A smart warehouse management method and system

Abstract

This invention discloses an intelligent warehouse management method and system, relating to the field of warehouse management technology. By scientifically assessing warehouse reputation and capabilities, it optimizes resource allocation, reduces logistics costs, and enhances sales data analysis capabilities, thereby improving a company's market responsiveness and decision-making intelligence. It helps companies quickly identify community warehouses that meet cooperation standards, optimize resource allocation, reduce unnecessary warehousing costs, utilize existing small business warehouse space, reduce investment and operating costs for new warehouses, achieve optimal resource allocation, improve overall efficiency, strengthen the connection between the company and the community through cooperation with local businesses, enhance brand image, and achieve rational resource allocation by analyzing sales data and inventory turnover rates in various communities. It maximizes the use of existing warehouse space, selects warehouses closer to customers, reduces transportation time and costs, and improves transportation efficiency.

Description

Technical Field

[0001] This invention relates to the field of warehouse management technology, and in particular to an intelligent warehouse management method and system. Background Technology

[0002] With rapid urbanization and dense urban populations, the demand for warehousing and logistics services in communities is increasing daily. Community warehousing can effectively solve the "last mile" delivery problem, improve logistics efficiency, and meet residents' needs for fast service. Intelligent warehouse management systems can help community warehouses with effective resource allocation and management; therefore, such systems have emerged.

[0003] Existing technologies, such as the invention patent application with publication number CN115187308A, disclose a sales data processing method and device based on a data warehouse, belonging to the field of data processing technology. This method addresses the technical problem of existing decision-making systems having simple evaluation factors, non-standardized criteria, and single data sources, leading to poor decision rationality and effectiveness. The method includes: acquiring comprehensive sales data related to various commodities operated by the enterprise; wherein the comprehensive sales data includes at least: enterprise-owned sales data, distributor sales data, and industry sales data; preprocessing the comprehensive sales data; constructing the enterprise's sales data warehouse based on the preprocessed comprehensive sales data; constructing a multi-dimensional decision-making system according to the enterprise's analytical needs; performing data analysis and processing on the data in the sales data warehouse through the multi-dimensional decision-making system and online analytical processing (OLAP) tools to obtain multi-dimensional decision information; and generating a pre-decision plan based on the multi-dimensional decision information.

[0004] Regarding the above solutions, the applicant of this invention has found that the above technology has at least the following technical problems: 1. Traditional warehouse management may not be able to make full use of existing warehouse space and resources, resulting in inventory backlog or insufficient space. It is necessary to invest in the construction or leasing of independent warehouse facilities, which leads to increased capital expenditure and operating costs. The maintenance and management costs of independent warehouses may be very high, especially when demand fluctuates.

[0005] In traditional warehousing models, warehouses may have idle space, leading to resource waste. The lack of distributed inventory management may result in excess or shortage of inventory, affecting overall operational efficiency. Large enterprises' warehousing facilities are often concentrated in specific areas, making it difficult to respond quickly to changes in demand within the community, which may lead to delivery delays. When facing market changes, they lack flexible warehousing solutions and find it difficult to quickly adjust inventory and distribution strategies.

[0006] Businesses may struggle to partner with local merchants, potentially alienating them from the community, impacting brand image and customer loyalty. A lack of deep understanding of local market demands may result in products and services failing to meet the actual needs of community consumers. Without community warehousing partnerships, individual warehousing facilities may operate independently, hindering data integration and leading to information asymmetry. The absence of flexible warehousing solutions may reduce the return on investment in advanced warehousing management technologies (such as automation and intelligent systems). Under traditional models, businesses may lack the motivation to innovate and adapt to rapidly changing market environments. Summary of the Invention

[0007] In view of the aforementioned existing problems, the present invention is proposed.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: collecting warehouse reputation impact parameters and warehouse capacity parameters for each community warehouse, calculating warehouse reputation impact factors, and calculating warehouse capacity evaluation coefficients; comparing the warehouse capacity evaluation coefficients of each community warehouse with the standard warehouse capacity evaluation coefficients, and selecting warehouses that meet the standards for potential cooperative community warehouses; analyzing the storage costs, transportation costs, and transportation time of potential cooperative community warehouses, and calculating decision factor evaluation coefficients; ranking potential cooperative community warehouses according to the decision factor evaluation coefficients, and selecting the warehouse with the highest evaluation coefficient as the best cooperative community warehouse; analyzing the sales data evaluation coefficients within the enterprise's target market range, comparing them with the sales data evaluation coefficients corresponding to each leased area in the database, and determining the most suitable leased area for the best cooperative community warehouse.

