Broadband salary optimization method and system

Abstract

The invention discloses a broadband salary optimization method and system, and belongs to the technical field of salary determination. The broadband salary optimization method comprises the steps that a post value evaluation score data set V (t) = {v1, v2,..., vm} of an enterprise is obtained, vi represents the value evaluation score of the ith post, and m is the number of posts; according to the distribution change of a post value evaluation score data set V (t), dynamically adjusting a quantile Qp (t), calculating an adjusted quantile # imgabs0 #, dividing a preliminary salary hierarchy interval # imgabs1 # to construct a strategic fitness function C (K), and optimizing a preliminary hierarchy number K in combination with an enterprise strategic target S to generate a candidate hierarchy number set; and calculating an optimized salary grade based on a dynamic amplification method. According to the method, the objectivity, strategic suitability and dynamic response capability of salary series, bandwidth and grade determination are improved, and a reusable methodology and tool support is provided for enterprise salary digital transformation.

Description

Technical Field

[0001] The present invention belongs to the technical field of salary determination, and particularly relates to a data-driven broadband salary optimization method and system. Background Art

[0002] In traditional enterprise salary management, there are often many problems in the methods for determining salary levels, bandwidths, and grades.

[0003] From the perspective of subjectivity, it usually relies on the experience and subjective judgment of managers to divide salary levels. For example, in some small enterprises, managers may only determine salary grades based on the superficial scope of job responsibilities and personal perception of the importance of the job, without scientifically and systematically considering the actual value contribution of the job. This results in significant differences in the division results of different managers, making it difficult to ensure fairness and reasonableness. Employees may reduce their work enthusiasm due to the perception of unfair salary distribution.

[0004] In terms of static nature, once the traditional salary system is determined, it is very difficult to adjust it in a timely manner according to the changes in the internal and external environments of the enterprise. With the intensification of market competition, industry technological innovation, and the expansion or contraction of the enterprise's own business, the value of jobs and the requirements for employees' skills will change. However, under the traditional method, salary levels and grades may remain unchanged for many years and cannot adapt to these changes. For example, an Internet enterprise initially focused on product R & D, and the salaries of technical positions were relatively high. But as the business developed, the importance of market operation and customer service became increasingly prominent. Due to the failure to adjust the salary system in a timely manner, the salaries of these positions did not match their actual value, resulting in brain drain.

[0005] In addition, the problem of strategic disconnection is also relatively prominent. The enterprise strategy is the core guiding the development direction of the enterprise, but traditional salary management methods often do not fully combine the enterprise strategic goals when determining salary parameters. For example, an enterprise formulates a development strategy centered on innovation and expects to attract and retain innovative talents. However, in salary design, insufficient incentives are given to positions with outstanding innovation achievements, and the setting of salary levels and grades cannot reflect the tilt towards innovation, making the salary system unable to effectively support the implementation of the enterprise strategy.

[0006] To sum up, the subjectivity, static nature, and strategic disconnection problems of traditional salary management methods seriously restrict the scientific nature and effectiveness of enterprise salary management, and it is difficult to meet the development needs of enterprises in a complex and changeable market environment. There is an urgent need for a new salary calculation method to solve these problems. Summary of the Invention

[0007] In view of the above-mentioned subjectivity, static nature, and strategic disconnection problems existing in traditional salary management methods, the present invention provides a broadband salary optimization method and system. The present invention combines dynamic quantile adjustment, strategic coverage optimization, and a mixed integer search algorithm, which can effectively solve the subjectivity, static nature, and strategic disconnection problems of traditional methods, thereby realizing the scientific, dynamic, and strategic adaptation of salary parameters.

[0008] To achieve the above object, the technical solution provided by the present invention is as follows:

[0009] The first aspect of the present invention provides a broadband salary optimization method, including:

[0010] Obtain the job value evaluation score data set V(t) = {v 1 , v 2 , …, v m} of the enterprise, where v i represents the job value evaluation score of the i-th job, and m is the number of jobs;

[0011] According to the distribution change of the job value evaluation score data set V(t), dynamically adjust the quantile Q p (t), calculate the adjusted quantile point and divide the preliminary salary level interval

[0012] Construct a strategic adaptation function C(K), optimize the preliminary number of levels K in combination with the enterprise strategic goal S, and generate a set of candidate number of levels;

[0013] Calculate the optimized salary grade number based on the dynamic increase method.

