Evaluation method, device and equipment for enterprise digital transformation and storage medium

Abstract

The invention provides an enterprise digital transformation evaluation method and device, equipment and a storage medium, and belongs to the technical field of data processing. The method comprises the steps of collecting index evaluation data of a target enterprise; performing score calculation processing on the index evaluation data to obtain an index score of the basic index; the index score is input into a maturity evaluation model, the maturity evaluation result of the digital transformation of the target enterprise is generated according to the maximum membership principle, and the maturity evaluation model is a hierarchical structure model with the maturity score as a target layer factor and with the ability factor and the evaluation index as a criterion layer factor; wherein the basic indexes are located at the bottom layer in the hierarchical structure model. Based on a maturity evaluation model construction method, a value chain theory and other theories, the actual situation and stage characteristics of discrete manufacturing enterprise digital transformation are analyzed, and a set of method which covers discrete manufacturing enterprise full-value chain business activities and can be flexibly adjusted according to evaluation application scenes is provided.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an evaluation method, apparatus, device and storage medium for enterprise digital transformation. Background Art

[0002] Digital transformation has become a powerful driving force and strategic measure to accelerate the digitalization, networking and intelligent development of the manufacturing industry and to consolidate and form core competitiveness. It is the key to fully promoting the implementation of the strategy of building a manufacturing power and the new journey of high-quality development.

[0003] The current research on the maturity evaluation model of the digital transformation capability of discrete manufacturing enterprises generally has many problems, such as coarse granularity of model indicators, low pertinence, poor accuracy, lack of evaluation of dimensions such as digital construction, and relatively single evaluation method, which has limited effectiveness in management practice. Summary of the Invention

[0004] This application provides an evaluation method, device, equipment, and storage medium for enterprise digital transformation, which can analyze the actual situation of digital transformation of discrete manufacturing enterprises. The technical solution is as follows:

[0005] On the one hand, embodiments of the present application provide a method for evaluating enterprise digital transformation, including:

[0006] Collect indicator evaluation data of target enterprises;

[0007] Performing score calculation on the indicator evaluation data to obtain the indicator score of the basic indicator;

[0008] The indicator scores are input into a maturity evaluation model, and a maturity evaluation result of the target enterprise's digital transformation is generated according to the maximum subordination principle. The maturity evaluation model is a hierarchical model with maturity scores as target layer factors and capability elements and evaluation indicators as criterion layer factors, wherein the basic indicators are located at the bottom layer of the hierarchical model.

[0009] On the other hand, an embodiment of the present application provides an evaluation device for enterprise digital transformation, including:

[0010] The collection module is used to collect the indicator evaluation data of the target enterprise;

[0011] A processing module, configured to perform score calculation processing on the indicator evaluation data to obtain the indicator score of the basic indicator;

[0012] An evaluation module is used to input the indicator scores into a maturity evaluation model and generate a maturity evaluation result of the target enterprise's digital transformation according to the maximum membership principle. The maturity evaluation model is a hierarchical model with maturity scores as the target layer and capability elements and evaluation indicators as the criterion layer, wherein the basic indicators are located at the bottom layer of the hierarchical model.

[0013] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the above aspects is implemented.

[0014] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. The computer program is loaded and executed by a processor to implement the method described in the above aspects.

[0015] On the other hand, an embodiment of the present application provides a computer program product or computer program, including computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes to implement the method described in the above aspects.

[0016] The technical solution provided by this application includes at least the following beneficial effects:

[0017] The evaluation method, device, equipment and storage medium for enterprise digital transformation provided in this application collect indicator evaluation data corresponding to basic indicators, convert them into corresponding scores, input them into a maturity evaluation model, and perform maturity evaluation according to the maximum membership principle. Based on the construction method of the maturity evaluation model, value chain theory and other theories, the actual situation and stage characteristics of the digital transformation of discrete manufacturing enterprises are analyzed, and a set of methods covering the entire value chain business activities of discrete manufacturing enterprises and which can be flexibly adjusted according to the evaluation application scenarios are proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0019] Figure 1 This is a flowchart of an evaluation method for enterprise digital transformation provided by an exemplary embodiment of the present application;

