CN114565344A - ERP e-commerce order inventory management system and method based on cloud platform - Google Patents
ERP e-commerce order inventory management system and method based on cloud platform Download PDFInfo
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Abstract
The invention discloses an ERP e-commerce order inventory management system and method based on a cloud platform, which comprises the following steps: the order data acquisition module extracts order data of the e-commerce through ERP in the cloud platform; the order data analysis module analyzes the obtained order data according to the order data of the e-commerce collected by the order data collection module to obtain a change curve of the e-commerce order; and the order data estimation module estimates the order data of the E-commerce according to the result obtained by the order data analysis module. And the inventory management module is used for realizing the management of the order inventory according to the estimation result of the order data obtained by the order data estimation module.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an ERP e-commerce order inventory management system and method based on a cloud platform.
Background
Along with the rapid development of computer technology, the application of the internet is gradually popularized by people, especially in the field of e-commerce, people can purchase goods of their own mood instruments without going out of home through the internet, great convenience is brought to the life of people, and merchants can sell their commodities to the whole country, but the current e-commerce order inventory management system has a great defect.
The current e-commerce order inventory management system can only count the quantity of inventory orders, cannot estimate the quantity of the orders, and cannot estimate the unsubscribing condition of a user according to the self condition of the user.
In view of the above situation, a cloud platform-based ERP e-commerce order inventory management system and method are needed.
Disclosure of Invention
The invention aims to provide a cloud platform-based ERP e-commerce order inventory management system and method, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an ERP e-commerce order inventory management system based on a cloud platform comprises:
the order data acquisition module extracts order data of the e-commerce through ERP in the cloud platform;
the order data analysis module analyzes the obtained order data according to the order data of the e-commerce collected by the order data collection module to obtain a change curve of the e-commerce order;
and the order data estimation module estimates the order data of the E-commerce according to the result obtained by the order data analysis module.
And the inventory management module is used for realizing the management of the order inventory according to the estimation result of the order data obtained by the order data estimation module.
According to the invention, through the cooperative cooperation of all the modules, the unified management of the order condition of the user is realized together, and the unsubscribe probability of the user is estimated by combining the historical browsing record of the user, so that the estimation of the stock order condition is realized, and the estimation precision is higher.
Further, when the order data acquisition module extracts order data of the e-commerce, the order data comprises the type and price of an order placed by a user and historical browsing records of each user in unit time before and after the order is placed,
the order data also includes historical order records for the user.
The order data acquisition module acquires the historical browsing records of each user in unit time before and after placing an order, and is used for analyzing the deviation condition between prices corresponding to the historical browsing records in unit time before and after placing the order, so as to judge whether the user is possible to have an order cancellation condition and how high the probability of the order cancellation is.
Further, the method for obtaining the change curve of the e-commerce order by the order data analysis module comprises the following steps:
s1.1, obtaining the quantity of the nth order corresponding to the ith first unit time in the second unit time in the historical order data
I is not less than 1 and not more than i1, wherein i1 represents the total number of the first unit time in the second unit time;
s1.2, obtaining
Corresponding coordinate data
The above-mentioned
To represent
The time difference between the time corresponding to the midpoint in the corresponding first unit time and the time corresponding to the starting point of the second unit time;
s1.3, inputting coordinate data corresponding to the same type of orders into a planar rectangular coordinate system one by one, wherein the origin of the planar rectangular coordinate system is a point corresponding to time difference of 0, the y axis is the number of nth orders corresponding to ith first unit time in second unit time in historical order data, and the x axis is the time difference between time corresponding to a middle point in the first unit time and time corresponding to a starting point of the second unit time;
s1.4, fitting a change curve fn of the e-commerce order corresponding to the nth order according to the coordinate data corresponding to the nth order.
In the process of curve fitting by the order data analysis module, the time difference is adopted by the coordinate data, so that a reference point is conveniently searched, and meanwhile, the order number after the first unit time based on the current time can be quickly estimated, so that a data basis is provided for estimation of the number of subsequent orders.
