Enterprise benchmarking analysis method and device

Abstract

The application discloses an enterprise benchmarking analysis method and device, which comprises the following steps: inputting the enterprise benchmarking demand of a user into a benchmarking enterprise recommendation model to obtain a recommended benchmarking enterprise list, wherein the enterprise benchmarking demand comprises a target enterprise; obtaining a benchmarking enterprise selected by the user from the benchmarking enterprise list; inputting the enterprise benchmarking demand of the user into a benchmarking index recommendation model to obtain a recommended benchmarking index list; obtaining a benchmarking index selected by the user from the benchmarking index list; establishing a benchmarking model according to the enterprise benchmarking demand and industry characteristics; collecting the enterprise information corresponding to the benchmarking indexes of the target enterprise and the benchmarking enterprises respectively; and analyzing the target enterprise and each benchmarking enterprise based on the collected enterprise information and the benchmarking model to obtain a benchmarking analysis result, wherein the benchmarking analysis result comprises the gap between the target enterprise and each benchmarking enterprise and optimization suggestions. The application can improve the accuracy and efficiency of enterprise benchmarking analysis.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to a method and apparatus for enterprise benchmarking analysis. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Benchmarking refers to an organization comparing itself to an organization with higher performance to achieve better results, continuously surpass itself, exceed benchmarks, pursue excellence, and promote organizational innovation and process reengineering. It is a systematic approach and process for finding, analyzing, and studying excellent products, services, designs, equipment, processes, and management practices to truly improve organizational performance. The benchmarking process involves selecting benchmark companies, collecting and processing data, conducting benchmarking, and finally writing a benchmarking report. The entire process requires experienced researchers and a significant amount of work to complete. However, even with this extensive work, the benchmarking analysis results may not be accurate enough, resulting in insufficient assistance to management and a poor management experience. Therefore, there is a need for an intelligent and automated benchmarking solution to improve the accuracy and efficiency of benchmarking analysis, provide precise decision support for management, and enhance the management experience. Summary of the Invention

[0004] Firstly, embodiments of the present invention provide a method for enterprise benchmarking analysis, which enables intelligent and automated enterprise benchmarking, improves the accuracy and efficiency of enterprise benchmarking analysis, provides precise decision support for enterprise managers, and enhances the experience of enterprise managers. The method includes:

[0005] The user's enterprise benchmarking requirements are input into the benchmarking enterprise recommendation model to obtain a list of recommended benchmarking enterprises. The benchmarking enterprise recommendation model is obtained by training an artificial intelligence model based on information from multiple enterprises in multiple dimensions. The enterprise benchmarking requirements include target enterprises.

[0006] Obtain the benchmark companies selected by the user from the list of benchmark companies;

[0007] The user's enterprise benchmarking requirements are input into the benchmarking indicator recommendation model to obtain a list of recommended benchmarking indicators. The benchmarking indicator recommendation model is obtained by training an artificial intelligence model based on multiple dimensions of features of each industry.

[0008] Obtain the benchmarking metrics selected by the user from the list of benchmarking metrics;

[0009] Based on the benchmarking needs of enterprises and industry characteristics, a benchmarking model is established, which includes intermediate indicators at multiple dimensions and benchmarking indicators at the bottom dimension.

[0010] Collect enterprise information corresponding to the benchmarking indicators of the target company and the benchmarking companies respectively;

[0011] Based on the collected enterprise information and the benchmarking model, the target enterprise and each benchmark enterprise are analyzed separately to obtain benchmarking analysis results. The benchmarking analysis results include the gap between the target enterprise and each benchmark enterprise and optimization suggestions.

[0012] Secondly, embodiments of the present invention also provide an enterprise benchmarking analysis device, which realizes intelligent and automated enterprise benchmarking, improves the accuracy and efficiency of enterprise benchmarking analysis, provides precise decision support for enterprise managers, and enhances the experience of enterprise managers. The device includes:

[0013] The benchmarking company recommendation module is used to input the user's benchmarking needs into the benchmarking company recommendation model to obtain a recommended list of benchmarking companies. The benchmarking company recommendation model is obtained by training an artificial intelligence model based on information from multiple companies in multiple dimensions. The benchmarking needs include target companies.

[0014] The benchmarking company identification module is used to obtain the benchmarking companies selected by the user from the benchmarking company list;

[0015] The benchmarking indicator recommendation module is used to input the user's enterprise benchmarking requirements into the benchmarking indicator recommendation model to obtain a recommended list of benchmarking indicators. The benchmarking indicator recommendation model is obtained by training an artificial intelligence model based on multiple dimensions of features of each industry.

[0016] The benchmarking indicator determination module is used to obtain the benchmarking indicators selected by the user from the benchmarking indicator list.

[0017] The benchmarking model building module is used to build a benchmarking model based on the company's benchmarking needs and industry characteristics. The benchmarking model includes intermediate indicators at multiple dimensions and benchmarking indicators at the bottom dimension.