[0020] Figure 2 is a flowchart of an evaluation method for enterprise digital transformation provided by another exemplary embodiment of the present application;

[0021] Figure 3This is a structural block diagram of an evaluation device for enterprise digital transformation provided by an exemplary embodiment of the present application;

[0022] Figure 4 It is a structural block diagram of a computer device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0024] Example 1

[0025] Please refer to Figure 1 , which shows a flow chart of an enterprise digital transformation evaluation method provided by an exemplary embodiment of the present application. The method includes the following steps:

[0026] Step 101: Collect indicator evaluation data of the target enterprise.

[0027] Step 102: Calculate the index evaluation data to obtain the index score of the basic index.

[0028] Step 103: Input the indicator scores into the maturity evaluation model and generate the maturity evaluation results of the target enterprise's digital transformation according to the maximum membership principle.

[0029] The maturity evaluation model is a hierarchical model with maturity score as the target layer factor and capability elements and evaluation indicators as the criterion layer factors, in which the basic indicators are located at the bottom of the hierarchical model.

[0030] In one possible implementation, the maturity evaluation model analyzes the enterprise's business, applications, data, and technology, and derives three capability elements as the basis of the model framework: digital transformation organizational capability, product full life cycle digital capability, and enterprise operation full value chain digital capability.

[0031] Among them, the organizational capabilities of digital transformation include indicators such as development planning, system mechanisms, and resource input; the digital capabilities of the product's entire life cycle include indicators such as R&D, manufacturing, comprehensive support, recycling and destruction; the digital capabilities of the entire value chain of enterprise operations include core key business processes such as scientific research, production, quality, and delivery, and are effectively integrated with financial, human resources and other management systems to promote enterprises to build a full-process, fully integrated operation management information platform.

[0032] The maturity assessment model is a hierarchical model that divides the decision-making objectives, considerations, and decision-making objects into top, middle, and bottom layers based on their interrelationships, creating a hierarchical diagram. The top layer represents the purpose of the decision and the problem to be solved, i.e., the maturity score. The bottom layer represents the alternatives for decision-making, i.e., the basic indicators mentioned above. The middle layer represents the considerations and decision-making criteria, i.e., the capability elements of the maturity assessment and the upper-level indicators. The top layer is called the objective layer, and the bottom layer is called the factor layer.

[0033] Computer equipment collects indicator evaluation data related to basic indicators, converts it into indicator scores corresponding to the basic indicators, and then calculates it layer by layer according to the maximum membership principle through the maturity evaluation model to finally obtain the maturity score of the target layer.

[0034] In one possible implementation, the model and method provided in the embodiments of the present application are applicable to the digital transformation maturity evaluation of discrete manufacturing enterprises. For different enterprises, the indicators and data collection content in the model can be replaced, thereby realizing an evaluation model that can be flexibly adjusted according to the evaluation scenario.

[0035] To sum up, the evaluation method for enterprise digital transformation provided in this application collects indicator evaluation data corresponding to basic indicators, converts them into corresponding scores, inputs them into the maturity evaluation model, and performs maturity evaluation according to the maximum membership principle. Based on the construction method of the maturity evaluation model, value chain theory, etc., the actual situation and stage characteristics of the digital transformation of discrete manufacturing enterprises are analyzed, and a set of methods covering the entire value chain business activities of discrete manufacturing enterprises is proposed, which can be flexibly adjusted according to the evaluation application scenarios.

[0036] Example 2

[0037] Please refer to Figure 2 , which shows a flow chart of an evaluation method for enterprise digital transformation provided by another exemplary embodiment of the present application. The method includes the following steps:

[0038] Step 201: construct a hierarchical structure model based on the basic principles of AHP.

[0039] The Analytic Hierarchy Process (AHP) is a method that treats a complex multi-objective decision-making problem as a system, decomposes the goal into multiple goals or criteria, and then decomposes it into several levels of multiple indicators (or criteria, constraints). The hierarchical single ranking (weight) and total ranking are calculated through the fuzzy quantification method of qualitative indicators as a systematic method for optimizing decision-making with multiple goals (multiple indicators) and multiple options.

