CN113627662B - Inventory data prediction method, apparatus, device and storage medium - Google Patents
Inventory data prediction method, apparatus, device and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for predicting inventory data, wherein the method comprises the following steps: inquiring store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal; inquiring historical purchasing data of one store in the store information; generating a predicted purchase order of the store according to the historical purchase data; inquiring and predicting the purchase quantity of the commodity SKU in the purchase order; judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store; and generating the predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity. The method has the advantage that inventory data are predicted in a targeted manner through store order prediction results based on artificial intelligence.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for inventory data prediction.
Background
In the related art, orders of a plurality of stores are collected through an internet platform mode, unified purchase and logistics goods taking are carried out on suppliers, and then the suppliers are distributed to corresponding stores through carrier vehicles according to the purchase orders, so that storage cost of stores such as convenience stores is reduced, and the purchase flexibility is improved.
Because of the randomness of the purchase quantity in the above modes, suppliers often fail to make efficient inventory estimates, resulting in inventory tension or backlog.
Disclosure of Invention
The summary of the application is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. The summary of the application is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the application provide inventory data prediction methods, apparatus, electronic devices, and computer storage media to solve the technical problems mentioned in the background section above.
As a first aspect of the present application, some embodiments of the present application provide an inventory data prediction method, including: inquiring store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal; inquiring historical purchasing data of one store in the store information; generating a predicted purchase order of the store according to the historical purchase data; inquiring and predicting the purchase quantity of the commodity SKU in the purchase order; judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store; if not, calculating the difference between the purchase quantity and the maximum shipping quantity of the store; judging whether the difference value is smaller than or equal to the maximum shipping quantity of other warehouses in the historical purchasing data of the shop, if so, taking the purchasing quantity as the predicted shipping quantity of the shop, and if not, taking the maximum shipping quantity of the shop in the historical shipping data of the warehouse terminal as the predicted shipping quantity of the shop; and generating the predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity.
As a second aspect of the present application, some embodiments of the present application provide an inventory data predicting device, including: a first inquiry module, configured to inquire store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal; the second inquiry module is used for inquiring historical purchasing data of one store in the store information; the prediction module is used for generating a predicted purchase order of the store according to the historical purchase data; the third query module is used for querying and predicting the purchase quantity of the commodity SKU in the purchase order; the first judging module is used for judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store; if not, calculating the difference between the purchase quantity and the maximum shipping quantity of the store; the second judging module is used for judging whether the difference value is smaller than or equal to the maximum shipping quantity of other warehouses in the historical purchasing data of the store, if so, taking the purchasing quantity as the predicted shipping quantity of the store, and if not, taking the maximum shipping quantity of the store in the historical shipping data of the warehouse terminal as the predicted shipping quantity of the store; and the output module is used for generating the predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity.
As a third aspect of the present application, some embodiments of the present application provide an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
As a fourth aspect of the present application, some embodiments of the present application provide a computer storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The application has the beneficial effects that: inventory data is targeted through store order forecast results based on artificial intelligence.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this specification. The drawings and their description are illustrative of the application and are not to be construed as unduly limiting the application.
In addition, the same or similar reference numerals denote the same or similar elements throughout the drawings. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
In the drawings:
FIG. 1 is a flow chart of a method of updating inventory data according to one embodiment of the application;
FIG. 2 is a flowchart of a portion of the steps in a method for inventory data prediction according to one embodiment of the application;
FIG. 3 is a flowchart of a portion of the steps in a method for inventory data prediction according to one embodiment of the application;
FIG. 4 is a flowchart of a portion of the steps in a method for inventory data prediction according to one embodiment of the application;
FIG. 5 is a block diagram of an inventory data forecasting device according to one embodiment of the present application;
fig. 6 is a schematic structural view of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the application have been illustrated in the accompanying drawings, it is to be understood that the application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the application are for illustration purposes only and are not intended to limit the scope of the present application.