[0009] As a preferred embodiment of the intelligent warehouse management method described in this invention, the method includes: the warehouse reputation impact parameters include the number of successful cooperation cases, customer retention rate, and positive review rate corresponding to each evaluation platform; the warehouse capacity parameters include the actual available area, the number of available shelves of each type, and the number of orders processed per unit time.

[0010] As a preferred embodiment of the intelligent warehouse management method described in this invention, the method includes: calculating the warehouse reputation impact factor by recording the number of successful cooperation cases, customer retention rate, and positive review rate on each evaluation platform for each sales season in each community warehouse. and

[0011] Where f represents the number corresponding to each community warehouse, f = 1, 2, ..., u, where u is any integer greater than 2; g represents the number corresponding to each sales season, g = 1, 2, ..., n, where n is any integer greater than 2; and h represents the number corresponding to each review platform, h = 1, 2, ..., m, where m is any integer greater than 2. Substituting these values ​​into the calculation formula:

[0012]

[0013] Where, α f The denots represent the warehouse reputation impact factors for each community warehouse. A′, B′, and C′ represent the standard number of successful cooperation cases, standard customer retention rate, and standard positive review rate on the evaluation platform for the set community warehouse sales season, respectively. μ1, μ2, and μ3 represent the weighting factors corresponding to the number of successful cooperation cases, customer retention rate, and positive review rate on the evaluation platform for the set community warehouse sales season, respectively.

[0014] As a preferred embodiment of the intelligent warehouse management method described in this invention, the method includes: calculating the warehouse capacity assessment coefficient by recording the actual available area corresponding to each sales season, the quantity of each type of available shelving, and the number of orders processed per unit time in each community warehouse as follows: and

[0015] Where y represents the number corresponding to each type of available shelf, y = 1, 2, ..., z, where z is any integer greater than 2, and k represents the number corresponding to each unit of time, k = 1, 2, ..., x, where x is any integer greater than 2. Substituting these values ​​into the formula:

[0016]

[0017] Where, β f The storage capacity assessment coefficients for each community warehouse are: D′, E′, and F′, which represent the standard actual usable area, standard quantity of available shelves, and standard number of orders processed per unit time for the set sales season of the community warehouse, respectively. π1, π2, and π3 represent the weighting factors for the actual usable area, the quantity of available shelves, and the number of orders processed per unit time for the set sales season of the community warehouse, respectively.

[0018] As a preferred embodiment of the intelligent warehouse management method of the present invention, the step of selecting warehouses that meet the standards for potential cooperative community warehouses includes comparing the warehouse capacity evaluation coefficient corresponding to each community warehouse with the warehouse capacity evaluation coefficient corresponding to a set standard community warehouse. If the warehouse capacity evaluation coefficient corresponding to a community warehouse is less than the warehouse capacity evaluation coefficient corresponding to the set standard community warehouse, then the current community warehouse is assessed as not meeting the standards for potential cooperative community warehouses. If the warehouse capacity evaluation coefficient corresponding to a community warehouse is greater than or equal to the warehouse capacity evaluation coefficient corresponding to the set standard community warehouse, then the current community warehouse is assessed as meeting the standards for potential cooperative community warehouses. The assessment of whether each community warehouse meets the standards for potential cooperative community warehouses yields a set of potential cooperative community warehouses within the enterprise's target market.

[0019] As a preferred embodiment of the intelligent warehouse management method described in this invention, the method includes: calculating the evaluation coefficient of decision factors, which involves denoting the storage cost corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, the transportation cost corresponding to each distributor, and the transportation time as G. t , and

[0020] Where t represents the ID corresponding to each potential cooperative community warehouse, t = 1, 2, ..., q, where q is any integer greater than 2, and r represents the ID corresponding to each distributor, r = 1, 2, ..., w, where w is any integer greater than 2. Substituting these values ​​into the calculation formula:

[0021]

[0022] Where, γ t The evaluation coefficients of decision factors corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection are represented by G′, H′, and R′, respectively, which are the standard storage cost, standard transportation cost, and standard transportation time corresponding to the set potential cooperative community warehouse, respectively. η1, η2, and η3 are the weight factors corresponding to the storage cost of the set potential cooperative community warehouse, the weight factor corresponding to the transportation cost of the distributor, and the weight factor corresponding to the transportation time, respectively.