[0040] In one possible implementation, the corresponding factors in the criterion layers, from top to bottom, are capability elements, superior indicators, and basic indicators. Schematically, a computer device constructs a hierarchical model based on the basic principles and steps of AHP. Layers 1, 2, and 3 of the target layer and criterion layer represent the comprehensive score of the digital transformation capability maturity of discrete manufacturing enterprises, capability elements, primary indicators, and secondary indicators, respectively. Secondary indicators are basic indicators. Table 1 shows a correspondence between primary and secondary indicators.

[0041]

[0042]

[0043] Table 1

[0044] Step 202: Generate evaluation weights corresponding to factors at each level.

[0045] Factors refer to evaluation indicators and capability elements in the model framework. In one possible implementation, evaluation weights are used to indicate the degree of influence between factors and / or the importance of factors. Step 202 specifically includes the following steps 202a to 202c:

[0046] Step 202a: Generate the first weight of each layer factor based on the basic principle of AHP. The first weight is used to indicate the importance of the factors at the same layer to the factors at the previous layer.

[0047] Specifically, the process of the computer device generating the first weight is as follows:

[0048] The 1-9 scaling method was used to construct the AHP judgment matrix of each layer of factors. The consistency test of the AHP judgment matrix was performed based on the eigenvector and the largest eigenroot of the AHP judgment matrix. The scales corresponding to the factors were adjusted based on the consistency test results until the AHP judgment matrix passed the consistency test. The eigenvector was determined as the first weight.

[0049] For example, for a certain criterion, the factors under it are compared in pairs and ranked according to their importance. ij This is the comparison of the importance of factor i and factor j. A scale of 1 indicates that factor i and factor j are equally important. A larger scale value means that factor i is more important than factor j to the corresponding factor at the previous level. For example, a scale of 3 indicates that factor i is slightly more important than factor j.

[0050] The computer device generates the judgment matrix of each layer of factors according to the comparison results of the two factors, and calculates the eigenvector Wi and the maximum eigenroot λ of the judgment matrix. max Perform consistency and randomness tests on the judgment matrix and adjust the ratio between indicators until the corresponding judgment matrix passes the consistency test. The test formula is:

[0051] CR=CI / RI

[0052] Where CR is the random consistency ratio of the judgment matrix; RI is the average random consistency index of the judgment matrix; CI is the consistency index of the judgment matrix, which is calculated by the following formula:

[0053]

[0054] Where n is the order of the judgment matrix.

[0055] RI can obtain the corresponding value of n by looking up the table, as shown in Table 2.

[0056] n 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49

[0057] Table 2

[0058] If CR is less than 0.1, the matrix is ​​considered to be relatively consistent, otherwise the ratio between the two indicators needs to be readjusted.

[0059] After the matrix has been checked for consistency, the computer device uses the AHP combined weight Wi of each factor. The eigenvector obtained from the judgment matrix is ​​the single-layer weight of each factor, that is, the first weight.

[0060] Step 202b: Calculate the second weight of the basic indicators based on the DEMATEL method. The second weight is used to indicate the degree of influence between the basic indicators.

[0061] Specifically, the process of calculating the second weight by the computer device is as follows:

[0062] The 0-4 scaling method is used to define the degree of influence between basic indicators and construct a comprehensive influence matrix; based on the comprehensive influence matrix, the influence, influence, centrality and causality of each basic indicator are calculated, where the influence is used to indicate the comprehensive influence of the corresponding basic indicator on other basic indicators, the influence is used to indicate the comprehensive influence value of the corresponding basic indicator by other basic indicators, the centrality is the sum of the influence and the influence, and the causality is the difference between the influence and the influence; based on the causality, the way in which the basic indicator affects other indicators is determined, and the centrality is determined as the second weight.

[0063] Computer equipment constructs a direct impact matrix C based on the secondary indicators corresponding to the criterion layer 3, and uses a scale of 0 to 4 to define the impact intensity between the secondary indicators, which respectively represent no impact, slightly weak impact, weak impact, strong impact, and strong impact between the two secondary indicators, and constructs a direct impact matrix C = (c ij ) n×n .

