CN115689442A - Commodity cross-regional supply chain management method, commodity cross-regional supply chain management device and commodity cross-regional supply chain management medium based on big data - Google Patents
Commodity cross-regional supply chain management method, commodity cross-regional supply chain management device and commodity cross-regional supply chain management medium based on big data Download PDFInfo
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Abstract
The invention discloses a commodity cross-regional supply chain management method, a device and a medium based on big data, which comprises the steps of obtaining various historical sales data of each sales region of a commodity, and determining the grade of the corresponding sales region according to the priority and the value weight of each historical sales data; acquiring inventory data of each sales area, and judging whether the inventory data is matched with preset inventory of the sales area; if not, acquiring total inventory data of each sales area, and allocating the inventory supply of the sales area based on the supply management model according to the level and the inventory condition of each sales area. The invention determines the level of each sales area through the historical sales data of the sales area, sets the corresponding preset inventory based on the level, and allocates the inventory of the sales area based on the supply management model, so that all the sales areas are transversely compared in the supply chain at the same time, the cross-area quick response of commodity supply is realized, and the linkage management of the supply chains of a plurality of sales areas is further realized.
Description
Technical Field
The invention belongs to the technical field of supply chain management, and particularly relates to a commodity cross-regional supply chain management method, device and medium based on big data.
Background
With the development of technology, the supply chain management of various industries gradually tends to be informationized and intelligentized. Supply chain management not only integrates upstream and downstream resources, but also the scientific planning and decision implementation thereof must be extremely fine, precise and accurate, otherwise the normal production and management order of an enterprise will be directly affected. For example: the stable and continuous development of the supply chain enables insulation material distribution enterprises to have stronger vitality and competitiveness in market competition, and therefore, more and more insulation material distribution enterprises begin to pay attention to related supply chain management.
The supply chain is a network structure formed by the supply and demand relationship among enterprises, and is a network structure formed by the connection of members such as raw material suppliers, producers, distributors, retailers and final consumers involved in the production and circulation of products with upstream and downstream members. Today, with high degree of information in the market, a huge amount of information is generated between members themselves and the members, and how to manage and monitor each supply chain information across areas so as to realize rapid reaction of commodities across areas becomes a problem to be solved urgently at present.
Disclosure of Invention
The invention aims to provide a commodity cross-regional supply chain management method, a commodity cross-regional supply chain management device and a commodity cross-regional supply chain management medium based on big data, and the commodity cross-regional supply chain management method, the commodity cross-regional supply chain management device and the commodity cross-regional supply chain management medium are used for solving the technical problem of how to manage and monitor each supply chain information of cross regions.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect provides a commodity cross-regional supply chain management method based on big data, which comprises the following steps:
acquiring various historical sales data of each sales area of the commodity, and determining the level of the corresponding sales area according to the priority and the value weight of each historical sales data;
acquiring inventory data of each sales area, and judging whether the inventory data is matched with preset inventory of the sales area of the corresponding level;
and if not, acquiring total inventory data of each sales area, and allocating the inventory supply of each sales area based on the supply management model according to the level and the inventory condition of each sales area.
In one possible design, the historical sales data includes at least commodity sales data, commodity goodness data, commodity complaint data, commodity repurchase data, and/or commodity return data.
In one possible design, determining the level of the corresponding sales area based on the priority and value weight of each historical sales data includes:
calculating posterior probabilities of each type of historical sales data relative to each level sales area, comparing the posterior probabilities of any two types of historical sales data in pairs, and marking the historical sales data with higher posterior probabilities until the comparison of all the historical sales data is completed;
counting the total mark quantity of each type of historical sales data, and sequencing all the historical sales data according to the total mark quantity so as to determine the priority of each type of historical sales data according to the sequencing result;
and calculating corresponding value weight according to the priority of each type of historical sales data, and calculating to obtain the level of the corresponding sales area.
In one possible design, the comparing the posterior probabilities of any two kinds of historical sales data two by two, and marking the historical sales data with the greater posterior probability includes:
based on a dual triangle arrangement method, arranging and combining M kinds of historical sales data according to M-1 rows and M-1 columns; wherein each row of historical sales data comprises a plurality of pairs of historical sales data;
comparing the posterior probabilities of one pair of data in each row of historical sales data respectively, and marking the data with higher posterior probability.
