Inventory management evaluation method, device, equipment, medium and program product

Abstract

The invention discloses an inventory management evaluation method and device, equipment, a medium and a program product, and belongs to the field of inventory management.The method comprises the steps that evaluation index data and evaluation index weights are obtained, and the evaluation index data represent data for evaluating different dimensions of inventory management; calculating an index score corresponding to the evaluation index data; and calculating a comprehensive score based on the evaluation index weight and the index score, the comprehensive score being used for evaluating the inventory management condition. According to the method, the index score corresponding to the evaluation index data is calculated, and the comprehensive score is calculated based on the evaluation index weight and the index score, so that the automatic calculation of the evaluation index data of different dimensions is realized, the unified comprehensive score is obtained, and the quantitative comprehensive evaluation of the inventory management condition is realized.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of inventory management, and in particular to an inventory management evaluation method, device, equipment, medium and program product. Background Art

[0002] Faced with increasingly severe competition, inventory management, as an important part of the enterprise management system, has a significant impact on the competitiveness of the enterprise.

[0003] In the related art, inventory management is performed by evaluating inventory management indicators such as inventory turnover rate, end-of-period inventory reduction rate, etc.

[0004] However, the related technologies have the problem that the dimensions of inventory management indicators are not unified, making it difficult to obtain comprehensive quantitative evaluation results. Summary of the invention

[0005] The present application provides an inventory management evaluation method, device, equipment, medium and program product, and the technical solution at least includes:

[0006] According to one aspect of an embodiment of the present application, a method for evaluating inventory management is provided. The method is executed by a computer device, and the method includes:

[0007] Obtaining evaluation index data and evaluation index weights, where the evaluation index data represents data for evaluating different dimensions of inventory management;

[0008] Calculate the indicator score corresponding to the evaluation indicator data;

[0009] Based on the evaluation index weights and index scores, a comprehensive score is calculated, which is used to evaluate the inventory management situation.

[0010] According to another aspect of an embodiment of the present application, there is provided an inventory management evaluation device, the device comprising:

[0011] An acquisition module is used to acquire evaluation index data and evaluation index weights, wherein the evaluation index data represents data for evaluating different dimensions of inventory management;

[0012] A calculation module, used to calculate the indicator score corresponding to the evaluation indicator data;

[0013] The calculation module is used to calculate the comprehensive score based on the evaluation index weights and index scores, and the comprehensive score is used to evaluate the inventory management situation.

[0014] According to another aspect of an embodiment of the present application, a computer device is provided, comprising: a processor and a memory, wherein at least one program is stored in the memory; the processor is used to execute at least one program in the memory to implement the inventory management evaluation method of the above-mentioned various aspects.

[0015] According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which at least one program is stored. The at least one program is loaded and executed by a processor to implement the inventory management evaluation method as described in the above aspects.

[0016] According to another aspect of an embodiment of the present application, a computer program product (or computer program) is provided, which includes computer instructions stored in a computer-readable storage medium, a processor obtains the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to implement the inventory management evaluation method as described in the above aspects.

[0017] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0018] This method calculates the indicator scores corresponding to the evaluation indicator data, and calculates the comprehensive scores based on the evaluation indicator weights and indicator scores, thereby realizing the automatic calculation of evaluation indicator data of different dimensions, obtaining a unified comprehensive score, and achieving a quantitative comprehensive evaluation of the inventory management situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A structural block diagram of a computer system provided by an exemplary embodiment of the present application is shown;

[0021] Figure 2 A flowchart of an inventory management evaluation method provided by an exemplary embodiment of the present application is shown;

[0022] Figure 3 A schematic diagram of an inventory management evaluation index model provided by an exemplary embodiment of the present application is shown;

[0023] Figure 4 A flowchart of an inventory management evaluation method provided by an exemplary embodiment of the present application is shown;

[0024] Figure 5 A schematic diagram of an inventory management evaluation system provided by an exemplary embodiment of the present application is shown;

[0025] Figure 6 A block diagram of an inventory management evaluation device provided by an exemplary embodiment of the present application is shown;

[0026] Figure 7 A schematic diagram of the structure of a computer device provided by an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0028] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0029] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0030] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0031] It should be understood that, although the terms first, second, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first parameter may also be referred to as the second parameter, and similarly, the second parameter may also be referred to as the first parameter. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0032] Figure 1 The structure block diagram of a computer system 100 provided by an exemplary embodiment of the present application is shown. The computer system 100 includes: a terminal device 120 and a server 140.

[0033] The terminal device 120 is connected to the server 140 via a wireless network or a wired network.

[0034] The server 140 includes at least one of a single server, multiple servers, a cloud computing platform, and a virtualization center. Exemplarily, the server 140 includes a processor 144 and a memory 142, and the memory 142 includes a receiving module 1421, a control module 1422, and a sending module 1423. The receiving module 1421 is used to receive a request sent by the terminal device 120; the control module 1422 is used for the operation of the inventory management evaluation system; and the sending module 1423 is used to send a response to the terminal device 120. The server 140 is used to provide background services for the terminal device 120. Optionally, the server 140 undertakes the main computing work, and the terminal device 120 undertakes the secondary computing work; or, the server 140 undertakes the secondary computing work, and the terminal device 120 undertakes the main computing work; or, the server 140 and the terminal device 120 adopt a distributed computing architecture for collaborative computing.

[0035] The device type of the terminal device 120 includes at least one of a smart phone, a smart watch, a smart TV, a tablet computer, an e-book reader, a laptop computer, and a desktop computer.

[0036] Those skilled in the art will appreciate that the number of the terminal devices 120 may be more or less. For example, the terminal device 120 may be only one, or the terminal devices 120 may be dozens or hundreds, or more. The embodiment of the present application does not limit the number and device type of the terminal devices 120.

