CN115375407B - Inventory management method and system of OMS - Google Patents
Inventory management method and system of OMS Download PDFInfo
- Publication number
- CN115375407B CN115375407B CN202211063223.3A CN202211063223A CN115375407B CN 115375407 B CN115375407 B CN 115375407B CN 202211063223 A CN202211063223 A CN 202211063223A CN 115375407 B CN115375407 B CN 115375407B
- Authority
- CN
- China
- Prior art keywords
- straight line
- estimated
- sales
- product identifier
- product
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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 a plurality of electronic commerce platforms, and classifies all orders according to product identifiers to obtain orders corresponding to each product; the computer equipment calculates the product quantity of each product identifier corresponding to the order, removes the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data, and obtains updated inventory data; the computing equipment predicts the estimated sales corresponding to the product identifier, and determines whether to generate a new order according to the estimated sales and the updated inventory data. The application has the advantage of high user experience.
Description
Technical Field
The application relates to the field of electronic equipment, in particular to an OMS inventory management method and system.
Background
The OMS (order management system) receives the customer order information, combines the stock information sent by the warehouse management system, classifies the order according to the customer and the degree of importance, and configures according to the stock of different warehouse sites. However, the existing OMS is inaccurate in inventory management of the e-commerce platform, thereby affecting the accuracy of inventory and reducing the user experience
Disclosure of Invention
The embodiment of the application provides an OMS inventory management method and system, which can realize effective inventory management of an E-commerce platform and improve user experience.
In a first aspect, an embodiment of the present application provides a method for inventory management of an OMS, including the steps of:
the computer equipment acquires all orders of a plurality of electronic commerce platforms, and classifies all orders according to product identifiers to obtain orders corresponding to each product;
the computer equipment calculates the product quantity of each product identifier corresponding to the order, removes the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data, and obtains updated inventory data;
the computing equipment predicts the estimated sales corresponding to the product identifier, and determines whether to generate a new order according to the estimated sales and the updated inventory data.
In a second aspect, there is provided an inventory management system of an OMS, the system comprising:
the acquiring unit is used for acquiring all orders of the plurality of electronic commerce platforms, and classifying all orders according to the product identifiers to obtain orders corresponding to each product;
the processing unit is used for calculating the product quantity of each product identifier corresponding to the order, and removing the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data to obtain updated inventory data; and estimating the estimated sales corresponding to the product identifier, and determining whether to generate a new order according to the estimated sales and the updated inventory data.
In a third aspect, a computer-readable storage medium storing a program for electronic data exchange is provided, wherein the program causes a terminal to execute the method provided in the first aspect.
The embodiment of the application has the following beneficial effects:
it can be seen that the technical scheme provided by the application is that the computer equipment acquires all orders of a plurality of electronic commerce platforms, and classifies all orders according to product identifiers to obtain orders corresponding to each product; the computer equipment calculates the product quantity of each product identifier corresponding to the order, removes the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data, and obtains updated inventory data; the computing equipment predicts the estimated sales corresponding to the product identifier, and determines whether to generate a new order according to the estimated sales and the updated inventory data. Therefore, the automatic management of the stock already ordered can be dynamically carried out, 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 of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a computer device
FIG. 2 is a flow chart of an OMS inventory management method according to the present application;
FIG. 3 is a schematic view of a first straight line and vertical distance provided by the present application;
fig. 4 is a schematic structural diagram of an OMS inventory management system according to the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" 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 listed steps or elements but may 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 may be included in at least one embodiment of the application. The appearances of such phrases 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. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, fig. 1 provides a computer device, which may be an electronic device of an IOS, an android, etc. system, or may be an electronic device of another system, for example, hong mo, etc., and the present application is not limited to the specific system, and as shown in fig. 1, the computer device may specifically include: the processor, memory, display screen, communication circuitry, and audio components (optional) may be connected by a bus or by other means, and the application is not limited to the specific manner of connection described above.
Electronic commerce requires a certain amount of inventory due to the high requirements of the response procedure to shipment, but the larger inventory would squeeze the funds, increasing the cost, and how to find a balance between cost and inventory is a problem that OMS need to solve. For electronic commerce, there are multiple platforms, such as, for example, panda, cat, spell, jindong, etc., different platforms may correspond to different stock already shipping channels, for electronic commerce platforms, many are shops that the manufacturer directly opens, but for shops all goods are not produced, so different stock management methods need to be provided for different products.