In one possible design, calculating a corresponding value weight according to the priority of each type of historical sales data, and calculating the level of the corresponding sales region includes:
determining the weight position of each type of historical sales data according to the priority of each type of historical sales data, wherein the weight position corresponding to the historical sales data with the priority of the ith level is s +1-i, and s represents the type of the historical sales data;
and calculating the value weight of each type of historical sales data according to the weight position, wherein the calculation formula is as follows:
wherein Wi represents the value weight of the historical sales data with the i-th level as the priority, and Di represents the weight position;
according to the value weight of each type of historical sales data, calculating to obtain the level H of the corresponding sales region k The calculation formula is as follows:
wherein k denotes the kth sales area, S g Indicates the g-th sales data, and N indicates the total number of categories of sales data of the k-th sales region.
In one possible design, acquiring inventory data of each sales area, and determining whether the inventory data matches a preset inventory of the sales area of a corresponding level includes:
acquiring the total number of warehouse inventory goods in each sales area and the total number of orders to be issued in the area;
according to the total number of the warehouse inventory goods and the total number of the orders to be issued, determining an inventory coefficient corresponding to actual inventory to be sold in the area based on the following formula, wherein the calculation formula is as follows:
Fj＝e×(Cj×b1-Dj×b2)； (2)
wherein Fj represents an inventory coefficient corresponding to the actual stock to be sold in the area, e represents a correction coefficient, cj represents the total number of warehouse stock commodities, dj represents the total number of orders to be delivered, b1 and b2 represent preset proportionality coefficients, and j represents the jth sales area;
and judging whether the inventory coefficient corresponding to the actual sales inventory of the area is matched with the coefficient of the preset inventory of the sales area of the corresponding level.
In one possible design, the provisioning management model is built as follows:
assuming that scheduling management is performed between the inventories of the sales regions, the management policy matrix of the k-th sales region in the supply chain system is as follows:
wherein a represents a policy point for allocating inventory management in the kth sales area;
wherein, the adaptation degree of the supply management model is as follows:
k 0 ＝P max /P min ； (4)
P′ 0 ＝(k 0 +k)P max ； (6)
wherein k is 0 Representing the initial degree of adaptation, P, of the Joint management model min And P max Represents the minimum and maximum profit earned by the kth sales region, P represents the total profit, P 0 Denotes the calculated remaining profit, P' 0 Representing the residual profit after the improvement of the fitness;
based on the adaptation degree of the supply management model, the supply management model is established as follows:
a second aspect provides a commodity cross-regional supply chain management device based on big data, comprising:
the level determining module is used for acquiring various historical sales data of each sales area of the commodity and determining the level of the corresponding sales area according to the priority and the value weight of each historical sales data;
the inventory matching module is used for acquiring inventory data of each sales area and judging whether the inventory data is matched with preset inventory of the sales area of the corresponding level;
and the supply allocation module is used for acquiring the total inventory data of each sales area if the total inventory data is not acquired, and allocating the inventory supply of each sales area based on the supply management model according to the level and the inventory condition of each sales area.
In one possible design, the historical sales data includes at least commodity sales data, commodity goodness data, commodity complaint data, commodity repurchase data, and/or commodity return data.
In one possible design, when determining the level of the corresponding sales region according to the priority and the value weight of each type of historical sales data, the level determination module is specifically configured to:
calculating posterior probabilities of each type of historical sales data relative to each level sales area, comparing the posterior probabilities of any two types of historical sales data in pairs, and marking the historical sales data with higher posterior probability until the comparison of all the historical sales data is completed;
counting the total mark quantity of each type of historical sales data, and sequencing all the historical sales data according to the total mark quantity so as to determine the priority of each type of historical sales data according to the sequencing result;
and calculating the corresponding value weight according to the priority of each type of historical sales data, and calculating to obtain the level of the corresponding sales area.
In a possible design, when comparing the posterior probabilities of any two types of historical sales data two by two and marking the historical sales data with the greater posterior probability, the level determining module is specifically configured to:
based on a dual triangle arrangement method, arranging and combining M kinds of historical sales data according to M-1 rows and M-1 columns; each row of historical sales data comprises a plurality of pairs of historical sales data;
comparing the posterior probabilities of one pair of data in each row of historical sales data, and marking the data with the higher posterior probability.