[0037] In the face of increasingly severe competition, inventory management is an important part of the enterprise management system, and the quality of management has a significant impact on the competitiveness of the enterprise. In the related art, inventory management is carried out by evaluating inventory management indicators such as inventory turnover rate and end-of-period inventory reduction rate. However, the related art has the problem that the dimensions of inventory management indicators are not unified, and it is difficult to obtain comprehensive quantitative evaluation results. Therefore, a unified dimension inventory management evaluation method is needed. Figure 2 A flowchart of an inventory management evaluation method provided by an exemplary embodiment of the present application is shown. The method is executed by a computer device, and the computer device includes at least one of a server and a terminal device. The method includes:

[0038] Step 210: Obtain evaluation index data.

[0039] The evaluation index data represents data for evaluating different dimensions of inventory management.

[0040] In some embodiments, the type of evaluation index data is pre-set by a person skilled in the art. The technicians analyze and study the enterprise's material supply management system, key points of material management work, and other contents to obtain three basic dimensions of inventory management evaluation, including: warehousing operation, inventory control, and comprehensive management, as primary indicators. Among them, warehousing operation refers to the management and operation of storage facilities, including the process of receiving, storing, transferring, outbound delivery, and inventory monitoring of goods; inventory control refers to a series of measures taken by the enterprise to monitor and manage its goods stored in warehouses or other places; comprehensive management refers to the management and control of inventory scale by considering multiple factors to achieve the best inventory management effect.

[0041] In some embodiments, each primary indicator includes multiple secondary indicators. For example, warehouse operation includes at least one of inventory turnover, inventory structure rationality, storage area utilization, and warehouse capacity utilization; inventory control includes at least one of end-of-period inventory reduction rate, high-age inventory reduction rate, and overstock inventory reduction rate; comprehensive management includes at least one of centralized reserve utilization rate, inventory scale assessment, agency storage and sales rate, and inventory accuracy rate. Evaluation indicator data includes primary indicator data and secondary indicator data.

[0042] Among them, the inventory turnover number refers to the number of times an item turns over within a period of time; the inventory structure rationality rate refers to the ratio of the sum of the inventory amount of conventional materials and the inventory amount of special reserve materials in the enterprise warehouse to the total inventory amount of materials; the storage area utilization rate refers to the ratio of the available area of ​​the warehouse to the actual use area; the warehouse capacity utilization rate refers to the ratio of the actual occupied warehouse storage space to the total volume of the warehouse; the end-of-period inventory reduction rate refers to the ratio of the difference between the end-of-period inventory and the beginning-of-period inventory within a certain period of time to the beginning-of-period inventory; the high-age inventory reduction rate refers to the reduction rate of inventory with an age of more than a certain period within a specific period of time; the backlog inventory reduction rate refers to the reduction rate of inventory materials with a storage time of more than one year and no clear use destination within a specific period of time; the centralized reserve utilization rate refers to the ratio of the amount of materials purchased by the enterprise from the centralized reserve unit to the total purchase amount of such materials of the enterprise; the inventory scale assessment refers to the ratio of the end-of-period inventory amount of the enterprise to the enterprise's operating income; the agency storage and sales rate refers to the ratio of the amount of materials purchased by the enterprise through agency storage and sales to the total amount of material purchases of the enterprise; the inventory accuracy rate refers to the ratio of the actual inventory quantity to the inventory quantity recorded on the books.

[0043] In some embodiments, the types of the above-mentioned primary indicators and secondary indicators can be modified, increased or decreased, and the embodiments of the present application do not limit this. For example, all technicians rate the necessity of all primary indicators and secondary indicators in 5 levels, and retain the primary indicators and secondary indicators with an average score greater than 3 as the primary indicators and secondary indicators used in the embodiments of the present application.

[0044] In some embodiments, after determining the primary indicators and secondary indicators, each technician will score their familiarity with different secondary indicators, the importance of different secondary indicators, and the evaluation basis for judging importance. The evaluation basis includes: at least one of practical experience, theoretical knowledge, references in the same industry, and intuitive feelings. The embodiments of this application are not limited to this.

[0045] In some embodiments, the reliability of technicians is represented using the authority coefficient (Cr):

[0046]

[0047] Among them, Ca is the technical personnel's evaluation basis coefficient, and Cs is the technical personnel's familiarity coefficient. The Cr value range is 0 to 1. When Cr is greater than 0.7, it means that the technical personnel is reliable. The assignment of Ca and Cs is shown in Table 1 and Table 2 respectively:

[0048] Table 1

[0049]

[0050] Table 2

[0051] Familiarity Familiarity coefficient Cs Very familiar 1.0 More familiar 0.75 General familiarity 0.50 Not so familiar 0.25 Very unfamiliar 0.00

[0052] The authority coefficient Cr of technicians is shown in Table 3:

[0053] Table 3

[0054]

[0055] According to Table 3, the authority coefficients of the technicians are all greater than 0.7, and they are all reliable technicians.

[0056] In some embodiments, the degree of coordination of the opinions of the technical personnel is calculated, that is, whether the technical personnel's judgment on the secondary indicators is consistent, and the coefficient of variation (CV) and Kendall's coordination coefficient (W) are used to represent the degree of coordination of the opinions of the technical personnel. The coefficient of variation is obtained by dividing the standard deviation corresponding to the secondary indicator data by the mean value, and the Kendall's coordination coefficient is obtained by relevant software such as the Statistical Package for the Social Sciences (SPSS). The smaller the coefficient of variation corresponding to a certain secondary indicator, the more consistent the judgment of the technical personnel on the secondary indicator is, with CV<0.25 as the working range. The W value range is 0 to 1. When the W value fluctuates between 0.4 and 0.5, the work can be completed. The closer the W value is to 1, the better the coordination of the technical personnel. The coefficient of variation CV is shown in Table 4:

[0057] Table 4

[0058] Secondary indicators Standard Deviation average value Coefficient of variation CV Inventory turnover times 0.4629 4.75 0.097 Reasonable inventory structure rate 0.5175 3.625 0.143 Storage area utilization 0.4629 2.75 0.168 Warehouse capacity utilization 0.5345 2.5 0.214 Ending inventory reduction rate 0.7559 4.5 0.168 High-age inventory reduction rate 0.4629 3.75 0.123 Inventory backlog reduction rate 0.4629 4.75 0.097 Storage utilization rate 0.5345 2.5 0.214 Inventory size assessment 0.5175 2.625 0.197 Storage and sales rate 0.5175 2.625 0.197 Inventory accuracy 0.5175 3.625 0.143

[0059] In some embodiments, the secondary indicators are screened based on the average and coefficient of variation of the importance score. For example, the secondary indicators are screened based on the value range of the average value of the importance score>3.5 and CV<0.25. As shown in Table 3, the CVs of the secondary indicators in Table 3 are all less than 0.25. The average values ​​of the importance scores corresponding to the secondary indicators in Table 3 are all greater than 3.5. Figure 3 The secondary indicators shown are used as an example of the secondary indicators after screening.