[0023] Arrange the evaluation coefficients of the decision factors for each potential cooperative community warehouse in the cooperative community warehouse collection in descending order, and select the potential cooperative community warehouse with the largest evaluation coefficient of decision factors in the cooperative community warehouse collection as the best cooperative community warehouse within the enterprise's target market range.

[0024] As a preferred embodiment of the intelligent warehouse management method described in this invention, the method includes: determining the most suitable rental area for the optimal cooperative community warehouse, which involves denoting the sales volume of each distributor and the inventory turnover rate of each product category within the enterprise's target market as Q. r and

[0025] Where r represents the distributor's ID, r = 1, 2, ..., w, where w is any integer greater than 2, and v represents the product category's ID, v = 1, 2, ..., s, where s is any integer greater than 2. Substituting these values ​​into the calculation formula:

[0026]

[0027] Among them, the sales data evaluation coefficient λ corresponding to the enterprise's target market is obtained, where Q′ and T′ are the standard sales volume and standard inventory turnover rate of the distributor and product category, respectively, within the enterprise's target market, and ω1 and ω2 are the weight factors corresponding to the sales volume of the distributor and the inventory turnover rate of the product category, respectively, within the enterprise's target market.

[0028] The sales data evaluation coefficient corresponding to the enterprise's target market is compared with the sales data evaluation coefficient corresponding to each leased area in the database. When the sales data evaluation coefficient corresponding to the enterprise's target market is the same as the sales data evaluation coefficient corresponding to the leased area in the database, the current leased area in the database is taken as the most suitable leased area corresponding to the best cooperative community warehouse in the enterprise's target market.

[0029] The database is used to store the sales data evaluation coefficients corresponding to each leased area.

[0030] Another object of the present invention is to provide an intelligent warehouse management system.

[0031] As a preferred embodiment of the intelligent warehouse management system described in this invention, it includes:

[0032] It includes modules for obtaining warehousing capacity assessment coefficients, potential cooperative community warehousing analysis, decision factor assessment coefficients, optimal cooperative community warehousing selection, and sales data assessment coefficients.

[0033] The warehousing capacity assessment coefficient acquisition module is used to acquire the warehousing reputation impact parameters and warehousing capacity parameters corresponding to each sales season in each community warehouse within the enterprise's target market range, and analyze to obtain the warehousing reputation impact factor and warehousing capacity assessment coefficient corresponding to each community warehouse.

[0034] The potential cooperative community warehouse analysis module is used to evaluate whether each community warehouse meets the standards for potential cooperative community warehouses based on the warehouse capacity evaluation coefficient corresponding to each community warehouse, and then obtain a collection of potential cooperative community warehouses within the enterprise's target market.

[0035] The decision factor evaluation coefficient acquisition module is used to acquire the decision factor parameters corresponding to each potential cooperative community warehouse in the potential cooperative community warehouse collection within the enterprise's target market scope. The decision factor parameters include storage costs, transportation costs and transportation time corresponding to each distributor, and then analyze and obtain the decision factor evaluation coefficients corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection.

[0036] The optimal cooperative community warehouse selection module is used to select the optimal cooperative community warehouse within the enterprise's target market range based on the decision factor evaluation coefficients corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection.

[0037] The sales data evaluation coefficient acquisition module is used to acquire the sales data of each distributor within the enterprise's target market. The sales data includes sales volume and inventory turnover rate of each product category. Then, the module analyzes and obtains the sales data evaluation coefficient within the enterprise's target market and evaluates the most suitable rental area for the best cooperative community warehouse within the enterprise's target market.

[0038] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of an intelligent warehouse management method.

[0039] A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of an intelligent warehouse management method.