[0014] In view of the subjectivity, static nature, and strategic disconnection problems existing in traditional salary management methods, the present invention provides a broadband salary optimization method. By dynamically adjusting the quantile in real time, the salary level interval is preliminarily divided based on the adjusted quantile; at the same time, a strategic adaptation function C(K) is constructed, the preliminary number of levels K is optimized in combination with the enterprise strategic goal S, and the salary grade number is calculated and optimized based on the dynamic increase method, thereby effectively improving the rationality of broadband salary calculation and realizing the scientific, dynamic, and strategic adaptation of salary parameters.

[0015] According to any of the technical solutions described in the first aspect of the present invention, the quantile Q p (t) is dynamically adjusted according to the following formula:

[0016]

[0017] Wherein, and σ V (t) are respectively the mean value and standard deviation of the job value evaluation scores at time t; is the mean change; λ(t) is the dynamic adjustment coefficient, and w(t) is the dynamic weight adjustment coefficient; is the third-order standardized matrix of the job value score, which is used to measure the skewness of the data distribution, that is, the degree of asymmetry of the data distribution.

[0018] Furthermore, the dynamic weight adjustment coefficient w(t) is calculated according to the following formula: The industry market volatility index and the enterprise business growth index can be specifically determined according to factors such as the market volatility of the enterprise's industry and the enterprise's own business growth trend; among them, is a very small positive number less than 1, which is used to prevent the denominator from being zero.

[0019] Furthermore, the dynamic adjustment coefficient where ∈ is a very small positive number less than 1, which is used to avoid λ(t) tending to infinity due to the denominator approaching 0.

[0020] According to any one of the technical solutions described in the first aspect of the present invention, according to the adjusted quantile The salary level interval is divided as follows:

[0021]

[0022] According to any one of the technical solutions described in the first aspect of the present invention, the job value evaluation score dataset V(t) is obtained through the expert scoring method or the job analysis questionnaire, and V(t) is subjected to outlier processing, and / or data points with |Z-score|>3 are removed, and / or Min-Max standardization processing; where Z-score is the standard score.

[0023] According to any one of the technical solutions described in the first aspect of the present invention, the construction of the strategic fitness function C(K), combined with the enterprise strategic goal S, optimizes the preliminary number of levels K, specifically including:

[0024] (i) Define the level importance weight V k is the sum of the job value evaluation scores corresponding to the kth salary level, and β k is a preset strategic influence factor, which is predefined through the enterprise strategic priority;

[0025] (ii) Define the fitness function:

[0026] Among them, n represents the number of indicators affecting the strategic fitness of the salary level, which can be specifically determined according to the enterprise strategic goal and the actual evaluation requirements; g iis the i-th index function for measuring the strategic fit of salary levels, and each index reflects the matching degree between the salary level and the strategic goal from different dimensions; α i is the strategic goal weight of the i-th index, and all weights satisfy These weights are set by the enterprise according to the strategic focus;

[0027] (iii) Construct the maximization objective function and satisfy the constraint K min ≤K≤K max and |Median(L k+1 ) - Median(L k )|≥δ min , and measure the strategic fit of the salary level number K through maximizing the objective function; where δ min represents the minimum difference between levels to prevent overlap; K min , K max are respectively the minimum and maximum limits of the salary level number K, which are formulated according to the enterprise strategy.

[0028] (iv) Output the optimal level number K and the corresponding salary range

[0029] According to any of the technical solutions described in the first aspect of the present invention, the indicators affecting the strategic fit of salary levels include, but are not limited to, the fairness index between levels and the competitiveness index of high-value positions.

[0030] According to any of the technical solutions described in the first aspect of the present invention, the salary grade number N is optimized by calculating based on the dynamic increase method, and the specific calculation formula of the salary grade number N is as follows:

[0031]

[0032] In the above formula, the logarithmic operation (log) is used to calculate the number of grades that can be divided within the salary bandwidth according to the increase rate r. The salary bandwidth Bandwidth k represents the salary bandwidth of the k-th level, and the salary increase rate r affects the number of grades. Among them, the salary increase rate r k is calculated as follows:

[0033] r k =r h ×e -λk

[0034] r h is the initial salary increase, which is determined by the enterprise strategy, λ is the coefficient controlling the decreasing speed, which is determined by the enterprise strategy, and k is the salary level.

[0035] According to any of the technical solutions described in the first aspect of the present invention, the salary bandwidth Bandwidth k is calculated as follows:

[0036]

[0037] S min,k =V min,k ×α

[0038] S max,k =V max,k ×β

[0039] Wherein, S max,k represents the upper limit of the k-th level bandwidth, and S min,k represents the lower limit of the k-th level bandwidth. V min and V max represent the minimum value and the maximum value in the job value score respectively. α and β are adjustment coefficients determined according to the enterprise salary strategy to ensure that the minimum salary level of the salary band meets the market competitiveness.