Referring to fig. 2, fig. 2 provides a flow chart of an OMS inventory management method, which is shown in fig. 2, and which may be performed by a computer device as shown in fig. 1, and which may include:
step S201, computer equipment acquires all orders of a plurality of electronic commerce platforms, and classifies all orders according to product identifiers to obtain orders corresponding to each product;
for example, the plurality of e-commerce platforms may specifically include: the OMS is capable of managing the e-commerce platform of inventory, such as, for example, heaven cat, panda, jindong, spell, etc., but for some e-commerce platforms of non-management inventory, such as, for example, jindong, amazon, etc., it has its own inventory and shipping flows, where the e-commerce platform does not include the e-commerce platform of self-management inventory as described above.
For example, the identification of each product is unique, that is, the product identification does not change with the change of the e-commerce platform, and it should be noted that the product identification herein does not indicate 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, accessory, and the like.
Step S202, calculating the product quantity of each order corresponding to the product identifier by computer equipment, and removing the product quantity corresponding to the product identifier from the inventory quantity 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 each product identifier corresponding to the order specifically includes:
the computer equipment calls a plurality of sales volumes of the day corresponding to the first product identifier in the electronic commerce platforms, determines the sum of the sales volumes to be the product quantity corresponding to the first product identifier, and traverses all the product identifiers to obtain the product quantity corresponding to each product identifier.
For example, the removing the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data to obtain updated inventory data may specifically include:
subtracting the product quantity represented by the product from the inventory quantity corresponding to the product identifier of the inventory data to obtain a remaining product quantity, taking the remaining product quantity as the inventory quantity of the product identifier in the updated inventory data, and traversing all the product identifiers to obtain all the inventory quantities in the updated inventory data.
Step S203, the computing device predicts the estimated sales corresponding to the product identifier, and determines whether to generate a new order according to the estimated sales and the updated inventory data.
According to the technical scheme provided by the application, the computer equipment acquires all orders of a plurality of electronic commerce platforms, and classifies all orders according to product identifiers to obtain orders corresponding to each product; the computer equipment calculates the product quantity of each product identifier corresponding to the order, removes the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data, and obtains updated inventory data; the computing equipment predicts the estimated sales corresponding to the product identifier, and determines whether to generate a new order according to the estimated sales and the updated inventory data. Therefore, the automatic management of the stock already ordered can be dynamically carried out, the utilization rate of the stock is improved, the cost of the stock is reduced, and the user experience is improved.
For example, the determining, by the computing device, whether to generate the new order according to the estimated sales corresponding to the estimated product identifier and the updated inventory data may specifically include:
the method comprises the steps of extracting a predicted first product identifier by computer equipment, extracting a sales 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 value in n days to obtain n-1 percentages, calculating an average value between the n-1 percentages to obtain a first average value, subtracting the predicted sales value from the updated stock quantity of the first product identifier to obtain a predicted residual value, and generating a new order corresponding to the first product identifier through OMS if the predicted residual value is lower than a first stock threshold value.
For example, the calculating the percentage difference between two adjacent days of the sales value for n days to obtain n-1 percentages may specifically include:
wherein x is n The sales on day n are indicated, and α is the first average.
For example, the determining, by the computing device, whether to generate the new order according to the estimated sales corresponding to the estimated product identifier and the updated inventory data may specifically include:
extracting a predicted first product identifier by computer equipment, extracting a pin value n days in a preset time interval before the first product identifier is the same as or larger than 5, constructing a first coordinate system of time and pin value, constructing n characteristic points in the first coordinate system, connecting the first points of the n characteristic points and the n point to form a straight line to obtain a first straight line, calculating n vertical distances (specifically, see fig. 3, n=5 in fig. 3) from the n characteristic points to the first straight line, wherein the characteristic points of the n vertical distances are positive above the first straight line, negative below the first straight line, calculating the sum of the n vertical distances to obtain a first sum, if the first sum belongs to the first threshold interval, not rotating the angle of the first straight line, if the first sum does not belong to the first threshold interval, rotating the angle of the first straight line for one time by taking the first point as an origin (namely, the first point is unchanged), calculating the sum of the n vertical distances until the sum of the n vertical distances belongs to the first threshold interval, stopping rotating the first straight line for one time, stopping rotating the first straight line after the first threshold interval, if the sum of the n vertical distances belongs to the first threshold interval, determining that the first straight line is not rotating the first straight line, if the first straight line is predicted, and if the first straight line is not rotating the first straight line is determined;
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 be referred to as a first estimated straight line mode), obtaining second n sales values of n days after the first n sales values of the first product identifier, and obtaining a third estimated straight line according to the second n sales values (the obtaining mode can be referred to as a first estimated straight line mode, and only changing a first point of the third estimated straight line into a terminal point of the second estimated straight line);
calculating the slope k1 of the first estimated straight line, the slope k2 of the second estimated straight line, and obtaining a fourth estimated straight line k4 after adjusting the third estimated straight line according to the following formula;
k4＝k3*(1+β)；
β＝(k2-k1)/k2；
wherein k3 is the slope of the third predicted line;
connecting the fourth estimated straight line after the first estimated straight line (namely determining the starting point of the fourth estimated straight line as the end point of the first estimated 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 estimated straight line is an estimated characteristic point of the next day, acquiring the estimated sales volume of the estimated characteristic point in the first coordinate system, subtracting the estimated sales volume from the updated stock volume of the first product identifier to obtain an estimated residual value, and generating a new order corresponding to the first product identifier through OMS if the estimated residual value is lower than the first stock threshold value.