[0060] In some embodiments, after completing the screening of the secondary indicators, the evaluation indicator data is obtained, and the evaluation indicator data includes data corresponding to the determined primary indicators and secondary indicators.

[0061] Step 220: Obtain evaluation index weights.

[0062] In some embodiments, the execution order of step 210 and step 220 is not limited, and step 210 may be executed first, or step 220 may be executed first.

[0063] In some embodiments, according to step 210, three first-level indicators are selected and 11 second-level indicators are formed as follows: Figure 3 The inventory management evaluation index model 300 is shown.

[0064] In some embodiments, the inventory management evaluation index model 300 is divided into three layers: a target layer 310, a criterion layer 320 and an indicator layer 330, wherein the inventory management evaluation index system 311 constitutes the target layer 310, and warehouse operations 321, inventory control 322, and comprehensive management 323 are first-level indicators that constitute the criterion layer 320; each first-level indicator contains multiple second-level indicators, and these second-level indicators constitute the indicator layer 330 of the evaluation system.

[0065] Exemplarily, warehouse operation 321 includes inventory turnover rate 331, inventory structure rationality rate 332, storage area utilization rate 333, and warehouse capacity utilization rate 334; inventory control 322 includes: end-of-period inventory reduction rate 335, high-age inventory reduction rate 336, and backlog inventory reduction rate 337; comprehensive management 323 includes: centralized reserve utilization rate 338, inventory scale assessment 339, agency storage and sales rate 3310, and inventory accuracy rate 3311.

[0066] In some embodiments, the evaluation index weight is obtained by multiplying the primary index weight and the secondary index weight, and the primary index weight and the secondary index weight are pre-set by technical personnel in this field.

[0067] Step 230: Calculate the index score corresponding to the evaluation index data.

[0068] In some embodiments, the evaluation index data of each enterprise is obtained through a data collection system, and the evaluation index data includes primary index data and secondary index data. For example, by accessing an enterprise resource planning (ERP) system, an electronic procurement system, etc. on a big data analysis platform, the business data of enterprise procurement, warehousing, outbound delivery, warehousing, etc. are collected, and based on big data technology, the evaluation index data of each enterprise is obtained through data aggregation, data cleaning, data analysis, data processing, etc.

[0069] In some embodiments, an evaluation level set is set, and the evaluation level set is a set of evaluation levels used to evaluate inventory management conditions. For example, four evaluation levels and corresponding value ranges are set: excellent [90, 100], good [80, 90], medium [60, 80], and poor [0, 60). The fuzzy judgment vector V used to represent the evaluation level set is (v 1 ,v 2 ,v 3 ,v 4 )=(95,85,70,30).

[0070] In some embodiments, the best value and the worst value are introduced, and the membership function is used to calculate the membership vector corresponding to the secondary index. The membership function represents the degree to which the secondary index belongs to different evaluation levels, and the membership vectors corresponding to all secondary indicators are combined to form an evaluation matrix R:

[0071]

[0072] Where n represents the number of evaluation level types, m represents the number of secondary indicators, in the embodiment of the present application, n=4, m=11, r mn Indicates the value corresponding to the nth evaluation level of the mth secondary indicator, for example, r 14 It represents the value corresponding to the 4th evaluation level of the 1st secondary indicator, that is, the value corresponding to the poor evaluation level of inventory turnover times.

[0073] In some embodiments, the fuzzy judgment vector V is multiplied by the evaluation matrix R to calculate the secondary indicator score set B:

[0074]

[0075] Among them, b m Represents the secondary indicator score of the mth secondary indicator.

[0076] Step 240: Calculate the comprehensive score based on the evaluation index weights and index scores.

[0077] Among them, the comprehensive score is used to evaluate the inventory management situation.

[0078] In some embodiments, the evaluation index weight W is multiplied by the index score (secondary index score set B) to calculate the comprehensive score S:

[0079]

[0080] Among them, w m It represents the evaluation index weight corresponding to the mth secondary index. The comprehensive score S is used to evaluate the inventory management situation. The corresponding evaluation level and value range are: excellent [90, 100], good [80, 90], medium [60, 80], and poor [0, 60).

[0081] In some embodiments, the secondary indicator weight is multiplied by the secondary indicator score, and the products are added to obtain the corresponding primary indicator score; the secondary indicator score is multiplied by the evaluation indicator weight, and the products are added to obtain the corresponding comprehensive score.

[0082] To sum up, the method provided in this embodiment calculates the indicator score corresponding to the evaluation indicator data, and calculates the comprehensive score based on the evaluation indicator weight and the indicator score, thereby realizing the automatic calculation of the evaluation indicator data of different dimensions, obtaining a unified comprehensive score, and achieving a quantitative comprehensive evaluation of the inventory management situation.

[0083] Figure 4 A flowchart showing an inventory management evaluation method provided by an exemplary embodiment of the present application is shown, the method comprising:

[0084] Step 210: Obtain evaluation index data.

[0085] Step 222: Obtain the weight of the first-level index and the weight of the second-level index as the evaluation index weight.

[0086] Among them, the evaluation index data represents the data corresponding to the evaluation index. The evaluation index includes primary indicators and secondary indicators. The secondary indicators are sub-indicators of the primary indicators. The evaluation index weight is obtained by multiplying the weight of the primary indicator by the weight of the secondary indicator. The weight of the primary indicator is used to represent the proportion of the primary indicator score corresponding to the primary indicator in the comprehensive score, and the weight of the secondary indicator is used to represent the proportion of the secondary indicator score corresponding to the secondary indicator in the primary indicator score.