[0040] The beneficial effects of this invention are as follows: By scientifically assessing the reputation and capabilities of warehouses, this invention optimizes resource allocation, reduces logistics costs, and enhances sales data analysis capabilities, thereby improving the market responsiveness and decision-making intelligence of enterprises. It helps enterprises quickly identify community warehouses that meet cooperation standards, optimize resource allocation, and by analyzing sales data and inventory turnover rates of each community, the system can achieve rational resource allocation, maximize the use of existing warehouse space, select warehouses closer to customers, reduce transportation time and costs, and improve transportation efficiency. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0042] Figure 1 This is a schematic diagram of an intelligent warehouse management method provided in one embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram of the working modules of an intelligent warehouse management system provided in one embodiment of the present invention. Detailed Implementation

[0044] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0045] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0047] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0048] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0049] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0050] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides an intelligent warehouse management method, including:

[0051] S1: Collect the warehouse reputation impact parameters and warehouse capacity parameters of each community warehouse, calculate the warehouse reputation impact factor, and calculate the warehouse capacity assessment coefficient.

[0052] Furthermore, the warehouse reputation impact parameters include the number of successful cooperation cases, customer retention rate, and positive review rate on each evaluation platform. Warehouse capacity parameters include actual available area, the number of available shelves of each type, and the number of orders processed per unit time.

[0053] It should be noted that the calculation of the warehouse reputation impact factor includes recording the number of successful cooperation cases, customer retention rate, and positive review rate on each evaluation platform for each sales season in each community warehouse. and

[0054] Where f represents the number corresponding to each community warehouse, f = 1, 2, ..., u, where u is any integer greater than 2; g represents the number corresponding to each sales season, g = 1, 2, ..., n, where n is any integer greater than 2; and h represents the number corresponding to each review platform, h = 1, 2, ..., m, where m is any integer greater than 2. Substituting these values ​​into the calculation formula:

[0055]

[0056] Where, α fThe denots represent the warehouse reputation impact factors for each community warehouse. A′, B′, and C′ represent the standard number of successful cooperation cases, standard customer retention rate, and standard positive review rate on the evaluation platform for the set community warehouse sales season, respectively. μ1, μ2, and μ3 represent the weighting factors corresponding to the number of successful cooperation cases, customer retention rate, and positive review rate on the evaluation platform for the set community warehouse sales season, respectively.

[0057] S2: Compare the storage capacity assessment coefficients of each community warehouse with the standard storage capacity assessment coefficients to select warehouses that meet the standards of potential cooperative community warehouses.

[0058] Furthermore, the calculation of the warehousing capacity assessment coefficient includes recording the actual available area for each sales season, the quantity of each type of available shelving, and the number of orders processed per unit time in each community warehouse as follows: and

[0059] Where y represents the number corresponding to each type of available shelf, y = 1, 2, ..., z, where z is any integer greater than 2, and k represents the number corresponding to each unit of time, k = 1, 2, ..., x, where x is any integer greater than 2. Substituting these values ​​into the formula:

[0060]

[0061] Where, β f The storage capacity assessment coefficients for each community warehouse are: D′, E′, and F′, which represent the standard actual usable area, standard quantity of available shelves, and standard number of orders processed per unit time for the set sales season of the community warehouse, respectively. π1, π2, and π3 represent the weighting factors for the actual usable area, the quantity of available shelves, and the number of orders processed per unit time for the set sales season of the community warehouse, respectively.

[0062] S3: Analyze the storage costs, transportation costs, and transportation time of potential cooperative community warehouses, and calculate the evaluation coefficient of decision factors.

[0063] Furthermore, the process of selecting warehouses that meet the standards for potential cooperative community warehouses includes comparing the warehouse capacity assessment coefficients corresponding to each community warehouse with the warehouse capacity assessment coefficients corresponding to the set standard community warehouses. If the warehouse capacity assessment coefficient of a community warehouse is less than the warehouse capacity assessment coefficient of the set standard community warehouse, then the current community warehouse is assessed as not meeting the standards for potential cooperative community warehouses. If the warehouse capacity assessment coefficient of a community warehouse is greater than or equal to the warehouse capacity assessment coefficient of the set standard community warehouse, then the current community warehouse is assessed as meeting the standards for potential cooperative community warehouses. By assessing whether each community warehouse meets the standards for potential cooperative community warehouses, a set of potential cooperative community warehouses within the enterprise's target market is obtained.