[0018] The enterprise information collection module is used to collect enterprise information corresponding to the benchmarking indicators of the target enterprise and the benchmarking enterprise, respectively.

[0019] The benchmarking analysis module is used to analyze the target company and each benchmark company based on the collected enterprise information and the benchmarking model, and obtain benchmarking analysis results. The benchmarking analysis results include the gap between the target company and each benchmark company and optimization suggestions.

[0020] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned enterprise benchmarking analysis method.

[0021] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned enterprise benchmarking analysis method.

[0022] Fifthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the above-mentioned enterprise benchmarking analysis method.

[0023] In this embodiment of the invention, the user's enterprise benchmarking requirements are input into a benchmarking enterprise recommendation model to obtain a recommended list of benchmarking enterprises. This model is obtained by training an artificial intelligence model based on multi-dimensional information from multiple enterprises. The enterprise benchmarking requirements include target enterprises. The user then selects benchmarking enterprises from the list. Next, the user's enterprise benchmarking requirements are input into a benchmarking indicator recommendation model to obtain a recommended list of benchmarking indicators. This model is obtained by training an artificial intelligence model based on multi-dimensional features of each industry. Finally, the user selects benchmarking indicators from the list. Based on the enterprise benchmarking requirements and industry characteristics, a benchmarking model is established, including multi-dimensional intermediate indicators and bottom-level benchmarking indicators. Enterprise information corresponding to the benchmarking indicators of both the target enterprise and the benchmarking enterprises is collected. Based on the collected enterprise information and the benchmarking model, the target enterprise and each benchmarking enterprise are analyzed to obtain benchmarking analysis results. These results include the gap between the target enterprise and each benchmarking enterprise, as well as optimization suggestions. Through the above steps, based on the intelligent benchmarking company recommendation model and benchmarking indicator recommendation model, a benchmarking company list and benchmarking indicator list that better meet the user's benchmarking needs are formed. The benchmarking model also takes into account the benchmarking needs of enterprises, thereby improving the accuracy and efficiency of enterprise benchmarking analysis, providing precise decision support for enterprise managers, and enhancing the experience of enterprise managers. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0025] Figure 1 This is a flowchart of the enterprise benchmarking analysis method in an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of the benchmarking model established in an embodiment of the present invention;

[0027] Figure 3 This is a flowchart of enterprise information collection in an embodiment of the present invention;

[0028] Figure 4 This is a structural block diagram of the enterprise benchmarking analysis device according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0031] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0032] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0033] Figure 1 The flowchart of the enterprise benchmarking analysis method in this embodiment of the invention includes:

[0034] Step 101: Input the user's enterprise benchmarking requirements into the benchmarking enterprise recommendation model to obtain a list of recommended benchmarking enterprises. The benchmarking enterprise recommendation model is obtained by training an artificial intelligence model based on information from multiple enterprises in multiple dimensions. The enterprise benchmarking requirements include target enterprises.

[0035] Step 102: Obtain the benchmark companies selected by the user from the list of benchmark companies;

[0036] Step 103: Input the user's enterprise benchmarking requirements into the benchmarking indicator recommendation model to obtain a list of recommended benchmarking indicators. The benchmarking indicator recommendation model is obtained by training an artificial intelligence model based on multiple dimensions of features of each industry.

[0037] Step 104: Obtain the benchmarking indicators selected by the user from the benchmarking indicator list;

[0038] Step 105: Based on the enterprise's benchmarking needs and industry characteristics, establish a benchmarking model, which includes multi-dimensional intermediate indicators and bottom-level benchmarking indicators.

[0039] Step 106: Collect enterprise information corresponding to the benchmarking indicators of the target enterprise and the benchmarking enterprise, respectively;

[0040] Step 107: Based on the collected enterprise information and the benchmarking model, analyze the target enterprise and each benchmark enterprise separately to obtain benchmarking analysis results. The benchmarking analysis results include the gap between the target enterprise and each benchmark enterprise and optimization suggestions.

[0041] In this embodiment of the invention, based on an intelligent benchmarking enterprise recommendation model and a benchmarking indicator recommendation model, a benchmarking enterprise list and a benchmarking indicator list that better meet the user's benchmarking needs are formed; the benchmarking model also takes into account the benchmarking needs of enterprises, thereby improving the accuracy and efficiency of enterprise benchmarking analysis, providing precise decision support for enterprise managers, and enhancing the experience of enterprise managers.

[0042] Each step is described in detail below.

[0043] In step 101, the user's enterprise benchmarking requirements are input into the benchmarking enterprise recommendation model to obtain a list of recommended benchmarking enterprises;

[0044] The benchmarking recommendation model eliminates the need for traditional manual screening, improving the intelligence and accuracy of benchmarking selection. Enterprise benchmarking requirements include target companies, as well as their size, industry, region, and objectives.