[0087] In some embodiments, the evaluation index weights are calculated by the primary index judgment matrix (criterion layer judgment matrix) and the secondary index judgment matrix (index layer judgment matrix). The technicians compare all the indicators in the same layer in pairs and score them according to the 9-level scaling method to calculate the primary index judgment matrix and the secondary index judgment matrix. The 9-level scaling method is shown in Table 5, where if the scale of the comparison between index i and index j is b ij , then the scale of index j compared with index i is b ji , b ij =1 / b ji .

[0088] Table 5

[0089] Scale value meaning 1 Equally important 3 More important 5 important 7 Very important 9 Very important 2、4、6、8 The compromise value of the above pairwise adjacent judgment

[0090] Among them, the table corresponding to the first-level indicator judgment matrix corresponding to technician A is shown in Table 6:

[0091] Table 6

[0092] First level indicator Warehouse Operations Inventory Control General Management First-level indicator weight Warehouse Operations 1 1 / 2 3 0.320 Inventory Control 2 1 4 0.557 General Management 1 / 3 1 / 4 1 0.123

[0093] Based on Table 6, the first-level indicator judgment matrix A corresponding to technician A is obtained:

[0094]

[0095] Normalize each column of the first-level indicator judgment matrix A to obtain the matrix A':

[0096]

[0097] Add up the normalized columns, and divide each element in the resulting vector by N to get the first-level indicator weight, where N is the number of types of first-level indicators. Here N = 3, i.e., 1 / 10 + 2 / 7 + 3 / 8 = 0.320, 3 / 5 + 4 / 7 + 1 / 2 = 0.557, and 1 / 10 + 1 / 7 + 1 / 8 = 0.123.

[0098] Taking the first-level indicator judgment matrix A as an example, calculate the maximum eigenvalue λ max The consistency test is performed with the consistency ratio CR.

[0099]

[0100]

[0101] The order n of the primary indicator judgment matrix A is 3, then the consistency index CI is:

[0102]

[0103] Consistency ratio CR:

[0104]

[0105] Among them, the random index (RI) is obtained by technical personnel by referring to relevant documents, and CR is less than 0.1, so the first-level index judgment matrix A passes the consistency test.

[0106] The principle of obtaining the first-level indicator judgment matrix and the inspection process corresponding to technicians B to technicians H is the same as that of the first-level indicator judgment matrix A, which will not be repeated here. The table corresponding to the first-level indicator judgment matrix corresponding to technicians A to technicians H is shown in Table 7:

[0107] Table 7

[0108] Technical Staff Warehouse Operations Inventory Control General Management Consistency ratio A 0.320 0.557 0.123 0.018 B 0.272 0.608 0.120 0.071 C 0.346 0.544 0.110 0.052 D 0.284 0.619 0.096 0.083 E 0.239 0.623 0.137 0.018 F 0.211 0.655 0.133 0.052 G 0.284 0.619 0.096 0.083 H 0.231 0.665 0.104 0.084 average value 0.274 0.611 0.115

[0109] According to Table 7, the consistency ratio of the first-level indicator judgment matrix corresponding to each technician is less than 0.1, and all pass the consistency test. Based on the average value in Table 7, the weight vector of the first-level indicator (warehouse operation, inventory control, and comprehensive management) is (0.274, 0.611, 0.115). Among them, inventory control accounts for a large proportion, which means that it is more important in inventory management evaluation, which is consistent with the management concept of "zero inventory" and "controlling the increase and reducing the stock".

[0110] The calculation method of the secondary indicator weight vector is the same as that of the primary indicator weight vector, and the secondary indicator weight vector is calculated based on the secondary indicator judgment matrix.

[0111] The table corresponding to the secondary indicator judgment matrix corresponding to warehousing operations is shown in Table 8:

[0112] Table 8

[0113]

[0114] The consistency ratios are all less than 0.1, passing the consistency test. Among them, the inventory turnover times and inventory structure rationality rate account for a relatively high proportion, reflecting the concept of attaching importance to daily inventory operation management.

[0115] The table corresponding to the secondary indicator judgment matrix corresponding to inventory control is shown in Table 9:

[0116] Table 9

[0117]

[0118]

[0119] The consistency ratios are all less than 0.1, passing the consistency test. Among them, the reduction rate of ending inventory accounts for a relatively high proportion, reflecting the management idea of ​​increasing the intensity of controlling inventory costs.

[0120] The table corresponding to the secondary indicator judgment matrix corresponding to comprehensive management is shown in Table 10:

[0121] Table 10

[0122]

[0123] The consistency ratios were all less than 0.1, passing the consistency test.

[0124] The calculation results of the first-level indicator weights and the second-level indicator weights are summarized. The summary results are shown in Table 11. The evaluation indicator weight is the product of the first-level indicator weight and the second-level indicator weight.

[0125] Table 11

[0126]

[0127]

[0128] It can be seen from Table 11 that inventory control accounts for a high proportion of the first-level indicators. Among the 11 second-level indicators, the evaluation index weights of mid-term inventory reduction rate, high-age inventory reduction rate, inventory turnover times, backlog inventory reduction rate, and inventory structure rationality rate occupy the top five, which are 0.366, 0.150, 0.130, 0.096, and 0.070, respectively, indicating that these second-level indicators have a significant impact on inventory management.

[0129] According to the above content, the first-level indicator judgment matrix and the second-level indicator judgment matrix are calculated, and the first-level indicator weight vector and the second-level indicator weight vector are calculated to obtain the first-level indicator weight vector and consistency ratio corresponding to each technician, as well as the corresponding second-level indicator weight vector and consistency ratio.

[0130] In some embodiments, according to the primary indicator judgment matrix corresponding to each technician, each primary indicator weight vector and the maximum eigenvalue of the primary indicator judgment matrix are calculated.

[0131] Based on the first-level indicator weight vector corresponding to each technician, the first-level indicator weight set W is obtained by weighted average. 1 :

[0132] W 1 =(w 1_1 , w 1_2 ,…,w 1_k );

[0133] Exemplarily, according to Table 11, W 1 =(0.274,0.611,0.115).

[0134] In some embodiments, according to the secondary indicator judgment matrix corresponding to each technician, each secondary indicator weight vector and the maximum eigenvalue of the secondary indicator judgment matrix are calculated.

[0135] Based on the secondary indicator weight vector corresponding to each technician, the secondary indicator weight set W is obtained by weighted average. 2 According to the above content, there are 3 first-level indicators, and the 3 first-level indicators correspond to 3 second-level indicator weight sets. Warehousing operation corresponds to the first and second-level indicator weight set W 21 :

[0136] W 21 =(w 211 , w 212 ,…,w 21t );

[0137] Inventory control corresponds to the second level indicator weight set W 22 :

[0138] W 22 =(w221 , w 222 ,…,w 22d );

[0139] Comprehensive management corresponds to the third and second level indicator weight set W 23 :

[0140] W 23 =(w 231 , w 232 ,…,w 23g );

[0141] Among them, t represents the number of secondary indicator types included in warehousing operations, d represents the number of secondary indicator types included in inventory control, g represents the number of secondary indicator types included in comprehensive management, and t, d, and g are natural numbers.

[0142] According to Table 11, W 21 =(0.474,0.257,0.157,0.112), W 22 =(0.598,0.245,0.157),W 23 =(0.459,0.272,0.166,0.103).

[0143] In some embodiments, the primary indicator weight W 1 and the secondary index weight W 2 The calculation results are summarized to obtain the evaluation index weight set W:

[0144] W=(w 1 , w 2 ,…,w m );

[0145] Among them, w m It represents the evaluation index weight corresponding to the mth secondary index, m represents the number of types of secondary indicators, m=t+d+g, and m is a natural number.

[0146] For example, in the case shown in Table 11, k=3, t=4, d=3, g=4, w 1 =w 1_1 *w 211 , w 2 =w 1_1 *w 212 , w 3 =w 1_1 *w 213 , w 4 =w 1_1 *w 214 , w 5 =w 1_2 *w 221 , w 6 =w1_2 *w 222 , w 7 =w 1_2 *w 223 , w 8 =w 1_3 *w 231 , w 9 =w 1_3 *w 232 , w 10 =w 1_3 *w 233 , w 11 =w 1_1 *w 234 , that is, W = (0.130, 0.070, 0.043, 0.031, 0.366, 0.150, 0.096, 0.053, 0.031, 0.019, 0.012).

[0147] Step 232: Calculate the membership vector corresponding to at least one secondary indicator in the evaluation index data through a membership function.

[0148] Among them, the membership function represents the degree to which at least one secondary indicator belongs to different evaluation levels.

[0149] In some embodiments, the evaluation index data of each enterprise is obtained through a data collection system, and the evaluation index data includes secondary index data. For example, by accessing an enterprise resource planning (ERP) system, an electronic procurement system, etc. on a big data analysis platform, the business data of enterprise procurement, warehousing, outbound delivery, storage, etc. are collected, and based on big data technology, the secondary index data of each enterprise is obtained through data aggregation, data cleaning, data analysis, data processing, etc.

[0150] For example, the secondary indicator data of LH10 enterprises are shown in Table 12:

[0151] Table 12

[0152]

[0153]

[0154] In some embodiments, an evaluation level set is set, and the evaluation level set is a set of evaluation levels used to evaluate inventory management conditions. For example, four evaluation levels and corresponding value ranges are set: excellent [90, 100], good [80, 90], medium [60, 80], and poor [0, 60). The fuzzy judgment vector V used to represent the evaluation level set is (v 1 ,v 2 ,v 3,v 4 )=(95,85,70,30).

[0155] In some embodiments, the optimal value and the worst value are introduced, and the secondary indicator score is calculated by using the membership function to calculate the membership vector. The indicator attribute of the secondary indicator with a larger value indicating a better evaluation is defined as a positive indicator, and the indicator attribute of the secondary indicator with a smaller value indicating a better evaluation is defined as a negative indicator.

[0156] For example, the indicator attributes, optimal values ​​and worst values ​​of the above secondary indicators are shown in Table 13:

[0157] Table 13

[0158] Secondary indicators Indicator properties Worst value Optimal value Inventory turnover times Positive indicators 2.36 42.1 Reasonable inventory structure rate Positive indicators 0.22 0.98 Storage area utilization Positive indicators 0.42 0.76 Warehouse capacity utilization Positive indicators 0.35 0.59 Ending inventory reduction rate Positive indicators -0.21 0.53 High-age inventory reduction rate Positive indicators -0.13 0.48 Overstock reduction rate Positive indicators -0.16 0.56 Centralized reserve utilization rate Positive indicators 0 0.73 Inventory size assessment Reverse Indicator 0.0078 0.00097 Storage and sales rate Positive indicators 0.37 0.86 Inventory accuracy Positive indicators 0.83 0.94

[0159] In some embodiments, it is assumed that the optimal value of the positive indicator is δ 1 , the worst value is δ 4 , δ 4 <δ 1 , the secondary indicator data is χ, [δ 4 ,δ 1 ] is divided into three intervals, and the division points are δ 2 , δ 3 , where δ 2 =δ 1 -(δ 1 -δ 4 ) / 3,δ 3 =δ 4 +(δ 1 -δ 4 ) / 3. Then the standard value corresponding to the evaluation level set (excellent, good, medium, poor) is (δ 1 , δ 2 , δ 3 , δ 4 ), the membership function of the secondary indicator data χ belonging to different standard values ​​is as follows:

[0160]

[0161]

[0162]

[0163]

[0164] In some embodiments, it is assumed that the optimal value of the reverse indicator is δ 1 , the worst value is δ 4 , δ 4 >δ 1, the secondary indicator data is χ, [δ 1 ,δ 4 ] is divided into three intervals, and the division points are δ 2 , δ 3 , where δ 2 =δ 1 +(δ 4 -δ 1 ) / 3,δ 3 =δ 4 -(δ 4 -δ 1 ) / 3. Then the standard value corresponding to the evaluation level set (excellent, good, medium, poor) is (δ 1 , δ 2 , δ 3 , δ 4 ), the membership function of the secondary indicator data χ belonging to different standard values ​​is the same as the principle of the positive indicator part, which will not be repeated here.

[0165] In some embodiments, the membership value corresponding to each secondary indicator at different evaluation levels is calculated through a membership function; the membership values ​​corresponding to each secondary indicator are arranged and combined horizontally to obtain a membership vector corresponding to each secondary indicator.

[0166] Step 234: Combine the membership vectors corresponding to at least one secondary index to obtain a first matrix.

[0167] In some embodiments, the membership vectors corresponding to at least one secondary indicator are respectively used as different row vectors of the first matrix; the row vectors corresponding to all secondary indicators are arranged and combined vertically to obtain the first matrix.

[0168] In some embodiments, based on the above-mentioned positive indicators, negative indicators and membership functions, the membership vectors corresponding to the secondary indicators are calculated, and the membership vectors corresponding to all secondary indicators are combined to form a first matrix, namely the evaluation matrix R:

[0169]

[0170] Where n represents the number of evaluation level types, m represents the number of secondary indicators, in the embodiment of the present application, n=4, m=11, r mn Indicates the value corresponding to the nth evaluation level of the mth secondary indicator, for example, r 14 It represents the value corresponding to the 4th evaluation level of the 1st secondary indicator, that is, the value corresponding to the poor evaluation level of inventory turnover times.

[0171] For example, the table corresponding to the membership vector of the secondary indicators of LH10 enterprises is shown in Table 14:

[0172] Table 14

[0173] Secondary indicators excellent good medium Poor Inventory turnover times 0.4861 0.5139 0 0 Reasonable inventory structure rate 0.9323 0.0677 0 0 Storage area utilization 0.0294 0.9706 0 0 Warehouse capacity utilization 0 0.50 0.50 0 Ending inventory reduction rate 0 0 0.0811 0.9189 High-age inventory reduction rate 1 0 0 0 Overstock reduction rate 0.8871 0.1129 0 0 Centralized reserve utilization rate 0 0 0.9676 0.0324 Inventory size assessment 0 0.2338 0.7662 1 Storage and sales rate 0 0.4694 0.5306 0 Inventory accuracy 0 0.6364 0.3636 0

[0174] Taking inventory turnover as an example, the corresponding value of LH10 enterprise is 35.2926, that is, χ = 35.2926. Inventory turnover is a positive indicator, the worst value is 2.36, and the best value is 42.1, that is, δ 1 =42.1,δ 4 =2.36, then δ 2 =δ 1 -(δ 1 -δ 4 ) / 3=28.853,δ 3 =δ 4 +(δ 1 -δ 4 ) / 3=15.607. According to the membership function, μδ 1 (χ) = (χ - δ 2 ) / (δ 1 -δ 2 )=(35.2926-28.853) / (42.1-28.853)=6.4396 / 13.247=0.4861,μδ 2 (χ)=(δ 1 -χ) / (δ 1 -δ 2 )=(42.1-35.2926) / (42.1-28.853)=6.8074 / 13.247=0.5139. The numerical calculation principle of the membership degree corresponding to other secondary indicators is the same, which will not be repeated here.

[0175] Step 236: Based on the first matrix, calculate the index score corresponding to the evaluation index data.

[0176] In some embodiments, the first matrix and the first vector are multiplied to obtain an indicator score corresponding to the evaluation indicator data;

[0177] The first vector is a row vector or a column vector used to represent the evaluation level, and the elements in the first vector represent the numerical values ​​corresponding to the evaluation level.

[0178] In some embodiments, the first vector (fuzzy evaluation vector V) and the first matrix (evaluation matrix R) are multiplied to calculate the indicator score (secondary indicator score set B):

[0179]

[0180] Among them, b m Represents the secondary indicator score of the mth secondary indicator.

[0181] For example, the inventory turnover number of LH10 enterprise is calculated The calculation principle of other secondary indicator scores is the same and will not be repeated here. The secondary indicator scores and evaluation levels of LH10 enterprises are shown in Table 15:

[0182] Table 15

[0183]

[0184] According to the evaluation level set, the scores of 11 secondary indicators are graded. The three excellent secondary indicators are: reasonable inventory structure rate, high-age inventory reduction rate, and backlog inventory reduction rate; the two good secondary indicators are: inventory turnover times and storage area utilization rate; the one poor secondary indicator is: end-of-period inventory reduction rate; the remaining five secondary indicators have medium evaluation levels.

[0185] Step 242: Multiply the product of the weight of a single first-level indicator and the weight of a second-level indicator by the corresponding second-level indicator score to calculate the comprehensive score of the single second-level indicator; add the comprehensive scores of all second-level indicators to calculate the comprehensive score.

[0186] In some embodiments, the secondary indicator weight is multiplied by the secondary indicator score, and the products are added to obtain the corresponding primary indicator score; the single secondary indicator score is multiplied by the corresponding evaluation indicator weight, the comprehensive score of the single secondary indicator is calculated, and then the comprehensive scores of all secondary indicators are added to obtain the corresponding comprehensive score.

[0187] For example, the comprehensive scores of LH10 enterprises are shown in Table 16:

[0188] Table 16

[0189]

[0190] Similarly, based on the evaluation index data of other companies, the secondary index scores of other companies are calculated according to the membership function and fuzzy judgment vector. Combined with the evaluation index weights, the comprehensive scores and evaluation levels of other companies are obtained, as shown in Table 17:

[0191] Table 17

[0192] Company Name Comprehensive score Rating LH1 80.4 good LH2 92.4 excellent LH3 63.3 medium LH4 69.8 medium LH5 73.8 medium LH6 73.8 medium LH7 77.3 medium LH8 83.8 good LH9 74.8 medium LH10 68.6 medium LH11 91.2 excellent LH12 70.1 medium LH13 75.7 medium LH14 68.8 medium LH15 53.8 Poor LH16 67.7 medium LH17 75.1 medium LH18 75.1 medium LH19 77.6 medium LH20 74.0 medium LH21 68.6 medium LH22 70.6 medium LH23 76.6 medium

[0193] For example, the comprehensive score of LH1 enterprise is 80.4, which is greater than 80 points and less than 90 points, and the evaluation level is good. Among all enterprises, the highest comprehensive score is LH2 enterprise, with a comprehensive score of 92.4. There are two enterprises with excellent evaluation level, one enterprise with poor evaluation level, two enterprises with good evaluation level, and the evaluation level of other enterprises is medium.

[0194] To sum up, the method provided in this embodiment calculates the indicator score corresponding to the evaluation indicator data, and calculates the comprehensive score based on the evaluation indicator weight and the indicator score, thereby realizing the automatic calculation of the evaluation indicator data of different dimensions, obtaining a unified comprehensive score, and achieving a quantitative comprehensive evaluation of the inventory management situation.

[0195] The method provided in this embodiment also calculates the primary indicator scores and the secondary indicator scores to perform targeted analysis on the aspects of inventory management that are performing well and those that need to be improved, thereby improving inventory management capabilities.

[0196] Figure 5 A schematic diagram of an inventory management evaluation system 500 provided by an exemplary embodiment of the present application is shown, and the inventory management evaluation system 500 includes:

[0197] The acquisition module 510 is used to collect target enterprise and evaluation index data, including material procurement data in the enterprise electronic procurement system; material procurement warehousing, material delivery, and material storage data in the enterprise ERP system; primary index, secondary index set and index calculation formula in the inventory management evaluation system; technical personnel's scoring information on the importance of primary index and secondary index. Among them, data such as material procurement, material procurement warehousing, material delivery, and material storage are used to calculate the secondary index data of inventory management of each enterprise; the primary index, secondary index set and index calculation formula of inventory management are used as the basic index data for data processing, calculation and inventory evaluation; the technical personnel's scoring information on the importance of primary index and secondary index is used to determine the primary index weight and secondary index weight, and to perform quantitative calculation and grading evaluation on inventory management evaluation.

[0198] The first calculation module 520 is used to calculate the weight of the first-level indicator and the weight of the second-level indicator by applying the hierarchical analysis method based on the scoring information of the technical personnel on the first-level indicator and the second-level indicator.

[0199] The second calculation module 530 is used to establish an inventory management evaluation index model based on the material procurement, warehousing and other data in the enterprise ERP system and electronic procurement system, in accordance with the data model design principles, through data cleaning, data analysis, field design, logical combing, data processing, etc., to automatically calculate the secondary index values ​​of each enterprise in different statistical periods (monthly or annually).

[0200] The third calculation module 540 is used to calculate the secondary indicator score of each enterprise based on the secondary indicator value of each enterprise and according to a preset score rule.

[0201] The fourth calculation module 550 is used to calculate the primary indicator score, comprehensive score and evaluation level of each enterprise based on the secondary indicator score and secondary indicator weight of each enterprise.

[0202] The query module 560 is used to query the secondary indicator scores, primary indicator scores, comprehensive scores and evaluation levels of different enterprises at different time points according to the set query conditions.

[0203] Display module 570 is used to display the enterprise inventory management evaluation in a multi-dimensional and visual manner, such as: enterprise inventory management indicator values, indicator scores, strong indicators and weak indicators; inventory management score trends of the same enterprise; ranking of inventory management scores of different enterprises; the top 5 and bottom 5 inventory management evaluation scores of enterprises, etc.

[0204] In the above embodiment, steps with the same sequence number can be considered as the same step. Figure 2 The corresponding embodiments and Figure 4 The corresponding embodiments may be implemented individually or in combination, and this application does not limit this.

[0205] Figure 6 A block diagram of an inventory management evaluation device provided by an exemplary embodiment of the present application is shown. The device includes: an acquisition module 610, a calculation module 620 and a combination module 630.

[0206] An acquisition module 610 is used to acquire evaluation index data and evaluation index weights, wherein the evaluation index data represents data for evaluating different dimensions of inventory management;

[0207] A calculation module 620 is used to calculate the index score corresponding to the evaluation index data;

[0208] The calculation module 620 is used to calculate a comprehensive score based on the evaluation index weights and the index scores, and the comprehensive score is used to evaluate the inventory management situation.

[0209] In a possible design of this embodiment, the calculation module 620 is used to calculate the membership vector corresponding to at least one secondary indicator in the evaluation index data through a membership function, and the membership function represents the degree to which at least one secondary indicator belongs to different evaluation levels;

[0210] A combining module 630, configured to combine the membership vectors corresponding to at least one secondary index to obtain a first matrix;

[0211] The calculation module 620 is used to calculate the indicator score corresponding to the evaluation indicator data based on the first matrix.

[0212] In a possible design of this embodiment, the calculation module 620 is used to calculate the membership value corresponding to each secondary indicator at different evaluation levels through a membership function;

[0213] The combination module 630 is used to horizontally arrange and combine the membership values ​​corresponding to each secondary indicator to obtain the membership vector corresponding to each secondary indicator.

[0214] In a possible design of this embodiment, the calculation module 620 is used to multiply the first matrix and the first vector to obtain an indicator score corresponding to the evaluation indicator data;

[0215] The first vector is a row vector or a column vector used to represent the evaluation level, and the elements in the first vector represent the numerical values ​​corresponding to the evaluation level.

[0216] In a possible design of this embodiment, the acquisition module 610 is used to obtain the weight of the first-level indicator and the weight of the second-level indicator as the evaluation indicator weight;

[0217] Among them, the evaluation index data represents the data corresponding to the evaluation index. The evaluation index includes primary indicators and secondary indicators. The secondary indicators are sub-indicators of the primary indicators. The evaluation index weight is obtained by multiplying the weight of the primary indicator by the weight of the secondary indicator. The weight of the primary indicator is used to represent the proportion of the primary indicator score corresponding to the primary indicator in the comprehensive score, and the weight of the secondary indicator is used to represent the proportion of the secondary indicator score corresponding to the secondary indicator in the primary indicator score.

[0218] In a possible design of this embodiment, the calculation module 620 is used to multiply the product of the weight of a single primary indicator and the weight of a secondary indicator by the corresponding secondary indicator score to calculate the comprehensive score of the single secondary indicator;

[0219] The calculation module 620 is used to add up the comprehensive scores of all the secondary indicators to calculate the comprehensive score.

[0220] An embodiment of the present application also provides a computer device, which includes: a processor and a memory, wherein at least one program is stored in the memory; the processor is used to execute at least one program in the memory to implement the inventory management evaluation method provided by the above-mentioned method embodiments.

[0221] Figure 7 700 is a block diagram of a computer device 700 provided by an exemplary embodiment of the present application. Generally, the computer device 700 includes: a processor 701 and a memory 702 .

[0222] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 may be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 701 may include a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 701 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0223] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is used to be executed by the processor 701 to implement the inventory management evaluation method provided in the method embodiment of the present application.

[0224] In some embodiments, the server 700 may also optionally include: an input interface 703 and an output interface 704. The processor 701, the memory 702 and the input interface 703, the output interface 704 may be connected via a bus or a signal line. Each peripheral device may be connected to the input interface 703, the output interface 704 via a bus, a signal line or a circuit board. The input interface 703 and the output interface 704 may be used to connect at least one peripheral device related to input / output to the processor 701 and the memory 702. In some embodiments, the processor 701, the memory 702 and the input interface 703, the output interface 704 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702 and the input interface 703, the output interface 704 may be implemented on a separate chip or circuit board, which is not limited in the embodiments of the present application.

[0225] Those skilled in the art will appreciate that the structure shown above does not constitute a limitation on the computer device 700 , and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0226] In an exemplary embodiment, a chip is also provided. The chip includes a programmable logic circuit and / or program instructions. When the chip runs on a computer device 700, it is used to implement the inventory management evaluation method provided in the embodiment of the present application.

[0227] In an exemplary embodiment, a computer program product is also provided, the computer program product includes computer instructions, the computer instructions are stored in a computer-readable storage medium. The processor reads the computer instructions from the computer-readable storage medium, and the processor reads and executes the computer instructions from the computer-readable storage medium to implement the inventory management evaluation method provided in the embodiment of the present application.

[0228] In an exemplary embodiment, a computer-readable storage medium is also provided, in which at least one program is stored. The at least one program is loaded and executed by a processor to implement the inventory management evaluation method provided in the embodiment of the present application.

[0229] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0230] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented with hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a general or special-purpose computer can access.

[0231] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for evaluating inventory management, characterized in that: The method is performed by a computer device, and the method comprises: Acquiring evaluation index data and obtaining evaluation index weights, wherein the evaluation index data represents data for evaluating different dimensions of the inventory management; Calculate the index score corresponding to the evaluation index data; Based on the evaluation index weights and the index scores, a comprehensive score is calculated, and the comprehensive score is used to evaluate the inventory management situation.

2. The method according to claim 1, characterized in that: The calculating the indicator score corresponding to the evaluation indicator data includes: Calculating a membership vector corresponding to at least one secondary indicator in the evaluation index data by means of a membership function, wherein the membership function represents the degree to which the at least one secondary indicator belongs to different evaluation levels; Combining the membership vectors corresponding to the at least one secondary index to obtain a first matrix; Based on the first matrix, the indicator score corresponding to the evaluation indicator data is calculated.

3. The method according to claim 2, characterized in that The calculating, by means of a membership function, a membership vector corresponding to at least one secondary indicator in the evaluation index data comprises: Calculate the membership value of each secondary indicator corresponding to the different evaluation levels through the membership function; The membership values ​​corresponding to each secondary indicator are arranged and combined horizontally to obtain the membership vector corresponding to each secondary indicator.

4. The method according to claim 2, characterized in that: The calculating, based on the first matrix, the indicator score corresponding to the evaluation indicator data includes: Multiplying the first matrix and the first vector to obtain an indicator score corresponding to the evaluation indicator data; The first vector is a row vector or a column vector used to represent the evaluation level, and the elements in the first vector represent the numerical values ​​corresponding to the evaluation level.

5. The method according to any one of claims 1 to 4, characterized in that: The obtaining of evaluation index weights includes: Obtaining the weight of the first-level index and the weight of the second-level index as the evaluation index weight; Among them, the evaluation index data represents the data corresponding to the evaluation index, and the evaluation index includes a primary index and a secondary index, and the secondary index is a sub-indicator of the primary index; the evaluation index weight is obtained by multiplying the weight of the primary index and the weight of the secondary index, and the weight of the primary index is used to represent the proportion of the primary index score corresponding to the primary index in the comprehensive score, and the weight of the secondary index is used to represent the proportion of the secondary index score corresponding to the secondary index in the primary index score.

6. The method according to claim 5, characterized in that The calculating of the comprehensive score based on the evaluation index weight and the index score comprises: The product of the weight of a single first-level indicator and the weight of a single second-level indicator is multiplied by the corresponding second-level indicator score to calculate the comprehensive score of the single second-level indicator; The comprehensive scores of all the secondary indicators are added together to calculate the comprehensive score.

7. An inventory management evaluation device, characterized in that: The device comprises: An acquisition module, used to acquire evaluation index data and evaluation index weights, wherein the evaluation index data represents data for evaluating different dimensions of the inventory management; A calculation module, used to calculate the index score corresponding to the evaluation index data; A calculation module is used to calculate a comprehensive score based on the evaluation index weight and the index score, and the comprehensive score is used to evaluate the inventory management situation.

8. A computer device, characterized in that: The computer device comprises: a processor and a memory, wherein at least one program is stored in the memory; the processor is used to execute the at least one program in the memory to implement the inventory management evaluation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program, and the at least one program is loaded and executed by a processor to implement the inventory management evaluation method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes computer instructions, which are stored in a computer-readable storage medium. The processor obtains the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to implement the inventory management evaluation method as described in any one of claims 1 to 6.

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