CN116050766A - Method, system and computer readable medium for dynamically adjusting the amount of goods spread - Google Patents

Method, system and computer readable medium for dynamically adjusting the amount of goods spread Download PDF

Info

Publication number
CN116050766A
CN116050766A CN202310027528.7A CN202310027528A CN116050766A CN 116050766 A CN116050766 A CN 116050766A CN 202310027528 A CN202310027528 A CN 202310027528A CN 116050766 A CN116050766 A CN 116050766A
Authority
CN
China
Prior art keywords
commodity
demand data
merchant
forecast
cost
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310027528.7A
Other languages
Chinese (zh)
Inventor
傅晓峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Elitesland Software System Co ltd
Original Assignee
Shanghai Elitesland Software System Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Elitesland Software System Co ltd filed Critical Shanghai Elitesland Software System Co ltd
Priority to CN202310027528.7A priority Critical patent/CN116050766A/en
Publication of CN116050766A publication Critical patent/CN116050766A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The method comprises the steps of firstly obtaining commodity forecast demand data of a merchant in a forecast period according to commodity historical demand data and logistics cost influence factors of storage nodes of each level of the merchant, then carrying out iterative computation according to the commodity forecast demand data and the logistics cost influence factors, calculating logistics costs under different paving plans, determining a target paving plan with optimal logistics cost in the paving plans, and adjusting the paving quantity according to the target paving plan. According to the scheme, on the basis of prediction based on historical data, the influence of logistics cost on a goods laying plan is further considered, the considered influence factors are more comprehensive, and after the goods laying plan with optimal logistics cost is determined in an iterative calculation mode, the goods laying quantity is dynamically adjusted based on the goods laying plan, so that the logistics cost of a merchant can be effectively reduced.