It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings. Embodiments of the application and features of the embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the devices in the embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The application will be described in detail below with reference to the drawings in connection with embodiments.
Referring to fig. 1, the inventory data prediction method of the present application includes the steps of:
s1: store information in historical shipping data of a warehouse terminal is queried in response to an inventory forecast request sent by the warehouse terminal for a commodity SKU.
S2: and inquiring historical purchasing data of one store in the store information.
S3: generating a predicted purchase order of the store according to the historical purchase data.
S4: inquiring and predicting the purchase quantity of the commodity SKU in the purchase order.
S5: judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store; if not, calculating the difference between the purchase quantity and the maximum shipping quantity of the store.
S6: judging whether the difference value is smaller than or equal to the maximum shipping quantity of other warehouses in the historical purchasing data of the shop, if so, taking the purchasing quantity as the predicted shipping quantity of the shop, and if not, taking the maximum shipping quantity of the shop in the historical shipping data of the warehouse terminal as the predicted shipping quantity of the shop.
S7: and generating the predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity.
As a specific solution, the warehouse terminal may be a smart phone or other form of smart device. The inventory forecast request may be a timed request or an instant request based on user action.
With continued reference to fig. 2, step S1 specifically includes the following steps: inquiring a delivery order corresponding to the delivery order received by the warehouse terminal according to an inquiry time range; store information is queried based on address data in the delivery order. Because of the order sharing mode, the pick order and the delivery order do not have a direct correspondence in the system.
With continued reference to fig. 3, step S3 specifically includes the following steps:
Collecting inventory data and sales data of a store;
Store state characteristic data are generated according to store inventory data and sales data;
Inputting store state characteristic data into an order prediction model to enable the order prediction model to output recommended purchase data and corresponding prediction confidence;
judging whether the prediction confidence coefficient is larger than a preset prediction confidence coefficient threshold value, and if so, generating a prediction purchase order according to the recommended purchase data.
Specifically, the store inventory data and the store sales data of the store are acquired as store inventory data and store sales data of the same day of the store.
In order to be able to timely provide suggestions of purchase orders for shops, as a preferred solution, the system triggers the execution of the automatic order generation method according to the application daily, i.e. the system runs the program daily according to user settings or system settings. Alternatively, the system may enable different stores to have different times to receive the predicted purchase order, avoiding anomalies in locking inventory due to centralized operations.
As another preferable mode, a trigger condition may be set, and when the trigger condition is satisfied, a program for realizing the automatic order generation method is automatically executed.
For example, when the store is checked every day and then the store inventory data and the store sales data are uploaded, the program is automatically triggered; or when store user uses user terminal to send request for automatic order to server (user can click recommendation button in user terminal APP interface), triggering program.
The store inventory data and store sales data may be managed by a program of the system platform, or may be provided and developed by a system to interface with other goods statistics programs of the store itself.
Store inventory data and sales data as referred to herein refer to SKU codes and corresponding quantity data, respectively, of items in inventory or sold on the day.
According to a general analysis scheme, a machine learning module is generally trained by adopting historical store inventory data and sales data respectively, then prediction of the store inventory data and the sales data is performed respectively, then the situation of possible picking is judged according to a prediction result, and then corresponding order suggestions and the like are generated according to the out-of-stock situation.
However, because of uncertainty in sales data, unless all commodities are included in the input data, the input data is huge and the invalid data is large, and finally the trained machine learning model cannot be converged.
In addition, although the store inventory data is relatively stable with respect to the sales data, the machine learning model is not converged due to the problem of the large number of items like the store inventory data.
For the above reasons, there is a major technical impediment to machine learning model training and prediction using store inventory data and sales data directly.
Based on the above, the technical scheme of the application adopts a new technical concept, specifically, as one scheme, after acquiring store inventory data and store sales data; and acquiring commodity SKUs and sales quantity of the first five positions of sales quantity and the existing stock quantity corresponding to the first five positions of commodities from sales data (the current day) to form a five-row and three-column matrix, wherein the matrix is used as store state characteristic data.