In one possible design, when the corresponding value weight is calculated according to the priority of each type of historical sales data and the level of the corresponding sales area is obtained through calculation, the level determination module is specifically configured to:
determining the weight position of each type of historical sales data according to the priority of each type of historical sales data, wherein the weight position corresponding to the historical sales data with the priority of the ith level is s +1-i, and s represents the type of the historical sales data;
and calculating the value weight of each type of historical sales data according to the weight position, wherein the calculation formula is as follows:
wherein Wi represents the value weight of the historical sales data with the i-th level as the priority, and Di represents the weight position;
according to the value weight of each type of historical sales data, calculating to obtain the level H of the corresponding sales region k The calculation formula is as follows:
where k denotes the kth sales region, S g Indicates the g-th sales data, and N indicates the total number of categories of sales data of the k-th sales region.
In a possible design, when acquiring inventory data of each sales area and determining whether the inventory data matches a preset inventory of the sales area of a corresponding level, the inventory matching module is specifically configured to:
acquiring the total number of warehouse inventory goods in each sales area and the total number of orders to be issued in the area;
according to the total number of the warehouse inventory goods and the total number of the orders to be issued, determining an inventory coefficient corresponding to actual inventory to be sold in the area based on the following formula, wherein the calculation formula is as follows:
Fj＝e×(Cj×b1-Dj×b2)； (2)
wherein Fj represents an inventory coefficient corresponding to the actual stock to be sold in the area, e represents a correction coefficient, cj represents the total number of warehouse stock commodities, dj represents the total number of orders to be delivered, b1 and b2 represent preset proportionality coefficients, and j represents the jth sales area;
and judging whether the inventory coefficient corresponding to the actual sales inventory of the area is matched with the coefficient of the preset inventory of the sales area of the corresponding level.
In one possible design, the provisioning management model is built as follows:
assuming that scheduling management is performed between the inventories of the sales regions, the management policy matrix of the k-th sales region in the supply chain system is as follows:
wherein a represents a policy point for allocating inventory management in the kth sales area;
wherein, the adaptation degree of the supply management model is as follows:
k 0 ＝P max /P min ； (4)
P′ 0 ＝(k 0 +k)P max ； (6)
wherein k is 0 Representing the initial fitness, P, of the Joint management model min And P max Represents the minimum and maximum profit margin for the k-th sales region, P represents the total profit, P 0 Denotes the calculated remaining profit, P' 0 Representing the residual profit after the improvement of the fitness;
based on the adaptation degree of the supply management model, the supply management model is established as follows:
in a third aspect, the present invention provides a computer-readable storage medium having stored thereon instructions for executing, when executed on a computer, the big-data based commodity cross-regional supply chain management method as described in any one of the possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer device, comprising a memory, a processor and a transceiver, which are sequentially connected in communication, wherein the memory is used for storing a computer program, the transceiver is used for sending and receiving messages, and the processor is used for reading the computer program and executing the big-data based commodity trans-regional supply chain management method as described in any one of the possible designs of the first aspect.
In a fifth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform a big-data based commodity supply chain management method as described in any one of the possible designs of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention determines the grade of the corresponding sale area according to the priority and the value weight of each historical sale data by acquiring various historical sale data of each sale area of the commodity; judging whether the inventory data is matched with the preset inventory of the sales area of the corresponding level or not by acquiring the inventory data of each sales area; and if not, acquiring total inventory data of each sales area, and allocating the inventory supply of the sales area based on the supply management model according to the level and the inventory condition of each sales area. The method and the system determine the level of each sales area through the historical sales data of the sales areas, set the corresponding preset inventory based on the level, and allocate the inventory of the sales area based on the supply management model when the current inventory of a certain sales area is not matched with the preset inventory, so that all the sales areas are simultaneously brought into the supply chain to be transversely compared to determine the priority of the sales areas, the cross-area quick response of commodity supply is realized, and the linkage management of the supply chains of a plurality of sales areas is further realized.
Drawings
Fig. 1 is a flowchart of a commodity cross-regional supply chain management method based on big data in an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the embodiments or the description in the prior art, it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts. It should be noted that the description of the embodiments is provided to help understanding of the present invention, and the present invention is not limited thereto.
Examples
In order to solve the technical problem of managing and monitoring supply chain information of cross-region, the embodiment of the application provides a commodity cross-region supply chain management method based on big data.