Description

Method, system and computer readable medium for dynamically adjusting the amount of goods spread
Technical Field
The present disclosure relates to the field of logistics storage technology, and in particular, to a method, a system, and a computer readable medium for dynamically adjusting a cargo amount.
Background
The quick-release products, namely quick-release consumer products (FMCG, fast Moving Consumer Goods), refer to daily necessities with shorter service life and higher consumption frequency, and are characterized by high consumption frequency and repeated use, thus occupying a large proportion in resident consumption.
At present, a quick-to-be-consumed goods laying scheme is mainly focused on establishing model prediction demand data based on characteristics of goods and historical data of merchants, for example, a first order calculation method based on static properties of goods and goods laying conditions is disclosed in the prior art, the model is trained by using a random forest algorithm according to static properties of historical goods, sales data, ordering data and shop data of goods laying, sales volume in sales duration is predicted, a predicted result is obtained, a safety stock calculation first order recommended volume is calculated by combining business rules, and then the first order recommended volume to a size is calculated according to order size ratio. However, the proposal just discloses analysis on commodity historical data, business rules and first order recommended quantity, and is not suitable for dynamically adjusting store goods quantity. Therefore, how to invent a scheme which has wide application and can dynamically adjust the shop goods quantity is an urgent problem in the technical field.
Disclosure of Invention
It is an object of the present application to provide a method, system and computer readable medium for dynamically adjusting the amount of inventory.
To achieve the above object, the present application provides a method for dynamically adjusting a cargo amount, the method comprising:
acquiring commodity forecast demand data of a merchant in a forecast period according to commodity historical demand data and logistics cost influence factors of all levels of storage nodes of the merchant;
carrying out iterative computation according to commodity forecast demand data and logistics cost influence factors, and calculating logistics costs under different shop cargo plans;
and determining a target cargo spreading plan with optimal logistics cost in the cargo spreading plans, and adjusting the cargo spreading quantity according to the target cargo spreading plan.
Further, according to commodity historical demand data and logistics cost influence factors of all levels of storage nodes of a merchant, acquiring commodity forecast demand data of the merchant in a forecast period, wherein the commodity forecast demand data comprises:
acquiring commodity historical demand data of all levels of storage nodes of a merchant;
selecting the logistic cost impact factor;
and carrying out integration analysis based on commodity historical demand data and logistics cost influence factors of all levels of storage nodes of the merchant, and obtaining commodity forecast demand data of the merchant in a forecast period.
Further, the commodity historical demand data includes commodity historical stock quantity, commodity historical purchase quantity and commodity historical sales quantity, and the commodity forecast demand data includes commodity forecast stock quantity, commodity forecast purchase quantity and commodity forecast sales quantity.
Further, the storage nodes of each level comprise a total warehouse, a city warehouse and a store.
Further, according to commodity historical demand data and logistics cost influence factors of all levels of storage nodes of a merchant, acquiring commodity forecast demand data of the merchant in a forecast period, wherein the commodity forecast demand data comprises:
and calculating by adopting a preset algorithm model according to commodity historical demand data and logistics cost influence factors of all levels of storage nodes of the merchant, and calculating commodity forecast demand data of the merchant in a forecast period.
Further, the algorithm model includes a weighted moving average algorithm or a primary exponential smoothing algorithm.
Further, the logistic cost impact factor comprises at least any one of the following: inventory holding cost, warehouse cost, transportation cost, information service cost.
Further, adjusting the amount of the spread according to the target spread plan includes:
generating a shop order for a shop goods planning system according to the target shop goods plan, and automatically adjusting the shop goods quantity on a shop goods list according to the shop order
The embodiment of the application also provides a system for dynamically adjusting the amount of the spread goods, which comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the system to execute the method for dynamically adjusting the amount of the spread goods.
Embodiments of the present application also provide a computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of dynamically adjusting a quantity of a store.
Compared with the prior art, in the scheme for dynamically adjusting the shop amount, commodity forecast demand data of a merchant in a forecast period can be obtained according to commodity historical demand data and logistics cost influence factors of all levels of storage nodes of the merchant, then iterative computation is carried out according to the commodity forecast demand data and the logistics cost influence factors, logistics costs under different shop plans are calculated, a target shop plan with optimal logistics cost is determined in the shop plans, and the shop amount is adjusted according to the target shop plan. According to the scheme, on the basis of prediction based on historical data, the influence of logistics cost on a goods laying plan is further considered, the considered influence factors are more comprehensive, and after the goods laying plan with optimal logistics cost is determined in an iterative calculation mode, the goods laying quantity is dynamically adjusted based on the goods laying plan, so that the logistics cost of a merchant can be effectively reduced.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is a process flow diagram of a method for dynamically adjusting a shipping volume according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a framework for performing an integration analysis in an embodiment of the present application;
FIG. 3 is a schematic view of the processing principle when the solution of the embodiment of the present application is adopted to implement the adjustment of the amount of goods spread;
the same or similar reference numbers in the drawings refer to the same or similar parts.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