As an extension, the number of rows of the matrix may be set according to the size of the store scale, and for a store with a large transaction amount, the number of rows of the matrix may be ten or more rows, for example, for a store with a large transaction amount, which has a large number of goods sold daily.
As a preferred solution, the total number of commodity categories in the store inventory is S, and the header value m=s×k is calculated, where K is the header percentage, which is determined according to the numerical interval in which S is located.
When the S is more than or equal to 300 and is more than 0, the value range of the head percentage is 7%; when the value of the head percentage is more than or equal to 1000 and is more than or equal to 300, the value range of the head percentage is 15 percent; when the value of the head percentage is more than or equal to 300 and is more than 0, the value range of the head percentage is 20 percent. The rectangular number of rows N is equal to the rounded value of the header value M.
The order prediction model may be trained using two schemes, with inputs being the store state characteristic data described above. The difference is the source of the output data in the training set.
The first way is: the method comprises the steps of processing historical data of stores, taking actual historical purchase orders as output data, wherein the output data is a matrix formed by commodity SKUs and purchase numbers in the purchase orders, determining two columns, specifically, how many rows are generated according to actual conditions of the purchase orders, and taking store state characteristic data of a day before the actual historical purchase orders occur as input data, so that a group of training data is formed. With such a scheme, the order forecast model is output as a matrix of commodity SKUs and purchase quantities.
The scheme has the advantages that the method can directly generate the purchase order, but due to the uncertainty of an output matrix, the order prediction model is equivalent to an empirical model, the sequence of inputting a training set and the setting of model parameters during training have great influence on final output, the accuracy fluctuation is high, the output has reference significance only by setting a high confidence threshold, and thus the problem of program circulation is caused during running. In this way, as a preferred scheme, the order prediction model may be a CNN neural network model.
The second way is: the order prediction model is built into a prediction type machine learning model, namely, the shop state characteristic data before the current day is used as input data, the shop state characteristic data on the current day is used as output data, and model training is carried out on the order prediction model, so that the output data and the output data are regular data and a determined matrix, and training and convergence are easy. In this way, as a preferred scheme, the order prediction model may be a BP neural network model.
Preferably, the order prediction model is constructed in a second manner, and the store status feature data (real matrix) of the tomorrow as described above is output, although not the direct purchase order data, in which the top commodity SKU, sales quantity, and stock quantity that may occur on the next day are predicted.
The system traverses sales quantity and inventory quantity data of all commodities in the prediction matrix and judges whether the sales quantity and inventory quantity data meet a preset relative relation or not. Specifically, the correlation is specifically that the number of sales of shops > the number of stock of shops x the balance coefficient; wherein the balance coefficient is in the range of 0.27 to 0.7.
For example, the balance coefficient takes a value of 0.5, and when the correlation judges that the sales number is more than 50% of the stock number, the commodity is selected into the purchase order.
As a specific solution, an order prediction model is trained for each store, that is, one order prediction model corresponds to one store or one store terminal device (one store may have a plurality of terminal devices). The data for each store is input into a corresponding order prediction model.
Preferably, if the predicted confidence is less than or equal to the predicted confidence threshold, returning to the step of collecting store inventory data and sales data, namely processing again by using the order prediction model, and outputting new output data and confidence.
Preferably, if the prediction confidence level cannot meet the prediction confidence level threshold all the time, for example, the prediction confidence level is still not met after exceeding the preset times, the purchase order is directly generated according to the store state characteristic data of the current day.
The judgment in steps S4 and S5 is to avoid that the store may have multiple suppliers, and if the store may overflow the actual supply demand of the current warehouse based on the number of predicted purchase orders alone, a large predicted misjudgment may result.