[0040] The second aspect of the present invention also provides a broadband salary optimization system, including:

[0041] A data acquisition module for real-time obtaining job value assessment scores and market salary data;

[0042] A preliminary salary level interval division module for dynamically adjusting the quantile Q p (t) according to the distribution change of the job value assessment score dataset V(t), calculating the adjusted quantile and dividing the preliminary salary level interval

[0043] A preliminary number of levels K optimization module for optimizing the preliminary number of levels K based on the strategic fitness function C(K) and combining with the enterprise strategic goal S to generate a candidate set of the number of levels;

[0044] A salary grade number calculation and optimization module for calculating and optimizing the salary grade number based on the dynamic increase method.

[0045] Furthermore, it further includes an output module for generating a visualization report of the optimized salary grade number and outputting it to the enterprise HR system.

[0046] In summary, by adopting the technical solution provided by the present invention, compared with the prior art, the following beneficial effects can be obtained:

[0047] The present invention provides a broadband salary optimization method, which is particularly applicable to the standardized design and dynamic optimization of salary levels, bandwidths, and grades in an enterprise's broadband salary model. By combining dynamic quantile adjustment, strategic coverage optimization, and a mixed integer search algorithm, it solves the problems of subjectivity, staticity, and strategic disconnection of traditional methods.

[0048] Specifically, the present invention proposes a dynamic quantile adjustment and strategic coverage optimization method (DQ-SCO). That is, first, dynamically adjust the quantiles according to the distribution change of the job value assessment scores, and divide the preliminary salary level intervals based on the adjusted quantiles. Then, optimize the preliminary number of levels in combination with the enterprise's strategic goals, so as to achieve a salary level division driven by both data and strategy, which is beneficial to ensuring the reliability of the salary level division. At the same time, the present invention further optimizes the calculation of the salary grades based on the dynamic increase method, realizing the standardized calculation of the salary grades, thereby enhancing the objectivity, strategic adaptability, and dynamic response ability of the determination of salary levels, bandwidths, and grades, and providing reusable methodology and tool support for the digital transformation of enterprise salaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is the full process of the DQ-SCO algorithm from data collection to salary grade calculation in the embodiment of the present invention, and the arrows represent the logical order;

[0050] Figure 2 It is a schematic diagram of the broadband salary optimization model in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] To further understand the content of the present invention, the present invention will be described in detail below in combination with the drawings and specific embodiments.

[0052] Combined with Figure 1 、 Figure 2 As shown, the broadband salary optimization method in the embodiment of the present invention includes:

[0053] Step 1. Obtain the job value assessment score data set V(t) = {v 1 , v 2 , …, v m} of the enterprise, where v i represents the value assessment score of the i-th job, and m is the number of jobs.

[0054] Specifically, in this embodiment, it is assumed that the enterprise evaluates each job through a job evaluation committee to obtain the job value assessment score data set V(t). Assume that the enterprise has 100 jobs, V(t) = {v 1 , v 1 , …, v 100}, and some data are shown in Table 1-1 below:

[0055] Table 1-1 Data Table of Post Value Scores

[0056]

[0057] Step 2: Dynamically adjust the quantile Q according to the distribution change of the post value evaluation score dataset V(t), calculate the adjusted quantile p (t), and divide the preliminary salary level intervals

[0058] In the embodiments of the present invention, the specific steps of dynamic quantile adjustment are as follows:

[0059] First, calculate the mean value of the post value scores and the standard deviation σ V (t) = 12;

[0060] Determine the dynamic adjustment coefficient ∈ takes a very small positive number. In the embodiments of the present invention, ∈ = 0.001, and the calculated result is

[0061] Calculate the result after dynamic adjustment of the quantile: Assume that the mean value at the previous moment is Then the mean value change amount is Assume that the current industry market fluctuation index is 0.8 and the enterprise business growth index is 1.2. Take a very small positive number Then the dynamic weight adjustment coefficient is Calculate the third-order standardized moment of the post value scores. After calculation

[0062] Taking p = 0.25 (i.e., the 25% quantile) as an example, the original quantile Q 0.25 (V(t)) = 60, and the adjusted quantile is:

[0063]

[0064] Divide the preliminary salary level intervals: Assume that 3 quantiles p are determined 1 = 0.25, p 2 = 0.5, p 3 = 0.75. After calculation Then the preliminary salary level intervals are:

[0065] Step 3: Construct a strategic fitness function C(K), and optimize the preliminary number of levels K in combination with the enterprise strategic goal S to generate a set of candidate numbers of levels.

[0066] ​Specifically, the construction of the strategic fitness function C(K) in the embodiments of the present invention is as follows:

[0067] Assume that the total scores of the job value evaluations corresponding to 4 salary levels are V 1 = 1200, V 2 = 1800, V 3 = 1500, V 4 = 2000; the preset strategic influence factor β 1 = 0.9, β 2 = 1, β 3 = 1.1, β 4 = 1.2, then the hierarchical importance weight Similarly, it can be calculated that w 2 ≈ 0.19, w 3 ≈ 0.18, w 4 ≈ 0.25.

[0068] Define the fitness function: Assume that according to the enterprise strategic goal and actual evaluation requirements, the number of indicators n affecting the strategic fitness of the salary level is determined to be 4, and the strategic goal weights α 1 = 0.3, α 2 = 0.2, α 3 = 0.3, α 4 = 0.2. In the embodiments of the present invention, the indicators affecting the strategic fitness of the salary level include the fairness index between levels, the competitiveness index of high-value positions, the management efficiency index, and the job stability index.

[0069] Fairness index between levels: Assume that the maximum median difference threshold γ allowed is 10. Taking and as examples,

[0070] Competitiveness index of high-value positions: Assume that the top 10% of the job value evaluation scores (i.e., Q 90％ (V) = 85) are high-value positions. There are no high-value positions in 2 , g There is 1 high-value position in with a score of 82, 2 The total score of the job value is 1800, and the total score of all high-value positions is 2500, then g

[0071] Management efficiency index: g 3 = -log(4) ≈ -1.39 (assuming 4 levels are currently considered).

[0072] Job stability index: Assume that g 4 is L kThe proportion of employees with more than 3 years of service in a position at each level, If the proportion in this is 0.4, then g 4 = 0.4.

[0073] Adaptive function Calculate The value of the adaptive function: Similarly, calculate other levels.

[0074] Optimize the initial number of levels K in combination with the enterprise strategic goal S, maximizing the objective function: Objective function The constraint condition is K min = 3, K max = 6, the minimum difference δ between levels min = 5. By calculating C(K) under different K values, determine the optimal number of levels. After calculation, when K = 4, C(K) is the largest.

[0075] Step 4. Calculate the optimized number of salary grades based on the dynamic increment method

[0076] Set the dynamic increment: In this embodiment, it is assumed that the enterprise determines the initial salary increment r h = 0.2, and the coefficient λ for controlling the decreasing speed = 0.1; then the salary increment ratio r for the first level 1 = r h × e -λ×1 ≈ 0.181, the second level r 2 ≈ 0.164, and so on.

[0077] Calculate the number of salary grades: Assume that α = 0.8 and β = 1.6 are determined according to the enterprise salary strategy. Taking the first level as an example, the minimum value V of the job value score min,1 = 45, and the maximum value V max,1 = 61. Then S min,1 = V min,1 × α = 36, S max,1 = V max,1 × β = 97.6, salary bandwidth Number of salary grades Similarly, calculate the number of salary grades for other levels.

[0078] Finally, the manufacturing enterprise determines 4 salary levels and the corresponding number of salary grades for each level, and constructs a broadband salary system based on data-driven and adapted to the enterprise strategy, which can effectively improve the scientificity and rationality of salary management.

[0079] The embodiment of the present invention also provides a broadband salary optimization system, including:

[0080] A data acquisition module for obtaining job value assessment scores and market salary data in real time;

[0081] Initial salary level range A division module for dynamically adjusting the quantile Q p (t) according to the distribution change of the job value assessment score dataset V(t), and calculating the adjusted quantile and dividing the initial salary level range

[0082] An initial level number K optimization module for optimizing the initial level number K based on the strategic fitness function C(K) and combining with the enterprise strategic goal S to generate a set of candidate level numbers;

[0083] A salary grade number calculation and optimization module for calculating and optimizing the salary grade number based on the dynamic increase method.

[0084] By adopting the broadband salary optimization system of this embodiment, the number of levels, bandwidth and grade number of the enterprise employees' salaries can be optimized and determined. The specific optimization calculation method is the same as the optimization calculation method described in any of the previous embodiments.

[0085] The above schematically describes the present invention and its implementation manners. This description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention. If those of ordinary skill in the art are inspired by it and without departing from the purpose of the present invention creation, they design similar structural manners and embodiments to this technical solution without creative efforts, which shall fall within the protection scope of the present invention.

Claims

1. A broadband salary optimization method, characterized in that: include: Get the enterprise's job value assessment score data set V(t) = {v1, v2, ..., v m }, where v i represents the value assessment score of the i-th position, and m is the number of positions; Dynamically adjust the quantile Q according to the distribution changes of the job value evaluation score dataset V(t) p (t), calculate the adjusted quantile And divide the initial salary level range Construct a strategic fitness function C(K), optimize the initial number of levels K based on the enterprise's strategic goal S, and generate a set of candidate number of levels; Calculate and optimize the number of salary grades based on the dynamic increase method.

2. The broadband salary optimization method according to claim 1, characterized in that: The quantile Q p (t) is dynamically adjusted according to the following formula: in, and σ V (r) are the mean and standard deviation of the job value assessment scores at time t; is the mean change; λ(t) is the dynamic adjustment coefficient, and w(t) is the dynamic weight adjustment coefficient.

3. The broadband salary optimization method according to claim 2, characterized in that: The dynamic weight adjustment coefficient w(t) is calculated according to the following formula: in, Used to prevent the denominator from being zero; and / or dynamic adjustment factor Where 0<∈<1 is used to prevent the denominator from being zero.

4. The broadband salary optimization method according to any one of claims 1 to 3, characterized in that: According to the adjusted quantile The salary levels are divided into the following categories:

5. The broadband salary optimization method according to any one of claims 1 to 3, characterized in that: The job value assessment score dataset V(t) is obtained through expert scoring method or job analysis questionnaire, and V(t) is processed for outliers, and / or data points with |Z-score|>3 are removed, and / or Min-Max normalization is performed; where Z-score is the standard score.

6. The broadband salary optimization method according to any one of claims 1 to 3, characterized in that: The strategic fitness function C(K) is constructed to optimize the initial number of levels K in combination with the enterprise strategic goal S, specifically including: (i) Define the importance weights of the levels V k is the sum of the job value assessment scores corresponding to the kth salary level, β k It is a preset strategic influencing factor, which is predefined by the enterprise strategic priorities; (ii) Define the adaptation function: Where n represents the number of indicators that affect the strategic fit of the salary level; g i is the i-th indicator function to measure the strategic fitness of the salary level; α i is the strategic goal weight of the ith indicator, and all weights satisfy (iii) Constructing the maximization objective function And satisfy the constraint K min ≤K≤K max and |Median(L k+1 )-Median(L k )|≥δ min , by maximizing the objective function to measure the fitness of the salary level number K in the strategic goal; where δ min Indicates the minimum inter-level difference; (iv) Output the optimal number of levels K and the corresponding salary range 7. The broadband salary optimization method according to claim 6, characterized in that: Indicators that affect the strategic fit of salary levels include but are not limited to inter-level fairness indicators and high-value job competitiveness indicators.

8. The broadband salary optimization method according to claim 6, characterized in that: The specific calculation formula for the number of salary grades N is as follows: In the above formula, Bandwidth k represents the salary bandwidth of the kth level, r k is the salary increase ratio of level k, which is calculated as follows: r k ＝r h ×e -λk Among them, r h is the initial salary increase, which is determined by the corporate strategy, λ is the coefficient that controls the rate of decline, which is determined by the corporate strategy, and k is the salary level.

9. The broadband salary optimization method according to claim 8, characterized in that: The pay bandwidth k The calculation formula is as follows: S min,k =V min,k ×α S max,k =V max,k ×β Among them, S max,k represents the upper limit of the k-th level bandwidth, S min,k represents the lower limit of the k-th level bandwidth, V min and V max They represent the minimum and maximum values ​​of the job value scores respectively, and α and β are adjustment coefficients determined according to the company's salary strategy.

10. A broadband salary optimization system, characterized in that: include: Data collection module, used to obtain job value assessment scores and market salary data in real time; Preliminary salary range The partitioning module is used to dynamically adjust the quantile Q according to the distribution changes of the job value assessment score dataset V(t) p (t), calculate the adjusted quantile And divide the initial salary level range The initial level number K optimization module is used to optimize the initial level number K based on the strategic fitness function C(K) and combined with the enterprise strategic goal S to generate a set of candidate level numbers; The salary grade calculation optimization module is used to calculate and optimize the salary grade based on the dynamic increase method.

Images