The corresponding sales volume is estimated according to the slope adjustment of the specific historical data, and compared with calculation by directly passing through the percentage, the calculation accuracy can be improved, the estimation of the sales volume is more accurate, and the user experience 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 comprising:
the acquiring unit 401 is configured to acquire all orders of the multiple e-commerce platforms, and classify all orders according to product identifiers to obtain orders corresponding to each product;
a processing unit 402, configured to calculate a product quantity of each product identifier corresponding to an order, and remove the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data to obtain updated inventory data; and estimating the estimated sales corresponding to the product identifier, and determining whether to generate a new order according to the estimated sales and the updated inventory data.
By way of example only, the present application is directed to a method of,
the processing unit is specifically configured to invoke a plurality of sales volumes of a day corresponding to a first product identifier in a plurality of e-commerce platforms, determine the sum of the sales volumes as a product number corresponding to the first product identifier, and traverse all the product identifiers to obtain a product number corresponding to each product identifier.
By way of example only, the present application is directed to a method of,
the processing unit is specifically configured to extract a predicted first product identifier from the computer device, extract a sales value of the first product identifier in n days within a preset time interval before the current day, calculate a difference percentage between two adjacent days of the sales value in n days to obtain n-1 percentages, calculate an average value between the n-1 percentages to obtain a first average value, calculate a predicted sales value of the first product identifier in the next day=the current day sales value (1+the first average value), subtract the predicted sales value from the updated repository volume of the first product identifier to obtain a predicted residual value, and generate a new order corresponding to the first product identifier through the OMS if the predicted residual value is lower than a first inventory threshold.
By way of example only, the present application is directed to a method of,
the processing unit is specifically configured to calculate a percentage difference between two adjacent days of the sales value for n days to obtain n-1 percentages, and specifically includes:
wherein x is n The sales on day n are indicated, and α is the first average.
The processing unit is specifically configured to extract an estimated first product identifier, extract a pin value of the first product identifier in a preset time interval before the current day for n days, where n is greater than or equal to 5, construct a first coordinate system of time and pin value, construct n feature points in the first coordinate system, connect a first point of the n feature points and the n point into a straight line to obtain a first straight line, calculate n vertical distances from the n feature points to the first straight line (specifically, see fig. 3, where n=5), where the feature points of the n vertical distances take a positive value above the first straight line, take a negative value below the first straight line, calculate a sum of the n vertical distances to obtain a first sum, if the first sum belongs to a first threshold interval, not rotate an angle of the first straight line, rotate the angle of the first straight line once with the first point as an origin (i.e., the first point is unchanged), calculate a sum of the n vertical distances until the n vertical distances and stop rotating the first straight line after the first straight line stops rotating the first straight line, determine that the sum of the n vertical distances belongs to the first straight line is a new straight line if the first sum belongs to the first threshold interval, and if the first sum of the first straight line rotates the first straight line is not determined to be 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 be referred to as a first estimated straight line mode), obtaining second n sales values of n days after the first n sales values of the first product identifier, and obtaining a third estimated straight line according to the second n sales values (the obtaining mode can be referred to as a first estimated straight line mode, and only changing a first point of the third estimated straight line into a terminal point of the second estimated straight line);
calculating the slope k1 of the first estimated straight line, the slope k2 of the second estimated straight line, and obtaining a fourth estimated straight line k4 after adjusting the third estimated straight line according to the following formula;
k4＝k3*(1+β)；
β＝(k2-k1)/k2；
wherein k3 is the slope of the third predicted line;
connecting the fourth estimated straight line after the first estimated straight line (namely determining the starting point of the fourth estimated straight line as the end point of the first estimated 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 estimated straight line is an estimated characteristic point of the next day, acquiring the estimated sales volume of the estimated characteristic point in the first coordinate system, subtracting the estimated sales volume from the updated stock volume of the first product identifier to obtain an estimated residual value, and generating a new order corresponding to the first product identifier through OMS if the estimated residual value is lower than the first stock threshold value.