The commodity cross-regional supply chain management method based on big data provided by the embodiment of the application is explained in detail below.
It should be noted that the commodity cross-regional supply chain management method based on big data provided in the embodiment of the present application may be applied to a terminal device of any operating system to execute a process of the method, where the terminal device includes but is not limited to an industrial computer, an IPAD tablet computer, a personal computer, a smart phone, and the like, and is not limited herein. For convenience of description, the embodiments of the present application are described with reference to an industrial computer as an implementation subject, unless otherwise specified. It is to be understood that the executing entity is not limited to the embodiment of the present application, and in some other embodiments, a smart phone or other types of terminal devices may be used as the executing entity.
As shown in fig. 1, which is a flowchart of a commodity cross-regional supply chain management method based on big data according to an embodiment of the present application, the commodity cross-regional supply chain management method based on big data includes, but is not limited to, steps S1 to S4 to implement:
s1, acquiring various historical sales data of each sales area of a commodity, and determining the level of the corresponding sales area according to the priority and the value weight of each type of historical sales data;
in step S1, the historical sales data at least includes commodity sales data, commodity favorable rating data, commodity complaint data, commodity repurchase data, and/or commodity return data; of course, it is understood that, in order to make the subsequent analysis result more comprehensive and accurate, the historical sales data in this embodiment is not limited to the above data, and other common sales data, such as the product browsing data, the product to be shipped data, and the like, are within the protection scope of the embodiment of the present application, and are not limited herein.
Preferably, after acquiring a plurality of types of historical sales data of each sales area of the commodity, the method further includes:
and after the historical sales data are preprocessed, the historical sales data are classified and stored in corresponding databases.
Preprocessing data, including but not limited to data cleaning of incomplete data and/or error data in historical sales data; the incomplete data mainly refers to data with information missing, such as the name of a supplier, the name of a branch company, regional information of a client, the unmatchable main list and the detailed list in a business system, and the like. Such data may be filtered through data cleansing and then content-complemented according to missing data items, such as by source of data acquisition, requesting data to source of data, etc. The error data mainly refers to data with an incorrect date format or data with a date exceeding a boundary, which causes operation failure of an ETL (Extract-Transform-Load, data warehouse), so that the data needs to be selected from a service system database in an SQL (structured query language) mode and sent to a corresponding department for limited term correction, and the corrected data is extracted. It should be noted that, in this embodiment, corresponding databases are created for different types of historical sales data, and after the data is cleaned, the business behavior data can be stored in the corresponding databases according to classification for calling in subsequent data analysis.
In a specific embodiment of step S1, determining the level of the corresponding sales area according to the priority and the value weight of each type of historical sales data includes:
s11, calculating posterior probabilities of each type of historical sales data relative to each level of sales area, comparing the posterior probabilities of any two types of historical sales data in pairs, and marking the historical sales data with higher posterior probabilities until the comparison of all the historical sales data is completed;
it should be noted that, when calculating the posterior probability of each historical sales data relative to each level sales area, the existing posterior probability calculation formula is adopted, and details are not repeated here.
Specifically, the posterior probabilities of any two kinds of historical sales data are compared pairwise, and the comparison comprises the following steps:
based on a dual triangle arrangement method, arranging and combining M kinds of historical sales data according to M-1 rows and M-1 columns; wherein each row of historical sales data comprises a plurality of pairs of historical sales data;
comparing the posterior probabilities of one pair of data in each row of historical sales data, and marking the data with the higher posterior probability.
More specifically, data with a high posterior probability is marked once, and preferably, data with a high posterior probability is marked once by circling; for example, when the historical sales data of the first row is compared, the specific comparison process is as follows:
mixing O with 1 Respectively sequentially react with O 2 ，O 3 ，O 4 ，...，O n-1 ，O n Comparing the posterior probabilities of (a); similarly, when the historical sales data of the second row is compared, the specific comparison process includes: mixing O with 2 Respectively sequentially react with O 3 ，O 4 ，...，O n-1 ，O n Comparing the posterior probabilities until the comparison of all risk factors is finished. For example, if judged to have O 1 Is superior to O 2 Then is at O 1 Adding a ring, otherwise, adding O 2 Adding a ring, and in the same way, adding O 2 And O 3 Comparing, and so on until the first row is compared; the comparison is performed in each row in a sequential manner until the n-1 th row, and the total comparison times are cumulative combination times, i.e. the
Next, the process is repeated. Then when all
After the two data in the item combination are compared, the priority of each data can be judged according to the number of times of adding circles, the most circles are optimal, the most circles are similar to the least circles, and if two or more data have the same number of circles, the priority rates of the two or more data are equal.
