Method and apparatus for predicting age structure of a company

Abstract

This invention provides a method and apparatus for predicting a company's age structure. The method includes: determining the company's age structure based on its talent structure; identifying multiple influencing factors on the age structure based on the company's current employee roster, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors; predicting the future growth rate of these multiple influencing factors using exponential smoothing based on the company's current employee roster; predicting the company's future talent structure and future age trend based on the future growth rate of these multiple influencing factors; and determining whether to implement early warning operations based on the company's future talent structure and future age trend. This method, by predicting the company's future talent structure and future age trend through exponential smoothing, can provide human resource managers with human resource planning clues, ensuring the implementation of insurance companies' strategies for rejuvenation.

Description

Technical Field

[0001] This invention relates to the field of exponential smoothing technology, and in particular to a method and apparatus for predicting the age structure of a company. Background Technology

[0002] In recent years, in order to firmly establish the strategic position of talent in leading development, various enterprises and institutions have introduced a series of talent policies and measures, with the aim of promoting the marketization, specialization and rejuvenation of talent, thus creating a new situation for enterprise talent development.

[0003] However, traditional statistical analysis of talent age in insurance companies cannot assess the talent structure and age trends at certain job levels. This is because: firstly, the data sample window is short, typically only providing 3 or 5 years of talent structure data, thus obscuring the trends of aging or rejuvenation; secondly, the age structure of certain job levels fluctuates significantly over 3 or 5 years, leading to volatility in age trends, which hinders HR assessment of these trends. For example, while the average age of headquarters employees increased from 2017 to 2022, the trend reversed after an initial increase in the last 3 years (2020-2022), making it impossible to describe the age trend at this level based on the current situation. Therefore, exponential smoothing methods are needed to predict talent structure and age trends over the next 10 years; providing early warning and offering a basis and clues for HR planning and mid-term risk control in determining talent rejuvenation strategies. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method and apparatus for predicting the age structure of a company, so as to predict the future talent structure and future age trend of the company through exponential smoothing, provide human resource managers with human resource planning clues, and ensure the implementation of the insurance company's youth development strategy.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting the age structure of a company. The method includes: determining the company's age structure based on its talent structure; determining multiple influencing factors of the age structure based on the company's roster of employees, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors; predicting the future growth rate of the multiple influencing factors based on the company's roster of employees using exponential smoothing; predicting the company's future talent structure and future age trend based on the future growth rate of the multiple influencing factors; and determining whether to conduct an early warning operation based on the company's future talent structure and future age trend.

[0006] In an optional embodiment of this application, the step of determining the company's age structure based on the company's talent structure includes: determining a baseline for the company's age structure based on the company's talent structure; and determining the company's age structure based on the baseline and preset age groups.

[0007] In optional embodiments of this application, the factors influencing the age structure include: joining the company, leaving the company, joining through job transfer, leaving through job transfer, being promoted internally, being promoted internally, being demoted internally, being demoted internally, natural age increase, and natural age decrease.

[0008] In an optional embodiment of this application, the step of predicting the future growth rate of multiple influencing factors based on the company's employee roster using exponential smoothing includes: determining the historical growth rate of multiple influencing factors based on the company's employee roster; and predicting the future growth rate of multiple influencing factors based on the historical growth rate of multiple influencing factors using exponential smoothing.

[0009] In an optional embodiment of this application, the step of predicting the future growth rate of multiple influencing factors based on the historical growth rate of multiple influencing factors by exponential smoothing includes: predicting the future growth rate of multiple influencing factors by the following formula: x=a×y+(1-a)×z; where x is the future growth rate, y is the actual value of the previous period determined based on the historical growth rate, z is the predicted value of the previous period determined based on the historical growth rate, and a is a preset smoothing coefficient.

[0010] In optional embodiments of this application, the above method further includes: performing trial calculations on multiple smoothing coefficients at each level and age group of the age structure, comparing the variance of the actual value and the predicted value of each trial calculation, and using the smoothing coefficient with the smallest variance as the smoothing coefficient of the age structure at that level and age group.

[0011] In an optional embodiment of this application, the steps of predicting the company's future talent structure and future age trend based on the future growth rate of multiple influencing factors include: predicting the company's future number of employees based on the future growth rate of multiple influencing factors and the company's current number of employees; determining the company's future talent structure based on the company's future number of employees; and determining the company's future age trend based on the company's future talent structure.

[0012] In an optional embodiment of this application, the step of determining the company's future age trend based on the company's future talent structure includes: determining the proportion of talent in each age group of the company based on the company's future talent structure; and performing a weighted calculation based on the preset weights of each age group of the company and the proportion of talent in each age group of the company to obtain the company's future age trend.

[0013] In an optional embodiment of this application, the step of determining whether to perform an early warning operation based on the company's future talent structure and future age trend includes: determining whether the company's future talent structure and future age trend meet the preset youth target; if the youth target is not met, performing an early warning operation.

[0014] Secondly, embodiments of the present invention also provide a device for predicting the age structure of a company. The device includes: an age structure determination module for determining the company's age structure based on its talent structure; an influencing factor determination module for determining multiple influencing factors of the age structure based on the company's roster of employees, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors; an influencing factor prediction module for predicting the future growth rate of multiple influencing factors based on the company's roster of employees using exponential smoothing; and an age structure prediction module for predicting the company's future talent structure and future age trend based on the future growth rate of multiple influencing factors, and determining whether to perform an early warning operation based on the company's future talent structure and future age trend.

[0015] The embodiments of the present invention bring the following beneficial effects: This invention provides a method and apparatus for predicting a company's age structure. The method determines the company's age structure based on its talent structure; it identifies multiple influencing factors on the age structure based on the company's current employee roster, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors; it predicts the future growth rate of these multiple influencing factors using exponential smoothing; it predicts the company's future talent structure and future age trend based on the future growth rate of these factors; and it determines whether to implement early warning operations based on the company's future talent structure and future age trend. This method, by predicting the company's future talent structure and future age trend through exponential smoothing, can provide human resource managers with human resource planning clues, ensuring the implementation of the insurance company's youth-oriented development strategy.

[0016] Other features and advantages of this disclosure will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0017] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for predicting the age structure of a company, as provided in an embodiment of the present invention; Figure 2 A schematic diagram illustrating a method for predicting the age structure of a company, provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a company age structure prediction device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Currently, existing methods for predicting company age structure include the full-period average method and the moving average method. For example, as shown in Table 1, the full-period average method can predict the age structure and age trend of the next 10 years using the average proportion over the six years from 2017 to 2022; the moving average method can predict the age structure and age trend of the next 10 years using the average proportion over the previous three years.

[0022] Table 1

[0023] However, the aforementioned full-period average method and moving average method have drawbacks such as forecast lag and narrow applicability: 1. Forecast lag: When the actual trend reverses, the moving average data reacts relatively slowly and takes some time to reflect the change.

[0024] 2. Narrow applicability: The full-period averaging method uses all past data of a time series equally, failing to eliminate the influence of long-term trends. The full-period averaging method is suitable for relatively stable proportions with minimal fluctuations in forecasting.

[0025] Based on this, embodiments of the present invention provide a method and apparatus for predicting the age structure of a company. Specifically, it provides a linear programming-based method for predicting age structure, which can utilize existing data to mine key factors influencing age trends and implementation strategies, and can also use exponential smoothing to predict age-related talent structure trends. To meet the rejuvenation development strategy of insurance companies, embodiments of the present invention can use exponential smoothing to fit the talent structure at each level to determine the proportions, thereby predicting the talent structure trend for the next 10 years. This allows human resource planning to identify aging job levels early, take necessary strategies, and then use this algorithm to observe whether the talent structure trend is becoming younger.

[0026] To facilitate understanding of this embodiment, a method for predicting the age structure of a company disclosed in this embodiment will first be described in detail.

[0027] Example 1: This invention provides a method for predicting the age structure of a company. Based on the above description, see [link to relevant documentation]. Figure 1 The flowchart shown illustrates a method for predicting the age structure of a company. This method includes the following steps: Step S102: Determine the company's age structure based on the company's talent structure.

[0028] See Figure 2 The diagram illustrates a method for predicting the age structure of a company. This embodiment can define the age structure.

[0029] To calculate the age structure, it is necessary to calculate the proportion of employees in the roster at each talent level. Therefore, the age structure must be defined first.

[0030] In some embodiments, a baseline for the company's age structure can be determined based on the company's talent structure; and the company's age structure can be determined based on the baseline and preset age groups.

[0031] In this embodiment, the different recruitment strategies of insurance companies lead to different age structures for different headquarters and branches, job levels, etc. For example, the headquarters recruits fresh graduates with a master's degree as the starting point, so considering the younger age group, 26 years old and below can be used as the benchmark; while the branches mostly recruit undergraduates as the starting point, so 23 years old and below can be used as the benchmark.

[0032] Therefore, the age structure can be defined by taking into account the existing talent structure of the head office and branches, as shown in Table 2.

[0033] Table 2

[0034] Step S104: Based on the company's roster of employees, and in combination with human resource planning intervention factors, company personnel transfer factors, and natural age increase factors, determine multiple influencing factors of age structure.

[0035] like Figure 2 As shown, this embodiment can uncover influencing factors.

[0036] This embodiment can calculate the growth rate from 2017 to 2022 based on the proportion of employees in the roster according to different age structures, and use the exponential smoothing formula to predict the personnel structure ratio for the next 10 years. However, directly predicting the number of employees in the roster would result in inaccurate data. The reason is: First, it ignores the impact of human resource planning intervention factors on the number of employees on the roster, such as onboarding and offboarding.

[0037] Secondly, it overlooks the impact of company personnel changes, such as promotions and demotions.

[0038] Third, it ignores the impact of natural aging. For example, if the employee was 26 years old in 2017 and aged one year in 2018, the employee's age structure changed from under 26 to 27-30 years old.

[0039] Therefore, this embodiment can determine the influencing factors affecting the age structure of the working population based on the above factors.

[0040] In some embodiments, factors influencing age structure include: onboarding, offboarding, transfers, transfers, internal promotions, internal demotions, internal demotions, natural aging, and natural age decline.

[0041] The factors influencing the age structure in this embodiment may include: (1) Onboarding: Onboarding situation under this job level and age level; for example, if someone is a deputy head of a secondary organization and is 27-30 years old when they are onboarding, then the number of onboarding in the 27-30 age group will increase by 1; this indicator can be used to implement strategies for human resource planning.

[0042] (2) Resignation: Resignation situation under this position level and age level; for example, when someone leaves, he is a deputy of a secondary institution and his age is 27-30 years old. Then, the number of resignations in the 27-30 age group increases by 1. Human resource planning can use this indicator to implement strategies.

[0043] (3) Transfer entry: When personnel are transferred from position level A to position level B, it is considered a transfer entry for position level B. For example, if someone is transferred from a deputy position in a third-level organization to a deputy position in a second-level organization and is 27-30 years old, this person is considered a transfer entry for the 27-30 age group of deputy positions in a second-level organization. This is used as an indicator for calculating the on-the-job factor.

[0044] (4) Transfer out: When personnel are transferred from position level A to position level B, it is considered a transfer out for position level A; for example, if someone is transferred from a deputy position in a third-level organization to a deputy position in a second-level organization and is 27-30 years old, this person is considered a transfer out for a deputy position in a third-level organization that is 27-30 years old; it is used as an indicator for calculating the on-the-job factor.

[0045] (5) Internal promotion: When a person is promoted from position level A to position level B due to personnel promotion, it is considered an internal promotion for position level B; for example, if a person is promoted from a headquarters employee to a head of a headquarters department and is 27-30 years old, this person is considered an internal promotion for a head of a headquarters department who is 27-30 years old; this is used as an indicator for calculating the on-the-job factor.

[0046] (6) Internal promotion: When a person is promoted from position level A to position level B due to personnel promotion, it is considered an internal promotion for position level A; for example, if a person is promoted from a headquarters employee to a head of a headquarters department, and is 27-30 years old, this person is considered an internal promotion for headquarters employees aged 27-30; this is used as an indicator for calculating the on-the-job factor.

[0047] (7) Internal demotion: When a person is demoted from position level A to position level B due to personnel promotion, it is considered an internal demotion for position level B. For example, if someone is demoted from a head of headquarters to a headquarters employee and is 27-30 years old, this person is considered an internal demotion for headquarters employees who are 27-30 years old. This is used as an indicator for calculating the on-the-job factor.

[0048] (8) Internal demotion: When a person is demoted from position level A to position level B due to a personnel promotion, it is considered an internal demotion for position level A. For example, if a person is demoted from a head of headquarters to a head of headquarters employee and is 27-30 years old, this person is considered an internal demotion for the head of headquarters who is 27-30 years old. This is used as an indicator for calculating the on-the-job factor.

[0049] (9) Natural age increase: Each year, the natural age increases to cross another age range; for example: a person is a headquarters employee, 26 years old in 2017, belonging to the age range of 26 years old and below, but in 2018, the natural age increase is 27 years old, belonging to the 27-30 age range. For headquarters employees in the 27-30 age range, this person's natural age increase is used as an indicator for calculating the on-the-job factor.

[0050] (10) Age dropout: If an employee naturally increases in age each year and crosses into another age range, then one employee in the original age group will drop out. For example, a headquarters employee was 26 years old in 2017 and belonged to the age range of 26 years old and below. However, in 2018, the employee naturally increased in age to 27 years old and belonged to the 27-30 age range. For headquarters employees aged 26 and below, this person is considered to have dropped out due to age dropout. This is used as an indicator for calculating the on-the-job factor.

[0051] Step S106: Predict the future growth rate of multiple influencing factors based on the company's employee roster using exponential smoothing.

[0052] like Figure 2 As shown, the growth rate can be calculated using the exponential smoothing method in this embodiment.

[0053] Exponential smoothing is a time series analysis and forecasting method. Its principle is that the exponentially smoothed value for any period is a weighted average of the actual observed value for that period and the exponentially smoothed value for the previous period. Exponential smoothing combines the advantages of both long-term averages and moving averages, assigning a gradually diminishing influence; that is, as the data moves further away, it is given weights that gradually converge to zero.

[0054] In some embodiments, the historical growth rates of multiple influencing factors can be determined based on the company’s roster of employees; and the future growth rates of multiple influencing factors can be predicted based on the historical growth rates of multiple influencing factors using an exponential smoothing method.

[0055] After defining the ten influencing factors, this embodiment requires calculating the growth rate of these ten factors as the historical growth rate to calculate the year-end employee roster. The growth rate calculation formula uses data from 2017 to 2022, with the previous year's employee roster as a constant denominator, to compare the growth rates of the ten factors. The calculation is as follows: (1) Enrollment rate (growth rate of enrollment): The number of new employees in this level and age group in the current year is calculated by dividing the number of employees in this level and age group in the previous year.

[0056] (2) Turnover rate (the rate of turnover): the number of people who leave this level and age group in the current year divided by the number of people who are employed in this level and age group in the previous year.

[0057] (3) Transfer rate (growth rate of transfers): the number of transfers to this level and age group in the current year divided by the number of employees in this level and age group in the previous year.

[0058] (4) Transfer rate (growth rate of transfers): the number of people transferred out of this level and age group in the current year divided by the number of people employed in this level and age group in the previous year.

[0059] (5) Internal promotion rate (internal promotion growth rate): the number of people promoted to or leaving this level and age group in the current year divided by the number of employees in the previous year.

[0060] (6) Internal promotion rate (internal promotion growth rate): the number of people promoted from this level and age group in the current year divided by the number of people employed in this level and age group in the previous year.

[0061] (7) Internal demotion rate (internal demotion growth rate): the number of people internally demoted in this level and age group in the current year divided by the number of people in this level and age group in the previous year.

[0062] (8) Internal demotion rate (growth rate of internal demotion): the number of internal demotions in this level and age group in the current year divided by the number of employees in this level and age group in the previous year.

[0063] (9) Natural growth rate of age (growth rate of natural age increase): the number of people in this level and age group who have grown naturally in the current year, divided by the number of people in this level and age group who were employed in the previous year.

[0064] (10) Age dropout rate (growth rate of age dropout): the number of people who drop out of this level and age group in the current year divided by the number of people in this level and age group in the previous year.

[0065] After calculating the historical growth rates of the ten major influencing factors from 2017 to 2022, this embodiment can use the exponential smoothing method to calculate the future growth rates of the ten major factors from 2023 to 2032, thereby predicting the roster of employees for the next 10 years.

[0066] In some embodiments, the future growth rate of multiple influencing factors can be predicted by the following formula: x = a × y + (1 - a) × z; where x is the future growth rate, y is the actual value of the previous period determined based on the historical growth rate, z is the predicted value of the previous period determined based on the historical growth rate, and a is a preset smoothing coefficient.

[0067] The smoothing coefficient 'a' ranges from [0,1]. A larger 'a' indicates a greater impact on historical data and a slower response to future trends. Conversely, a smaller 'a' indicates a smaller impact on historical data and a faster response to future trends.

[0068] In some embodiments, multiple smoothing coefficients can be trial-calculated at each level and age group of the age structure, and the variance of the actual value and the predicted value of each trial-calculation can be compared; the smoothing coefficient with the smallest variance can be used as the smoothing coefficient of the age structure at that level and age group.

[0069] In this embodiment, a brute-force calculation method can be used to perform trial calculations for each level and age group in the range [0,1] using the exponential smoothing formula (currently 10 calculations, namely 0, 0.1, 0.2...0.9, 1). Each trial calculation compares the variance of the actual value and the predicted value for 2017-2022, and takes the value with the smallest variance as the smoothing coefficient a for this level and age group.

[0070] This invention can use exponential smoothing to calculate the growth rate and then fit the percentage of employees using business rules. Compared to directly predicting the percentage of employees, the exponential smoothing method considers multiple factors and provides a more accurate prediction.

[0071] It should be noted that, in this embodiment, in addition to using the exponential smoothing method, the average method or the moving average method can also be used instead of the exponential smoothing method to calculate all growth rates in order to quickly predict the number of employees in the future.

[0072] Step S108: Based on the future growth rate of multiple influencing factors, predict the company's future talent structure and future age trend, and determine whether to take early warning action based on the company's future talent structure and future age trend.

[0073] like Figure 2 As shown, this embodiment can fit the talent structure.

[0074] In this embodiment, the goal of fitting the talent agency is to calculate the number of people at this level and in this age group over the next 10 years. It primarily uses the future growth rate of the ten key factors of talent planning to calculate the number of people for each of these ten factors in the coming year.

[0075] In some embodiments, the company’s future number of employees can be predicted based on the future growth rate of multiple influencing factors and the company’s current number of employees; the company’s future talent structure can be determined based on the company’s future number of employees; and the company’s future age trend can be determined based on the company’s future talent structure.

[0076] The formula for calculating the future talent structure is as follows: Forecast annual increase in number of employees = Forecast annual growth rate (i.e., future growth rate) × Number of employees in the year preceding the forecast. For example: Number of employees hired in 2023 = 2023 hiring rate (exponentially smoothed and already predicted) × Number of employees at the end of 2022.

[0077] This embodiment can calculate the number of employees for the predicted year based on the ten factors, and then calculate the roster of employees for the corresponding level and age group. The calculation formula is as follows: Number of employees for the predicted year (i.e., the number of employees in the future) = Number of employees on the roster at the end of the previous year (i.e., the number of employees) + Inflow for the predicted year - Outflow for the predicted year. Wherein, Inflow = Inflow due to job transfer + New hires + Internal promotions + Internal demotions + Natural age increase; Outflow = Outflow due to job transfer + Resignations + Internal promotions + Internal demotions + Natural age decrease.

[0078] For example, as shown in Table 3, the predicted talent structure for deputy positions in headquarters departments shows that the 46-50 age group, based on the current growth trend, will decrease to 19% in 10 years. Meanwhile, the 41-45 age group will increase to 42% in 10 years. This aligns with the strategic goal of rejuvenating the workforce; therefore, no intervention is necessary at this stage.

[0079] Table 3

[0080] like Figure 2 As shown, this embodiment can predict age trends.

[0081] In some embodiments, the proportion of talent in each age group of the company can be determined based on the company's future talent structure; the company's future age trend can be obtained by weighting the company's age groups based on the preset weights of each age group and the proportion of talent in each age group.

[0082] In this embodiment, age trends can be predicted based on talent structure using the following formulas. For example, the age trends of company and organizational leadership levels can be predicted. : ;in, The percentage of those aged 26 and under. The percentage of those aged 27-30 The percentage of those aged 27-30 The percentage of those aged 31-35 The percentage of those aged 36-40 The percentage of those aged 41-45 The percentage of those aged 46-50 The percentage of those aged 51-55 The percentage is between 56 and 60 years old.

[0083] Age trends of institutional employees : ;in, The percentage of those aged 23 and under. The percentage of those aged 24-30 The percentage of those aged 27-30 The percentage of those aged 31-35 The percentage of those aged 36-40 The percentage of those aged 41-45 The percentage of those aged 46-50 The percentage of those aged 51-55 The percentage is between 56 and 60 years old.

[0084] For example, the predicted age of employees in secondary institutions based on talent structure could be: 31.56 in 2024, 31.43 in 2025, 31.33 in 2026, 31.22 in 2027, 31.15 in 2028, 31.05 in 2029, 30.94 in 2030, 30.84 in 2031, 30.73 in 2032, and 30.62 in 2033.

[0085] In some embodiments, it can be determined whether the company's future talent structure and future age trends meet the preset youth development goals; if the youth development goals are not met, an early warning operation is performed.

[0086] In this embodiment, the future talent structure and future age trends predicted by the aforementioned steps can be used to determine whether the formula will meet the preset youth-oriented target in the future. If the formula meets the youth-oriented target in the future, no warning operation can be performed; if the formula does not meet the youth-oriented target in the future, a warning operation can be performed. This provides a basis and clues for human resource planning to determine the talent youth-oriented strategy, and can help human resources understand the current talent structure trend, locate the aging level, and quickly formulate age intervention strategies.

[0087] This invention provides a method for predicting a company's age structure. The method determines the company's age structure based on its talent structure; it identifies multiple influencing factors based on the company's current employee roster, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors; it predicts the future growth rate of these influencing factors using exponential smoothing based on the company's current employee roster; it then predicts the company's future talent structure and future age trend based on the future growth rate of these factors; and finally, it determines whether to implement early warning measures based on the company's future talent structure and future age trend. This method, by predicting the company's future talent structure and future age trend through exponential smoothing, can provide human resource managers with human resource planning clues, ensuring the implementation of insurance companies' strategies for rejuvenation.

[0088] The method provided in this invention can use exponential smoothing to calculate the growth rate of ten key characteristics, which helps to reduce the impact of noise and makes the prediction of the number of employees more accurate. It can also use the prediction of ten influencing factors to calculate the number of employees, helping human resources to identify the aging level and prepare for human resources to quickly formulate intervention strategies. At the same time, based on the intervention strategy (number of new hires and departures) provided by human resources, the growth rate can be calculated using exponential smoothing. After updating the growth rates of new hires and departures, the percentage of employees and average age in the roster after intervention can be predicted in the next 10 years, allowing human resources planning to judge whether the policy implementation is effective.

[0089] Example 2: Corresponding to the above method embodiments, this invention provides a device for predicting the age structure of a company. See [link to relevant documentation]. Figure 3 The diagram shows a structural schematic of a company's age structure prediction device. The company's age structure prediction device includes: Age structure determination module 31 is used to determine the company's age structure based on the company's talent structure. The influencing factor determination module 32 is used to determine multiple influencing factors of age structure based on the company's current employee roster, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors. The influencing factor prediction module 33 is used to predict the future growth rate of multiple influencing factors based on the company's current employee roster using an exponential smoothing method. The age structure prediction module 34 is used to predict the company's future talent structure and future age trend based on the future growth rate of multiple influencing factors, and to determine whether to take early warning action based on the company's future talent structure and future age trend.

[0090] This invention provides a device for predicting a company's age structure. It determines the company's age structure based on its talent structure; identifies multiple influencing factors based on the company's current employee roster, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors; predicts the future growth rate of these multiple influencing factors using exponential smoothing; predicts the company's future talent structure and future age trend based on the future growth rate of these factors; and determines whether to implement early warning operations based on the company's future talent structure and future age trend. This method, by predicting the company's future talent structure and future age trend through exponential smoothing, can provide human resource managers with human resource planning clues, ensuring the implementation of insurance companies' strategies for rejuvenation.

[0091] The aforementioned age structure determination module is used to determine the baseline of the company's age structure based on the company's talent structure; and to determine the company's age structure based on the baseline and preset age ranges.

[0092] The factors influencing the aforementioned age structure include: joining the company, leaving the company, joining through job transfers, leaving through job transfers, being promoted internally, being promoted internally, being demoted internally, being demoted internally, natural aging, and natural aging.

[0093] The aforementioned influencing factor prediction module is used to determine the historical growth rate of multiple influencing factors based on the company's current employee roster; and to predict the future growth rate of multiple influencing factors based on their historical growth rates using an exponential smoothing method.

[0094] The aforementioned influencing factor prediction module is used to predict the future growth rate of multiple influencing factors using the following formula: x = a × y + (1 - a) × z; where x is the future growth rate, y is the actual value of the previous period determined based on the historical growth rate, z is the predicted value of the previous period determined based on the historical growth rate, and a is a preset smoothing coefficient.

[0095] The aforementioned device's influencing factor prediction module is also used to perform trial calculations on multiple smoothing coefficients at various levels and age groups of the age structure, compare the variance of the actual value and the predicted value of each trial calculation, and use the smoothing coefficient with the smallest variance as the smoothing coefficient of the age structure at that level and age group.

[0096] The aforementioned age structure prediction module is used to predict the company's future number of employees based on the future growth rates of multiple influencing factors and the company's current number of employees; to determine the company's future talent structure based on the company's future number of employees; and to determine the company's future age trend based on the company's future talent structure.

[0097] The aforementioned age structure prediction module is used to determine the proportion of talent in each age group of the company based on the company's future talent structure; it performs a weighted calculation based on the preset weights of each age group and the proportion of talent in each age group to obtain the company's future age trend.

[0098] The aforementioned age structure prediction module is used to determine whether the company's future talent structure and future age trends meet the preset youth development goals; if the youth development goals are not met, an early warning operation is performed.

[0099] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the company age structure prediction device described above can be referred to the corresponding process in the embodiments of the aforementioned company age structure prediction method, and will not be repeated here.

[0100] Example 3: This invention also provides an electronic device for running the above-described method for predicting the age structure of companies; see [link to related documentation]. Figure 4 The diagram shows the structure of an electronic device, which includes a memory 100 and a processor 101. The memory 100 stores one or more computer instructions, which are executed by the processor 101 to implement the aforementioned method for predicting the company's age structure.

[0101] Furthermore, Figure 4 The electronic device shown also includes a bus 102 and a communication interface 103, with the processor 101, the communication interface 103 and the memory 100 connected via the bus 102.

[0102] The memory 100 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 103 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 102 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0103] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. Processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 100, and processor 101 reads information from memory 100 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0104] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the aforementioned method for predicting the company's age structure. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0105] The computer program product of the company age structure prediction method and apparatus provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and / or device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0108] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0110] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting the age structure of a company, characterized in that, The method includes: The company's age structure is determined based on its talent structure. Based on the company's current employee roster, and in conjunction with human resource planning intervention factors, company personnel transfer factors, and natural age increase factors, multiple influencing factors of the age structure are determined. Predict the future growth rates of several of the aforementioned influencing factors based on the company’s current employee roster using an exponential smoothing method; Based on the future growth rates of multiple influencing factors, the company's future talent structure and future age trends are predicted, and a decision is made on whether to issue an early warning based on the company's future talent structure and future age trends.

2. The method according to claim 1, characterized in that, The steps for determining the company's age structure based on its talent structure include: Determine the baseline for the company's age structure based on the company's talent structure; The company's age structure is determined based on the baseline and preset age ranges.

3. The method according to claim 1, characterized in that, The factors influencing the age structure include: joining the company, leaving the company, joining through job transfers, leaving through job transfers, being promoted internally, being promoted internally, being demoted internally, being demoted internally, natural age increase, and natural age decrease.

4. The method according to claim 1, characterized in that, The steps of predicting the future growth rate of multiple influencing factors based on the company's employee roster using exponential smoothing include: The historical growth rates of several of the aforementioned influencing factors were determined based on the company's current employee roster. The future growth rate of several influencing factors is predicted based on their historical growth rates using an exponential smoothing method.

5. The method according to claim 4, characterized in that, The step of predicting the future growth rate of multiple influencing factors based on their historical growth rates using exponential smoothing includes: The future growth rate of multiple influencing factors is predicted by the following formula: x = a × y + (1 - a) × z; where x is the future growth rate, y is the actual value of the previous period determined based on the historical growth rate, z is the predicted value of the previous period determined based on the historical growth rate, and a is a preset smoothing coefficient.

6. The method according to claim 5, characterized in that, The method further includes: Multiple smoothing coefficients are tested at each level and age group of the age structure, and the variance of the actual and predicted values ​​of each test is compared. The smoothing coefficient with the smallest variance is used as the smoothing coefficient for the age structure at that level and in that age group.

7. The method according to claim 1, characterized in that, The steps for predicting the company's future talent structure and future age trends based on the future growth rates of multiple influencing factors include: The company’s future number of employees is predicted based on the future growth rates of multiple influencing factors and the company’s current number of employees, and the company’s future talent structure is determined based on the company’s future number of employees. The company's future age trend is determined based on its future talent structure.

8. The method according to claim 7, characterized in that, The steps for determining the company's future age trend based on its future talent structure include: The proportion of talent in each age group in the company is determined based on the company's future talent structure. The company's future age trend is obtained by weighting the company's age groups based on preset weights and the proportion of talent in each age group.

9. The method according to claim 1, characterized in that, The steps for determining whether to implement early warning operations based on the company's future talent structure and future age trends include: Determine whether the company's preset youth development target is met based on its future talent structure and future age trends. If the stated youthfulness target is not met, an early warning action will be taken.

10. A device for predicting the age structure of a company, characterized in that, The device includes: An age structure determination module is used to determine the company's age structure based on the company's talent structure. The influencing factor determination module is used to determine multiple influencing factors of the age structure based on the company's current employee roster, combined with human resource planning intervention factors, company personnel transfer factors, and natural age growth factors. The influencing factor prediction module is used to predict the future growth rate of multiple influencing factors based on the company's employee roster using an exponential smoothing method. The age structure prediction module is used to predict the company's future talent structure and future age trend based on the future growth rates of multiple influencing factors, and to determine whether to issue an early warning based on the company's future talent structure and future age trend.

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