According to the method for dynamically adjusting the goods laying quantity, firstly, commodity prediction demand data of a merchant in a prediction period can be obtained according to commodity historical demand data and logistics cost influence factors of storage nodes of each level of the merchant, then iterative computation is carried out according to the commodity prediction demand data and the logistics cost influence factors, logistics costs under different goods laying plans are calculated, a target goods laying plan with optimal logistics cost is determined in the goods laying plans, and the goods laying quantity is adjusted according to the target goods laying plan. According to the scheme, on the basis of prediction based on historical data, the influence of logistics cost on a goods laying plan is further considered, the considered influence factors are more comprehensive, and after the goods laying plan with optimal logistics cost is determined in an iterative calculation mode, the goods laying quantity is dynamically adjusted based on the goods laying plan, so that the logistics cost of a merchant can be effectively reduced.
In a practical scenario, the execution subject of the method may be a user device, a network device, or a device formed by integrating the user device and the network device through a network, or may be an application running on the device. The user equipment comprises, but is not limited to, various terminal equipment such as computers, mobile phones, tablet computers and the like; the network device includes, but is not limited to, a network host, a single network server, a server in a plurality of network servers or a server in a distributed cloud network, etc. The distributed Cloud network described herein is made up of a large number of hosts or web servers based on Cloud Computing (Cloud Computing).
Fig. 1 shows a process flow of a method for dynamically adjusting a cargo amount according to an embodiment of the present application, which at least includes the following steps:
step S101, acquiring commodity forecast demand data of a merchant in a forecast period according to commodity historical demand data and logistics cost influence factors of storage nodes of each level of the merchant.
The storage nodes at each level include storage nodes of multiple levels divided by merchants according to actual operation areas and logistics management plans, for example, in this embodiment, the storage nodes may be divided into 3 levels, including a total warehouse, a city warehouse and a store. The commodity historical demand data is commodity demand data of the storage node in a certain past time period, for example, the commodity demand data of the storage node in the previous months or quarters can include commodity historical stock quantity, commodity historical purchase quantity and commodity historical sales quantity, and is used for representing demand information of the corresponding storage node on specific commodities in a past time period. The commodity forecast demand data in the forecast period refers to commodity demand data in a time period in which the amount of the paved commodity needs to be forecast and adjusted, and may be commodity demand data in the next month or the next quarter in the future, for example, specifically, commodity forecast inventory, commodity forecast purchase, commodity forecast sales, and the like may be included.
The logistic cost influencing factor refers to various factors influencing the logistic cost of the commodity in the whole commodity paving process, for example, the logistic cost influencing factor in the embodiment can at least comprise at least any one of inventory holding cost, storage cost, transportation cost and information service cost. The inventory holding cost refers to the fund cost occupied by the inventory commodity, and the inventory service cost comprises relevant insurance, tax and the like. The warehouse cost mainly comprises the cost of warehouse facility equipment such as construction, purchase or lease and the cost brought by various warehouse operations. The transportation cost mainly comprises related fees for realizing commodity transportation, including labor fees such as wages of transportation personnel, welfare and the like; operating costs, such as operating vehicle fuel costs, depreciation, road transport management costs; other fees, such as travel fees, etc. The information service cost is related fees including travel fees, conference fees, financial fees, management information system fees and other miscellaneous fees for the logistics management of enterprises.
In some embodiments of the present application, the commodity historical demand data of the various levels of warehouse nodes of the merchant may be obtained first, for example, when the various levels of warehouse nodes of the merchant include one total warehouse, 3 city warehouses, and 10 stores, the commodity historical demand data about these warehouse nodes may be obtained. If the data of the next quarter is to be predicted according to the historical data of the past three quarters and the amount of goods to be spread is to be dynamically adjusted in the embodiment, the historical demand data of the goods of the past three quarters can be obtained when the historical data of the goods of each stage of storage nodes are obtained. After acquiring the historical demand data for the commodity, the logistic cost impact factor may be selected. In each different application scenario, the influence degree of different logistics cost influence factors on logistics cost is different, so that the appropriate logistics cost influence factors can be selected according to the actual application scenario. For example, four logistic cost impact factors, namely, inventory holding cost, warehouse cost, transportation cost and information service cost, can be selected in this embodiment. After the commodity historical demand data and the logistics cost influence factors are determined, integrated analysis can be performed based on the commodity historical demand data and the logistics cost influence factors of all levels of storage nodes of the merchant, and commodity prediction demand data of the merchant in a prediction period can be obtained.
In some embodiments of the present application, a preset algorithm model may be adopted when calculating the commodity forecast demand data, that is, the commodity forecast demand data of the merchant in the forecast period may be calculated by adopting the preset algorithm model according to the commodity historical demand data and the logistic cost influence factor of the storage nodes of each level of the merchant. In an actual scene, a plurality of preset algorithm models can be provided for users to use so as to better adapt to different application scenes. For example, the algorithm model in this embodiment may include an exponential smoothing algorithm or a weighted moving average algorithm. The formula of the exponential smoothing algorithm is as follows:
S t ＝α·y t +(1-α)·S t-1
wherein S is t For the preliminary sales prediction in the prediction period, α is a smooth index, which may be set to a value greater than 0 and less than 1, y t S is the actual historical sales in the previous relevant sales period t-1 Representing the predicted historical sales volume in the previous associated sales cycle. For example, take the scenario of calculating sales prediction of a certain household appliance in the fourth quarter of the year, y t For the actual historical sales of the third quarter of the year, S t-1 The predicted sales amount of the household appliance in the third quarter of the present year, namely the predicted result of the sales predicted amount in the third quarter of the present year in the predicted period, can be calculated by the above disclosure t 。
And the formula of the weighted moving average algorithm is as follows:
Figure BDA0004045759100000061
wherein Q is the preliminary sales forecast of the commodity in the forecast period, Q i For the historical sales of the commodity in the ith relevant sales period, W i The historical sales of the commodity in the ith relevant sales period is weighted, and n is the number of relevant sales periods. Taking a scenario of calculating sales prediction quantity of a certain household appliance in the fourth quarter of the present year as an example, Q 1 、Q 2 、Q 3 Historical sales of the first quarter, the second quarter and the third quarter of the year respectively, W 1 、W 2 、W 3 Respectively Q 1 、Q 2 、Q 3 The corresponding weight, n is 3, so that the pattern can be calculated by the above disclosureThe sales of the household appliance in the fourth quarter of the year predict Q.
And step S102, carrying out iterative computation according to commodity forecast demand data and logistics cost influence factors, and calculating logistics costs under different shop cargo plans. Iterative computation refers to that after the input data is changed, computation is performed again based on new input data to obtain a computation result under the new data. For example, the input data in this embodiment are the commodity forecast demand data and the logistics cost influencing factors, and under different shop plans, the commodity forecast demand data and the logistics cost influencing factors may all change, so that the commodity forecast demand data or the logistics cost influencing factors can be updated by adjusting the shop plans, thereby obtaining new input data, and performing iterative calculation again, so as to calculate the logistics cost under different shop plans.
In an actual scene, part of the processing procedure in iterative computation can be realized by using a distributed data analysis server, namely, the distributed data analysis server analyzes data and carries out related processing to obtain commodity prediction demand data. The architecture of the distributed data analysis server is shown in fig. 2, and includes a data processing module 210, an artificial intelligence module 220, and a data synchronization module 230, where the data processing module is configured to calculate and analyze the commodity historical demand data and the logistics cost impact factor data to obtain the commodity prediction demand data; the artificial intelligent module is used for establishing an algorithm model on line according to the related data; and the data synchronization module is used for carrying out iterative computation after the commodity historical demand data and the logistics cost influence factor change and synchronously updating the commodity forecast demand data.
And step S103, determining a target cargo spreading plan with optimal logistics cost in the cargo spreading plans, and adjusting the cargo spreading quantity according to the target cargo spreading plan. In this embodiment, the optimal logistics cost is that the corresponding shop plan can be completed with the lowest logistics cost. For example, in the case of the lay plan plan_1, the logistic cost under the lay plan is calculated as a. When some links of the shop goods plan are modified to obtain a new shop goods plan plan_2, commodity forecast demand data and logistics cost influence factors are changed, input data of iterative calculation are synchronously updated at the moment, and logistics cost corresponding to the shop goods plan plan_2 can be calculated to be b. Similarly, the logistics costs corresponding to the shop planning plan_3 and plan_4 can be calculated as c and d respectively, and if the numerical relationship between these logistics costs is: b < c < a < d, it is possible to determine that the logistics cost is optimal when using the shop plan 2. Thus, the target cargo laying plan is determined to be the plan_2, and the cargo laying amount can be adjusted according to the target cargo laying plan plan_2 to be the most suitable cargo laying amount.
When the goods laying amount is adjusted according to the target goods laying plan, a goods laying instruction for the shop goods laying planning system can be generated according to the target goods laying plan, and the goods laying amount on the goods laying list can be automatically adjusted according to the goods laying instruction, so that the shop goods laying planning system can be directly docked, and the adjustment of the goods laying amount is more convenient.
Fig. 3 shows a processing principle when the solution of the embodiment of the present application is adopted to realize dynamic adjustment of the goods laying amount, where the goods forecast demand data is determined by using historical demand data (including total warehouse historical data, city warehouse historical data, store historical data) of goods and combining logistics cost influence factors (including inventory holding cost, warehouse cost, transportation cost and information service cost), an operation plan, a sales plan and a goods laying plan are generated, then algorithm iterative computation is performed according to the demand data and the logistics cost influence factors to find out optimal logistics cost under different goods laying plans, and a goods laying instruction for a store goods laying planning system is generated, and then the goods laying amount on a goods laying list is automatically adjusted according to the goods laying instruction.
In addition, the embodiment of the application also provides a system for dynamically adjusting the amount of the shop goods, which comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the system to execute the method for dynamically adjusting the amount of the shop goods.
In particular, the methods and/or embodiments of the present application may be implemented as a computer software program. For example, the present embodiment also includes a computer program product comprising a computer program loaded on a computer readable medium, the computer program comprising program code for performing the method shown in the flowchart. The above-described functions defined in the method of the present application are performed when the computer program is executed by a processing unit.
It should be noted that, the computer readable medium described in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowchart or block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As another aspect, the present application also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more computer program instructions executable by a processor to implement the methods and/or aspects of the various embodiments of the present application described above.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, using Application Specific Integrated Circuits (ASIC), a general purpose computer or any other similar hardware device. In some embodiments, the software programs of the present application may be executed by a processor to implement the above steps or functions. Likewise, the software programs of the present application (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. In addition, some steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the apparatus claims can also be implemented by means of one unit or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.