By way of example, the processing unit 402 in the embodiment of the present application may also be used to execute a refinement, an alternative, etc. of the embodiment shown in fig. 2, which is not described here again.
The embodiment of the present application also provides a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, where the computer program causes a computer to execute part or all of the steps of any one of the OMS inventory management methods described in the method embodiments above.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of an inventory management method of an OMS as described in any of the method embodiments above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (5)
1. A method of inventory management for an OMS, the method comprising the steps of:
the computer equipment acquires all orders of a plurality of electronic commerce platforms, and classifies all orders according to product identifiers to obtain orders corresponding to each product;
the computer equipment calculates the product quantity of each product identifier corresponding to the order, removes the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data, and obtains updated inventory data;
the computing device predicts the estimated sales corresponding to the product identifier, and determines whether to generate a new order according to the estimated sales and the updated inventory data, specifically comprising:
extracting a predicted first product identifier by computer equipment, extracting a pin value n days in a preset time interval before the first product identifier, wherein n is more than or equal to 5, constructing a first coordinate system of time and pin value, constructing n characteristic points in the first coordinate system, connecting the first points of the n characteristic points and the n point into a straight line to obtain a first straight line, calculating n vertical distances from the n characteristic points to the first straight line, wherein the value of the characteristic point of the n vertical distances is positive above the first straight line, the value of the characteristic point of the n vertical distances is negative below the first straight line, calculating the sum of the n vertical distances to obtain a first sum, if the first sum does not belong to the first threshold interval, rotating the angle of the first straight line by the first point for every time, calculating the sum of the n vertical distances until the sum of the n vertical distances belongs to the first threshold interval, stopping rotating to obtain a first updated straight line, and if the first straight line does not rotate to the first straight line, determining the first straight line to be the predicted first straight line, if the first straight line is the first straight line to be the predicted straight line, and if the first straight line is not rotated to be 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, obtaining second n sales values of n days after the first n sales values of the first product identifier, and obtaining a third estimated straight line according to the second n sales values;
calculating the slope k1 of the first estimated straight line, the slope k2 of the second estimated straight line, and obtaining a fourth estimated straight line k4 after adjusting the third estimated straight line according to the following formula;
k4＝k3*(1+β)；
β＝(k2-k1)/k2；
wherein k3 is the slope of the third predicted line;
connecting a fourth estimated straight line with the first estimated straight line, constructing a next antenna on the second day in the first coordinate system, wherein the intersection point of the next antenna and the fourth estimated straight line is an estimated characteristic point on the next day, acquiring the estimated sales value of the estimated characteristic point on the first coordinate system to obtain the estimated sales on the next day, subtracting the estimated sales from the updated stock quantity of the first product identifier to obtain an estimated residual value, and generating a new order corresponding to the first product identifier through the OMS if the estimated residual value is lower than the first inventory threshold;
or, the calculating device estimates the estimated sales corresponding to the product identifier, and determining whether to generate a new order according to the estimated sales and the updated inventory data specifically includes:
the method comprises the steps that 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, calculates a difference percentage between two adjacent days of the sales value in n days to obtain n-1 percentages, calculates an average value between the n-1 percentages to obtain a first average value, calculates a predicted sales value of the first product identifier in the next day = the current day (1+the first average value), subtracts the predicted sales value from a repository amount of the updated first product identifier to obtain a predicted residual value, and generates a new order corresponding to the first product identifier through OMS if the predicted residual value is lower than a first inventory threshold; the calculating of the percentage difference between two adjacent days of the sales value of n days to obtain n-1 percentages specifically comprises:
wherein x is n The sales on day n are indicated, and α is the first average.
2. The OMS inventory management method according to claim 1, wherein the computing by the computer device the product quantity for each product identification corresponding to an order specifically comprises:
the computer equipment calls a plurality of sales volumes of the day corresponding to the first product identifier in the electronic commerce platforms, determines the sum of the sales volumes to be the product quantity corresponding to the first product identifier, and traverses all the product identifiers to obtain the product quantity corresponding to each product identifier.