[0064] S4: Based on the evaluation coefficient of decision factors, rank the potential cooperative community warehouses and select the warehouse with the highest evaluation coefficient as the best cooperative community warehouse.

[0065] Furthermore, the calculation of the decision factor evaluation coefficient includes denoting the storage cost corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, the transportation cost corresponding to each distributor, and the transportation time as G. t , and

[0066] Where t represents the ID corresponding to each potential cooperative community warehouse, t = 1, 2, ..., q, where q is any integer greater than 2, and r represents the ID corresponding to each distributor, r = 1, 2, ..., w, where w is any integer greater than 2. Substituting these values ​​into the calculation formula:

[0067]

[0068] Where, γ t The evaluation coefficients of decision factors corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection are represented by G′, H′, and R′, respectively, which are the standard storage cost, standard transportation cost, and standard transportation time corresponding to the set potential cooperative community warehouse, respectively. η1, η2, and η3 are the weight factors corresponding to the storage cost of the set potential cooperative community warehouse, the weight factor corresponding to the transportation cost of the distributor, and the weight factor corresponding to the transportation time, respectively.

[0069] Arrange the evaluation coefficients of the decision factors for each potential cooperative community warehouse in the cooperative community warehouse collection in descending order, and select the potential cooperative community warehouse with the largest evaluation coefficient of decision factors in the cooperative community warehouse collection as the best cooperative community warehouse within the enterprise's target market range.

[0070] S5: Analyze the sales data evaluation coefficient within the enterprise's target market range, compare it with the sales data evaluation coefficient corresponding to each leased area in the database, and determine the most suitable leased area for the best cooperative community warehouse.

[0071] Furthermore, determining the optimal rental area for the best cooperative community warehouse includes denoting the sales volume of each distributor and the inventory turnover rate of each product category within the enterprise's target market as Q. r and

[0072] Where r represents the distributor's ID, r = 1, 2, ..., w, where w is any integer greater than 2, and v represents the product category's ID, v = 1, 2, ..., s, where s is any integer greater than 2. Substituting these values ​​into the calculation formula:

[0073]

[0074] Among them, the sales data evaluation coefficient λ corresponding to the enterprise's target market is obtained, where Q′ and T′ are the standard sales volume and standard inventory turnover rate of the distributor and product category, respectively, within the enterprise's target market, and ω1 and ω2 are the weight factors corresponding to the sales volume of the distributor and the inventory turnover rate of the product category, respectively, within the enterprise's target market.

[0075] The sales data evaluation coefficient corresponding to the enterprise's target market is compared with the sales data evaluation coefficient corresponding to each leased area in the database. When the sales data evaluation coefficient corresponding to the enterprise's target market is the same as the sales data evaluation coefficient corresponding to the leased area in the database, the current leased area in the database is taken as the most suitable leased area corresponding to the best cooperative community warehouse in the enterprise's target market.

[0076] The database is used to store the sales data evaluation coefficients corresponding to each leased area.

[0077] In Example 2, if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0079] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0080] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0081] Example 3, referring to Figure 2 As an embodiment of the present invention, an intelligent warehouse management system is provided, characterized in that it includes a warehouse capacity assessment coefficient acquisition module, a potential cooperative community warehouse analysis module, a decision factor assessment coefficient acquisition module, an optimal cooperative community warehouse selection module, and a sales data assessment coefficient acquisition module.

[0082] The warehousing capacity assessment coefficient acquisition module is used to obtain the warehousing reputation impact parameters and warehousing capacity parameters corresponding to each sales season in each community warehouse within the enterprise's target market range, and analyze to obtain the warehousing reputation impact factor and warehousing capacity assessment coefficient corresponding to each community warehouse;

[0083] The potential cooperative community warehousing analysis module is used to evaluate whether each community warehouse meets the standards for potential cooperative community warehousing based on the warehousing capacity assessment coefficient corresponding to each community warehouse, thereby obtaining a collection of potential cooperative community warehouses within the enterprise's target market.

[0084] The decision factor evaluation coefficient acquisition module is used to obtain the decision factor parameters corresponding to each potential cooperative community warehouse in the potential cooperative community warehouse collection within the enterprise's target market scope. The decision factor parameters include storage costs, transportation costs and transportation time corresponding to each distributor, and then analyzes and obtains the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection.

[0085] The Optimal Partner Community Warehouse Selection Module is used to select the optimal partner community warehouse within the enterprise's target market range based on the evaluation coefficients of decision factors corresponding to each potential partner community warehouse in the partner community warehouse collection.

[0086] The sales data evaluation coefficient acquisition module is used to obtain the sales data of each distributor within the enterprise's target market. The sales data includes sales volume and inventory turnover rate for each product category. Then, the module analyzes and obtains the sales data evaluation coefficient within the enterprise's target market and evaluates the most suitable rental area for the best cooperative community warehouse within the enterprise's target market.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An intelligent warehouse management method, characterized in that: include, Collect warehouse reputation impact parameters and warehouse capacity parameters for each community warehouse, calculate warehouse reputation impact factor, and calculate warehouse capacity assessment coefficient; The calculation of warehouse reputation impact factors includes recording the number of successful cooperation cases, customer retention rate, and positive review rate on various evaluation platforms for each community warehouse in each sales season. , and ; in, This indicates the corresponding number for each community warehouse. u is any integer greater than 2, and g represents the number corresponding to each sales season. n is any integer greater than 2, and h represents the number corresponding to each evaluation platform. Let m be any integer greater than 2. Substitute this into the calculation formula: in, This indicates the warehouse reputation impact factor corresponding to each community warehouse. These are the standard number of successful cooperation cases, standard customer retention rate, and standard positive review rate corresponding to the set community warehouse sales season. The weighting factors are: the number of successful cooperation cases in the set sales season of community warehousing; the weighting factor for customer retention rate; and the weighting factor for positive review rate on the evaluation platform. The warehousing capacity evaluation coefficient of each community warehouse is compared with the standard warehousing capacity evaluation coefficient to screen out warehouses that meet the standards of potential cooperative community warehouses. The calculation of the warehousing capacity assessment coefficient includes recording the actual available area for each sales season, the quantity of each type of available shelving, and the number of orders processed per unit time in each community warehouse. , and ; Where y represents the number corresponding to each type of available shelf. z is any integer greater than 2, and k represents the number corresponding to each unit of time. Let x be any integer greater than 2. Substitute it into the formula: in, This refers to the storage capacity assessment coefficient for each community warehouse. These are the standard actual usable area, the standard quantity of available shelving for each type, and the standard number of orders processed per unit time, corresponding to the set community warehouse sales season. The weighting factors are: the actual available area of ​​the community warehouse during the set sales season; the weighting factors are: the number of available shelves of each type; and the weighting factors are: the number of orders processed per unit time. The storage costs, transportation costs, and transportation time of potential cooperative community warehouses are analyzed, and the evaluation coefficients of decision factors are calculated. The calculation of decision factor evaluation coefficients includes, respectively, recording the storage costs corresponding to each potential cooperative community warehouse in the potential cooperative community warehouse collection, the transportation costs corresponding to each distributor, and the transportation time as follows: , and ; Where t represents the ID number corresponding to each potential partner community warehouse. q is any integer greater than 2, and r represents the number corresponding to each distributor. Let w be any integer greater than 2. Substitute this into the calculation formula: in, This represents the evaluation coefficient of the decision factors for each potential cooperative community warehouse in the potential cooperative community warehouse collection. These are the standard storage costs for potential partner community warehouses, the standard transportation costs for distributors, and the standard transportation time, respectively. The weighting factors are: the storage cost of potential partner community warehouses, the transportation cost of distributors, and the transportation time. Based on the evaluation coefficients of the decision factors, the potential partner community warehouses are ranked, and the warehouse with the highest evaluation coefficient is selected as the best partner community warehouse. Analyze the sales data evaluation coefficients within the company's target market area and compare them with the sales data evaluation coefficients corresponding to each leased area in the database to determine the most suitable leased area for the best cooperative community warehouse. Determining the optimal rental area for the best cooperative community warehouse includes denoting the sales volume of each distributor and the inventory turnover rate of each product category within the company's target market as follows: and ; Where v represents the product number corresponding to each category. s is any integer greater than 2. Substitute it into the calculation formula: Among them, the sales data evaluation coefficient corresponding to the enterprise's target market scope is obtained. ,in, , These are the standard sales volume for distributors within the defined target market area of ​​the enterprise, and the standard inventory turnover rate for each product category. These are the weighting factors corresponding to the sales volume of distributors within the set target market of the enterprise and the weighting factors corresponding to the inventory turnover rate of product categories. The sales data evaluation coefficient corresponding to the enterprise's target market is compared with the sales data evaluation coefficient corresponding to each leased area in the database. When the sales data evaluation coefficient corresponding to the enterprise's target market is the same as the sales data evaluation coefficient corresponding to the leased area in the database, the current leased area in the database is taken as the most suitable leased area corresponding to the best cooperative community warehouse in the enterprise's target market. The database is used to store the sales data evaluation coefficients corresponding to each leased area.

