CN115375407A - Inventory management method and system of OMS (open machine system) - Google Patents

Inventory management method and system of OMS (open machine system) Download PDF

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CN115375407A
CN115375407A CN202211063223.3A CN202211063223A CN115375407A CN 115375407 A CN115375407 A CN 115375407A CN 202211063223 A CN202211063223 A CN 202211063223A CN 115375407 A CN115375407 A CN 115375407A
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product
estimated
sales
value
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CN115375407B (en
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艾小松
张雨
李永志
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Shenzhen Lianyu Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • G06Q30/0635—Processing of requisition or of purchase orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0623—Item investigation
    • G06Q30/0625—Directed, with specific intent or strategy

Abstract

The application provides an OMS inventory management method and system, wherein the method comprises the following steps: the computer equipment acquires all orders of the E-commerce platforms, and classifies all the orders according to the product identification to obtain the order corresponding to each product; the computer equipment calculates the product quantity of each order corresponding to the product identification, and removes the product quantity corresponding to the product identification from the inventory corresponding to the product identification of the inventory data to obtain updated inventory data; and the computing equipment estimates the estimated sales amount corresponding to the product identification, and determines whether to generate a new order or not according to the estimated sales amount and the updated inventory data. The method and the device have the advantage of high user experience.

Description

Inventory management method and system of OMS (open machine system)
Technical Field
The invention relates to the field of electronic equipment, in particular to an inventory management method and system of an OMS (operation management system).
Background
And the OMS (order management system) receives the order information of the customer, combines the inventory information sent by the storage management system, classifies the order according to the customer and the criticality degree, and configures according to the inventory of different storage places. However, the existing OMS is inaccurate in inventory management of the E-commerce platform, so that the inventory accuracy is influenced, and the user experience is reduced
Disclosure of Invention
The embodiment of the invention provides an inventory management method and system of an OMS (operation management system), which can realize effective management of the inventory of an e-commerce platform and improve the user experience.
In a first aspect, an embodiment of the present invention provides an inventory management method for an OMS, where the method includes the following steps:
the computer equipment acquires all orders of the E-commerce platforms, and classifies all the orders according to the product identification to obtain the order corresponding to each product;
the computer equipment calculates the product quantity of each order corresponding to the product identification, and removes the product quantity corresponding to the product identification from the inventory corresponding to the product identification of the inventory data to obtain updated inventory data;
and the computing equipment estimates the estimated sales volume corresponding to the product identification, and determines whether to generate a new order or not according to the estimated sales volume and the updated inventory data.
In a second aspect, there is provided an inventory management system of an OMS, the system comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring all orders of a plurality of E-commerce platforms and classifying all the orders according to product identifications to obtain orders corresponding to each product;
the processing unit is used for calculating the product quantity of each order corresponding to the product identifier, and removing the product quantity corresponding to the product identifier from the inventory corresponding to the product identifier of the inventory data to obtain updated inventory data; and estimating the estimated sales amount corresponding to the product identification, and determining whether to generate a new order or not according to the estimated sales amount and the updated inventory data.
In a third aspect, a computer-readable storage medium is provided, which stores a program for electronic data exchange, wherein the program causes a terminal to execute the method provided in the first aspect.
The embodiment of the invention has the following beneficial effects:
according to the technical scheme, the computer equipment acquires all orders of the E-commerce platforms, and classifies all the orders according to the product identification to obtain the order corresponding to each product; the computer equipment calculates the product quantity of each order corresponding to the product identification, and removes the product quantity corresponding to the product identification from the inventory corresponding to the product identification of the inventory data to obtain updated inventory data; and the computing equipment estimates the estimated sales amount corresponding to the product identification, and determines whether to generate a new order or not according to the estimated sales amount and the updated inventory data. Therefore, the orders of the stock can be dynamically and automatically managed, the utilization rate of the stock is improved, the cost of the stock is reduced, and the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a computer device
FIG. 2 is a flow chart illustrating an OMS inventory management method provided herein;
FIG. 3 is a first line and vertical distance schematic provided herein;
fig. 4 is a schematic structural diagram of an inventory management system of an OMS according to the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of the invention and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 provides a computer device, which may be an electronic device of an IOS system, an android system, or the like, or may also be an electronic device of another system, such as hong meng, or the like, and the present application does not limit the above specific system, and as shown in fig. 1, the computer device may specifically include: the processor, the memory, the display screen, the communication circuit and the audio component (optional), and the above components may be connected by a bus, and may also be connected by other ways, and the present application does not limit the specific way of the above connection.
Electronic commerce requires a certain amount of inventory due to the high requirement of a response program for delivery, but the large inventory can push against fund, the cost is increased, and how to find a balance between the cost and the inventory is a problem to be solved by the OMS. For the e-commerce, there are multiple platforms, such as panning, skyward, amazada, kyoto, etc., different platforms may correspond to different stock already-shipped channels, and for the e-commerce platform, many stores are directly opened by manufacturers, but all the stocks in the stores are not produced, so that different stock management modes need to be provided for different products.
Referring to fig. 2, fig. 2 provides a schematic flowchart of an inventory management method of an OMS, where the method is shown in fig. 2, and the method may be implemented in a computer device shown in fig. 1, and the method may specifically include:
step S201, obtaining all orders of a plurality of E-commerce platforms by computer equipment, and classifying all orders according to product identifications to obtain orders corresponding to each product;
for example, the plurality of e-commerce platforms may specifically include: the OMS can manage the e-commerce platform of the inventory, such as Tianmao, taobao, jingdong, shuduo and the like, but for some e-commerce platforms which do not manage the inventory, such as Jingdong self-management, amazon and the like, the e-commerce platform has own inventory and delivery processes, and the e-commerce platform does not contain the e-commerce platform which manages the inventory by the e-commerce platform.
For example, the identification of each product is unique, i.e. does not change with the change of the e-commerce platform, it should be noted that the product identification herein does not mean that the same product has one identification, and in practical application, the identification of the same product is completely different due to the difference of size, color, accessories and the like.
Step S202, calculating the product quantity of each order corresponding to each product identifier by the computer equipment, and removing the product quantity corresponding to the product identifier from the inventory corresponding to the product identifier of the inventory data to obtain updated inventory data;
for example, the calculating, by the computer device, the product quantity of the order corresponding to each product identifier specifically includes:
the computer equipment calls a plurality of sales volumes of the same day corresponding to the first product identification in the E-commerce platforms, determines the sum of the sales volumes as the product quantity corresponding to the first product identification, and traverses all the product identifications to obtain the product quantity corresponding to each product identification.
For example, the step of removing the quantity of the product corresponding to the product identifier from the inventory corresponding to the product identifier of the inventory data to obtain the updated inventory data may specifically include:
and subtracting the product quantity represented by the product from the inventory corresponding to the product identifier of the inventory data to obtain the residual product quantity, taking the residual product quantity as the inventory of the product identifier in the updated inventory data, and traversing all the product identifiers to obtain all the inventory in the updated inventory data.
Step S203, the computing device estimates the estimated sales amount corresponding to the product identification, and determines whether to generate a new order according to the estimated sales amount and the updated inventory data.
According to the technical scheme, the computer equipment acquires all orders of a plurality of E-commerce platforms, and classifies all the orders according to product identifications to obtain orders corresponding to each product; the computer equipment calculates the product quantity of each order corresponding to the product identification, and removes the product quantity corresponding to the product identification from the inventory corresponding to the product identification of the inventory data to obtain updated inventory data; and the computing equipment estimates the estimated sales volume corresponding to the product identification, and determines whether to generate a new order or not according to the estimated sales volume and the updated inventory data. Therefore, the orders of the stock can be dynamically and automatically managed, the utilization rate of the stock is improved, the cost of the stock is reduced, and the user experience is improved.
For example, the estimating, by the computing device, an estimated sales amount corresponding to the product identifier, and determining whether to generate a new order according to the estimated sales amount and the updated inventory data may specifically include:
the computer equipment extracts an estimated first product identification, extracts a sales volume value of the first product identification within a preset time interval n days before the current day, calculates a difference percentage between two adjacent days of the sales volume value of the n days to obtain n-1 percentages, calculates an average value between the n-1 percentages to obtain a first average value, calculates the estimated sales volume of the first product identification on the next day = sales volume of the current day (1 + the first average value), subtracts the estimated sales volume from the updated stock quantity of the first product identification to obtain an estimated residual value, and generates a new order corresponding to the first product identification through an OMS if the estimated residual value is lower than a first stock threshold value.