3. An OMS inventory management system, the system comprising:
the acquiring unit is used for acquiring all orders of the plurality of electronic commerce platforms, and classifying all orders according to the product identifiers to obtain orders corresponding to each product;
the processing unit is used for calculating the product quantity of each product identifier corresponding to the order, and removing the product quantity corresponding to the product identifier from the inventory quantity corresponding to the product identifier of the inventory data to obtain updated inventory data; estimating estimated sales corresponding to the product identifier, and determining whether to generate a new order according to the estimated sales and the updated inventory data;
extracting a predicted first product identifier, extracting a pin 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, constructing a first coordinate system of time and pin value, constructing n characteristic points in the first coordinate system, connecting the first points and the n th points of the n characteristic points into a straight line to obtain a first straight line, calculating n vertical distances from the n characteristic points to the first straight line, wherein the value of the characteristic points of the n vertical distances is positive above the first straight line, the value of the characteristic points of the n vertical distances is negative below the first straight line, calculating the sum of the n vertical distances to obtain a first sum, if the first sum does not belong to the first threshold interval, rotating the angle of the first straight line by taking the first point as an origin, stopping rotating after every rotation of the angle of the first straight line once, calculating the sum of the n vertical distances until the sum of the n vertical distances belongs to the first threshold interval, and obtaining a first updated straight line, if the first straight line does not rotate the first straight line is the predicted straight line, and if the first sum of the first straight line does not rotate the first straight line is the predicted 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, obtaining second n sales values of n days after the first n sales values of the first product identifier, and obtaining a third estimated straight line according to the second n sales values;
calculating the slope k1 of the first estimated straight line, the slope k2 of the second estimated straight line, and obtaining a fourth estimated straight line k4 after adjusting the third estimated straight line according to the following formula;
k4＝k3*(1+β)；
β＝(k2-k1)/k2；
wherein k3 is the slope of the third predicted line;
connecting a fourth estimated straight line with the first estimated straight line, constructing a next antenna on the second day in the first coordinate system, wherein the intersection point of the next antenna and the fourth estimated straight line is an estimated characteristic point on the next day, acquiring the estimated sales value of the estimated characteristic point on the first coordinate system to obtain the estimated sales on the next day, subtracting the estimated sales from the updated stock quantity of the first product identifier to obtain an estimated residual value, and generating a new order corresponding to the first product identifier through the OMS if the estimated residual value is lower than the first inventory threshold;
or, the processing unit is specifically configured to extract a predicted first product identifier from the computer device, extract a sales value of the first product identifier in a preset time interval before the current day for n days, calculate a difference percentage between two adjacent days of the sales 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 a predicted sales value of the first product identifier for the next day=the sales value of the current day (1+the first average value), subtract the predicted sales value from the updated repository amount of the first product identifier to obtain a predicted residual value, and generate a new order corresponding to the first product identifier through the OMS if the predicted residual value is lower than a first inventory threshold;
the processing unit is specifically configured to calculate a percentage difference between two adjacent days of the sales value for n days to obtain n-1 percentages, and specifically includes:
wherein x is n The sales on day n are indicated, and α is the first average.
4. The OMS inventory management system of claim 3, wherein,
the processing unit is specifically configured to invoke a plurality of sales volumes of a day corresponding to a first product identifier in a plurality of e-commerce platforms, determine the sum of the sales volumes as a product number corresponding to the first product identifier, and traverse all the product identifiers to obtain a product number corresponding to each product identifier.