[0045] The benchmark enterprise recommendation model is obtained by training an artificial intelligence model. The training set consists of information from multiple enterprises across multiple dimensions. These dimensions include at least one of the following: enterprise type, enterprise size, industry information, and region. Enterprise types include foreign, state-owned, private, collective, or mixed-ownership enterprises; enterprise sizes include large, medium, small, and micro enterprises; and industries include those in the same or related industries. During training, the benchmark enterprise recommendation model comprehensively compares and ranks enterprises based on their performance in terms of financial health, market performance, and operational efficiency to obtain a list of benchmark enterprises.

[0046] The benchmarking company recommendation model includes:

[0047] The requirements analysis module is used to analyze a company's benchmarking requirements and extract keywords. For example, if the purpose of benchmarking is to pursue excellence and continuous improvement, then the keywords are pursuit of excellence and continuous improvement, company size, industry, and regional requirements. If the purpose of benchmarking is to analyze competitors within the industry, then the keywords are competitors within the industry, company size, industry, and regional requirements. If the purpose of benchmarking is to improve international business capabilities, then the keywords are international business capabilities.

[0048] The benchmarking module analyzes data on companies' financial health, market performance, and operational efficiency based on keywords. It then sorts the companies according to the analyzed data to generate a list of benchmarking companies. For example, if the keywords are "pursuit of excellence" and "continuous improvement," the module filters companies with better performance. If the keywords are "industry competitors," the module filters companies with similar business scopes and backgrounds. If the keywords are "international operating capabilities," the module selects companies in the same industry overseas.

[0049] In addition, the benchmarking module considers historical benchmarking experience when analyzing company data. Historical benchmarking experience refers to the experience of selecting companies based on different benchmarking needs in previous benchmarking analyses, along with the specific reasons for choosing these companies. This data helps the large model better understand its benchmarking preferences. Based on the above information, the benchmarking module can provide recommended companies and selection rules, allowing users to choose companies for their current benchmarking needs.

[0050] In one embodiment, after obtaining the recommended list of benchmark companies, the method further includes:

[0051] Obtain benchmarking recommendations from the benchmarking recommendation model, wherein the benchmarking recommendations are suggested benchmarking companies derived from the industry change trends predicted by the benchmarking recommendation model;

[0052] If the suggested benchmarking company is not in the benchmarking company list, the suggested benchmarking company will be added to the benchmarking company list.

[0053] These benchmarking recommendations not only consider the current needs of enterprises, but also predict possible industry trends through benchmarking recommendation models, providing more forward-looking benchmarking recommendations.

[0054] In step 102, the benchmark companies selected by the user from the list of benchmark companies are obtained;

[0055] Specifically, users can select at least one benchmarking company. Of course, this embodiment of the invention also supports users manually filling in the benchmarking company.

[0056] In step 103, the user's enterprise benchmarking requirements are input into the benchmarking indicator recommendation model to obtain a list of recommended benchmarking indicators. The benchmarking indicator recommendation model is obtained by training an artificial intelligence model based on multiple dimensions of features of each industry.

[0057] The benchmark recommendation model includes:

[0058] The requirement analysis module is used to analyze the benchmarking requirements of enterprises and extract keywords. The extraction method is similar to that before, so it will not be repeated here. The requirement analysis module of the benchmarking enterprise recommendation model can be reused.

[0059] The benchmarking indicator screening module is used to analyze industry standards and historical data based on keywords to obtain a list of benchmarking indicators.

[0060] The benchmark recommendation model is obtained by training an artificial intelligence model. The training set consists of information from multiple enterprises across multiple dimensions. The multiple dimensions of the industry include at least one of demand, supply, production, marketing, and finance.

[0061] For example, in the financial industry, core benchmarking indicators such as debt-to-equity ratio and net profit margin might be recommended, while in the manufacturing industry, indicators such as production cost and inventory turnover rate might be recommended. Benchmarking indicator recommendation models can avoid oversights caused by manual selection of benchmarking indicators and improve the accuracy of benchmarking.

[0062] The industry's multiple dimensions include at least one of demand, supply, production, marketing, and finance.

[0063] In step 104, the benchmarking indicators selected by the user from the benchmarking indicator list are obtained;

[0064] In one embodiment, after obtaining the recommended list of benchmarking metrics, the method further includes:

[0065] The system displays the benchmarking indicator list and the benchmarking indicators in the preset indicator library to the user, wherein the same benchmarking indicators in the benchmarking indicator list and the preset indicator library are marked the same.

[0066] Obtaining the benchmarking metrics selected by the user from the benchmarking metric list includes: obtaining the benchmarking metrics selected by the user from the displayed benchmarking metrics.

[0067] The pre-set indicator library covers common financial, operational, and market indicators. These indicators encompass various aspects of a company's performance, such as financial health, production efficiency, and market share, making the benchmarking analysis more comprehensive.