For example, the calculating the percentage of the difference between two adjacent days of the n-day sales value to obtain n-1 percentages may specifically include:
Figure BDA0003827148550000051
wherein x is n The sales value on day n is shown and α is the first average.
For example, the step of the computing device predicting the predicted sales amount corresponding to the product identifier, and determining whether to generate a new order according to the predicted sales amount and the updated inventory data may specifically include:
the computer equipment extracts a predicted first product identifier, extracts a sales value of the first product identifier in n days in a preset time interval before the current day, wherein n is more than or equal to 5, constructs a first coordinate system of time and sales value, constructs n characteristic points in the first coordinate system, connects the first points and the nth points of the n characteristic points into a straight line to obtain a first straight line, calculates n vertical distances from the n characteristic points to the first straight line (specifically, see n =5 in fig. 3), wherein the characteristic points of the n vertical distances have positive values above the first straight line and negative values below the first straight line, calculates the sum of the n vertical distances to obtain a first sum, if the first sum belongs to the first threshold interval, the angle of the first straight line is not rotated, if the first sum does not belong to the first threshold interval, the angle of the first straight line is rotated with the first point as the origin (namely, the angle of the first straight line is not changed), and if the first sum of the n vertical distances is calculated once per rotation of the first straight line, the angle of the predicted straight line until the sum of the n vertical distances and the n is not belong to the first threshold interval, and the first straight line is updated, and the first straight line is determined as the updated first straight line angle, if the first straight line is not rotated and the updated, the first straight line updated angle is not updated, and the updated first straight line updated angle of the first straight line;
extracting first n sales values of the first product identifier corresponding to n days in the last year, obtaining a second estimated straight line according to the first n sales values (the obtaining mode can refer to the mode of the first estimated straight line), obtaining second n sales values of the first product identifier n days after the first n sales values, and obtaining a third estimated straight line according to the second n sales values (the obtaining mode can refer to the mode of the first estimated straight line, wherein only a first point of the third estimated straight line is changed into an end point of the second estimated straight line);
calculating the slope k1 of the first estimated straight line and the slope k2 of the second estimated straight line, and adjusting the third estimated straight line according to the following formula to obtain a fourth estimated straight line k4;
k4＝k3*(1+β)；
β＝(k2-k1)/k2；
wherein k3 is the slope of the third predicted line;
connecting the fourth predicted straight line behind the first predicted straight line (namely determining the starting point of the fourth predicted straight line as the end point of the first predicted straight line), constructing a next antenna on the second day (the day after the day) in the first coordinate system, wherein the intersection point of the next antenna and the fourth predicted straight line is the predicted characteristic point on the next day, obtaining the predicted sales amount of the predicted characteristic point on the first coordinate system to obtain the predicted sales amount on the next day, subtracting the predicted sales amount from the stock amount of the updated first product identifier to obtain the predicted residual value, and if the predicted residual value is lower than the first stock threshold value, generating a new order corresponding to the first product identifier through an OMS.
In the method, the corresponding sales volume is estimated according to the slope adjustment of the specific historical data, and compared with the method that the calculation precision is improved by directly calculating the percentage, the estimation on the sales volume is more accurate, and the experience degree of a user is improved.
Referring to fig. 4, fig. 4 provides a schematic structural diagram of an inventory management system of an OMS, the system being applied to a computer device, the system including:
the acquiring unit 401 is configured to acquire all orders of multiple e-commerce platforms, and classify all orders according to product identifiers to obtain an order corresponding to each product;
a processing unit 402, configured to calculate a product quantity of each order corresponding to each product identifier, and remove the product quantity corresponding to the product identifier from the inventory corresponding to the product identifier of the inventory data to obtain updated inventory data; and estimating the estimated sales amount corresponding to the product identification, and determining whether to generate a new order or not according to the estimated sales amount and the updated inventory data.
As an example of this, it is possible to provide,
the processing unit is specifically configured to call a plurality of sales volumes of the same day corresponding to the first product identifiers in the plurality of e-commerce platforms, determine a sum of the sales volumes as a product quantity corresponding to the first product identifier, and traverse all the product identifiers to obtain the product quantity corresponding to each product identifier.
As an example of this, it is possible to use,
the processing unit is specifically configured to extract an estimated first product identifier by the computer device, extract a sales volume value of the first product identifier for n days in a preset time interval before the current day, calculate a percentage of a difference between two adjacent days of the sales volume value for n days to obtain n-1 percentages, calculate an average value between the n-1 percentages to obtain a first average value, calculate an estimated sales volume of the first product identifier for the next day = a sales volume of the current day (1 + the first average value), subtract the estimated sales volume from the stock volume of the updated first product identifier to obtain an estimated remaining value, and generate a new order corresponding to the first product identifier through the OMS if the estimated remaining value is lower than a first stock threshold value.
As an example of this, it is possible to provide,
the processing unit is specifically configured to calculate a percentage of a difference between two adjacent days of the n-day sales value to obtain n-1 percentages, and specifically includes:
Figure BDA0003827148550000071
wherein x is n The sales value on day n is shown and α is the first average.
The processing unit is specifically configured to extract a predicted first product identifier, extract a sales value of the first product identifier for n days in a preset time interval before the current day, where n is greater than or equal to 5, construct a first coordinate system of time and sales value, construct n feature points in the first coordinate system, connect the first point and the nth point of the n feature points into a straight line to obtain a first straight line, calculate n perpendicular distances from the n feature points to the first straight line (specifically, see fig. 3 where n =5 is shown), where the feature points of the n perpendicular distances have positive values above the first straight line and negative values below the first straight line, calculate a sum of the n perpendicular distances to obtain a first sum, if the first sum belongs to the first threshold interval, rotate the angle of the first straight line, if the first sum does not belong to the first threshold interval, rotate the angle of the first straight line with the first point as an origin (i.e., the first point is not changed), determine the first straight line updated angle if the first straight line does not rotate the first sum of the n perpendicular distances to the first threshold interval once, and determine the first straight line updated angle if the first straight line updated the first straight line does not rotate the first sum of the predicted angle;
extracting first n sales values of the first product identification corresponding to n days in the last year, obtaining a second estimated straight line according to the first n sales values (the obtaining mode can refer to the mode of the first estimated straight line), obtaining second n sales values of n days after the first n sales values of the first product identification, and obtaining a third estimated straight line according to the second n sales values (the obtaining mode can refer to the mode of the first estimated straight line, wherein only a first point of the third estimated straight line is changed into a terminal point of the second estimated straight line);
calculating the slope k1 of the first estimated line and the slope k2 of the second estimated line, and adjusting the third estimated line according to the following formula to obtain a fourth estimated line k4;
k4＝k3*(1+β)；
β＝(k2-k1)/k2；
wherein k3 is the slope of the third predicted line;
connecting the fourth predicted straight line behind the first predicted straight line (namely determining the starting point of the fourth predicted straight line as the end point of the first predicted straight line), constructing a next antenna on the second day (the day after the day) in the first coordinate system, wherein the intersection point of the next antenna and the fourth predicted straight line is the predicted characteristic point on the next day, obtaining the predicted sales amount of the predicted characteristic point on the first coordinate system to obtain the predicted sales amount on the next day, subtracting the predicted sales amount from the stock amount of the updated first product identifier to obtain the predicted residual value, and if the predicted residual value is lower than the first stock threshold value, generating a new order corresponding to the first product identifier through an OMS.
For example, the processing unit 402 in the embodiment of the present application may also be used to execute the refinement scheme, the alternative scheme, and the like in the embodiment shown in fig. 2, which is not described herein again.
Embodiments of the present invention further provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to perform part or all of the steps of any one of the inventory management methods of the OMS as described in the above method embodiments.
Embodiments of the present invention further provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program, the computer program being operable to cause a computer to perform some or all of the steps of any of the inventory management methods of OMS as set forth in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may be performed in other orders or concurrently according to the present invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required to practice the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical or other form.
Those skilled in the art will appreciate that all or part of the steps of the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, the memory including: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The above embodiments of the present invention are described in detail, and the principle and the implementation of the present invention are explained by applying specific embodiments, and the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (9)

1. A method for inventory management in an OMS, the method comprising the steps of:
the computer equipment acquires all orders of the E-commerce platforms, and classifies all the orders according to the product identification to obtain the order corresponding to each product;
the computer equipment calculates the product quantity of each order corresponding to the product identification, and removes the product quantity corresponding to the product identification from the inventory corresponding to the product identification of the inventory data to obtain updated inventory data;
and the computing equipment estimates the estimated sales amount corresponding to the product identification, and determines whether to generate a new order or not according to the estimated sales amount and the updated inventory data.
2. The inventory management method of an OMS according to claim 1, wherein the calculating, by the computer device, the quantity of the product of the order corresponding to each product identifier specifically includes:
the computer device calls a plurality of sales volumes of the same day corresponding to the first product identification in the E-commerce platforms, determines the sum of the sales volumes as the product quantity corresponding to the first product identification, and traverses all the product identifications to obtain the product quantity corresponding to each product identification.
3. The inventory management method of the OMS of claim 1 or claim 2, wherein the computing device estimates an estimated sales amount corresponding to the product identifier, and the determining whether to generate a new order according to the estimated sales amount and the updated inventory data specifically comprises:
the computer equipment extracts an estimated first product identification, extracts a sales volume value of the first product identification within a preset time interval n days before the current day, calculates a difference percentage between two adjacent days of the sales volume value of the n days to obtain n-1 percentages, calculates an average value between the n-1 percentages to obtain a first average value, calculates the estimated sales volume of the first product identification on the next day = sales volume of the current day (1 + the first average value), subtracts the estimated sales volume from the updated stock quantity of the first product identification to obtain an estimated residual value, and generates a new order corresponding to the first product identification through an OMS if the estimated residual value is lower than a first stock threshold value.
4. The method of claim 3, wherein the calculating the percentage difference between two adjacent days of the n-day sales value to obtain n-1 percentages specifically comprises:
Figure FDA0003827148540000011
wherein x is n The sales value on day n is shown and α is the first mean value.
5. An inventory management system of an OMS, the system comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring all orders of a plurality of E-commerce platforms and classifying all the orders according to product identifications to obtain orders corresponding to each product;
the processing unit is used for calculating the product quantity of each order corresponding to each product identifier, and removing the product quantity corresponding to the product identifier from the inventory corresponding to the product identifier of the inventory data to obtain updated inventory data; and estimating the estimated sales amount corresponding to the product identification, and determining whether to generate a new order or not according to the estimated sales amount and the updated inventory data.
6. The OMS inventory management system according to claim 5,
the processing unit is specifically configured to call a plurality of sales volumes of the same day corresponding to the first product identifiers in the plurality of e-commerce platforms, determine a sum of the sales volumes as a product quantity corresponding to the first product identifier, and traverse all the product identifiers to obtain the product quantity corresponding to each product identifier.
7. The inventory management system of the OMS according to claim 5 or 6,
the processing unit is specifically used for extracting an estimated first product identifier by the computer device, extracting a sales volume value of the first product identifier in n days in a preset time interval before the current day, calculating a difference percentage between two adjacent days of the sales volume value in the n days to obtain n-1 percentages, calculating an average value between the n-1 percentages to obtain a first average value, calculating an estimated sales volume of the first product identifier in the next day = sales volume in the current day (1 + the first average value), subtracting the estimated sales volume from the stock volume of the updated first product identifier to obtain an estimated residual value, and if the estimated residual value is lower than a first stock threshold value, generating a new order corresponding to the first product identifier through the OMS.
8. The OMS inventory management system according to claim 7,
the processing unit is specifically configured to calculate a percentage of a difference between two adjacent days of the n-day sales value to obtain n-1 percentages, and specifically includes:
Figure FDA0003827148540000021
wherein x is n The sales value on day n is shown and α is the first mean value.
9. A computer-readable storage medium storing a program for electronic data exchange, wherein the program causes a terminal to perform the method as provided in any one of claims 1-4.
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