S12, counting the total mark quantity of each type of historical sales data, and sequencing all the historical sales data according to the total mark quantity to determine the priority of each type of historical sales data according to the sequencing result;
and S13, calculating corresponding value weight according to the priority of each type of historical sales data, and calculating to obtain the level of the corresponding sales area.
In a possible design, when calculating a corresponding value weight according to a priority of each type of historical sales data and calculating a level of a corresponding sales area, the method specifically includes:
determining the weight position of each type of historical sales data according to the priority of each type of historical sales data, wherein the weight position corresponding to the historical sales data with the priority of the ith level is s +1-i, and s represents the type of the historical sales data;
and calculating the value weight of each type of historical sales data according to the weight position, wherein the calculation formula is as follows:
wherein Wi represents the value weight of the historical sales data with the i-th level as the priority, and Di represents the weight position;
according to the value weight of each type of historical sales data, calculating to obtain the level H of the corresponding sales region k The calculation formula is as follows:
wherein k denotes the kth sales area, S g Indicates the g-th sales data, and N indicates the total number of categories of sales data of the k-th sales area.
S2, acquiring inventory data of each sales area, and judging whether the inventory data is matched with preset inventory of the sales area of the corresponding level;
in a specific implementation manner of step S2, acquiring inventory data of each sales area, and determining whether the inventory data matches a preset inventory of the sales area of a corresponding level includes:
acquiring the total number of warehouse inventory goods in each sales area and the total number of orders to be issued in the area;
according to the total number of the warehouse inventory goods and the total number of the orders to be issued, determining an inventory coefficient corresponding to actual inventory to be sold in the area based on the following formula, wherein the calculation formula is as follows:
Fj＝e×(Cj×b1-Dj×b2)； (2)
wherein Fj represents an inventory coefficient corresponding to the actual stock to be sold in the area, e represents a correction coefficient, cj represents the total number of warehouse stock commodities, dj represents the total number of orders to be delivered, b1 and b2 represent preset proportionality coefficients, and j represents the jth sales area;
and judging whether the inventory coefficient corresponding to the actual sales inventory of the area is matched with the coefficient of the preset inventory of the sales area of the corresponding level.
Specifically, in this embodiment, a preset inventory is set for each level of sales area, a coefficient of the preset inventory is calculated based on the total inventory, and when the inventory coefficient corresponding to the actual sales inventory in the area is smaller than the preset inventory coefficient, the inventory is considered to be insufficient, otherwise, the inventory is considered to meet the demand. Of course, it can be understood that in this embodiment, the level of each sales area and the corresponding preset inventory coefficient may be dynamically adjusted according to historical sales data, and the value thereof is not limited here.
And S3, if not, acquiring total inventory data of each sales area, and allocating inventory supply of each sales area based on a supply management model according to the level and the inventory condition of each sales area.
In step S3, the process of constructing the supply management model is as follows:
assuming that the scheduling management is performed between the inventories of the sales regions, the management policy matrix of the kth sales region in the supply chain system is as follows:
wherein a represents a policy point for allocating inventory management in the kth sales area;
wherein, the adaptation degree of the supply management model is as follows:
k 0 ＝P max /P min ； (4)
P′ 0 ＝(k 0 +k)P max ； (6)
wherein k is 0 Representing the initial fitness, P, of the Joint management model min And P max Represents the minimum and maximum profit margin for the k-th sales region, P represents the total profit, P 0 Denotes the calculated remaining profit, P' 0 Representing the residual profit after the improvement of the fitness;
based on the adaptation degree of the supply management model, the supply management model is established as follows:
based on the disclosure, the embodiment of the application acquires various historical sales data of each sales area of the commodity, and determines the level of the corresponding sales area according to the priority and the value weight of each type of historical sales data; judging whether the inventory data is matched with the preset inventory of the sales area of the corresponding level or not by acquiring the inventory data of each sales area; and if not, acquiring total inventory data of each sales area, and allocating the inventory supply of the sales area based on the supply management model according to the level and the inventory condition of each sales area. The method and the system determine the level of each sales area through the historical sales data of the sales areas, set the corresponding preset inventory based on the level, and allocate the inventory of the sales area based on the supply management model when the current inventory of a certain sales area is not matched with the preset inventory, so that all the sales areas are simultaneously brought into the supply chain to be transversely compared to determine the priority of the sales areas, the cross-area quick response of commodity supply is realized, and the linkage management of the supply chains of a plurality of sales areas is further realized.
A second aspect provides a commodity cross-regional supply chain management device based on big data, comprising:
the level determining module is used for acquiring various historical sales data of each sales area of the commodity and determining the level of the corresponding sales area according to the priority and the value weight of each historical sales data;
the inventory matching module is used for acquiring inventory data of each sales area and judging whether the inventory data is matched with preset inventory of the sales area of the corresponding level;
and the supply allocation module is used for acquiring the total inventory data of each sales area if the total inventory data is not acquired, and allocating the inventory supply of each sales area based on the supply management model according to the level and the inventory condition of each sales area.
In one possible design, the historical sales data includes at least commodity sales data, commodity goodness data, commodity complaint data, commodity repurchase data, and/or commodity return data.
In one possible design, when determining the level of the corresponding sales region according to the priority and the value weight of each type of historical sales data, the level determination module is specifically configured to:
calculating posterior probabilities of each type of historical sales data relative to each level sales area, comparing the posterior probabilities of any two types of historical sales data in pairs, and marking the historical sales data with higher posterior probability until the comparison of all the historical sales data is completed;
counting the total mark quantity of each type of historical sales data, and sequencing all the historical sales data according to the total mark quantity so as to determine the priority of each type of historical sales data according to the sequencing result;
and calculating corresponding value weight according to the priority of each type of historical sales data, and calculating to obtain the level of the corresponding sales area.
In a possible design, when comparing the posterior probabilities of any two types of historical sales data two by two and marking the historical sales data with the greater posterior probability, the level determining module is specifically configured to:
based on a dual triangle arrangement method, arranging and combining M kinds of historical sales data according to M-1 rows and M-1 columns; wherein each row of historical sales data comprises a plurality of pairs of historical sales data;
comparing the posterior probabilities of one pair of data in each row of historical sales data, and marking the data with the higher posterior probability.
In one possible design, when the corresponding value weight is calculated according to the priority of each type of historical sales data and the level of the corresponding sales area is obtained through calculation, the level determination module is specifically configured to:
determining the weight position of each type of historical sales data according to the priority of each type of historical sales data, wherein the weight position corresponding to the historical sales data with the priority of the ith level is s +1-i, and s represents the type of the historical sales data;
and calculating the value weight of each type of historical sales data according to the weight position, wherein the calculation formula is as follows:
wherein Wi represents the value weight of the historical sales data with the i-th level as the priority, and Di represents the weight position;
according to the value weight of each type of historical sales data, calculating to obtain the level H of the corresponding sales region k The calculation formula is as follows:
wherein k denotes the kth sales area, S g Showing the g sales data, N TableThe total number of types of sales data of the k-th sales region is shown.
In one possible design, when acquiring inventory data of each sales area and determining whether the inventory data matches a preset inventory of the sales area of a corresponding level, the inventory matching module is specifically configured to:
acquiring the total number of warehouse inventory goods in each sales area and the total number of orders to be issued in the area;
according to the total number of the warehouse inventory goods and the total number of the orders to be issued, determining an inventory coefficient corresponding to actual inventory to be sold in the area based on the following formula, wherein the calculation formula is as follows:
Fj＝e×(Cj×b1-Dj×b2)； (2)
wherein Fj represents an inventory coefficient corresponding to the actual stock to be sold in the area, e represents a correction coefficient, cj represents the total number of warehouse stock commodities, dj represents the total number of orders to be delivered, b1 and b2 represent preset proportionality coefficients, and j represents the jth sales area;
and judging whether the inventory coefficient corresponding to the actual sales inventory of the area is matched with the coefficient of the preset inventory of the sales area of the corresponding level.
In one possible design, the provisioning management model is built as follows:
assuming that scheduling management is performed between the inventories of the sales regions, the management policy matrix of the k-th sales region in the supply chain system is as follows:
wherein a represents a policy point for allocating inventory management in the kth sales area;
wherein, the adaptation degree of the supply management model is as follows:
k 0 ＝P max /P min ； (4)
P′ 0 ＝(k 0 +k)P max ； (6)
wherein k is 0 Representing the initial degree of adaptation, P, of the Joint management model min And P max Represents the minimum and maximum profit earned by the kth sales region, P represents the total profit, P 0 Denotes the calculated remaining profit, P' 0 Representing the residual profit after the improvement of the fitness;
based on the adaptation degree of the supply management model, the supply management model is established as follows:
for the working process, working details and technical effects of the foregoing apparatus provided in the second aspect of this embodiment, reference may be made to the method described in any one of the first aspect or the first aspect, which is not described herein again.
In a third aspect, the present invention provides a computer-readable storage medium having stored thereon instructions for executing, when executed on a computer, the big-data based commodity cross-regional supply chain management method as described in any one of the possible designs of the first aspect.
The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, flash disks and/or Memory sticks (Memory sticks), etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
For the working process, working details and technical effects of the foregoing computer-readable storage medium provided in the third aspect of this embodiment, reference may be made to the method described in the first aspect or any one of the possible designs of the first aspect, which is not described herein again.
In a fourth aspect, the present invention provides a computer device, comprising a memory, a processor and a transceiver, which are sequentially connected in communication, wherein the memory is used for storing a computer program, the transceiver is used for sending and receiving messages, and the processor is used for reading the computer program and executing the big-data based commodity trans-regional supply chain management method as described in any one of the possible designs of the first aspect.
For example, the Memory may include, but is not limited to, a Random-Access Memory (RAM), a Read-Only Memory (ROM), a Flash Memory (Flash Memory), a First-in First-out (FIFO), and/or a First-in Last-out (FILO), and the like; the processor may not be limited to the microprocessor employing the model number STM32F105 family; the transceiver may be, but is not limited to, a WiFi (wireless fidelity) wireless transceiver, a bluetooth wireless transceiver, a GPRS (General Packet Radio Service) wireless transceiver, and/or a ZigBee (ZigBee protocol, low power local area network protocol based on ieee802.15.4 standard) wireless transceiver, etc. In addition, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.
For the working process, working details and technical effects of the foregoing computer device provided in the fourth aspect of this embodiment, reference may be made to the method described in the first aspect or any one of the possible designs of the first aspect, which is not described herein again.
In a fifth aspect, the present invention provides a computer program product containing instructions which, when run on a computer, cause the computer to perform a method for big data based supply chain management of goods across regions as described in any one of the possible designs of the first aspect.
For the working process, the working details and the technical effects of the computer program product containing the instructions provided in the fifth aspect of the present embodiment, reference may be made to the method described in the first aspect or any one of the possible designs of the first aspect, and details are not described herein again.
Finally, it should be noted that: the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present 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 (10)
1. The commodity cross-regional supply chain management method based on big data is characterized by comprising the following steps:
acquiring various historical sales data of each sales area of the commodity, and determining the level of the corresponding sales area according to the priority and the value weight of each historical sales data;
acquiring inventory data of each sales area, and judging whether the inventory data is matched with preset inventory of the sales area of the corresponding level;
and if not, acquiring total inventory data of each sales area, and allocating the inventory supply of each sales area based on the supply management model according to the level and the inventory condition of each sales area.
2. The big-data-based commodity cross-regional supply chain management method according to claim 1, wherein the historical sales data at least comprises commodity sales data, commodity goodwill data, commodity complaint data, commodity re-purchase data and/or commodity return data.
3. The big data based commodity cross-regional supply chain management method according to claim 2, wherein determining the level of the corresponding sales region according to the priority and the value weight of each historical sales data comprises:
calculating posterior probabilities of each type of historical sales data relative to each level sales area, comparing the posterior probabilities of any two types of historical sales data in pairs, and marking the historical sales data with higher posterior probability until the comparison of all the historical sales data is completed;
counting the total mark quantity of each type of historical sales data, and sequencing all the historical sales data according to the total mark quantity so as to determine the priority of each type of historical sales data according to the sequencing result;
and calculating the corresponding value weight according to the priority of each type of historical sales data, and calculating to obtain the level of the corresponding sales area.
4. The commodity trans-regional supply chain management method based on big data according to claim 3, wherein the comparing of the posterior probabilities of any two kinds of historical sales data in pairs and marking the historical sales data with the higher posterior probability comprises:
based on a dual triangle arrangement method, arranging and combining M kinds of historical sales data according to M-1 rows and M-1 columns; wherein each row of historical sales data comprises a plurality of pairs of historical sales data;
comparing the posterior probabilities of one pair of data in each row of historical sales data, and marking the data with the higher posterior probability.
5. The big data based commodity cross-regional supply chain management method according to claim 3, wherein calculating a corresponding value weight according to the priority of each type of historical sales data and calculating the level of a corresponding sales region comprises:
determining the weight position of each type of historical sales data according to the priority of each type of historical sales data, wherein the weight position corresponding to the historical sales data with the priority of the ith level is s +1-i, and s represents the type of the historical sales data;
and calculating the value weight of each type of historical sales data according to the weight position, wherein the calculation formula is as follows:
wherein Wi represents the value weight of the historical sales data with the i-th level as the priority, and Di represents the weight position;
according to the value weight of each type of historical sales data, calculating to obtain the level H of the corresponding sales region k The calculation formula is as follows:
wherein k denotes the kth sales area, S g Indicates the g-th sales data, and N indicates the total number of categories of sales data of the k-th sales area.
6. The method as claimed in claim 1, wherein the step of obtaining inventory data of each sales area and determining whether the inventory data matches a preset inventory of the sales area of the corresponding level comprises:
acquiring the total number of warehouse inventory goods in each sales area and the total number of orders to be issued in the area;
according to the total number of the warehouse inventory goods and the total number of the orders to be issued, determining an inventory coefficient corresponding to actual inventory to be sold in the area based on the following formula, wherein the calculation formula is as follows:
Fj＝e×(Cj×b1-Dj×b2)； (2)
wherein Fj represents an inventory coefficient corresponding to the actual stock to be sold in the area, e represents a correction coefficient, cj represents the total number of warehouse stock commodities, dj represents the total number of orders to be delivered, b1 and b2 represent preset proportionality coefficients, and j represents the jth sales area;
and judging whether the inventory coefficient corresponding to the actual sales inventory of the area is matched with the coefficient of the preset inventory of the sales area of the corresponding level.
7. The commodity cross-regional supply chain management method based on big data according to claim 1, wherein the supply management model is constructed by the following steps:
assuming that scheduling management is performed between the inventories of the sales regions, the management policy matrix of the k-th sales region in the supply chain system is as follows:
wherein a represents a policy point for allocating inventory management in the kth sales area;
wherein, the adaptation degree of the supply management model is as follows:
k 0 ＝P max /P min ； (4)
P′ 0 ＝(k 0 +k)P max ； (6)
wherein k is 0 Representing the initial fitness, P, of the Joint management model min And P max Represents the minimum and maximum profit margin for the k-th sales region, P represents the total profit, P 0 Denotes the calculated remaining profit, P' 0 Representing the residual profit after the improvement of the fitness;
based on the adaptation degree of the supply management model, the supply management model is established as follows:
8. commodity transregional supply chain management device based on big data, its characterized in that includes:
the level determining module is used for acquiring various historical sales data of each sales area of the commodity and determining the level of the corresponding sales area according to the priority and the value weight of each historical sales data;
the inventory matching module is used for acquiring inventory data of each sales area and judging whether the inventory data is matched with preset inventory of the sales area of the corresponding level;
and the supply allocation module is used for acquiring the total inventory data of each sales area if the total inventory data is not acquired, and allocating the inventory supply of each sales area based on the supply management model according to the level and the inventory condition of each sales area.
9. The device according to claim 8, wherein the historical sales data at least comprises commodity sales data, commodity goodness data, commodity complaint data, commodity repurchase data, and/or commodity return data.
10. A storage medium having stored thereon instructions for executing the big data based commodity trans-regional supply chain management method according to any one of claims 1 to 8 when the instructions are run on a computer.
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| CN116933941A (en) * | 2023-07-27 | 2023-10-24 | 郑州软通合力计算机技术有限公司 | Intelligent supply chain logistics intelligent optimization method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116933941A (en) * | 2023-07-27 | 2023-10-24 | 郑州软通合力计算机技术有限公司 | Intelligent supply chain logistics intelligent optimization method, system and storage medium |
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