5. A computer-readable storage medium storing a program for electronic data exchange, wherein the program causes a terminal to perform the method provided in any one of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211063223.3A CN115375407B (en) | 2022-09-01 | 2022-09-01 | Inventory management method and system of OMS |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211063223.3A CN115375407B (en) | 2022-09-01 | 2022-09-01 | Inventory management method and system of OMS |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115375407A CN115375407A (en) | 2022-11-22 |
| CN115375407B true CN115375407B (en) | 2023-09-19 |
Family
ID=84068648
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211063223.3A Active CN115375407B (en) | 2022-09-01 | 2022-09-01 | Inventory management method and system of OMS |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115375407B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017155767A1 (en) * | 2016-03-05 | 2017-09-14 | Home Depot International, Inc. | Optimistic product order reservation system and method |
| CN108846599A (en) * | 2018-03-29 | 2018-11-20 | 宏图物流股份有限公司 | A kind of method and device of logistics order management |
| CN112561414A (en) * | 2019-09-25 | 2021-03-26 | 顺丰科技有限公司 | Inventory management method, device and computer readable storage medium |
| CN113191795A (en) * | 2021-04-15 | 2021-07-30 | 杭州白秋科技有限公司 | Commodity display quantity estimation method, commodity display quantity estimation device, commodity display quantity estimation equipment and storage medium |
| CN113902383A (en) * | 2021-11-23 | 2022-01-07 | 杭州拼便宜网络科技有限公司 | Inventory processing method, device, equipment and storage medium based on e-commerce platform |
| US11334846B1 (en) * | 2021-09-15 | 2022-05-17 | Fabfitfun, Inc. | Systems and computer-implemented methods for inventory management |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11475405B2 (en) * | 2018-11-20 | 2022-10-18 | Target Brands, Inc. | Store-based order fulfillment system |
-
2022
- 2022-09-01 CN CN202211063223.3A patent/CN115375407B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017155767A1 (en) * | 2016-03-05 | 2017-09-14 | Home Depot International, Inc. | Optimistic product order reservation system and method |
| CN108846599A (en) * | 2018-03-29 | 2018-11-20 | 宏图物流股份有限公司 | A kind of method and device of logistics order management |
| CN112561414A (en) * | 2019-09-25 | 2021-03-26 | 顺丰科技有限公司 | Inventory management method, device and computer readable storage medium |
| CN113191795A (en) * | 2021-04-15 | 2021-07-30 | 杭州白秋科技有限公司 | Commodity display quantity estimation method, commodity display quantity estimation device, commodity display quantity estimation equipment and storage medium |
| US11334846B1 (en) * | 2021-09-15 | 2022-05-17 | Fabfitfun, Inc. | Systems and computer-implemented methods for inventory management |
| CN113902383A (en) * | 2021-11-23 | 2022-01-07 | 杭州拼便宜网络科技有限公司 | Inventory processing method, device, equipment and storage medium based on e-commerce platform |
Non-Patent Citations (2)
| Title |
|---|
| 方雄伟 ; .红狮公司供应链管理系统设计与实现.湖北经济学院学报(人文社会科学版).2010,(第07期),全文. * |
| 红狮公司供应链管理系统设计与实现;方雄伟;;湖北经济学院学报(人文社会科学版)(第07期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115375407A (en) | 2022-11-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110493016B (en) | Flow service charging method and device | |
| US9779406B2 (en) | User feature identification method and apparatus | |
| EP2524345A1 (en) | Attribute aggregation for standard product unit | |
| CN110705475A (en) | Method, apparatus, medium, and device for target object recognition | |
| CN115936582A (en) | Commodity replenishment method, system, electronic device and readable storage medium | |
| CN110782139A (en) | Daily yield daily clearing accounting method, device, equipment and storage medium based on block chain | |
| CN114663015A (en) | Replenishment method and device | |
| CN115375407B (en) | Inventory management method and system of OMS | |
| CN111754330A (en) | Interest-counting device and method for regular account to be paid in advance | |
| CN115827994A (en) | Data processing method, device, equipment and storage medium | |
| CN115309730A (en) | Data auditing method and device, electronic equipment and storage medium | |
| CN103984779A (en) | Data updating method and device | |
| CN112001759A (en) | Warehouse price data prediction method and device, electronic equipment and readable storage medium | |
| CN112199418B (en) | State identification method, device and equipment for industrial object | |
| CN114443311B (en) | Third-party service configuration method and device and electronic equipment | |
| CN115293096B (en) | Line generation method, device, electronic equipment and storage medium | |
| CN114694138B (en) | Road surface detection method, device and equipment applied to intelligent driving | |
| CN117150215B (en) | Assessment result determining method and device, electronic equipment and storage medium | |
| CN110110176B (en) | Data display method and device | |
| CN111815867B (en) | Settling processing method and device for storage cabinet and storage medium | |
| CN114529397A (en) | Method and device for determining client loan risk level, electronic equipment and storage medium | |
| CN117333104A (en) | Inventory synchronization method and device, electronic equipment and storage medium | |
| CN112966210A (en) | Method and device for storing user data | |
| CN117216066A (en) | Material code generation method, device, equipment and storage medium | |
| CN114169962A (en) | Data processing method and device for traffic management |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |