position utilization rate is calculated by using a Hungarian algorithm.
The invention also discloses a computer system, comprising:
one or more processors; and
a memory associated with the one or more processors for storing program instructions that, when read and executed by the one or more processors, perform the method as described above.
Fig. 4 illustrates an architecture of a computer system, which may include, in particular, a processor 1510, a video display adapter 1511, a disk drive 1512, an input/output interface 1513, a network interface 1514, and a memory 1520. The processor 1510, video display adapter 1511, disk drive 1512, input/output interface 1513, network interface 1514, and memory 1520 may be communicatively coupled via a communication bus 1530.
The processor 1510 may be implemented by a general CPU (central processing unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute related programs to implement the technical solution provided by the present application.
The Memory 1520 may be implemented in the form of a ROM (Read only Memory), a RAM (random access Memory), a static storage device, a dynamic storage device, or the like. The memory 1520 may store an operating system 1521 for controlling the operation of the computer system 1500, a Basic Input Output System (BIOS) for controlling low-level operations of the computer system 1500. In addition, a web browser 1523, a data storage management system 1524, an icon font processing system 1525, and the like can also be stored. The icon font processing system 1525 may be an application program that implements the operations of the foregoing steps in this embodiment of the application. In summary, when the technical solution provided by the present application is implemented by software or firmware, the relevant program codes are stored in the memory 1520 and called for execution by the processor 1510.
The input/output interface 1513 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The network interface 1514 is used to connect a communication module (not shown) to enable the device to communicatively interact with other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
The bus 1530 includes a path to transfer information between the various components of the device, such as the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, and the memory 1520.
In addition, the computer system 1500 may also obtain information of specific extraction conditions from the virtual resource object extraction condition information database 1541 for performing condition judgment, and the like.
It should be noted that although the above devices only show the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, the memory 1520, the bus 1530, etc., in a specific implementation, the devices may also include other components necessary for proper operation. Furthermore, it will be understood by those skilled in the art that the apparatus described above may also include only the components necessary to implement the solution of the present application, and not necessarily all of the components shown in the figures.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like, and includes several instructions for enabling a computer device (which may be a personal computer, a cloud server, or a network device) to execute the method according to the embodiments or some parts of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The data processing method, device and apparatus provided by the present application are introduced in detail, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiment is only used to help understand the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific embodiments and the application range may be changed. In view of the above, the description should not be taken as limiting the application.
Claims (10)
1. A method of restocking, comprising:
determining the safety stock days of a specific commodity in the sorting area and the near M-day average daily delivery quantity of the specific commodity;
determining the safe inventory of the specific commodity in the sorting area, wherein the safe inventory is equal to the product of the safe inventory days of the specific commodity and the near M balance average daily delivery amount;
and monitoring whether the actual inventory of the specific commodity in the sorting area is lower than the safe inventory of the specific commodity, and if so, performing replenishment operation.
2. The method of claim 1, wherein the determining the number of safe inventory days for the particular item in the picking zone is based on α and β, wherein α is the number of backorders for the particular item in the picking zone for N days, and β is the number of backorders for the particular item minus the target number of backorders for the particular item for N days.
3. The method of claim 2, wherein the number of days in safety stock for the particular good is determined by an iterative formula of
Initial safety stock days + k1 × α -k2 × β, wherein k1 and k2 are preset coefficients.
4. The method of claim 3, wherein the near N days is near 30 days and the target replenishment count is 2.
5. The method of any one of claims 1 to 4, wherein said performing restocking operations comprises: determining the replenishment quantity of the specific commodity and replenishing the specific commodity in the picking area, wherein the replenishment quantity of the specific commodity is equal to the predicted delivery quantity of the specific commodity in the future L days minus the actual stock quantity of the specific commodity in the picking area.
6. The method of claim 5, wherein the predicted L-day future shipment of the specific commodity is:
and predicting the delivery volume of the specific commodity in the future L days by adopting a delivery volume prediction model based on the commodity description data, the user behavior data, the promotion data, the time data, the historical sales data, the weather data and/or the price data of the specific commodity.
7. The method of claim 5, wherein the performing restocking operations further comprises: based on the bin utilization maximization objective, a restocking destination bin for the particular item at the picking zone is determined.
8. The method as recited in claim 7, wherein determining a replenishment destination bin for the particular item at the picking zone based on the bin utilization maximization objective comprises:
and calculating the replenishment destination bin which meets the maximization of bin utilization rate by using the Hungarian algorithm.
9. A replenishment device, comprising:
the safety stock days determining unit is used for determining the safety stock days of the specific goods in the sorting area;
an average daily shipment amount determining unit for determining an average daily shipment amount of the specific commodity in the last M days;
the safety stock determining unit is used for determining the safety stock of the specific commodity in the sorting area, and the safety stock is equal to the product of the number of safety stock days of the specific commodity and the average daily delivery amount of the near M balance;
and the monitoring unit is used for monitoring whether the actual stock of the specific commodity in the sorting area is lower than the safe stock of the specific commodity, and if so, the replenishment operation is carried out.
10. A computer system, comprising:
one or more processors; and
a memory associated with the one or more processors for storing program instructions that, when read and executed by the one or more processors, perform the method of any of claims 1-8.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626515A (en) * | 2020-05-29 | 2020-09-04 | 浙江百世技术有限公司 | Intelligent replenishment system |
| CN112184340A (en) * | 2020-11-06 | 2021-01-05 | 杭州拼便宜网络科技有限公司 | Automatic replenishment system for fast-eliminated products and working method thereof |
| CN113762828A (en) * | 2020-08-03 | 2021-12-07 | 北京京东振世信息技术有限公司 | Replenishment method, replenishment device, electronic equipment and storage medium |
| CN116911747A (en) * | 2023-09-14 | 2023-10-20 | 南京龟兔赛跑软件研究院有限公司 | Integrated management platform for agricultural product transaction and sales |
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2019
- 2019-12-13 CN CN201911289591.8A patent/CN111126903A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626515A (en) * | 2020-05-29 | 2020-09-04 | 浙江百世技术有限公司 | Intelligent replenishment system |
| CN111626515B (en) * | 2020-05-29 | 2022-07-05 | 浙江百世技术有限公司 | Intelligent replenishment system |
| CN113762828A (en) * | 2020-08-03 | 2021-12-07 | 北京京东振世信息技术有限公司 | Replenishment method, replenishment device, electronic equipment and storage medium |
| CN113762828B (en) * | 2020-08-03 | 2024-04-09 | 北京京东振世信息技术有限公司 | Method and device for supplementing goods, electronic equipment and storage medium |
| CN112184340A (en) * | 2020-11-06 | 2021-01-05 | 杭州拼便宜网络科技有限公司 | Automatic replenishment system for fast-eliminated products and working method thereof |
| CN116911747A (en) * | 2023-09-14 | 2023-10-20 | 南京龟兔赛跑软件研究院有限公司 | Integrated management platform for agricultural product transaction and sales |
| CN116911747B (en) * | 2023-09-14 | 2023-12-19 | 南京龟兔赛跑软件研究院有限公司 | Integrated management platform for agricultural product transaction and sales |
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Application publication date: 20200508 |