[0068] In one embodiment, the method further includes:

[0069] While displaying the list of benchmarking indicators and the benchmarking indicators in the preset indicator library to the user, editable options are displayed on each benchmarking indicator in the preset indicator library.

[0070] After a user selects an editable option for a benchmarking indicator, the system allows the user to make custom edits to that benchmarking indicator.

[0071] Displays user-defined and edited benchmarking metrics;

[0072] It receives benchmarks selected by the user from the benchmark list, the preset benchmark library, and user-defined benchmarks.

[0073] Specifically, after receiving user-defined edits to the benchmark metric, it's necessary to validate the user-defined benchmark metric, including whether there is data source support and whether the calculation formula is correct. For example, users in the manufacturing industry might focus on production cycle time, and could create a benchmark metric based on similar current metrics, while users in the internet industry are more concerned with user activity, and could create a benchmark metric based on user activity.

[0074] In step 105, a benchmarking model is established based on the enterprise's benchmarking needs and industry characteristics. The benchmarking model includes intermediate indicators with multiple dimensions and benchmarking indicators with a bottom dimension.

[0075] In one embodiment, a benchmarking model is established based on the company's benchmarking needs and industry characteristics, including:

[0076] Keyword extraction was performed on the benchmarking needs and industry characteristics of the aforementioned enterprises;

[0077] The keywords and preset multi-dimensional intermediate indicators are fused to obtain the fused intermediate indicators.

[0078] The integrated intermediate metrics are compared with the underlying dimension metrics to form a benchmarking model.

[0079] Specifically, fusion means that if a keyword is semantically similar to an intermediate indicator, then that intermediate indicator is used as a required intermediate indicator. The steps for establishing the benchmarking model described above can be implemented using a large-scale model. Combining the logical reasoning and tool-calling capabilities of a large-scale model, data analysis can be performed from multiple dimensions, including financial analysis, operational efficiency analysis, and market performance analysis.

[0080] In another embodiment, multiple benchmarking models can be preset (e.g., financial analysis models, market analysis models, etc.). When integrating the keywords and preset multi-dimensional intermediate indicators, a large model can automatically select the appropriate model based on the company's specific needs and industry characteristics. Selection is based on keywords; for example, when selecting a financial analysis model, the large model will adjust the calculation weights of the financial analysis model according to the company's benchmarking needs and key industry characteristics. During actual benchmarking, the large model continuously learns and provides feedback mechanisms to adjust the benchmarking models in real time. For example, if market trends change, the large model will dynamically update the calculation weights of the benchmarking models to ensure the accuracy and timeliness of the analysis results.

[0081] Specifically, the benchmarking model also includes calculation methods for analyzing the target company and each benchmark company.

[0082] The established benchmarking model typically includes multiple dimensions, such as scale and strength, efficiency and effectiveness, and product excellence. Each dimension can contain intermediate or specific benchmarking indicators for sub-dimensions; for example, scale and strength may include indicators such as operating revenue and total assets. The benchmarking model is calculated starting from the leaf nodes, using methods such as entropy weighting, standardized value methods, and arithmetic progressions. The Analytic Hierarchy Process (AHP) is used to calculate the values ​​and corresponding weights of each benchmarking indicator and intermediate indicator. The indicators (intermediate and benchmarking indicators) under each dimension are multiplied by their weights and then summed to obtain the score for that dimension. Each level is scored using this method, ultimately yielding the company's score in the overall benchmarking model.

[0083] Figure 2 This is a schematic diagram of the benchmarking model established in an embodiment of the present invention. See also... Figure 2 As can be seen, the underlying dimensions are definite benchmarks, while the other dimensions are intermediate indicators.

[0084] In one embodiment, the method further includes:

[0085] Based on the calculation parameters in the calculation method for each benchmarking indicator, determine the enterprise information to be collected for each benchmarking indicator. Specifically, the data used in the calculation parameters indicates the enterprise information to be collected. For example, if the benchmarking indicator is total assets, and the calculation formula is Total Assets = Total Liabilities + Owner's Equity or Current Assets + Non-current Assets, then the calculation parameters are total liabilities, owner's equity or current assets, and non-current assets.

[0086] In step 106, collect enterprise information corresponding to the benchmarking indicators of the target enterprise and the benchmarking enterprise, respectively;

[0087] See Figure 3This is a flowchart of enterprise information collection in an embodiment of the present invention. In one embodiment, enterprise information corresponding to the benchmarking indicators of the target enterprise and the benchmark enterprise is collected respectively, including:

[0088] Step 301: Invoke the data collection model to obtain enterprise information of the target enterprise and benchmark enterprises from public data sources and the enterprise's internal data sources. The data collection model collects enterprise information through API or web crawler technology.

[0089] Step 302: Clean the collected enterprise information to remove redundant data, outliers and incomplete information to obtain cleaned enterprise information;

[0090] Step 303: Verify the cleaned enterprise information, remove information with abnormal fluctuations and information that does not conform to industry standards, and obtain the verified enterprise information.