[0064] Furthermore, the normalized matrix A is constructed based on the direct impact matrix C:

[0065]

[0066] Furthermore, a comprehensive influence matrix T=A(EA)-1 is constructed, where E is a unit matrix of the same order as T.

[0067] According to the comprehensive influence matrix, the influence degree Di, the influenced degree Gi, the centrality (Di+Gi) and the causal degree (Di-Gi) among the secondary indicators are calculated as follows:

[0068]

[0069] When the causal degree (Di-Gi) is greater than 0, it means that the secondary indicator has a greater impact on other secondary indicators in the model and is a causal factor; when the causal degree (Di-Gi) is less than 0, it means that the secondary indicator is greatly influenced by other secondary indicators in the model and is a result factor. Centrality (Di+Gi) indicates the importance of the secondary indicator in model evaluation.

[0070] Step 202c: Generate a modified weight of the basic indicator by combining the first weight and the second weight.

[0071] Computer equipment organically combines AHP and DEMATEL and introduces comprehensive influence.

[0072] The calculation formula for the modified weight includes:

[0073]

[0074] Among them, Q i is the modified weight, W i is the first weight, D i +G i is the second weight.

[0075] Step 203: Collect indicator evaluation data of the target enterprise.

[0076] Specifically, step 203 includes the following steps:

[0077] Step 203a: A questionnaire is formed based on data collection questions of various basic indicators.

[0078] Step 203b: Collect the questionnaire response results and generate indicator evaluation data. The response results include qualitative data and quantitative data for describing the indicator completion degree.

[0079] Indicatively, technical personnel may design 1 to 6 data collection items for each basic indicator in advance, form a questionnaire, and use a 0-5 scale method to determine the weight of the data collection items corresponding to each basic indicator.

[0080] Step 204: perform score calculation on the indicator evaluation data to obtain the indicator score of the basic indicator.

[0081] The collected indicators include both qualitative indicators in the form of single-choice and multiple-choice, as well as quantitative data such as the digitalization rate of production equipment, which reflects the level of digital transformation capabilities of enterprises. The scoring of qualitative indicators is obtained through quantitative processing, with a score range of 0 to 100. Quantitative data can be normalized using the standard deviation method, which converts the actual value x collected into i Use z-score to standardize the collection index score s i , the score range is 0 to 100 points, s i The calculation formula is as follows:

[0082]

[0083]

[0084]

[0085] In step 205, the indicator scores are input into the maturity evaluation model, and the maturity evaluation results are generated based on the indicator weights of the factors at each layer and in accordance with the maximum subordination principle.

[0086] Specifically, the computer device generates a maturity evaluation result according to the maximum subordination principle based on the revised weight of the basic indicator and the first weight of other layer factors.

[0087] In principle, the digital transformation process for manufacturing enterprises is defined by five capability maturity levels: L1—Starting Application (0-20 points), L2—System Application (20-40 points), L3—Integration and Integration (40-60 points), L4—Converged Application (60-80 points), and L5—Industry Chain Collaboration (80-100 points). L1-L4 encompass the process of developing internal digital capabilities within manufacturing enterprises, from single-point applications to integration and integration, and then to innovative transformation of business and management models. L5 extends beyond the manufacturing enterprise, integrating the collaborative R&D capabilities of enterprises based on product matching relationships, and emphasizing the comprehensive capabilities of enterprises to collaborate and interconnect with upstream and downstream supply chains and build an industrial ecosystem.

[0088] In the embodiment of the present application, by collecting indicator evaluation data corresponding to basic indicators and converting them into corresponding scores, the maturity evaluation model is input and the maturity evaluation is performed according to the maximum membership principle. Based on the construction method of the maturity evaluation model, value chain theory and other theories, the actual situation, stage characteristics and focus of the digital transformation of discrete manufacturing enterprises are analyzed, and a set of methods covering the entire value chain business activities of discrete manufacturing enterprises is proposed, which can be flexibly adjusted according to the evaluation application scenarios.