2. The intelligent warehouse management method as described in claim 1, characterized in that: The warehouse reputation impact parameters include the number of successful cooperation cases, customer retention rate, and positive review rate on each evaluation platform. Warehouse capacity parameters include actual available area, the number of available shelves of each type, and the number of orders processed per unit time.

3. The intelligent warehouse management method as described in claim 2, characterized in that: The process of selecting warehouses that meet the standards for potential cooperative community warehouses involves comparing the warehouse capacity assessment coefficient of each community warehouse with the warehouse capacity assessment coefficient of a set standard community warehouse. If the warehouse capacity assessment coefficient of a community warehouse is less than the warehouse capacity assessment coefficient of the set standard community warehouse, then the current community warehouse is assessed as not meeting the standards for potential cooperative community warehouses. If the warehouse capacity assessment coefficient of a community warehouse is greater than or equal to the warehouse capacity assessment coefficient of the set standard community warehouse, then the current community warehouse is assessed as meeting the standards for potential cooperative community warehouses. By assessing whether each community warehouse meets the standards for potential cooperative community warehouses, a set of potential cooperative community warehouses within the enterprise's target market is obtained.

4. The intelligent warehouse management method as described in claim 3, characterized in that: Arrange the decision factor evaluation coefficients of each potential cooperative community warehouse in the potential cooperative community warehouse collection in descending order, and select the potential cooperative community warehouse with the largest decision factor evaluation coefficient in the potential cooperative community warehouse collection as the best cooperative community warehouse within the enterprise's target market range.

5. A system employing an intelligent warehouse management method as described in any one of claims 1 to 4, characterized in that: It includes modules for obtaining warehousing capacity assessment coefficients, potential cooperative community warehousing analysis, decision factor assessment coefficients, optimal cooperative community warehousing selection, and sales data assessment coefficients. The warehousing capacity assessment coefficient acquisition module is used to acquire the warehousing reputation impact parameters and warehousing capacity parameters corresponding to each sales season in each community warehouse within the enterprise's target market range, and analyze to obtain the warehousing reputation impact factor and warehousing capacity assessment coefficient corresponding to each community warehouse. The potential cooperative community warehouse analysis module is used to evaluate whether each community warehouse meets the standards for potential cooperative community warehouses based on the warehouse capacity evaluation coefficient corresponding to each community warehouse, and then obtain a collection of potential cooperative community warehouses within the enterprise's target market. The decision factor evaluation coefficient acquisition module is used to acquire the decision factor parameters corresponding to each potential cooperative community warehouse in the potential cooperative community warehouse collection within the enterprise's target market scope. The decision factor parameters include storage costs, transportation costs and transportation time corresponding to each distributor, and then analyze and obtain the decision factor evaluation coefficients corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection. The optimal cooperative community warehouse selection module is used to select the optimal cooperative community warehouse within the enterprise's target market range based on the decision factor evaluation coefficients corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection. The sales data evaluation coefficient acquisition module is used to acquire the sales data of each distributor within the enterprise's target market. The sales data includes sales volume and inventory turnover rate of each product category. Then, the module analyzes and obtains the sales data evaluation coefficient within the enterprise's target market and evaluates the most suitable rental area for the best cooperative community warehouse within the enterprise's target market.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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