[0091] Specifically, internal data sources include enterprise ERP and CRM systems, while public data sources include industry reports, publicly available financial data, and market research. The enterprise information collected by the data acquisition model includes various data types. If it is HTML, the data acquisition model extracts relevant enterprise information based on extraction rules and webpage content; if it is JSON, the enterprise information needs to be parsed; if it is PDF, the relevant enterprise information can be extracted by recognizing the PDF content and extracting relevant enterprise information through a multimodal large model.

[0092] When cleaning the collected enterprise information, data problems can be marked so that users can manually verify them; after the cleaned enterprise information is verified, data problems can also be automatically marked for manual verification.

[0093] Verification of cleaned enterprise information can be based on historical data, using a large model formed by anomaly detection algorithms to validate newly collected data. For example, if a company's financial indicator shows an anomaly (such as abnormal fluctuations or significant deviations from industry standards), the large model will issue a warning, indicating that the data may be inaccurate. Furthermore, after the verified enterprise information is obtained, it can be automatically standardized using different standards (such as currency units, time ranges, etc.) to ensure comparability between data from different companies and guarantee data reliability. This verification process can significantly improve the data quality of benchmarking analysis.

[0094] In step 107, based on the collected enterprise information and the benchmarking model, the target enterprise and each benchmark enterprise are analyzed to obtain benchmarking analysis results. The benchmarking analysis results include the gap between the target enterprise and each benchmark enterprise and optimization suggestions.

[0095] In one embodiment, based on the collected enterprise information and the benchmarking model, the target enterprise and each benchmark enterprise are analyzed separately to obtain benchmarking analysis results, including:

[0096] Based on the collected enterprise information, the benchmarking indicator values ​​and intermediate indicator values ​​of the target enterprise are calculated in order from the bottom dimension to the top dimension of the benchmarking model, as well as the benchmarking indicator values ​​and intermediate indicator values ​​of each benchmark enterprise.

[0097] Based on the benchmarking index value and the intermediate index value, the gap between the target company and each benchmark company under the benchmarking index value and the intermediate index value is obtained;

[0098] The gap is input into the optimization suggestion recommendation model to obtain optimization suggestions corresponding to each benchmark indicator value and intermediate indicator value. The optimization suggestion recommendation model is obtained by training the artificial intelligence model with the enterprise information to be collected.

[0099] Specifically, the gaps can be in key indicators such as revenue, profit margin, market share, and production efficiency. These gaps can be categorized and ranked among multiple companies from highest to lowest, with the overall results divided into three levels. After determining the company's own level, companies in the other two levels are selected for comparison. The purpose of benchmarking analysis is to identify gaps and strengths, helping company managers adjust and optimize their strategies based on the benchmarking results.

[0100] In addition, embodiments of the present invention can also determine risk analysis indicators; collect data for risk analysis indicators; calculate risk analysis indicator values ​​for the target company and benchmark companies based on the collected data; generate a risk report based on the differences between the risk analysis indicator values ​​of the target company and benchmark companies.

[0101] In one embodiment, the method further includes:

[0102] A benchmarking analysis report is generated based on each benchmarking indicator value and intermediate indicator value of the target company, the corresponding gap, and the corresponding optimization suggestions.

[0103] The generated benchmarking analysis report will be presented to the user.

[0104] Key considerations for receiving user feedback;

[0105] Based on the focus areas, the steps for calculating the corresponding benchmark values ​​are as follows:

[0106] Add the calculation steps to the benchmarking analysis report.

[0107] Specifically, benchmarking analysis reports can be automatically generated using large models.

[0108] The large-scale model can generate structured reports, including charts, data analysis results, and text descriptions, based on collected data and the analysis results of benchmark models. It can also generate customized report content based on the user's input of key areas of interest.

[0109] For example, if users are particularly focused on market share, the large model will analyze that metric in detail and provide detailed calculation steps and optimization suggestions. The large model is adaptive, dynamically adjusting its analytical focus based on company feedback during report generation. For instance, companies can adjust their benchmarking targets, and the report content will be automatically optimized to meet the latest needs. The report not only includes current benchmarking analysis results, but the large model can also predict future performance based on historical data and market trends, and provide optimization strategies. For example, based on benchmarking analysis results, the large model can provide specific suggestions for reducing production costs or increasing market share (optimizing prompts based on historical benchmarking experience, ultimately outputting satisfactory results, while simultaneously using the more powerful large model to enhance effectiveness).

[0110] The benchmarking analysis report supports the visualization of data, such as bar charts, line charts, and pie charts, allowing users to intuitively see the performance differences of various companies on different indicators.

[0111] In addition, the system can customize report templates to meet the specific needs of businesses. Businesses can choose to focus on only certain key metrics or conduct in-depth analysis of data in a specific area.

[0112] After benchmarking analysis is completed, companies can adjust their operational strategies or improve business processes based on the optimization suggestions in the report. Simultaneously, the large-scale model can optimize indicator selection and benchmarking models based on company feedback to adapt to the ever-changing market environment. By saving historical benchmarking records, companies can assess their progress by comparing historical reports longitudinally and continuously optimize future benchmarking processes.

[0113] The solutions proposed in this invention involve the integration of enterprise management and artificial intelligence, particularly the application of large-scale pre-trained models (large models) in enterprise benchmarking. The technical fields of this invention cover multiple areas such as enterprise management, artificial intelligence, big data analysis, decision support systems, and information system automation, demonstrating innovative applications through multidisciplinary integration.

[0114] This invention also proposes an enterprise benchmarking analysis generation device, the principle of which is similar to the enterprise benchmarking analysis generation method, and will not be described in detail here.

[0115] Figure 4 This is a schematic diagram of the enterprise benchmarking analysis generation device in an embodiment of the present invention, including:

[0116] The benchmarking company recommendation module 401 is used to input the user's benchmarking needs into the benchmarking company recommendation model to obtain a recommended list of benchmarking companies. The benchmarking company recommendation model is obtained by training an artificial intelligence model based on information from multiple companies in multiple dimensions. The benchmarking needs include target companies.

[0117] The benchmarking company determination module 402 is used to obtain the benchmarking companies selected by the user from the benchmarking company list;

[0118] The benchmarking indicator recommendation module 403 is used to input the user's enterprise benchmarking requirements into the benchmarking indicator recommendation model to obtain a recommended list of benchmarking indicators. The benchmarking indicator recommendation model is obtained by training an artificial intelligence model based on multiple dimensions of features of each industry.

[0119] The benchmarking indicator determination module 404 is used to obtain the benchmarking indicator selected by the user from the benchmarking indicator list;

[0120] The benchmarking model building module 405 is used to build a benchmarking model based on the enterprise's benchmarking needs and industry characteristics. The benchmarking model includes intermediate indicators at multiple dimensions and benchmarking indicators at the bottom dimension.

[0121] The enterprise information collection module 406 is used to collect enterprise information corresponding to the benchmarking indicators of the target enterprise and the benchmarking enterprise, respectively.

[0122] The benchmarking analysis module 407 is used to analyze the target company and each benchmark company based on the collected enterprise information and the benchmarking model to obtain benchmarking analysis results. The benchmarking analysis results include the gap between the target company and each benchmark company and optimization suggestions.

[0123] In one embodiment, the multiple dimensions of the enterprise include at least one of enterprise type, enterprise size, industry information, and region;

[0124] The industry's multiple dimensions include at least one of demand, supply, production, marketing, and finance.

[0125] In one embodiment, the benchmarking company determination module is further configured to:

[0126] After obtaining the recommended list of benchmark companies, the benchmarking suggestions output by the benchmarking recommendation model are obtained. The benchmarking suggestions are suggested benchmark companies derived from the industry change trends predicted by the benchmarking recommendation model.

[0127] If the suggested benchmarking company is not in the benchmarking company list, the suggested benchmarking company will be added to the benchmarking company list.

[0128] In one embodiment, the benchmarking indicator determination module is further configured to:

[0129] After obtaining the recommended list of benchmarking indicators, the user is shown the list of benchmarking indicators and the benchmarking indicators in the preset indicator library. The same benchmarking indicators in the benchmarking indicator list and the preset indicator library are marked the same.

[0130] Obtaining the benchmarking metrics selected by the user from the benchmarking metric list includes: obtaining the benchmarking metrics selected by the user from the displayed benchmarking metrics.

[0131] In one embodiment, the benchmarking indicator determination module is further configured to:

[0132] While displaying the list of benchmarking indicators and the benchmarking indicators in the preset indicator library to the user, editable options are displayed on each benchmarking indicator in the preset indicator library.

[0133] After a user selects an editable option for a benchmarking indicator, the system allows the user to make custom edits to that benchmarking indicator.

[0134] Displays user-defined and edited benchmarking metrics;

[0135] It receives benchmarks selected by the user from the benchmark list, the preset benchmark library, and user-defined benchmarks.

[0136] In one embodiment, the enterprise information collection module is further used for:

[0137] Based on the calculation parameters in the calculation method of each benchmarking indicator, determine the enterprise information that needs to be collected for each benchmarking indicator.

[0138] In one embodiment, the benchmarking model building module is further configured to:

[0139] Keyword extraction was performed on the benchmarking needs and industry characteristics of the aforementioned enterprises;

[0140] The keywords and preset multi-dimensional intermediate indicators are fused to obtain the fused intermediate indicators.

[0141] The integrated intermediate metrics are compared with the underlying dimension metrics to form a benchmarking model.

[0142] In one embodiment, the enterprise information collection module is further used for:

[0143] The data collection model is invoked to obtain enterprise information of the target enterprise and benchmark enterprises from public data sources and internal data sources of the enterprise. The data collection model collects enterprise information through API or web crawling technology.

[0144] The collected enterprise information is cleaned to remove redundant data, outliers, and incomplete information, resulting in cleaned enterprise information.

[0145] The information of the cleaned companies is verified, and information with abnormal fluctuations and that does not conform to industry standards is removed to obtain verified company information.

[0146] In one embodiment, the benchmarking analysis module is further used for:

[0147] Based on the collected enterprise information, the benchmarking indicator values ​​and intermediate indicator values ​​of the target enterprise are calculated in order from the bottom dimension to the top dimension of the benchmarking model, as well as the benchmarking indicator values ​​and intermediate indicator values ​​of each benchmark enterprise.

[0148] Based on the benchmarking index value and the intermediate index value, the gap between the target company and each benchmark company under the benchmarking index value and the intermediate index value is obtained;

[0149] The gap is input into the optimization suggestion recommendation model to obtain optimization suggestions corresponding to each benchmark indicator value and intermediate indicator value. The optimization suggestion recommendation model is obtained by training the artificial intelligence model with the enterprise information to be collected.

[0150] In one embodiment, the apparatus further includes a benchmarking analysis report generation module, used for:

[0151] A benchmarking analysis report is generated based on each benchmarking indicator value and intermediate indicator value of the target company, the corresponding gap, and the corresponding optimization suggestions.

[0152] The generated benchmarking analysis report will be presented to the user.

[0153] Key considerations for receiving user feedback;

[0154] Based on the focus areas, the steps for calculating the corresponding benchmark values ​​are as follows:

[0155] Add the calculation steps to the benchmarking analysis report.

[0156] In summary, the method and apparatus proposed in the embodiments of the present invention have the following beneficial effects:

[0157] 1. Intelligent benchmarking suggestions: By analyzing companies inside and outside the industry, the system automatically provides suggestions for benchmarking companies, eliminating reliance on traditional manual screening methods and improving the intelligence and accuracy of benchmarking company selection.

[0158] 2. Data Collection and Verification: The ability to participate in and optimize the collection and verification process of benchmarking data, ensuring the quality and reliability of benchmarking data, is particularly crucial for enterprises to evaluate and learn from the strengths of competitors.

[0159] 3. Indicator selection and benchmarking model construction: Able to participate in the process of indicator selection and benchmarking model creation, recommend indicators and build models for users based on benchmarking requirements, automatically optimize and adjust benchmarking models, and ensure the adaptability and relevance of benchmarking analysis.

[0160] 4. Adaptive benchmarking report generation: Based on the benchmarking business needs, automatically generate customized benchmarking reports, making report generation more efficient and ensuring that the report content is more in line with the actual needs of the enterprise.

[0161] This invention also provides a computer device. Figure 5 This is a schematic diagram of a computer device in an embodiment of the present invention. The computer device 500 includes a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 530, it implements the above-mentioned enterprise benchmarking analysis method.

[0162] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned enterprise benchmarking analysis method.

[0163] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the aforementioned enterprise benchmarking analysis method.

[0164] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0166] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0168] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for enterprise benchmarking analysis, characterized in that, include: The user's enterprise benchmarking requirements are input into the benchmarking enterprise recommendation model to obtain a list of recommended benchmarking enterprises. The benchmarking enterprise recommendation model is obtained by training an artificial intelligence model based on information from multiple enterprises in multiple dimensions. The enterprise benchmarking requirements include target enterprises. Obtain the benchmark companies selected by the user from the list of benchmark companies; The user's enterprise benchmarking requirements are input into the benchmarking indicator recommendation model to obtain a list of recommended benchmarking indicators. The benchmarking indicator recommendation model is obtained by training an artificial intelligence model based on multiple dimensions of features of each industry. Obtain the benchmarking metrics selected by the user from the list of benchmarking metrics; Based on the benchmarking needs of enterprises and industry characteristics, a benchmarking model is established, which includes intermediate indicators at multiple dimensions and benchmarking indicators at the bottom dimension. Collect enterprise information corresponding to the benchmarking indicators of the target company and the benchmarking companies respectively; Based on the collected enterprise information and the benchmarking model, the target enterprise and each benchmark enterprise are analyzed separately to obtain benchmarking analysis results. The benchmarking analysis results include the gap between the target enterprise and each benchmark enterprise and optimization suggestions.

2. The method according to claim 1, characterized in that, The multiple dimensions of the enterprise include at least one of the following: enterprise type, enterprise size, industry information, and region. The industry's multiple dimensions include at least one of demand, supply, production, marketing, and finance.

3. The method according to claim 1, characterized in that, After obtaining the recommended list of benchmark companies, the following are also included: Obtain benchmarking recommendations from the benchmarking recommendation model, wherein the benchmarking recommendations are suggested benchmarking companies derived from the industry change trends predicted by the benchmarking recommendation model; If the suggested benchmarking company is not in the benchmarking company list, the suggested benchmarking company will be added to the benchmarking company list.

4. The method according to claim 1, characterized in that, After obtaining the recommended list of benchmarking metrics, the following are also included: The system displays the benchmarking indicator list and the benchmarking indicators in the preset indicator library to the user, wherein the same benchmarking indicators in the benchmarking indicator list and the preset indicator library are marked the same. Obtaining the benchmarking metrics selected by the user from the benchmarking metric list includes: obtaining the benchmarking metrics selected by the user from the displayed benchmarking metrics.

5. The method according to claim 4, characterized in that, Also includes: While displaying the list of benchmarking indicators and the benchmarking indicators in the preset indicator library to the user, editable options are displayed on each benchmarking indicator in the preset indicator library. After a user selects an editable option for a benchmarking indicator, the system allows the user to make custom edits to that benchmarking indicator. Displays user-defined and edited benchmarking metrics; It receives benchmarks selected by the user from the benchmark list, the preset benchmark library, and user-defined benchmarks.

6. The method according to claim 1, characterized in that, Also includes: Based on the calculation parameters in the calculation method of each benchmarking indicator, determine the enterprise information that needs to be collected for each benchmarking indicator.

7. The method according to claim 1, characterized in that, Based on the company's benchmarking needs and industry characteristics, establish a benchmarking model, including: Keyword extraction was performed on the benchmarking needs and industry characteristics of the aforementioned enterprises; The keywords and preset multi-dimensional intermediate indicators are fused to obtain the fused intermediate indicators. The integrated intermediate metrics are compared with the underlying dimension metrics to form a benchmarking model.

8. The method according to claim 1, characterized in that, Collect enterprise information corresponding to the benchmarking indicators of both the target company and the benchmarking companies, including: The data collection model is invoked to obtain enterprise information of the target enterprise and benchmark enterprises from public data sources and internal data sources of the enterprise. The data collection model collects enterprise information through API or web crawling technology. The collected enterprise information is cleaned to remove redundant data, outliers, and incomplete information, resulting in cleaned enterprise information. The information of the cleaned companies is verified, and information with abnormal fluctuations and that does not conform to industry standards is removed to obtain verified company information.

9. The method according to claim 1, characterized in that, Based on the collected enterprise information and the benchmarking model, the target enterprise and each benchmark enterprise are analyzed separately to obtain benchmarking analysis results, including: Based on the collected enterprise information, the benchmarking indicator values ​​and intermediate indicator values ​​of the target enterprise are calculated in order from the bottom dimension to the top dimension of the benchmarking model, as well as the benchmarking indicator values ​​and intermediate indicator values ​​of each benchmark enterprise. Based on the benchmarking index value and the intermediate index value, the gap between the target company and each benchmark company under the benchmarking index value and the intermediate index value is obtained; The gap is input into the optimization suggestion recommendation model to obtain optimization suggestions corresponding to each benchmark indicator value and intermediate indicator value. The optimization suggestion recommendation model is obtained by training the artificial intelligence model with the enterprise information to be collected.

10. The method according to claim 1, characterized in that, Also includes: A benchmarking analysis report is generated based on each benchmarking indicator value and intermediate indicator value of the target company, the corresponding gap, and the corresponding optimization suggestions. The generated benchmarking analysis report will be presented to the user. Key considerations for receiving user feedback; Based on the focus areas, the steps for calculating the corresponding benchmark values ​​are as follows: Add the calculation steps to the benchmarking analysis report.

11. A benchmarking analysis device for enterprises, characterized in that, include: The benchmarking company recommendation module is used to input the user's benchmarking needs into the benchmarking company recommendation model to obtain a recommended list of benchmarking companies. The benchmarking company recommendation model is obtained by training an artificial intelligence model based on information from multiple companies in multiple dimensions. The benchmarking needs include target companies. The benchmarking company identification module is used to obtain the benchmarking companies selected by the user from the benchmarking company list; The benchmarking indicator recommendation module is used to input the user's enterprise benchmarking requirements into the benchmarking indicator recommendation model to obtain a recommended list of benchmarking indicators. The benchmarking indicator recommendation model is obtained by training an artificial intelligence model based on multiple dimensions of features of each industry. The benchmarking indicator determination module is used to obtain the benchmarking indicators selected by the user from the benchmarking indicator list. The benchmarking model building module is used to build a benchmarking model based on the company's benchmarking needs and industry characteristics. The benchmarking model includes intermediate indicators at multiple dimensions and benchmarking indicators at the bottom dimension. The enterprise information collection module is used to collect enterprise information corresponding to the benchmarking indicators of the target enterprise and the benchmarking enterprise, respectively. The benchmarking analysis module is used to analyze the target company and each benchmark company based on the collected enterprise information and the benchmarking model, and obtain benchmarking analysis results. The benchmarking analysis results include the gap between the target company and each benchmark company and optimization suggestions.

12. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 10.

14. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 10.

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