[0089] Example 3

[0090] In combination with the above embodiments, the evaluation method for enterprise digital transformation provided in the embodiments of the present application includes the following steps:

[0091] 1. Build an evaluation model

[0092] The evaluation model analyzes the enterprise's business, applications, data, and technology, and derives three capability elements as the basis of the model framework: digital transformation organizational capability, product full life cycle digital capability, and enterprise operation full value chain digital capability.

[0093] 2. Set the weight of evaluation indicators

[0094] 1) Use the AHP method to determine the combined weight of each indicator.

[0095] a. A hierarchical model is constructed based on the basic principles and steps of AHP. The target layer and criterion layers 1, 2, and 3 are the comprehensive score, capability factors, primary indicators, and secondary indicators of the digital transformation capability maturity of discrete manufacturing enterprises, respectively.

[0096] b. Use the 1-9 scaling method to construct the judgment matrix of each layer of indicators, calculate the eigenvector wi and the maximum eigenroot λmax of the judgment matrix, perform consistency and randomness tests on the judgment matrix, and adjust the ratio between indicators until the corresponding judgment matrix passes the consistency test.

[0097] c. Calculate the AHP combined weight Wi of each indicator. The eigenvector obtained from the judgment matrix is ​​the single-layer weight of each indicator.

[0098] 2) Calculate the degree of influence between secondary indicators based on the DEMATEL method.

[0099] a. Based on the secondary indicators corresponding to the criterion layer 3, the direct impact matrix C is constructed. The impact intensity between the secondary indicators is defined using a scale of 0 to 4, which represents no impact, slightly weak impact, weak impact, strong impact, and strong impact between the two secondary indicators.

[0100] b. Construct a normalized matrix

[0101] Construct a comprehensive influence matrix T = A(EA)-1, where E is the unit matrix of the same order as T.

[0102] c. Calculate the influence degree Di, influenced degree Gi, centrality (Di+Gi) and causality (Di-Gi) among each secondary indicator based on the comprehensive influence matrix.

[0103] When the causal degree (Di-Gi) is greater than 0, it means that the secondary indicator has a greater impact on other secondary indicators in the model and is a causal factor; when the causal degree (Di-Gi) is less than 0, it means that the secondary indicator is greatly influenced by other secondary indicators in the model and is a result factor. Centrality (Di+Gi) indicates the importance of the secondary indicator in model evaluation.

[0104] 3) Modify the indicator weights based on AHP-DEMATEL.

[0105] AHP and DEMATEL are organically combined and comprehensive influence is introduced.

[0106] 3. Calculation of collection index scores

[0107] 4. Maturity level calculation

[0108] During the evaluation phase, discrete manufacturing enterprises complete data collection by filling out questionnaires, applying the fuzzy comprehensive evaluation method, and dividing them into maturity levels based on the maximum membership principle. The maturity score and maturity level of the previous level indicator are calculated from the maturity score and maturity level of the next level indicator, and finally the level and score of the enterprise's digital transformation capability maturity evaluation are obtained.

[0109] Example 4

[0110] Please refer to Figure 3 , which shows a structural block diagram of an enterprise digital transformation evaluation device provided by an exemplary embodiment of the present application, the device includes:

[0111] The collection module 301 is used to collect the indicator evaluation data of the target enterprise;

[0112] The processing module 302 is used to perform score calculation processing on the indicator evaluation data to obtain the indicator score of the basic indicator;

[0113] The evaluation module 303 is used to input the indicator score into the maturity evaluation model and generate the maturity evaluation result of the digital transformation of the target enterprise according to the maximum membership principle. The maturity evaluation model is a hierarchical model with the maturity score as the target layer and the capability elements and evaluation indicators as the criterion layer, wherein the basic indicators are located at the bottom layer of the hierarchical model.

[0114] Optionally, the device further includes:

[0115] A construction module is used to construct the hierarchical structure model based on the basic principle of AHP, wherein the factors corresponding to the criterion levels from top to bottom are the capability elements, the upper indicators and the basic indicators respectively;

[0116] A generation module, configured to generate evaluation weights corresponding to factors at each layer, wherein the evaluation weights are used to indicate the degree of influence between factors and / or the importance of factors;

[0117] The evaluation module 303 is further configured to:

[0118] The indicator scores are input into the maturity evaluation model, and the maturity evaluation results are generated according to the maximum subordination principle based on the indicator weights of the factors at each layer.

[0119] Optionally, the generating module is further configured to:

[0120] Generate a first weight of each layer factor based on the AHP basic principle, wherein the first weight is used to indicate the importance of the same layer factor to the previous layer factor;

[0121] Calculating a second weight of the basic indicator based on the DEMATEL method, where the second weight is used to indicate the degree of influence between the basic indicators;

[0122] generating a modified weight of the basic indicator by combining the first weight and the second weight;

[0123] The evaluation module 303 is further configured to:

[0124] Based on the modified weight of the basic indicator and the first weight of other layer factors, the maturity evaluation result is generated according to the maximum membership principle.

[0125] Optionally, the generating module is further configured to:

[0126] The 1-9 scaling method was used to construct the AHP judgment matrix of each layer of factors;

[0127] Performing a consistency check on the AHP judgment matrix based on the eigenvectors and the maximum eigenroot of the AHP judgment matrix;

[0128] Adjusting the scales corresponding to the factors based on the consistency test results until the AHP judgment matrix passes the consistency test;

[0129] The feature vector is determined as the first weight.

[0130] Optionally, the generating module is further configured to:

[0131] The 0-4 scale method is used to define the degree of influence between the basic indicators and construct a comprehensive influence matrix;

[0132] Based on the comprehensive influence matrix, the influence degree, the influenced degree, the centrality and the causal degree of each basic indicator are calculated, wherein the influence degree is used to indicate the comprehensive influence index of the corresponding basic indicator on other basic indicators, the influenced degree is used to indicate the comprehensive influence value of the corresponding basic indicator by other basic indicators, the centrality is the sum of the influence degree and the influenced degree, and the causal degree is the difference between the influence degree and the influenced degree;

[0133] The influence of the basic indicator on other indicators is determined based on the cause degree, and the centrality is determined as the second weight.

[0134] Optionally, the calculation formula for the modified weight includes:

[0135]

[0136] Among them, Q i is the correction weight, W i is the first weight, D i +G i is the second weight.

[0137] Optionally, the acquisition module 301 is further configured to:

[0138] The questionnaire is composed of data collection questions based on each of the basic indicators mentioned above;

[0139] Collecting the response results of the questionnaire to generate the indicator evaluation data, wherein the response results include qualitative data and quantitative data for describing the completion degree of the indicator;

[0140] The processing module 302 is further configured to:

[0141] The qualitative data is quantified based on the target score interval, and the quantitative data is standardized based on the target score interval to obtain the indicator score of each basic indicator.

[0142] Example 5

[0143] An embodiment of the present application provides an electronic device; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 4As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and memory 405. The communication bus 402 is configured to enable communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include standard wired and wireless interfaces. The processor 401 is configured to execute a program stored in the memory, describing a method for evaluating enterprise digital transformation, to implement the steps of the method provided in the above-described embodiment.

[0144] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the method described in the above embodiment.

[0145] An embodiment of the present application further provides a computer program product, which runs on a processor of a computer device, causing the computer device to execute the method described in the above embodiment.

[0146] It should be noted that the descriptions of the above storage medium, electronic device, and remote control embodiments are similar to the descriptions of the above method embodiments and have similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0147] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0148] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, object, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, object, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, method, object, or apparatus comprising the element.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0150] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0151] In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0152] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROMs), magnetic disks, optical disks, and other media that can store program codes.

[0153] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0154] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for evaluating enterprise digital transformation, characterized by: include: Collect indicator evaluation data of target enterprises; Performing score calculation on the indicator evaluation data to obtain the indicator score of the basic indicator; The indicator scores are input into a maturity evaluation model, and a maturity evaluation result of the target enterprise's digital transformation is generated according to the maximum subordination principle. The maturity evaluation model is a hierarchical model with maturity scores as target layer factors and capability elements and evaluation indicators as criterion layer factors, wherein the basic indicators are located at the bottom layer of the hierarchical model.

2. The method according to claim 1, characterized in that Before collecting the indicator evaluation data, the method includes: The hierarchical model is constructed based on the basic principle of AHP, wherein the factors corresponding to the criterion levels from top to bottom are the capability elements, the upper indicators and the basic indicators respectively; Generate evaluation weights corresponding to factors at each level, wherein the evaluation weights are used to indicate the degree of influence between factors and / or the importance of factors; Inputting the indicator scores into the maturity evaluation model and generating the maturity evaluation results of the target enterprise's digital transformation according to the maximum membership principle include: The indicator scores are input into the maturity evaluation model, and the maturity evaluation results are generated according to the maximum subordination principle based on the indicator weights of the factors at each layer.

3. The method according to claim 2, characterized in that The generation of evaluation weights corresponding to factors at each level includes: Generate a first weight of each layer factor based on the AHP basic principle, wherein the first weight is used to indicate the importance of the same layer factor to the previous layer factor; Calculating a second weight of the basic indicator based on the DEMATEL method, where the second weight is used to indicate the degree of influence between the basic indicators; generating a modified weight of the basic indicator by combining the first weight and the second weight; The indicator weights based on the factors at each layer are used to generate the maturity evaluation results according to the maximum subordination principle, including: Based on the modified weight of the basic indicator and the first weight of other layer factors, the maturity evaluation result is generated according to the maximum membership principle.

4. The method according to claim 3, characterized in that The first weights of the factors at each layer are generated based on the basic principle of AHP, including: The 1-9 scaling method was used to construct the AHP judgment matrix of each layer of factors; Performing a consistency check on the AHP judgment matrix based on the eigenvectors and the maximum eigenroot of the AHP judgment matrix; Adjusting the scales corresponding to the factors based on the consistency test results until the AHP judgment matrix passes the consistency test; The feature vector is determined as the first weight.

5. The method according to claim 3, characterized in that The second weight of the basic indicator is calculated based on the DEMATEL method, including: The 0-4 scale method is used to define the degree of influence between the basic indicators and to construct a comprehensive influence matrix; Based on the comprehensive influence matrix, the influence degree, the influenced degree, the centrality and the causal degree of each basic indicator are calculated, wherein the influence degree is used to indicate the comprehensive influence index of the corresponding basic indicator on other basic indicators, the influenced degree is used to indicate the comprehensive influence value of the corresponding basic indicator by other basic indicators, the centrality is the sum of the influence degree and the influenced degree, and the causal degree is the difference between the influence degree and the influenced degree; The influence of the basic indicator on other indicators is determined based on the cause degree, and the centrality is determined as the second weight.

6. The method according to claim 3, characterized in that The calculation formula of the modified weight includes: Among them, Q i is the correction weight, W i is the first weight, D i +G i is the second weight.

7. The method according to any one of claims 1 to 6, characterized in that: The target enterprise's index evaluation data collected includes: The questionnaire is composed of data collection questions based on each of the basic indicators mentioned above; Collecting the response results of the questionnaire to generate the indicator evaluation data, wherein the response results include qualitative data and quantitative data for describing the completion degree of the indicator; The step of performing score calculation on the indicator evaluation data to obtain the indicator score of the basic indicator includes: The qualitative data is quantified based on the target score interval, and the quantitative data is standardized based on the target score interval to obtain the indicator score of each basic indicator.

8. An evaluation device for enterprise digital transformation, characterized in that: include: The collection module is used to collect the indicator evaluation data of the target enterprise; A processing module, configured to perform score calculation processing on the indicator evaluation data to obtain the indicator score of the basic indicator; An evaluation module is used to input the indicator scores into a maturity evaluation model and generate a maturity evaluation result of the target enterprise's digital transformation according to the maximum membership principle. The maturity evaluation model is a hierarchical model with maturity scores as the target layer and capability elements and evaluation indicators as the criterion layer, wherein the basic indicators are located at the bottom layer of the hierarchical model.

9. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that A computer program is stored, and the computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 7.

11. A computer program product or a computer program, characterized in that The method comprises computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes to implement the method according to any one of claims 1 to 7.

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