Further, in the process of fitting the variation curve fn of the e-commerce order corresponding to the nth order in the step S1.4, a corresponding e-commerce order variation curve is obtained by using a fitting curve template,
the fitted curve template includes a plurality of types,
when obtaining the change curve fn of the E-commerce order, respectively adopting different fitting curve templates to perform curve fitting on the coordinate data corresponding to the nth order to respectively obtain each corresponding fitting curve,
and then respectively calculating the sum of the distances between each data coordinate data in each fitting curve and the corresponding fitting curve, and selecting the smallest fitting curve after the distance as a change curve fn of the e-commerce order corresponding to the nth order.
In the process of curve fitting, a plurality of fitting curve templates are adopted, so that the change trend of the order is analyzed from a plurality of angles, the change trend corresponding to the fitting curve is more accurate, and the accuracy of the estimation result of the order data in the follow-up process is further improved.
Further, the order data analysis module analyzes the historical browsing records of each user who places the order in unit time before and after placing the order to obtain the probability of each user quitting the order,
the method for obtaining the probability of order quitting of each user by the order data analysis module comprises the following steps:
s2.1, respectively obtaining historical browsing records of the order placing user in unit time before and after placing the order,
recording historical browsing records corresponding to the commodity types corresponding to the nth order in historical browsing records of the jth order placing user in the nth order in unit time before order placing into a blank set one by one to obtain
Recording historical browsing records corresponding to the commodity types corresponding to the nth order in historical browsing records of the jth order-placing user in the nth order in unit time after order placing into a blank set one by one to obtain
S2.2, obtaining
The order prices corresponding to the browsing records are sequenced from small to large, and the corresponding serial numbers and the corresponding prices when placing the order are obtained
Obtaining a user ordering preference value by the ratio of the total number of the browsing records;
s2.3, obtaining
Ordering prices corresponding to all browsing records in the S2.2 are sorted from small to large, and the user order preference value obtained in the S2.2 is multiplied by
The total number of the browsing records in the database is obtained to obtain a first comparison product ranking sequence number;
s2.4, obtaining the product price corresponding to the first comparison product ranking serial number of S2.3 and recording the product price as
The described
The product price corresponding to the first comparison product ranking serial number corresponding to the jth order placing user in the nth order is represented;
s2.5, calculating the deviation coefficient between the product price corresponding to the first comparative product ranking serial number corresponding to the jth order placing user in the nth order and the order placing price corresponding to the user
The above-mentioned
The above-mentioned
Representing the price corresponding to the order placing of the jth order placing user in the nth order;
s2.6, obtaining the deviation coefficient
Comparing with a first preset value and a second preset value, wherein the first preset value is larger than the second preset value,
when in use
Greater than or equal to a first preset value or
When the value is less than or equal to the second preset value, the judgment is made
Is invalid and will
The value of (a) is changed to 0,
when in use
When the value is less than the first preset value and greater than the second preset value, the judgment is made
Is effective, and
the value of (a) is not changed.
Invention determination
Whether the deviation value is valid or not is determined in order to lock a range in which the deviation value is proportional to the unsubscribing probability, considering that the deviation value is large, which causes a large difference in the price of the product (in this case, the difference exceeds a certain range, and the possibility of unsubscribing by the user becomes small).
Further, the order data analysis module obtains various order unsubscribing conditions of the user according to historical order records of the user, calculates order unsubscribing probabilities of the user every other third unit time, calculates an average value of the order unsubscribing probabilities of the user in each third unit time corresponding to each user in a fourth unit time, and records the average value of the order unsubscribing probabilities of the jth user corresponding to the nth order as the average value of the order unsubscribing probabilities in each third unit time corresponding to the jth user corresponding to the nth order
Further, the method for estimating the order data of the e-commerce by the order data estimation module comprises the following steps:
s3.1, calculating fnm corresponding to a change curve fn of an e-commerce order corresponding to the nth order after the first unit time based on the current time and fnm1 average value of the change curve fn of the e-commerce order corresponding to the nth order in the second unit time corresponding to the first unit time,
the above-mentioned
S3.2, obtaining a deviation coefficient between the product price corresponding to the first comparative product ranking serial number corresponding to the jth order placing user in the nth order in S2.6 and the order placing price corresponding to the user
S3.3, obtaining the average value of order unsubscribing probabilities in each third unit time corresponding to the jth user corresponding to the nth order and recording the average value as
S3.4, calculating the order unsubscribe number corresponding to the nth order after the first unit time based on the current time, wherein the order unsubscribe number is
r is a first coefficient, r is obtained through database query, the minimum value of fnm and fnm1 is represented by { fnm, fnm1} min, and jn is the total number of order placing users corresponding to the nth order;
s3.5, obtaining a first estimated value Q1n of the nth order data of the e-commerce,
the above-mentioned
Where { fnm, fnm1} max represents the maximum of fnm and fnm 1;
s3.6, obtaining the first estimated unsubscribing rate
The above-mentioned
S3.7, the first estimated unsubscribing rate
And
and a third preset value beta are compared,
when in use
And is
Then, it is determined that the first estimated value Q1n and the first coefficient r of the nth order data of the power utility need to be calibrated,
the value of r after calibration is
The value corresponding to r, denoted as r1,
the described
The calibrated r1 is saved in a database, and the original r value is replaced,
the calibrated first estimate being equal to
When in use
And is
Then, it is determined that the first estimated value Q1n and the first coefficient r of the nth order data of the power utility need not be calibrated.