Claims (10)

1. A method of dynamically adjusting a quantity of a store, the method comprising:
acquiring commodity forecast demand data of a merchant in a forecast period according to commodity historical demand data and logistics cost influence factors of all levels of storage nodes of the merchant;
carrying out iterative computation according to commodity forecast demand data and logistics cost influence factors, and calculating logistics costs under different shop cargo plans;
and determining a target cargo spreading plan with optimal logistics cost in the cargo spreading plans, and adjusting the cargo spreading quantity according to the target cargo spreading plan.
2. The method of claim 1, wherein obtaining commodity forecast demand data for the merchant over a forecast period based on commodity historical demand data and logistic cost impact factors for each level of warehousing nodes of the merchant, comprises:
acquiring commodity historical demand data of all levels of storage nodes of a merchant;
selecting the logistic cost impact factor;
and carrying out integration analysis based on commodity historical demand data and logistics cost influence factors of all levels of storage nodes of the merchant, and obtaining commodity forecast demand data of the merchant in a forecast period.
3. The method of claim 2, wherein the commodity historical demand data comprises a commodity historical inventory, a commodity historical purchase amount, and a commodity historical sales amount, and wherein the commodity forecast demand data comprises a commodity forecast inventory, a commodity forecast purchase amount, and a commodity forecast sales amount.
4. The method of claim 1, wherein the levels of warehousing nodes comprise a total warehouse, a city warehouse, and a store.
5. The method of claim 1, wherein obtaining commodity forecast demand data for the merchant over a forecast period based on commodity historical demand data and logistic cost impact factors for each level of warehousing nodes of the merchant, comprises:
and calculating by adopting a preset algorithm model according to commodity historical demand data and logistics cost influence factors of all levels of storage nodes of the merchant, and calculating commodity forecast demand data of the merchant in a forecast period.
6. The method of claim 5, wherein the algorithm model comprises a weighted moving average algorithm or a primary exponential smoothing algorithm.
7. The method of claim 1, wherein the logistic cost impact factor comprises at least any one of: inventory holding cost, warehouse cost, transportation cost, information service cost.
8. The method of claim 1, wherein adjusting the volume of the store according to the target store plan comprises:
and generating a shop order for a shop goods planning system according to the target shop goods plan, and automatically adjusting the shop goods quantity on a shop goods list according to the shop order.
9. A system for dynamically adjusting a quantity of a store, wherein the system comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the system to perform the method of any one of claims 1 to 8.
10. A computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of any of claims 1 to 8.
CN202310027528.7A 2023-01-09 2023-01-09 Method, system and computer readable medium for dynamically adjusting the amount of goods spread Pending CN116050766A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310027528.7A CN116050766A (en) 2023-01-09 2023-01-09 Method, system and computer readable medium for dynamically adjusting the amount of goods spread

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310027528.7A CN116050766A (en) 2023-01-09 2023-01-09 Method, system and computer readable medium for dynamically adjusting the amount of goods spread

Publications (1)

Publication Number Publication Date
CN116050766A true CN116050766A (en) 2023-05-02

Family

ID=86112854

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310027528.7A Pending CN116050766A (en) 2023-01-09 2023-01-09 Method, system and computer readable medium for dynamically adjusting the amount of goods spread

Country Status (1)

Country Link
CN (1) CN116050766A (en)

Similar Documents

Publication Publication Date Title
CN109993484B (en) Data processing method and system, medium and computing device
Olanrewaju et al. Supplier selection decision making in disaster response
US7958026B2 (en) Hierarchical transaction filtering
Bozkaya et al. A GIS-based optimization framework for competitive multi-facility location-routing problem
US20200057918A1 (en) Systems and methods for training artificial intelligence to predict utilization of resources
US20170206500A1 (en) Real-time determination of delivery/shipping using multi-shipment rate cards
US20150254589A1 (en) System and Method to Provide Inventory Optimization in a Multi-Echelon Supply Chain Network
US11301794B2 (en) Machine for labor optimization for efficient shipping
US10387925B2 (en) System and method for integrating retail price optimization for revenue and profit with business rules
Ashayeri et al. Economic value added of supply chain demand planning: A system dynamics simulation
US20070129981A1 (en) Business solution management
Becker et al. ANP-based analysis of ICT usage in Central European enterprises
US20220026874A1 (en) System and method for high-mix wheels for capacity planning resource planning and material resource planning
US20090043684A1 (en) Transaction Allocation
KR102392212B1 (en) Method, device and system for managing inventory and analising sales for mart operation
Saccani et al. Improving spare parts management for field services: a model and a case study for the repair kit problem
CN116128408A (en) Intelligent replenishment method, system and computer readable medium
EP2343683A1 (en) Data relationship preservation in a multidimension data hierarchy
Yenisey A flow-network approach for equilibrium of material requirements planning
US20160110733A1 (en) Inventory management system
Zarindast et al. A robust multi-objective global supplier selection model under currency fluctuation and price discount
CN114444782A (en) Enterprise asset management method and device, storage medium and computer equipment
Rafke et al. Simulating fleet procurement in an Indonesian logistics company
CN112288484A (en) Commodity automatic pricing method and device, electronic equipment and storage medium
Babaei et al. A comprehensive blockchain-enabled supply chain network design: an iterative model versus an integrated model

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination