Enterprise organization adjustment evaluation method and system based on transverse and longitudinal comparison
By employing a comparative approach to assessing organizational restructuring, combining entropy weighting and the TOPSIS method, a multi-dimensional indicator system and weighted evaluation matrix are constructed. Euclidean distance and proximity are calculated, solving the problem of low assessment accuracy caused by single-dimensional comparison in existing technologies and achieving precise assessment of organizational restructuring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 国网浙江省电力有限公司新昌县供电公司
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-14
AI Technical Summary
Existing methods for assessing organizational restructuring often rely on single-dimensional comparisons, leading to low accuracy in the assessment results.
This study employs a corporate organizational adjustment assessment method based on horizontal and vertical comparisons, combined with entropy weighting and the TOPSIS method. It calculates objective weights through a multi-dimensional indicator system, information entropy, and difference coefficients, constructs a weighted evaluation matrix, calculates Euclidean distance and proximity, and achieves simultaneous comparison of multiple objects and time-series comparison of a single object to obtain accurate assessment results.
This approach achieves unified and comparable comprehensive performance across all evaluation subjects, avoiding bias caused by a single perspective. Through cross-validation across two dimensions, it accurately assesses the actual effectiveness of organizational adjustments within enterprises.
Smart Images
Figure CN121860451A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of enterprise evaluation technology, and in particular to an enterprise organizational adjustment evaluation method and system based on horizontal and vertical comparisons. Background Technology
[0002] Against the backdrop of economic transformation and upgrading and high-quality development, enterprises face fierce market competition and pressure to transform and upgrade. Organizational adjustment has become a key measure to optimize governance structure and enhance core competitiveness. A scientific and effective organizational adjustment evaluation system can help enterprises accurately diagnose operational performance, identify strengths and weaknesses, optimize resource allocation, and provide reliable support for strategic decision-making. Therefore, it has become a core requirement for sustainable enterprise development.
[0003] Currently, assessment techniques related to corporate organizational restructuring mainly fall into three core categories: First, financial performance-based evaluation systems. These systems focus on financial indicators such as revenue and profit, offering strong quantification but a singular perspective. They tend to overemphasize short-term profitability while neglecting non-financial dimensions such as innovation capabilities, service quality, and human resource development. Second, ESG-based evaluation systems. While these systems focus on sustainable development, they suffer from poor data availability, low standardization, and a lack of targeted assessments of core business indicators such as operational efficiency and process optimization. Third, industry regulatory-based performance evaluation systems. Although these systems possess professional authority, their scope is narrow, focusing primarily on the technical and economic indicators of production units while ignoring governance aspects such as overall corporate strategy, value creation, and customer service. This leads to biased and fragmented assessment results. Therefore, it is evident that existing assessment methods for corporate organizational restructuring all suffer from a focus on single-dimensional comparisons, resulting in relatively low accuracy of the assessment results. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of existing technologies that use single-dimensional comparison to evaluate corporate organizational adjustments, resulting in low accuracy of evaluation results. This invention provides a method and system for evaluating corporate organizational adjustments based on horizontal and vertical comparisons. It combines entropy weight and TOPSIS method to achieve quantitative evaluation of different evaluation objects. Furthermore, through horizontal evaluation of multiple objects in the same period and vertical evaluation of single object in time series comparison, it achieves accurate evaluation of the effectiveness of corporate organizational adjustments.
[0005] The objective of this invention is achieved through the following technical solution: Organizational restructuring assessment methods based on horizontal and vertical comparisons include: Positive and negative indicators are selected based on preset dimensions, and a multi-dimensional evaluation indicator system is constructed based on the selected indicators. Extract indicator data for each entity to be evaluated during the enterprise organizational restructuring, and preprocess the indicator data; Calculate the information entropy and difference coefficient of each evaluation indicator, determine the objective weight of each evaluation indicator, and construct a weighted evaluation matrix by combining the preprocessed indicator data; The positive and negative ideal solutions of the index are calculated based on the weighted evaluation matrix, and the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions is calculated to obtain the corresponding closeness. By combining the corresponding proximity calculation results, the assessment results of enterprise organizational adjustment are obtained through the same-period comparison of multiple objects to be assessed and the time-series comparison of a single object to be assessed.
[0006] Furthermore, the calculation of the information entropy and difference coefficient of each evaluation indicator, and the determination of the objective weight of each evaluation indicator, includes: Based on the preprocessed indicator data, the feature weight of each object to be evaluated under each evaluation indicator is obtained, and the information entropy of each evaluation indicator is calculated in combination with the preset standardization coefficient. The difference coefficients are calculated based on the information entropy of each evaluation indicator, and the difference coefficients of each evaluation indicator are normalized. The objective weight of each evaluation indicator is determined based on the proportion of its normalized difference coefficient in the sum of the difference coefficients of all evaluation indicators.
[0007] Furthermore, the construction of the weighted evaluation matrix by combining the preprocessed indicator data includes: The row and column dimensions of the weighted evaluation matrix are set based on each evaluation indicator and each object to be evaluated; Based on the preprocessed indicator data of each object to be evaluated under each evaluation indicator, and combined with the objective weight of each evaluation indicator, a weighted calculation is performed to obtain the corresponding weighted indicator value. Using the objects to be evaluated as rows and the evaluation indicators as columns, a weighted evaluation matrix is constructed according to a preset order, and the elements of the weighted evaluation matrix are assigned values based on the calculated corresponding weighted indicator values.
[0008] Furthermore, the step of calculating the positive and negative ideal solutions of the index based on the weighted evaluation matrix, and calculating the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions to obtain the corresponding closeness, includes: Traverse all columns of the weighted evaluation matrix, take the maximum value of each column as the optimal performance value of the corresponding evaluation index, arrange the optimal performance values of all evaluation indexes in column order, and obtain the positive ideal solution vector. Traverse all columns of the weighted evaluation matrix, take the minimum value of each column as the worst performance value of the corresponding evaluation index, arrange the worst performance values of all evaluation indicators in column order, and obtain the negative ideal solution vector. Based on the element values in the weighted evaluation matrix, calculate the Euclidean distance between each object to be evaluated and the positive ideal solution vector, as well as the Euclidean distance between each object to be evaluated and the negative ideal solution vector. The proximity is calculated based on the Euclidean distance between each object to be evaluated and the positive ideal solution vector, and the Euclidean distance between each object to be evaluated and the negative ideal solution vector.
[0009] Furthermore, the extraction of indicator data for each entity to be evaluated during the enterprise organizational restructuring, and the preprocessing of the indicator data, include: The data collection cycle is identified based on the assessment needs, and the corresponding data source is identified based on the indicator type and the evaluation object type of each object to be assessed. Extract the indicator data of each object to be evaluated within the data collection period from the corresponding data source, and perform data cleaning processing. The cleaned indicator data is standardized and normalized to obtain preprocessed indicator data.
[0010] Furthermore, the process of combining the corresponding proximity calculation results to obtain the enterprise organizational adjustment assessment results through contemporaneous comparison of multiple assessment objects and time-series comparison of a single assessment object includes: Based on the preset time series, the proximity calculation results of all objects to be evaluated under each time series are extracted and sorted in descending order according to proximity. The proximity fluctuation information of each object to be evaluated is determined based on the sorting results. Based on the preset time series, the preprocessed indicator data of each object to be evaluated under all time series is obtained, and the corresponding objective weights are combined to perform weighted summation to obtain the performance evaluation score of each object to be evaluated at each time point. The organizational adjustment assessment results are obtained based on the proximity fluctuation information of each subject to be assessed and the performance evaluation scores at each time point.
[0011] Furthermore, the process of obtaining the organizational adjustment assessment results based on the proximity fluctuation information of each assessed object and the effectiveness assessment score at each time point includes: Based on the proximity fluctuation information of each object to be evaluated, the horizontal competitive position and its changing trend can be located; The longitudinal improvement magnitude and its development trend are identified based on the performance evaluation scores at each time point. Individual evaluation results for each object to be evaluated are obtained based on the changing trends of the corresponding horizontal competitive positions and the development trends of the vertical improvement magnitude. By integrating the individual evaluation results of each entity to be evaluated based on the changing trends of horizontal competitive position and the development trends of vertical improvement, the organizational adjustment evaluation results of the enterprise are obtained.
[0012] Furthermore, the process of integrating the individual evaluation results of each entity to be evaluated according to the changing trends of horizontal competitive position and the development trends of vertical improvement magnitude to obtain the organizational adjustment evaluation results includes: The objects to be evaluated are classified based on the changing trends of horizontal competitive positions and the development trends of vertical improvement. Based on the individual assessment results of each subject to be assessed, identify the corresponding ranking fluctuation driving indicators and score fluctuation weakness indicators, and combine the classification results to obtain the common characteristics of each category of subjects to be assessed; Based on the common characteristics of each type of object to be evaluated and the corresponding number of objects to be evaluated, the results of the enterprise organizational adjustment evaluation are obtained.
[0013] Furthermore, after obtaining the assessment results of the enterprise organizational restructuring, the following actions are also taken: The results of the enterprise organizational adjustment assessment can be visualized using two-dimensional heat maps or spatial dynamic trajectory maps.
[0014] A corporate organizational restructuring assessment system based on horizontal and vertical comparisons, used to perform any of the above-mentioned corporate organizational restructuring assessment methods, including: The indicator selection module is used to filter positive and negative indicators based on preset dimensions, and to construct a multi-dimensional evaluation indicator system based on the selected indicators. The data processing module is used to extract indicator data of each object to be evaluated in the enterprise organizational adjustment and to preprocess the indicator data. The data analysis module is used to calculate the information entropy and difference coefficient of each evaluation indicator, determine the objective weight of each evaluation indicator, construct a weighted evaluation matrix by combining the preprocessed indicator data, calculate the positive and negative ideal solutions of the indicators based on the weighted evaluation matrix, and calculate the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions to obtain the corresponding closeness. The assessment module is used to combine the corresponding proximity calculation results to obtain the assessment results of enterprise organizational adjustment through the same-period comparison of multiple objects to be assessed and the time-series comparison of a single object to be assessed.
[0015] The beneficial effects of this invention are: First, the entropy weight method is used to objectively determine the indicator weights, avoiding subjective bias. Then, the TOPSIS method is combined to calculate the closeness, transforming the organizational adjustment effectiveness of different evaluation objects into quantitative results within a unified range, achieving uniform comparability of the overall performance of each object. Furthermore, a horizontal evaluation using simultaneous comparison of multiple objects identifies the external competitive position and changes of each object, while a vertical evaluation using time-series comparison of a single object tracks the magnitude and trend of internal improvements. This dual-dimensional cross-validation avoids the one-sidedness caused by a single perspective, restoring the actual effectiveness of organizational adjustments and achieving accurate evaluation of enterprise organizational adjustments. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of one structure of the present invention. Detailed Implementation
[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0018] Example: Evaluation methods for organizational restructuring based on horizontal and vertical comparisons, such as Figure 1 As shown, it includes: Positive and negative indicators are selected based on preset dimensions, and a multi-dimensional evaluation indicator system is constructed based on the selected indicators. Extract indicator data for each entity to be evaluated during the enterprise organizational restructuring, and preprocess the indicator data; Calculate the information entropy and difference coefficient of each evaluation indicator, determine the objective weight of each evaluation indicator, and construct a weighted evaluation matrix by combining the preprocessed indicator data; The positive and negative ideal solutions of the index are calculated based on the weighted evaluation matrix, and the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions is calculated to obtain the corresponding closeness. By combining the corresponding proximity calculation results, the assessment results of enterprise organizational adjustment are obtained through the same-period comparison of multiple objects to be assessed and the time-series comparison of a single object to be assessed.
[0019] The preset dimensions include value, development, efficiency, and service dimensions. The value dimension focuses on budget execution and investment efficiency, the development dimension focuses on talent development and market expansion, the efficiency dimension optimizes operational processes and energy utilization, and the service dimension improves quality and customer experience.
[0020] Based on pre-defined dimensions, expert evaluation was conducted across value, development, efficiency, and service dimensions to select and define the scope of comprehensive evaluation indicators. Specifically, 16 secondary indicators were chosen to construct a multi-dimensional evaluation indicator system. These secondary indicators include revenue growth rate, operating profit margin, cost control rate, R&D investment ratio, market share, employee training coverage, process turnover rate, project delivery cycle, resource utilization rate, inventory turnover rate, customer satisfaction, complaint resolution rate, service response time, and customer retention rate.
[0021] Furthermore, the indicator attributes are divided into positive and negative attributes, represented by the symbols + and - respectively. Positive indicators represent indicators that have a positive impact on the effectiveness of organizational reform evaluation, and the larger the value, the better. Conversely, negative indicators are better, and the smaller the value, the better.
[0022] The constructed multi-dimensional evaluation index system is shown in Table 1: Table 1. Evaluation Indicator System for Organizational Reform
[0023] Based on the established multi-dimensional evaluation indicator system, the responsible entities are first identified according to the indicator dimensions to preliminarily identify potential evaluation targets. The data provision capabilities of these preliminarily identified targets are then verified to determine the final evaluation targets. Next, the time period covered by the current evaluation is determined, such as monthly, quarterly, or annually, and the indicator data of the evaluation targets within the corresponding time period is extracted for subsequent evaluation processing.
[0024] The extracted raw indicator data may contain errors, inconsistent formats, and missing values, and the data sources are scattered, which can lead to evaluation bias if used directly. Furthermore, different evaluation indicators use different units of measurement, and direct comparisons can distort results due to differences in units. Therefore, the extracted indicator data undergoes further preprocessing to remove outliers, standardize data formats, and complete or process missing values. This prevents defects in the raw data from interfering with subsequent analysis, ensures data integrity and standardization, and eliminates the influence of units.
[0025] The step of extracting indicator data for each entity to be evaluated during enterprise organizational restructuring and preprocessing the indicator data includes: The data collection cycle is identified based on the assessment needs, and the corresponding data source is identified based on the indicator type and the evaluation object type of each object to be assessed. Extract the indicator data of each object to be evaluated within the data collection period from the corresponding data source, and perform data cleaning processing. The cleaned indicator data is standardized and normalized to obtain preprocessed indicator data.
[0026] If the assessment focuses on tracking short-term results, a monthly or quarterly cycle can be selected. If the assessment focuses on the long-term impact of reforms, an annual cycle should be used to ensure that the data collection cycle matches the update frequency of the assessment objectives and indicators.
[0027] Meanwhile, based on the type of indicator and the type of each object to be evaluated, the corresponding data source is identified. The data sources for both positive and negative indicators are determined by their responsible entities. For example, indicators such as revenue growth rate and operating profit margin in the value dimension correspond to the financial statement system of the finance department and the revenue statistics ledger of the business unit; indicators such as R&D investment ratio and employee training coverage in the development dimension are linked to the funding records of the R&D department and the training files of the human resources department; indicators such as process turnover rate and inventory turnover rate in the efficiency dimension are linked to the process monitoring system of the operations management department and the warehousing records of the inventory management department; and indicators such as customer satisfaction and complaint resolution rate in the service dimension come from the feedback ledger of the customer service department and the after-sales support system. This ensures that the data source for each indicator can provide objective and quantifiable raw data and is directly related to the business scope of the object to be evaluated.
[0028] Then, the raw data of indicators for each object to be evaluated within the predetermined collection period are extracted from the identified corresponding data sources, covering all time-series nodes within the collection period. The extracted data undergoes data cleaning processing: outliers and logically contradictory data are removed, and the date format and numerical precision in different reports are standardized to unify the data format. Finally, missing values are filled based on the average level of similar objects or interpolated according to data trends to avoid interference from defects in the raw data in subsequent analysis, ensuring the standardization and reliability of the data.
[0029] Finally, the cleaned indicator data is standardized and normalized. Different standardization formulas are used for positive and negative indicators. While mapping all indicator data to a unified range and eliminating the differences in the units of measurement between different indicators, the logic of the superiority or inferiority of negative indicators is unified to the case that the larger the value, the better, so as to ensure that all types of indicators have a basis for horizontal comparison.
[0030] For positive indicators, the standardized formula is: ; For negative indicators, the standardized formula is: ; in, For the first The first subject to be evaluated in the first The raw values of each evaluation indicator For the first When the first evaluation indicator is a positive indicator, the second... The first subject to be evaluated in the first Standardized values for each evaluation indicator For the first When the first evaluation indicator is a negative indicator, the second... The first subject to be evaluated in the first Standardized values for each evaluation indicator The number of objects to be evaluated. To assess the number of indicators.
[0031] The standardized indicator data is then normalized. The normalization expression is as follows: ; in, For the first Under the evaluation indicators, the first The characteristic weight of the first object to be evaluated, i.e., the weight of the first object. The first subject to be evaluated in the first The size of the value of each evaluation indicator relative to the total value of all objects for that indicator. These are the standardized values.
[0032] To avoid subjectivity in the evaluation weights, the data distribution characteristics of the preprocessed indicator data are statistically analyzed to set corresponding objective weights, ensuring the accuracy of the final evaluation results. Specifically, this embodiment uses the entropy weight method to set the objective weights of the evaluation indicators. By calculating information entropy and the difference coefficient, the dispersion and information contribution of the indicator data are quantified, ensuring the accuracy of the set objective weights.
[0033] The calculation of the information entropy and difference coefficient of each evaluation indicator, and the determination of the objective weight of each evaluation indicator, includes: Based on the preprocessed indicator data, the feature weight of each object to be evaluated under each evaluation indicator is obtained, and the information entropy of each evaluation indicator is calculated in combination with the preset standardization coefficient. The difference coefficients are calculated based on the information entropy of each evaluation indicator, and the difference coefficients of each evaluation indicator are normalized. The objective weight of each evaluation indicator is determined based on the proportion of its normalized difference coefficient in the sum of the difference coefficients of all evaluation indicators.
[0034] After the preprocessed index data is normalized, the normalized data presented is the corresponding feature weight. Based on the feature weight, the corresponding information entropy is calculated in combination with the preset standardization coefficient.
[0035] The formula for calculating the information entropy is: ; in, For the first The information entropy of each evaluation indicator assesses the degree of dispersion of each value; constants The coefficient is the standardized coefficient.
[0036] By standardizing the coefficients, the entropy values are uniformly mapped to a reasonable range, thus avoiding distortion of the entropy calculation results due to differences in the number of objects to be evaluated.
[0037] The degree of dispersion of indicator data is measured by the information entropy obtained. The smaller the entropy value, the greater the difference in performance of each object to be evaluated under the indicator, the stronger the indicator's ability to distinguish the evaluation results, and the more effective information it can provide. Conversely, the larger the entropy value, the more concentrated the indicator data, the weaker the distinguishability, and the lower the information contribution.
[0038] Based on the calculated information entropy, the corresponding difference coefficient is calculated using the following formula: ; in, For the first The coefficient of difference of each evaluation indicator The larger the value, the more information the indicator provides, and the more important it is. The smaller the value, the less information the indicator provides, and the less important it is.
[0039] Furthermore, in order to convert the difference coefficients into values that can be directly used for weight allocation, the difference coefficients of all evaluation indicators need to be normalized. This process ensures that the sum of the difference coefficients of all indicators is 1, eliminating the magnitude interference between the difference coefficients of different indicators and ensuring that the difference coefficients of each indicator are comparable.
[0040] Finally, the objective weight of each evaluation indicator is determined based on its proportion in the sum of the difference coefficients of all evaluation indicators after normalization.
[0041] The formula for calculating the objective weights of the evaluation indicators is as follows: ; in, For the first Objective weights of each evaluation indicator.
[0042] The objective weights determined by the entropy weight method only quantify the information contribution and importance of each indicator, without combining them with the specific data of the object to be evaluated. Further, a weighted evaluation matrix is constructed by combining the preprocessed indicator data to calculate the standardized data of each object to be evaluated on each indicator with the corresponding objective weights. This ensures that each element in the matrix carries both the object's performance and the importance of the indicator, amplifying the numerical differences of core indicators and reasonably weakening the influence of secondary indicators. This ensures that the evaluation results can focus on the indicators that play a key role in organizational adjustment, thereby improving the relevance and authenticity of the evaluation.
[0043] The construction of the weighted evaluation matrix by combining the preprocessed indicator data includes: The row and column dimensions of the weighted evaluation matrix are set based on each evaluation indicator and each object to be evaluated; Based on the preprocessed indicator data of each object to be evaluated under each evaluation indicator, and combined with the objective weight of each evaluation indicator, a weighted calculation is performed to obtain the corresponding weighted indicator value. Using the objects to be evaluated as rows and the evaluation indicators as columns, a weighted evaluation matrix is constructed according to a preset order, and the elements of the weighted evaluation matrix are assigned values based on the calculated corresponding weighted indicator values.
[0044] Using the objects to be evaluated as the row dimension, it covers all internal entities involved in the organizational adjustment and evaluation. Using the evaluation indicators as the column dimension, it includes all secondary indicators under the four dimensions of value, development, efficiency, and service, ensuring the adaptability of the matrix structure.
[0045] The preprocessed data is already comparable. The objective weights quantify the information contribution of the indicators. By multiplying the standardized values of the indicators with their corresponding objective weights, each value carries the dual information of the object's relative performance on the indicator and the importance level of the indicator. This allows the numerical differences of high-weight indicators to be amplified, while the influence of low-weight indicators is reasonably weakened, thereby accurately reflecting the differentiated contributions of different indicators to the effectiveness of organizational adjustments.
[0046] It should be noted that the constructed objective weights need to be calculated based on the corresponding dimension weights. In this embodiment, the weights of the value dimension, development dimension, efficiency dimension, and service dimension are set to 0.28, 0.28, 0.22, and 0.22, respectively.
[0047] The final expression for the weighted evaluation matrix is: ; in, The values are weighted and standardized, taking into account both data standardization and indicator weights.
[0048] While weighted evaluation matrices integrate standardized data and objective weights, the best and worst performances of each indicator are scattered across different evaluated objects, making it impossible to directly measure the overall merits of a single object. Therefore, the TOPSIS method is further introduced to achieve a quantitative assessment of the effectiveness of organizational restructuring within enterprises.
[0049] When ranking the merits of objects using the TOPSIS method, the positive and negative ideal solutions of the indicators are first calculated using a weighted evaluation matrix to establish a unified comparison benchmark and avoid confusion in judgments under multiple indicator dimensions. The comprehensive difference between the single evaluation object and the benchmark is quantified by calculating the Euclidean distance between the object and the corresponding positive and negative ideal solutions. Finally, the closeness is obtained by the distance ratio to achieve unified comparability and ranking for all objects to be evaluated.
[0050] The step of calculating the positive and negative ideal solutions of the index based on the weighted evaluation matrix, and calculating the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions to obtain the corresponding closeness, includes: Traverse all columns of the weighted evaluation matrix, take the maximum value of each column as the optimal performance value of the corresponding evaluation index, arrange the optimal performance values of all evaluation indexes in column order, and obtain the positive ideal solution vector. Traverse all columns of the weighted evaluation matrix, take the minimum value of each column as the worst performance value of the corresponding evaluation index, arrange the worst performance values of all evaluation indicators in column order, and obtain the negative ideal solution vector. Based on the element values in the weighted evaluation matrix, calculate the Euclidean distance between each object to be evaluated and the positive ideal solution vector, as well as the Euclidean distance between each object to be evaluated and the negative ideal solution vector. The proximity is calculated based on the Euclidean distance between each object to be evaluated and the positive ideal solution vector, and the Euclidean distance between each object to be evaluated and the negative ideal solution vector.
[0051] The calculation expression for the positive ideal solution is: ; The expression for calculating the negative ideal solution is: ; in, For the first The positive ideal solution for each evaluation indicator For the first The negative ideal solution of each evaluation indicator The weighted evaluation matrix is the first... Line number The element values of the column.
[0052] The obtained positive ideal solution vector is The negative ideal solution vector is .
[0053] The obtained positive ideal solution is a virtual optimal solution, which can be characterized by each attribute value being the maximum value of all objects to be evaluated on that attribute. The negative ideal solution is a virtual worst solution, which can be characterized by each attribute value being the minimum value of all objects to be evaluated on that attribute.
[0054] When determining the proximity to the ideal solution, the distance from each object to be evaluated to the positive ideal solution is calculated using Euclidean distance. and distance to the negative ideal solution Its expression is: ; ; in, For the first The distance between an object to be evaluated and the ideal solution; the smaller this value, the closer the object is to the optimal state. For the first The distance between an object to be evaluated and the negative ideal solution; the larger this value, the further the object is from the worst state.
[0055] The proximity is further calculated based on the calculated distance, and its calculation expression is as follows: ; in, For the first The relative closeness of the evaluated objects is the final score. Because... and ,therefore ,so . The closer it is to 1, the better. The larger and The smaller the value, the closer the evaluated object is to the positive ideal solution and the farther it is from the negative ideal solution, the higher the evaluation. The closer it is to 0, the lower the evaluation.
[0056] The obtained proximity score can only reflect the overall performance of a single object, but cannot directly determine its relative superiority or inferiority among all the objects being evaluated. Further, by comparing the proximity scores of all objects to be evaluated within the same time period and ranking them in descending order, we can quickly locate the horizontal competitive position of each object. A higher ranking indicates better overall performance within the same period, while fluctuations in ranking reflect changes in its position within the competitive landscape. This avoids viewing the quantitative results of a single object in isolation and achieves a comprehensive evaluation result.
[0057] Furthermore, a single time-series correlation cannot reflect the long-term effects of organizational adjustments, while corporate organizational reforms are often implemented in phases, requiring tracking of the dynamic improvement process. Therefore, further time-series comparisons are used to extract the correlation and effectiveness evaluation scores of the same subject under evaluation at different time points, to intuitively present its improvement range and development trend. A continuous increase in scores indicates that the adjustment measures are effective, while fluctuations or declines suggest that there is room for optimization, allowing the evaluation results to focus on the dynamic effectiveness of organizational adjustments.
[0058] By comparing two dimensions, the actual effectiveness of organizational adjustments can be fully revealed. Horizontal comparison clarifies the organization's position within the group, while vertical comparison clarifies its own growth trajectory, ensuring the objectivity and accuracy of the evaluation results.
[0059] The step of combining the corresponding proximity calculation results to obtain the enterprise organizational adjustment assessment results through contemporaneous comparison of multiple assessment objects and time-series comparison of a single assessment object includes: Based on the preset time series, the proximity calculation results of all objects to be evaluated under each time series are extracted and sorted in descending order according to proximity. The proximity fluctuation information of each object to be evaluated is determined based on the sorting results. Based on the preset time series, the preprocessed indicator data of each object to be evaluated under all time series is obtained, and the corresponding objective weights are combined to perform weighted summation to obtain the performance evaluation score of each object to be evaluated at each time point. The organizational adjustment assessment results are obtained based on the proximity fluctuation information of each subject to be assessed and the performance evaluation scores at each time point.
[0060] The preset time series is set according to the established evaluation period. For each time series, the corresponding proximity score has been converted into a quantitative value in the 0-1 range using the TOPSIS method, which can directly reflect the overall performance of the object during the same period. After extracting the corresponding proximity score data, the objects are sorted in descending order of proximity score to obtain the horizontal ranking of each object in each time series. By comparing the ranking changes of different time series, the proximity score fluctuation information is determined. The proximity score fluctuation information includes the magnitude of ranking rise and fall, fluctuation frequency, and ranking zone, in order to capture the positional changes of each object in the competitive landscape during the same period.
[0061] Based on the same preset time series, preprocessed indicator data of each object to be evaluated is extracted in all time series. Combined with the previously determined objective weights, all indicator data of each object at a single time point are weighted and summed, and converted into a performance evaluation score with a full score of 100. This score is the absolute performance quantification value of the object at that time point, which can intuitively reflect the organizational adjustment effectiveness level of a single object at a specific time point.
[0062] The formula for calculating the performance evaluation score is as follows: ; in, For the first The individual to be evaluated at time The absolute score of the longitudinal evaluation For the first The individual to be evaluated at time For the first The standardized mean of each evaluation indicator. Indicating the first in the longitudinal evaluation The first subject to be evaluated for the first Evaluation weights for each assessment indicator.
[0063] The two types of data were cross-validated to ultimately form the assessment results of the enterprise organizational restructuring. The process of obtaining organizational adjustment evaluation results based on the proximity fluctuation information of each evaluated object and the effectiveness evaluation score at each time point includes: Based on the proximity fluctuation information of each object to be evaluated, the horizontal competitive position and its changing trend can be located; The longitudinal improvement magnitude and its development trend are identified based on the performance evaluation scores at each time point. Individual evaluation results for each object to be evaluated are obtained based on the changing trends of the corresponding horizontal competitive positions and the development trends of the vertical improvement magnitude. By integrating the individual evaluation results of each entity to be evaluated based on the changing trends of horizontal competitive position and the development trends of vertical improvement, the organizational adjustment evaluation results of the enterprise are obtained.
[0064] The proximity fluctuation information comes from the proximity ranking results at different time series. By comparing the ranking changes of the same evaluated object within a preset time series, its horizontal competitive position is clarified. A higher ranking indicates better overall performance among all evaluated objects in the same period, while a ranking in the middle or lower range reflects insufficient competitive advantage. At the same time, by analyzing the ranking trajectory over continuous time series, the trend of change is further identified. A continuously rising ranking indicates gradually strengthening horizontal competitiveness, a stable ranking indicates a solid competitive position, and a declining ranking indicates a disadvantage in the competition during the same period.
[0065] Based on the calculated effectiveness evaluation scores, the longitudinal improvement magnitude is calculated by the score differences of the same evaluated object at different time series. A positive difference indicates that the organizational adjustments have actually improved during that period, while a negative difference or a difference close to zero indicates insufficient improvement. Furthermore, the development trend is judged by the score trajectory over continuous time series. A continuous increase in scores indicates that the organizational adjustment measures have been effectively implemented, showing a steady improvement trend, while fluctuating scores or a continuous decline suggest that there is room for optimization of the adjustment strategy.
[0066] By combining the trends in horizontal competitive position and vertical improvement, a precise profile of the organizational adjustment effectiveness of each individual entity under evaluation is created. Cross-validation of these horizontal and vertical trends avoids the bias of a single-dimensional judgment. For example, if an entity's horizontal ranking continues to rise and its vertical score steadily increases, it indicates that it possesses a significant advantage in its peer competition and has achieved substantial improvement. The individual evaluation conclusion is that the organizational adjustment has been remarkably effective, and the measures are appropriate and efficient. If an entity's horizontal ranking rises but its vertical score remains unchanged, it may be due to a relative advantage resulting from the decline in the performance of other entities in the same period, rather than proactive improvement. The conclusion should indicate that the improvement in horizontal competitiveness stems from changes in the external landscape, and its own improvement is insufficient. If an entity's vertical score increases but its horizontal ranking declines, it indicates that it has made progress but has not kept pace with the improvement of similar entities in the same period. The conclusion is that internal improvements are effective, but its horizontal competitive advantage still needs to be strengthened.
[0067] Then, by combining the individual evaluation results of all the entities to be evaluated, the overall effectiveness evaluation results of the organizational restructuring of the enterprise are obtained.
[0068] The process of integrating the individual evaluation results of each entity to be evaluated according to the changing trends of horizontal competitive position and the development trends of vertical improvement magnitude to obtain the organizational adjustment evaluation results includes: The objects to be evaluated are classified based on the changing trends of horizontal competitive positions and the development trends of vertical improvement. Based on the individual assessment results of each subject to be assessed, identify the corresponding ranking fluctuation driving indicators and score fluctuation weakness indicators, and combine the classification results to obtain the common characteristics of each category of subjects to be assessed; Based on the common characteristics of each type of object to be evaluated and the corresponding number of objects to be evaluated, the results of the enterprise organizational adjustment evaluation are obtained.
[0069] First, the scattered individual performances are aggregated according to their development trends by classifying the objects to be evaluated, avoiding fragmented conclusions caused by isolated analysis. Specifically, the classification dimensions consist of a combination of horizontal changes and vertical development, including types with strengthened competitive advantages and continuous self-improvement through both horizontal and vertical increases; types with improved competitive positions through both horizontal and vertical increases but without substantial breakthroughs; types with self-improvement through both horizontal stability and vertical increases but with unchanged competitive landscape; types with stable performance without significant fluctuations through both horizontal stability and vertical stability; and types with intensified competitive disadvantages or stagnation through both horizontal decline and vertical stability or decline.
[0070] Further, by comparing the performance of secondary indicators before and after the ranking fluctuations of the evaluated objects, the ranking fluctuation drivers that have the core impact on horizontal ranking changes are identified. Simultaneously, the score fluctuation bottleneck indicators that cause vertical scores to fall short of expectations are identified. Then, based on the classification results, the common characteristics of each category of evaluated objects are integrated to pinpoint the root causes of performance changes and improve the comprehensiveness of subsequent evaluation results. Specifically, the contribution can be calculated by multiplying the indicator weight and the rate of change of the indicator value, and then the indicator with the highest contribution is selected as the ranking fluctuation driver, while indicators with declining values and high weight percentages are identified as score fluctuation bottleneck indicators.
[0071] The number and proportion of each category of objects to be evaluated are statistically analyzed. If the proportion of objects that are rising horizontally and rising vertically exceeds 50%, it indicates that the overall organizational adjustment of the enterprise has achieved significant results. If the proportion of objects that are declining horizontally and stagnating vertically is too high, it suggests that there is a systematic deviation in the overall adjustment strategy. Combined with the common characteristics identified, the final evaluation results of the enterprise organizational adjustment are given.
[0072] The evaluation results of the enterprise organizational restructuring include the overall effectiveness of the restructuring, its advantages, and its shortcomings.
[0073] Because the organizational restructuring assessment results contain multi-dimensional information, including horizontal and vertical trends and classifications of each assessed entity, as well as detailed data such as ranking-driven indicators and score-deficit indicators, the original results are presented in data tables and textual conclusions, which are abstract and fragmented. Therefore, after obtaining the organizational restructuring assessment results, the following steps are also taken: The results of the enterprise organizational adjustment assessment can be visualized using two-dimensional heat maps or spatial dynamic trajectory maps.
[0074] By visualizing two-dimensional heatmaps and spatial dynamic trajectory maps, abstract data is transformed into intuitive graphics, integrating and presenting multiple pieces of information through coordinate dimensions, color depth, and trajectory changes.
[0075] Taking a heatmap as an example, the objects to be evaluated can be arranged in rows, time sequence in columns, and colors to represent performance quality, thus enabling a visual display of the evaluation results of organizational adjustments.
[0076] Another aspect of this embodiment also provides an enterprise organizational adjustment assessment system based on horizontal and vertical comparisons, including: The indicator selection module is used to filter positive and negative indicators based on preset dimensions, and to construct a multi-dimensional evaluation indicator system based on the selected indicators. The data processing module is used to extract indicator data of each object to be evaluated in the enterprise organizational adjustment and to preprocess the indicator data. The data analysis module is used to calculate the information entropy and difference coefficient of each evaluation indicator, determine the objective weight of each evaluation indicator, construct a weighted evaluation matrix by combining the preprocessed indicator data, calculate the positive and negative ideal solutions of the indicators based on the weighted evaluation matrix, and calculate the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions to obtain the corresponding closeness. The assessment module is used to combine the corresponding proximity calculation results to obtain the assessment results of enterprise organizational adjustment through the same-period comparison of multiple objects to be assessed and the time-series comparison of a single object to be assessed.
[0077] The indicator selection module, data processing module, data analysis module, and evaluation module are all data processing components with data processing capabilities, and each is equipped with a corresponding external data port to retrieve the required data.
[0078] It also features a human-computer interaction interface that can visualize the acquired enterprise organizational adjustment assessment results.
[0079] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications may be made without departing from the technical solutions described in the claims.
Claims
1. A method for evaluating enterprise organizational restructuring based on horizontal and vertical comparisons, characterized in that: include: Positive and negative indicators are selected based on preset dimensions, and a multi-dimensional evaluation indicator system is constructed based on the selected indicators. Extract indicator data for each entity to be evaluated during the enterprise organizational restructuring, and preprocess the indicator data; Calculate the information entropy and difference coefficient of each evaluation indicator, determine the objective weight of each evaluation indicator, and construct a weighted evaluation matrix by combining the preprocessed indicator data; The positive and negative ideal solutions of the index are calculated based on the weighted evaluation matrix, and the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions is calculated to obtain the corresponding closeness. By combining the corresponding proximity calculation results, the assessment results of enterprise organizational adjustment are obtained through the same-period comparison of multiple objects to be assessed and the time-series comparison of a single object to be assessed.
2. The enterprise organizational adjustment assessment method based on horizontal and vertical comparison as described in claim 1, characterized in that, The calculation of the information entropy and difference coefficient of each evaluation indicator, and the determination of the objective weight of each evaluation indicator, includes: Based on the preprocessed indicator data, the feature weight of each object to be evaluated under each evaluation indicator is obtained, and the information entropy of each evaluation indicator is calculated in combination with the preset standardization coefficient. The difference coefficients are calculated based on the information entropy of each evaluation indicator, and the difference coefficients of each evaluation indicator are normalized. The objective weight of each evaluation indicator is determined based on the proportion of its normalized difference coefficient in the sum of the difference coefficients of all evaluation indicators.
3. The enterprise organizational adjustment assessment method based on horizontal and vertical comparison as described in claim 1, characterized in that, The construction of the weighted evaluation matrix by combining the preprocessed indicator data includes: The row and column dimensions of the weighted evaluation matrix are set based on each evaluation indicator and each object to be evaluated; Based on the preprocessed indicator data of each object to be evaluated under each evaluation indicator, and combined with the objective weight of each evaluation indicator, a weighted calculation is performed to obtain the corresponding weighted indicator value. Using the objects to be evaluated as rows and the evaluation indicators as columns, a weighted evaluation matrix is constructed according to a preset order, and the elements of the weighted evaluation matrix are assigned values based on the calculated corresponding weighted indicator values.
4. The enterprise organizational adjustment evaluation method based on horizontal and vertical comparison as described in claim 3, characterized in that, The calculation of positive and negative ideal solutions for the index based on the weighted evaluation matrix, and the calculation of the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions to obtain the corresponding closeness, includes: Traverse all columns of the weighted evaluation matrix, take the maximum value of each column as the optimal performance value of the corresponding evaluation index, arrange the optimal performance values of all evaluation indexes in column order, and obtain the positive ideal solution vector. Traverse all columns of the weighted evaluation matrix, take the minimum value of each column as the worst performance value of the corresponding evaluation index, arrange the worst performance values of all evaluation indicators in column order, and obtain the negative ideal solution vector. Based on the element values in the weighted evaluation matrix, calculate the Euclidean distance between each object to be evaluated and the positive ideal solution vector, as well as the Euclidean distance between each object to be evaluated and the negative ideal solution vector. The proximity is calculated based on the Euclidean distance between each object to be evaluated and the positive ideal solution vector, and the Euclidean distance between each object to be evaluated and the negative ideal solution vector.
5. The enterprise organizational adjustment assessment method based on horizontal and vertical comparison as described in claim 1, characterized in that, The extraction of indicator data for each entity to be evaluated in the enterprise organizational restructuring, and the preprocessing of the indicator data, include: The data collection cycle is identified based on the assessment needs, and the corresponding data source is identified based on the indicator type and the evaluation object type of each object to be assessed. Extract the indicator data of each object to be evaluated within the data collection period from the corresponding data source, and perform data cleaning processing. The cleaned indicator data is standardized and normalized to obtain preprocessed indicator data.
6. The enterprise organizational adjustment assessment method based on horizontal and vertical comparison according to claim 1, characterized in that, The process of combining the corresponding proximity calculation results to obtain the enterprise organizational adjustment assessment results through contemporaneous comparison of multiple assessment objects and time-series comparison of a single assessment object includes: Based on the preset time series, the proximity calculation results of all objects to be evaluated under each time series are extracted and sorted in descending order according to proximity. The proximity fluctuation information of each object to be evaluated is determined based on the sorting results. Based on the preset time series, the preprocessed indicator data of each object to be evaluated under all time series is obtained, and the corresponding objective weights are combined to perform weighted summation to obtain the performance evaluation score of each object to be evaluated at each time point. The organizational adjustment assessment results are obtained based on the proximity fluctuation information of each subject to be assessed and the performance evaluation scores at each time point.
7. The enterprise organizational adjustment assessment method based on horizontal and vertical comparison as described in claim 6, characterized in that, The assessment results of organizational restructuring are obtained based on the proximity fluctuation information of each assessed object and the performance assessment score at each time point, including: Based on the proximity fluctuation information of each object to be evaluated, the horizontal competitive position and its changing trend can be located; The longitudinal improvement magnitude and its development trend are identified based on the performance evaluation scores at each time point. Individual evaluation results for each object to be evaluated are obtained based on the changing trends of the corresponding horizontal competitive positions and the development trends of the vertical improvement magnitude. By integrating the individual evaluation results of each entity to be evaluated based on the changing trends of horizontal competitive position and the development trends of vertical improvement, the organizational adjustment evaluation results of the enterprise are obtained.
8. The enterprise organizational adjustment evaluation method based on horizontal and vertical comparison according to claim 7, characterized in that, The process involves integrating the individual evaluation results of each entity to be evaluated based on the changing trends of horizontal competitive position and the development trends of vertical improvement magnitude to obtain the organizational adjustment evaluation results, including: The objects to be evaluated are classified based on the changing trends of horizontal competitive positions and the development trends of vertical improvement. Based on the individual assessment results of each subject to be assessed, identify the corresponding ranking fluctuation driving indicators and score fluctuation weakness indicators, and combine the classification results to obtain the common characteristics of each category of subjects to be assessed; Based on the common characteristics of each type of object to be evaluated and the corresponding number of objects to be evaluated, the results of the enterprise organizational adjustment evaluation are obtained.
9. The enterprise organizational adjustment assessment method based on horizontal and vertical comparison as described in claim 1, characterized in that, After obtaining the assessment results of the enterprise organizational restructuring, the following also applies: The results of the enterprise organizational adjustment assessment can be visualized using two-dimensional heat maps or spatial dynamic trajectory maps.
10. A corporate organizational restructuring assessment system based on horizontal and vertical comparisons, used to execute the corporate organizational restructuring assessment method according to any one of claims 1 to 9, characterized in that, include: The indicator selection module is used to filter positive and negative indicators based on preset dimensions, and to construct a multi-dimensional evaluation indicator system based on the selected indicators. The data processing module is used to extract indicator data of each object to be evaluated in the enterprise organizational adjustment and to preprocess the indicator data. The data analysis module is used to calculate the information entropy and difference coefficient of each evaluation indicator, determine the objective weight of each evaluation indicator, construct a weighted evaluation matrix by combining the preprocessed indicator data, calculate the positive and negative ideal solutions of the indicators based on the weighted evaluation matrix, and calculate the Euclidean distance between each object to be evaluated and the positive and negative ideal solutions to obtain the corresponding closeness. The assessment module is used to combine the corresponding proximity calculation results to obtain the assessment results of enterprise organizational adjustment through the same-period comparison of multiple objects to be assessed and the time-series comparison of a single object to be assessed.