The present invention obtains fnm and fnm1 because both of them can reflect the estimated value of the nth order after the first unit time based on the current time, calculate { fnm, fnm1} max and calculate
To estimate the minimum withdrawal amount and normality of the n-th orderThe maximum sales volume of the order can estimate the sales volume of the products in the stock to a certain extent as soon as possible, and the stock is managed to avoid the occurrence of out-of-stock situations.
Further, when the inventory management module manages the inventory,
when the number of the order in the nth order in the stock is less than Qn, the product corresponding to the nth order in the stock needs to be restocked,
when the number of orders in the nth order in the stock is more than or equal to Qn, the stock does not need to be processed.
An ERP e-commerce order inventory management method based on a cloud platform comprises the following steps:
s1, the order data acquisition module extracts order data of the e-commerce through ERP in the cloud platform;
s2, the order data analysis module analyzes the obtained order data according to the order data of the E-commerce collected by the order data collection module to obtain a change curve of the E-commerce order;
and S3, the order data estimation module estimates the order data of the E-commerce according to the result obtained by the order data analysis module.
And S4, the stock management module realizes the management of the order stock according to the estimation result of the order data obtained by the order data estimation module.
Compared with the prior art, the invention has the following beneficial effects: the invention can realize the estimation of the user unsubscribe probability by combining the historical browsing records of the user, further realize the estimation of the stock order condition, has higher estimation precision, avoids the occurrence of stock shortage and is convenient for merchants to effectively manage the stock.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic structural diagram of an ERP e-commerce order inventory management method based on a cloud platform according to the present invention;
FIG. 2 is a schematic flow chart of a method for obtaining the probability of each user returning an order by an order data analysis module in the ERP e-commerce order inventory management system based on the cloud platform according to the invention;
fig. 3 is a flow diagram of an ERP e-commerce order inventory management method based on a cloud platform according to the present invention.
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1-3, the present invention provides a technical solution: an ERP e-commerce order inventory management system based on a cloud platform comprises:
the order data acquisition module extracts order data of the e-commerce through ERP in the cloud platform;
the order data analysis module analyzes the obtained order data according to the order data of the e-commerce collected by the order data collection module to obtain a change curve of the e-commerce order;
and the order data estimation module estimates the order data of the E-commerce according to the result obtained by the order data analysis module.
And the inventory management module is used for realizing the management of the order inventory according to the estimation result of the order data obtained by the order data estimation module.
According to the invention, through the cooperative cooperation of all the modules, the unified management of the order condition of the user is realized together, and the unsubscribe probability of the user is estimated by combining the historical browsing record of the user, so that the estimation of the stock order condition is realized, and the estimation precision is higher.
When the order data acquisition module extracts order data of the E-commerce, the order data comprises the type and price of an order placed by a user and historical browsing records of each user in unit time before and after the order is placed,
the order data also includes historical order records for the user.
The order data acquisition module acquires the historical browsing records of each user in unit time before and after placing an order, and is used for analyzing the deviation condition between prices corresponding to the historical browsing records in unit time before and after placing the order, so as to judge whether the user is possible to have an order cancellation condition and how high the probability of the order cancellation is.
The method for obtaining the change curve of the E-commerce order by the order data analysis module comprises the following steps of:
s1.1, obtaining the quantity of the nth order corresponding to the ith first unit time in the second unit time in the historical order data
I is not less than 1 and not more than i1, wherein i1 represents the total number of the first unit time in the second unit time;
s1.2, obtaining
Corresponding coordinate data
The described
To represent
The time difference between the time corresponding to the midpoint in the corresponding first unit time and the time corresponding to the starting point of the second unit time;
s1.3, inputting coordinate data corresponding to orders of the same type into a planar rectangular coordinate system one by one, wherein the origin of the planar rectangular coordinate system is a point corresponding to time difference of 0, the y axis is the number of nth orders corresponding to ith first unit time in second unit time in historical order data, and the x axis is the time difference between time corresponding to a middle point in the first unit time and time corresponding to a starting point of the second unit time;
s1.4, fitting a change curve fn of the e-commerce order corresponding to the nth order according to the coordinate data corresponding to the nth order.
In the process of curve fitting by the order data analysis module, the time difference is adopted by the coordinate data, so that a reference point is conveniently searched, and meanwhile, the order number after the first unit time based on the current time can be quickly estimated, so that a data basis is provided for estimation of the number of subsequent orders.
In the process of fitting the variation curve fn of the e-commerce order corresponding to the nth order in the S1.4, a corresponding e-commerce order variation curve is obtained by adopting a fitting curve template,
the fitted curve template includes a plurality of types,
when obtaining the change curve fn of the E-commerce order, respectively adopting different fitting curve templates to perform curve fitting on the coordinate data corresponding to the nth order to respectively obtain each corresponding fitting curve,
and then respectively calculating the sum of the distances between each data coordinate data in each fitting curve and the corresponding fitting curve, and selecting the smallest fitting curve after the distance as a change curve fn of the e-commerce order corresponding to the nth order.
In the process of curve fitting, a plurality of fitting curve templates are adopted, so that the change trend of the order is analyzed from a plurality of angles, the change trend corresponding to the fitting curve is more accurate, and the accuracy of the estimation result of the order data in the follow-up process is further improved.
The order data analysis module also analyzes the historical browsing records of each user who places the order in unit time before and after placing the order to obtain the probability of each user for quitting the order,
the method for obtaining the probability of order quitting of each user by the order data analysis module comprises the following steps:
s2.1, respectively obtaining historical browsing records of the order-placing user in unit time before and after placing the order,
recording historical browsing records corresponding to the commodity types corresponding to the nth order in historical browsing records of the jth order placing user in the nth order in unit time before order placing into a blank set one by one to obtain
Recording historical browsing records corresponding to the commodity types corresponding to the nth order in historical browsing records of the jth order-placing user in the nth order in unit time after order placing into a blank set one by one to obtain
S2.2, obtaining
The order prices corresponding to the browsing records are sequenced from small to large, and the corresponding serial numbers and the corresponding prices when placing the order are obtained
Obtaining a user ordering preference value by the ratio of the total number of the browsing records;
s2.3, obtaining
Ordering prices corresponding to all browsing records in the S2.2 are sorted from small to large, and the user order preference value obtained in the S2.2 is multiplied by
The total number of the browsing records in the database is obtained to obtain a first comparison product ranking sequence number;
s2.4, obtaining the product price corresponding to the first comparison product ranking serial number of S2.3, and recording as
The above-mentioned
The product price corresponding to the first comparison product ranking serial number corresponding to the jth order placing user in the nth order is represented;
s2.5, calculating the deviation coefficient between the product price corresponding to the first comparative product ranking serial number corresponding to the jth order placing user in the nth order and the order placing price corresponding to the user
The above-mentioned
The above-mentioned
Representing the price corresponding to the order placing of the jth order placing user in the nth order;
s2.6, obtaining the deviation coefficient
Comparing with a first preset value and a second preset value, wherein the first preset value is larger than the second preset value,
when the temperature is higher than the set temperature
Greater than or equal to a first predetermined value or
When the value is less than or equal to the second preset value, the judgment is made
Is invalid and will
The value of (a) is changed to 0,
when in use
When the value is less than the first preset value and greater than the second preset value, the judgment is made
Is effective, and
the value of (a) is not changed.
Invention determination
Whether the deviation value is valid or not is determined in order to lock a range in which the deviation value is proportional to the unsubscribing probability, considering that the deviation value is large, which causes a large difference in the price of the product (in this case, the difference exceeds a certain range, and the possibility of unsubscribing by the user becomes small).
The order data analysis module also obtains various order unsubscribing conditions of the user according to the historical order record of the user, calculates the order unsubscribing probability of the user in every third unit time, calculates the average value of the order unsubscribing probability of each user in each third unit time in the fourth unit time, and records the average value of the order unsubscribing probability of each jth user in the jth unit time corresponding to the nth order as the order unsubscribing probability of each user in each third unit time
The method for estimating the order data of the E-commerce by the order data estimation module comprises the following steps:
s3.1, calculating fnm corresponding to a change curve fn of an e-commerce order corresponding to the nth order after the first unit time based on the current time and fnm1 average value of the change curve fn of the e-commerce order corresponding to the nth order in the second unit time corresponding to the first unit time,
the above-mentioned
S3.2, obtaining a deviation coefficient between the product price corresponding to the first comparative product ranking serial number corresponding to the jth order placing user in the nth order in S2.6 and the order placing price corresponding to the user
S3.3, obtaining the average value of order unsubscribing probabilities in each third unit time corresponding to the jth user corresponding to the nth order and recording the average value as
S3.4, calculating the order unsubscribe number corresponding to the nth order after the first unit time based on the current time, wherein the order unsubscribe number is
r is a first coefficient, r is obtained through database query, the minimum value of fnm and fnm1 is represented by { fnm, fnm1} min, and jn is the total number of order placing users corresponding to the nth order;
s3.5, obtaining a first estimated value Q1n of the nth order data of the e-commerce,
the above-mentioned
Where { fnm, fnm1} max represents the maximum of fnm and fnm 1;
s3.6, obtaining the first estimated unsubscribing rate
The above-mentioned
S3.7, the first estimated unsubscribing rate
And
and a third preset value beta are compared,
when in use
And is
Then, it is determined that the first estimated value Q1n and the first coefficient r of the nth order data of the power utility need to be calibrated,
the value of r after calibration is
The value corresponding to r, denoted as r1,
the described
The calibrated r1 is saved to the database, and replaces the original r value,
the calibrated first estimate being equal to
When in use
And is
Then, it is determined that the first estimated value Q1n and the first coefficient r of the nth order data of the e-commerce are not needed to be calibrated.
In this embodiment, if the value f3m corresponding to the change curve f3 of the e-commerce order corresponding to the 3 rd order after the first unit time based on the current time is 4,
the variation curve f3 of the e-commerce order corresponding to the 3 rd order is that the mean value f3m1 of the e-commerce order corresponding to the first unit time in the second unit time is 5, r is equal to 8, beta is equal to 0.25,
if there are two users in the historical order,
corresponding to the first user
The content of the organic acid is 0.1,
the content of the organic acid is 0.1,
corresponding to a second user
The content of the acid-base reaction product is 0.15,
the content of the organic acid is 0.1,
the number of unsubscribes is
The first estimate is 4, 5 max-0.4 to 4.6,
because of the fact that
There is no need to calibrate the first estimated value Q1n and the first coefficient r of the nth order data of the power utility.
When the inventory management module manages the inventory,
when the number of the order in the nth order in the stock is less than Qn, the product corresponding to the nth order in the stock needs to be restocked,
when the number of orders in the nth order in the stock is more than or equal to Qn, the stock does not need to be processed.
An ERP e-commerce order inventory management method based on a cloud platform comprises the following steps:
s1, the order data acquisition module extracts order data of the e-commerce through ERP in the cloud platform;
s2, the order data analysis module analyzes the obtained order data according to the order data of the E-commerce collected by the order data collection module to obtain a change curve of the E-commerce order;
and S3, the order data estimation module estimates the order data of the E-commerce according to the result obtained by the order data analysis module.
And S4, the stock management module realizes the management of the order stock according to the estimation result of the order data obtained by the order data estimation module.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An ERP e-commerce order inventory management system based on a cloud platform is characterized by comprising:
the order data acquisition module extracts order data of the e-commerce through ERP in the cloud platform;
the order data analysis module analyzes the obtained order data according to the order data of the e-commerce collected by the order data collection module to obtain a change curve of the e-commerce order;
and the order data estimation module estimates the order data of the E-commerce according to the result obtained by the order data analysis module.
And the inventory management module is used for realizing the management of the order inventory according to the estimation result of the order data obtained by the order data estimation module.
2. The ERP e-commerce order inventory management system based on the cloud platform as claimed in claim 1, wherein: when the order data acquisition module extracts order data of the E-commerce, the order data comprises the type and price of an order placed by a user and historical browsing records of each user in unit time before and after the order is placed,
the order data also includes historical order records for the user.
3. The ERP e-commerce order inventory management system based on the cloud platform as claimed in claim 2, wherein: the method for obtaining the change curve of the E-commerce order by the order data analysis module comprises the following steps of:
s1.1, acquiring the quantity of the nth order corresponding to the ith first unit time in the second unit time T2 in the historical order data
I is not less than 1 and not more than i1, wherein i1 represents the total number of the first unit time in the second unit time;
s1.2, obtaining
Corresponding coordinate data
The above-mentioned
To represent
The time difference between the time corresponding to the midpoint in the corresponding first unit time and the time corresponding to the starting point of the second unit time;
s1.3, inputting coordinate data corresponding to the same type of orders into a planar rectangular coordinate system one by one, wherein the origin of the planar rectangular coordinate system is a point corresponding to time difference of 0, the y axis is the number of nth orders corresponding to ith first unit time in second unit time in historical order data, and the x axis is the time difference between time corresponding to a middle point in the first unit time and time corresponding to a starting point of the second unit time;
s1.4, fitting a change curve fn of the e-commerce order corresponding to the nth order according to the coordinate data corresponding to the nth order.
4. The ERP e-commerce order inventory management system based on the cloud platform as claimed in claim 3, wherein: in the process of fitting the variation curve fn of the e-commerce order corresponding to the nth order in the S1.4, a corresponding e-commerce order variation curve is obtained by adopting a fitting curve template,
the fitted curve template includes a plurality of types,
when obtaining the change curve fn of the E-commerce order, respectively adopting different fitting curve templates to perform curve fitting on the coordinate data corresponding to the nth order to respectively obtain each corresponding fitting curve,
and then respectively calculating the sum of the distances between each data coordinate data in each fitting curve and the corresponding fitting curve, and selecting the smallest fitting curve after the distance as a change curve fn of the e-commerce order corresponding to the nth order.
5. The ERP e-commerce order inventory management system based on the cloud platform as claimed in claim 4, wherein: the order data analysis module also analyzes the historical browsing records of each user who places the order in unit time before and after placing the order to obtain the probability of each user for quitting the order,
the method for obtaining the probability of order quitting of each user by the order data analysis module comprises the following steps:
s2.1, respectively obtaining historical browsing records of the order placing user in unit time before and after placing the order,
recording historical browsing records corresponding to the commodity types corresponding to the nth order in historical browsing records of the jth order placing user in the nth order in unit time before order placing into a blank set one by one to obtain
Recording historical browsing records corresponding to the commodity types corresponding to the nth order in historical browsing records of the jth order-placing user in the nth order in unit time after order placing into a blank set one by one to obtain
S2.2, obtaining
The order prices corresponding to the browsing records are sequenced from small to large, and the corresponding serial numbers and the corresponding prices when placing the order are obtained
Obtaining a user ordering preference value by the ratio of the total number of the browsing records;
s2.3, obtaining
Ordering prices corresponding to all browsing records in the S2.2 are sorted from small to large, and the user order preference value obtained in the S2.2 is multiplied by
The total number of the browsing records in the database is obtained to obtain a first comparison product ranking sequence number;
s2.4, obtaining the product price corresponding to the first comparison product ranking serial number of S2.3 and recording the product price as
The above-mentioned
The product price corresponding to the first comparison product ranking serial number corresponding to the jth order placing user in the nth order is represented;
s2.5, calculating the deviation coefficient between the product price corresponding to the first comparative product ranking serial number corresponding to the jth order placing user in the nth order and the order placing price corresponding to the user
The above-mentioned
The described
Representing the price corresponding to the order placing of the jth order placing user in the nth order;
s2.6, obtaining the deviation coefficient
Comparing with a first preset value and a second preset value, wherein the first preset value is larger than the second preset value,
when in use
Greater than or equal to a first preset value or
When the value is less than or equal to the second preset value, the judgment is made
Is invalid and will
The value of (a) is changed to 0,
when in use
When the value is less than the first preset value and greater than the second preset value, the judgment is made
Is effective, and
the value of (a) is not changed.
6. The ERP e-commerce order inventory management system based on the cloud platform as claimed in claim 5, wherein: the order data analysis module also obtains various order unsubscribing conditions of the user according to the historical order record of the user, calculates the order unsubscribing probability of the user in every third unit time, calculates the average value of the order unsubscribing probability of each user in each third unit time in the fourth unit time, and records the average value of the order unsubscribing probability of each jth user in the jth unit time corresponding to the nth order as the order unsubscribing probability of each user in each third unit time
7. The ERP e-commerce order inventory management system based on the cloud platform as claimed in claim 6, wherein: the method for estimating the order data of the E-commerce by the order data estimation module comprises the following steps:
s3.1, calculating fnm corresponding to a change curve fn of an e-commerce order corresponding to the nth order after the first unit time based on the current time and fnm1 average value of the change curve fn of the e-commerce order corresponding to the nth order in the second unit time corresponding to the first unit time,
the above-mentioned
S3.2, obtaining a deviation coefficient between the product price corresponding to the first comparative product ranking serial number corresponding to the jth order placing user in the nth order in S2.6 and the order placing price corresponding to the user
S3.3, obtaining the average value of order unsubscribing probabilities in each third unit time corresponding to the jth user corresponding to the nth order and recording the average value as
S3.4, calculating the order unsubscribe number corresponding to the nth order after the first unit time based on the current time, wherein the order unsubscribe number is
r is a first coefficient, r is obtained through database query, the minimum value of fnm and fnm1 is represented by { fnm, fnm1} min, and jn is the total number of order placing users corresponding to the nth order;
s3.5, obtaining a first estimated value Q1n of the nth order data of the e-commerce,
the above-mentioned
Wherein { fnm, fnm1} max denotes the sum of fnm and fnm1Maximum value of (d);
s3.6, obtaining the first estimated unsubscribing rate
The above-mentioned
S3.7, the first estimated unsubscribing rate
And
and a third preset value beta are compared,
when in use
And is
Then, it is determined that the first estimated value Q1n and the first coefficient r of the nth order data of the power utility need to be calibrated,
the value of r after calibration is
The value corresponding to r, denoted as r1,
the above-mentioned
The calibrated r1 is saved to the database, and replaces the original r value,
the calibrated first estimate being equal to
When in use
And is
Then, it is determined that the first estimated value Q1n and the first coefficient r of the nth order data of the power utility need not be calibrated.
8. The ERP e-commerce order inventory management system based on the cloud platform as claimed in claim 7, wherein: when the inventory management module manages the inventory,
when the number of the order in the nth order in the stock is less than Qn, the product corresponding to the nth order in the stock needs to be restocked,
when the number of orders in the nth order in the stock is more than or equal to Qn, the stock does not need to be processed.
9. The ERP e-commerce order inventory management method based on the cloud platform as claimed in any one of claims 1 to 8, wherein: the method comprises the following steps:
s1, the order data acquisition module extracts order data of the e-commerce through ERP in the cloud platform;
s2, the order data analysis module analyzes the obtained order data according to the order data of the E-commerce collected by the order data collection module to obtain a change curve of the E-commerce order;
s3, the order data pre-estimation module pre-estimates the order data of the E-commerce according to the result obtained by the order data analysis module;
and S4, the stock management module realizes the management of the order stock according to the estimation result of the order data obtained by the order data estimation module.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115423575A (en) * | 2022-11-03 | 2022-12-02 | 孩子王儿童用品股份有限公司 | Digital analysis management system and method based on Internet |
| CN116503000A (en) * | 2023-06-27 | 2023-07-28 | 广州晨安网络科技有限公司 | Manufacturing order inventory ERP management method and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115423575A (en) * | 2022-11-03 | 2022-12-02 | 孩子王儿童用品股份有限公司 | Digital analysis management system and method based on Internet |
| CN116503000A (en) * | 2023-06-27 | 2023-07-28 | 广州晨安网络科技有限公司 | Manufacturing order inventory ERP management method and system |
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