With continued reference to fig. 4, step S7 specifically includes the following steps: calculating the total value of the predicted shipping quantity of all shops in the shop information as the inventory consumption total of the commodity SKU; and calculating the difference value between the existing inventory quantity and the inventory consumption total quantity of the commodity SKU as the predicted inventory quantity of the commodity SKU.
With continued reference to fig. 5, as an embodiment, an inventory data predicting device includes: a first inquiry module, configured to inquire store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal; the second inquiry module is used for inquiring historical purchasing data of one store in the store information; the prediction module is used for generating a predicted purchase order of the store according to the historical purchase data; the third query module is used for querying and predicting the purchase quantity of the commodity SKU in the purchase order; the first judging module is used for judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store; if not, calculating the difference between the purchase quantity and the maximum shipping quantity of the store; the second judging module is used for judging whether the difference value is smaller than or equal to the maximum shipping quantity of other warehouses in the historical purchasing data of the store, if so, taking the purchasing quantity as the predicted shipping quantity of the store, and if not, taking the maximum shipping quantity of the store in the historical shipping data of the warehouse terminal as the predicted shipping quantity of the store; and the output module is used for generating the predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity.
As shown in fig. 6, the electronic device 800 may include a processing means (e.g., a central processor, a graphics processor, etc.) 801, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
In general, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like: an output device 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, etc.; including storage 808, such as magnetic tape, hard disk, etc.: communication means 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 6 shows an electronic device 800 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 6 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the application include a computer program product comprising a computer program embodied on a computer storage medium, the computer program comprising program code for performing the methods shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via communication device 809, or from storage device 808, or from ROM 802. The above-described functions defined in the methods of some embodiments of the present application are performed when the computer program is executed by the processing means 801.
It should be noted that, in some embodiments of the present application, the computer storage medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In some embodiments of the application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the application, however, the computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperTextTransferProtocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer storage medium may be included in the electronic device or may exist alone without being incorporated in the electronic device. The computer storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: inquiring store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal; inquiring historical purchasing data of one store in the store information; generating a predicted purchase order of the store according to the historical purchase data; inquiring and predicting the purchase quantity of the commodity SKU in the purchase order; judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store; if not, calculating the difference between the purchase quantity and the maximum shipping quantity of the store; judging whether the difference value is smaller than or equal to the maximum shipping quantity of other warehouses in the historical purchasing data of the shop, if so, taking the purchasing quantity as the predicted shipping quantity of the shop, and if not, taking the maximum shipping quantity of the shop in the historical shipping data of the warehouse terminal as the predicted shipping quantity of the shop; and generating the predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity.
Computer program code for carrying out operations for certain embodiments of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, or combinations thereof: such as the "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The above description is only illustrative of the few preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application in the embodiments of the present application is not limited to the specific combination of the above technical features, but also encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the application. Such as the above-described features, are mutually replaced with the technical features having similar functions (but not limited to) disclosed in the embodiments of the present application.
Claims (8)
1. A method of inventory data prediction, comprising:
inquiring store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal;
inquiring historical purchasing data of one store in the store information;
generating a predicted purchase order of the store according to the historical purchase data;
inquiring the purchase quantity of the commodity SKU in the predicted purchase order;
Judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store;
If not, calculating the difference value between the purchase quantity and the maximum shipping quantity of the store;
Judging whether the difference value is smaller than or equal to the maximum shipping quantity of other warehouses in the historical purchasing data of the store, if so, taking the purchasing quantity as the predicted shipping quantity of the store, and if not, taking the maximum shipping quantity of the store in the historical shipping data of the warehouse terminal as the predicted shipping quantity of the store;
generating a predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity;
the querying store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal includes:
Inquiring a delivery order corresponding to the goods taking order received by the warehouse terminal according to an inquiry time range; inquiring the store information according to the address data in the delivery order;
the generating a predicted purchase order of the store according to the historical purchase data comprises the following steps:
Collecting inventory data and sales data of a store;
store inventory data and sales data of the store generate store status feature data;
Inputting store state characteristic data into an order prediction model to enable the order prediction model to output recommended purchase data and corresponding prediction confidence;
judging whether the prediction confidence coefficient is larger than a preset prediction confidence coefficient threshold value, and if so, generating a prediction purchase order according to the recommended purchase data.
2. The inventory data prediction method of claim 1, wherein the order prediction model is a BP neural network model.
3. The inventory data forecasting method of claim 1, wherein the order forecasting model is a CNN neural network model.
4. The inventory data forecasting method of claim 1, wherein the generating a forecasted inventory quantity for transmission to the warehouse terminal based on the forecasted shipping quantity comprises:
and calculating the total value of the predicted shipping quantity of all shops in the shop information as the inventory consumption total of the commodity SKU.
5. The inventory data forecasting method of claim 4, wherein the generating a forecasted inventory quantity for transmission to the warehouse terminal based on the forecasted shipping quantity comprises:
and calculating the difference value between the existing inventory quantity of the commodity SKU and the inventory consumption total quantity as the predicted inventory quantity of the commodity SKU.
6. An inventory data forecasting device, comprising:
A first query module, configured to query store information in historical shipping data of a warehouse terminal in response to an inventory forecast request sent by the warehouse terminal for a commodity SKU;
The second inquiry module is used for inquiring historical purchasing data of one store in the store information; the prediction module is used for generating a predicted purchase order of the store according to the historical purchase data;
The third query module is used for querying the purchase quantity of the commodity SKU in the predicted purchase order;
The first judging module is used for judging whether the purchase quantity is smaller than the maximum shipment quantity of the store in the historical shipment data of the warehouse terminal, and if so, taking the purchase quantity as the predicted shipment quantity of the store; if not, calculating the difference value between the purchase quantity and the maximum shipping quantity of the store;
The second judging module is used for judging whether the difference value is smaller than or equal to the maximum shipping quantity of other warehouses in the historical purchasing data of the store, if so, taking the purchasing quantity as the predicted shipping quantity of the store, and if not, taking the maximum shipping quantity of the store in the historical shipping data of the warehouse terminal as the predicted shipping quantity of the store;
the output module is used for generating the predicted inventory quantity sent to the warehouse terminal according to the predicted shipping quantity;
the querying store information in historical shipping data of a warehouse terminal in response to an inventory forecast request about a commodity SKU sent by the warehouse terminal includes:
Inquiring a delivery order corresponding to the goods taking order received by the warehouse terminal according to an inquiry time range; inquiring the store information according to the address data in the delivery order;
the generating a predicted purchase order of the store according to the historical purchase data comprises the following steps:
Collecting inventory data and sales data of a store;
store inventory data and sales data of the store generate store status feature data;
Inputting store state characteristic data into an order prediction model to enable the order prediction model to output recommended purchase data and corresponding prediction confidence;
judging whether the prediction confidence coefficient is larger than a preset prediction confidence coefficient threshold value, and if so, generating a prediction purchase order according to the recommended purchase data.
7. An electronic device, comprising:
one or more processors; a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the processor to implement the method of any of claims 1-5.
8. A computer storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of any of claims 1 to 5.
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| WO2018221164A1 (en) * | 2017-05-31 | 2018-12-06 | ベンダーサービス株式会社 | Inventory control and ordering device, inventory control and ordering method, and computer program |
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| CN112308681A (en) * | 2020-11-16 | 2021-02-02 | 杭州拼便宜网络科技有限公司 | Inventory processing method and device, electronic equipment and storage medium |
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| WO2018221164A1 (en) * | 2017-05-31 | 2018-12-06 | ベンダーサービス株式会社 | Inventory control and ordering device, inventory control and ordering method, and computer program |
| CN110880084A (en) * | 2018-09-06 | 2020-03-13 | 北京京东振世信息技术有限公司 | Warehouse replenishment method and device |
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