CN113269514A - Enterprise health degree measuring method, device and system - Google Patents
Enterprise health degree measuring method, device and system Download PDFInfo
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Abstract
The method is used for measuring the health degree of the enterprise based on a three-order data system. The third-order data system is a data system formed by combining fifty-seven dimensions of data selected from three aspects of a general layer, an organizational layer and an individual layer in enterprise human resource data. The method is a method for measuring the health degree of the enterprise according to any human resource data in the third-order data system of the enterprise. The establishment of a three-order data system realizes the selection and arrangement of the structured data, thereby realizing the data coverage on the human resources of enterprises in fixed layers, realizing the normalized arrangement and collection of the HR data of each enterprise, and when the human resource problem of the enterprises occurs, the data can be reflected on one or more human resources data in the three-order data system, thereby realizing the measurement of the health degree of the enterprises by comparing the human resources data with the evaluation value range.
Description
Technical Field
The application relates to the technical field of enterprise service based on big data, in particular to a method, a device and a system for measuring enterprise health degree.
Background
At present, with the continuous development of network technology and the establishment of an HR management system of an enterprise, the development of a value service platform of human resources of the enterprise is more and more concerned, such as an e-HR system. Besides online network management of enterprise data, such service platforms are also required to assist in monitoring the health status of the enterprise.
However, for different enterprises, the difference of enterprise data is large, and the unstructured data in the enterprise data is too much, so that the establishment of an evaluation system of the health state of the enterprise is influenced, and the contribution of a service platform to the development planning of the enterprise is reduced.
Based on the above description, there is a need in the related art to establish an enterprise health measurement system applicable to different enterprises.
Disclosure of Invention
The application aims to provide a method, a device and a system for measuring the enterprise health degree, so as to be suitable for measuring the enterprise health degree of different enterprises.
In a first aspect, the present application provides a method for measuring enterprise health, including:
acquiring enterprise data;
establishing a third-order data system formed by combining a plurality of human resource data according to enterprise data, wherein the third-order data system comprises:
first-order data including per-class per-capita income, per-capita profit, per-capita overtime, per-capita cost/income ratio, labor cost/expenditure ratio, absenteeism rate, manager/department absenteeism rate, periodic overtime cost, per-capita training cost, training efficiency, active leave-time rate, passive leave-time rate, talent loss rate, manager/department leave-time rate, spacious labor cost, personnel change cost, HR population percentage, HR cost percentage, and average lead-up period;
second-order data including an enrollment period, a recruitment period, direct recruitment cost, a recruitment channel, a first-year rate of leaving, a first-year turnover rate, a first-month turnover rate, recruitment satisfaction, candidate satisfaction, job applicant number, recruitment rate, total recruitment cost, offer acceptance rate, job vacancy rate, recruitment completion rate, recruitment conversion rate, channel recruitment cost, post adaptation cost and promotion rate of an organization hierarchy;
and third-order data comprising average age, average job age, retirement rate, average commute distance, employee employment degree, work satisfaction degree, rise range, comprehensive per-capita income, comprehensive per-capita profit and comprehensive per-capita cost of the general class;
acquiring intention analysis data and required enterprise data, and establishing an industry model according to the required enterprise data, wherein the intention analysis data is one or more of the human resource data;
calculating industry data matched with intention analysis data according to the industry model, and generating an evaluation value range according to the industry data;
and when the intention analysis data exceeds the evaluation value range, the enterprise health degree is low.
By adopting the technical scheme, the enterprise data corresponds to the enterprises one to one, namely each enterprise has own enterprise data, and the data has structured data and unstructured data. The establishment of a three-order data system realizes the selection and arrangement of the structured data, thereby realizing the data coverage on the human resources of enterprises in fixed layers, realizing the normalized arrangement and collection of the HR data of each enterprise, and when the human resource problem of the enterprises occurs, the data can be reflected on one or more human resources data in the three-order data system, thereby realizing the measurement of the health degree of the enterprises by comparing the human resources data with the evaluation value range.
In a preferred embodiment, calculating industry data from the industry model that matches an intent analysis data includes:
in historical data, selecting a peer enterprise by using the industry model;
acquiring intention analysis data of the peer enterprise;
and calculating an average value of the intention analysis data of the same-row enterprises as the industry data.
In a preferred embodiment, calculating industry data from the industry model that matches an intent analysis data further comprises:
and acquiring personalized parameters, and selecting the enterprises in the same row by using the industry model and the personalized parameters in historical data.
In a preferred embodiment, the demand enterprise data includes a domain, a location, and a size automatically extracted according to the demand enterprise information.
In a preferred embodiment, the demand enterprise data includes at least one predetermined model parameter, and the industry model is established according to the model parameter.
In a second aspect, the present application further provides an enterprise health measuring device, including:
the first data acquisition module is used for acquiring enterprise data;
the data processing module is used for establishing a third-order data system formed by combining a plurality of human resource data according to enterprise data, and the third-order data system comprises:
first-order data including per-class per-capita income, per-capita profit, per-capita overtime, per-capita cost/income ratio, labor cost/expenditure ratio, absenteeism rate, manager/department absenteeism rate, periodic overtime cost, per-capita training cost, training efficiency, active leave-time rate, passive leave-time rate, talent loss rate, manager/department leave-time rate, spacious labor cost, personnel change cost, HR population percentage, HR cost percentage, and average lead-up period;
second-order data including an enrollment period, a recruitment period, direct recruitment cost, a recruitment channel, a first-year rate of leaving, a first-year turnover rate, a first-month turnover rate, recruitment satisfaction, candidate satisfaction, job applicant number, recruitment rate, total recruitment cost, offer acceptance rate, job vacancy rate, recruitment completion rate, recruitment conversion rate, channel recruitment cost, post adaptation cost and promotion rate of an organization hierarchy;
and third-order data comprising average age, average job age, retirement rate, average commute distance, employee employment degree, work satisfaction degree, rise range, comprehensive per-capita income, comprehensive per-capita profit and comprehensive per-capita cost of the general class;
the second data acquisition module is used for acquiring intention analysis data and required enterprise data, wherein the intention analysis data is one or more of the human resource data;
the model building module is used for building an industry model according to the required enterprise data,
the data calculation module is used for calculating industry data matched with intention analysis data according to the industry model and generating an evaluation value range according to the industry data;
and the health degree judging module outputs a low judgment result when the intention analysis data exceeds the evaluation value range.
In a preferred embodiment, the data calculation module comprises:
the data selection unit is used for selecting the enterprises in the same row by using the industry model in historical data;
the data acquisition unit is used for acquiring intention analysis data of the same-row enterprise;
and the data calculation unit is used for calculating the average value of the intention analysis data of the same-row enterprise as the industry data.
In a third aspect, the present application further provides an enterprise health measuring system, where the system includes:
one or more memories for storing instructions;
one or more processors configured to receive and execute the instructions to cause performance of the method recited in any of claims 1-5.
In summary, the present application at least includes the following beneficial technical effects:
the establishment of a three-order data system realizes the selection and the arrangement of the structured data, thereby realizing the data coverage on the human resources of enterprises in fixed layers and providing a data basis for the health measurement of the enterprises.
Drawings
Fig. 1 is a third-order data architecture topology diagram of an embodiment of the present application.
Fig. 2 is a schematic flow chart of an enterprise health degree measurement method according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
Referring to fig. 1, an embodiment of the present application provides a method for measuring the health degree of an enterprise, where the method measures the health degree of the enterprise based on a third-order data system. The third-order data system is a data system formed by combining fifty-seven dimensions of data selected from three aspects of a general layer, an organizational layer and an individual layer in enterprise human resource data. The method is a method for measuring the health degree of the enterprise according to any human resource data in the third-order data system of the enterprise.
In order to express the method described in the present application more clearly, first, a composition of each human resource data in a third-order data system is introduced, where the human resource data is converted from enterprise data inside an enterprise, and specifically, the third-order data system includes:
first order data, including personal hierarchy
Per-capita income, total payroll income/total number of employees;
the total income of the full-time staff, the total income of salary/the total number of the full-time staff;
per-capita profits, total profit/total number of employees;
the total profits of the full-time staff, total profit amount/total number of the full-time staff;
the man-average overtime length represents the ratio of overtime working hours to total working hours, the total working hours represent the sum of the overtime working hours and contract working hours, and the contract working hours represent the working hours specified by the work contract between the staff and the enterprise;
per-person cost, total labor cost/total number of employees;
total staff per capita cost, total labor cost/total staff count;
cost of labor/revenue ratio, total cost of labor/organizational revenue;
labor cost/expenditure ratio, total labor cost/total organization expenditure;
absence rate, absence days/total number of workdays;
manager/department absent rate, number of absent days per unit/total number of working days per unit;
periodic overtime cost, overtime payroll/total payroll for each period;
per-person training costs, training costs/total costs;
training efficiency, training cost/training effect of each employee;
active job leaving rate, voluntary number of persons leaving/total number of employees at work;
passive job leaving rate, number of involuntary persons leaving/total number of employees at work;
talent loss rate, number of people with high potential/total number of employees in work;
the loss rate, the number of people leaving/the total number of employees at work;
the manager/department job leaving rate is based on the range covered by one manager (or one department), and the job leaving rate of the unit in a certain time period;
the labor cost and the total expense of absenteeism;
personnel change cost, total turnaround cost;
HR, the number of people, the number of full-time employees engaged in human resource work/the total number of full-time employees;
HR cost ratio, total cost of human resources/total cost of full-time employees;
and average lead period, average time before lead;
second order data, including organizational hierarchy
The period of entry, the number of days from the release of the position vacancy to the engagement of the candidate;
a recruitment period, the number of days from proximity to the candidate being enrolled;
direct recruitment cost, total recruitment cost/number of new employees;
the recruitment channel is used for attracting the number of recruitment channels of talents;
the first-year departure rate, which is the number of employees leaving the organization within 1 year;
first year turnover rate, number of employees who leave the job in a year/total number of new employees;
first month turnover rate, number of employees who left the organization/total number of new employees within 1 month;
recruitment satisfaction, number of employees with good performance/total number of employees;
candidate satisfaction, number of employees/total number of employees who are satisfied with the new job;
job applicant number, total applicant/number of job openings;
engaging rate, enrollment candidates/total number of candidates;
total recruitment cost, (internal total + external total)/total number of hired people;
offer acceptance rate, the number of applicants who accept/are provided with job opportunities;
job vacancy rate, total number of open jobs/total number of jobs in an organization;
recruitment completion rate, total number of people who complete the recruitment application/total number of people who begin the application;
recruitment conversion rate, the number of applicants successfully completing the corresponding stage/the total number of applicants entering the corresponding stage;
channel conversion rate, total number of resumes shown in the channel/number of applications in the channel, wherein the channel is a recruitment channel, such as BOSS, intelligent union and the like;
channel recruitment cost, advertisement expenditure per channel/number of successful applicants per platform;
the post fitting cost enables a person to reach the total cost related to the latest state;
promoting rate, promoting employee number/employee number;
and third order data, including generic hierarchy
Average age, age sum of all employees/number of employees;
average work age, service age of all employees/total number of people;
retirement rate, number of retired personnel/number of staff;
average commute distance, in units of average distance (kilometers) from home;
the employee's contribution degree, the number of employees/number of employees who meet the contribution standard;
work satisfaction, the number of people who are satisfied with the work/total number of people;
salary range, (current salary-salary of previous year)/salary of previous year.
Integrating per-capita income, total employee income/total employee number;
the comprehensive per-capita income of full-time employees, the total income of full-time employees/the total number of full-time employees;
per-capita comprehensive profit, total financial profit/total number of employees;
the whole staff has gross profit and the total profit of the whole staff;
comprehensive per-capita cost, financial staff cost/total number of staff;
the total cost of the full-time staff, the sum of the financial staff cost of the full-time staff/the total number of the full-time staff.
The method for measuring the enterprise health degree is described based on the third-order data system, and comprises the following steps:
and S101, acquiring enterprise data. The enterprise data represents enterprise internal data related to the enterprise, such as total labor cost, total number of employees, number of persons who leave, and the like.
And S102, establishing the third-order data system formed by combining the human resource data according to enterprise data.
And S103, acquiring intention analysis data and required enterprise data, and establishing an industry model according to the required enterprise data.
The requirement enterprises evaluate the self health degree through the enterprise health degree measuring method provided by the application.
The intention analysis data is one or more of the human resource data, such as the first year job leaving rate and the like; the required enterprise data is data of an enterprise tag representing the industry of the required enterprise, in a preferred example, enterprise information such as enterprise investigation and the like is automatically acquired from internet data, and then the field, position and scale of the enterprise are automatically extracted through the enterprise information to serve as the required enterprise data; in another example, the demand enterprise data is the field, the location, and the scale of the enterprise automatic upload, and in this embodiment, the acquisition source of the demand enterprise data is not limited uniquely, as long as the data capable of classifying the peer enterprises of the demand enterprise can be acquired.
After the required enterprise data is obtained, an industry model is established according to the required enterprise data, such as the field, the position and the scale, namely, the same-row enterprises with the same field, position and scale as the required enterprise data are obtained through screening.
In a preferred example, the demand enterprise data includes at least one preset model parameter, the model parameter is data input by the demand enterprise in advance, and the model parameter is human resource data in the third-order data system.
In the process of establishing an industry model, the same-row enterprises are selected as enterprise selection rules according to the required enterprise data such as the field, the position and the scale, wherein the same-row enterprises can be all the same-row enterprises stored in a historical database, and can also be part of the same-row enterprises selected after the same-row enterprises are sorted according to the quality of model parameters. In one embodiment, the number of the model parameters is multiple, and then threshold selection is performed on the enterprises in the same row according to a preset threshold of each model parameter, so that the final enterprises in the same row are selected to form the industry model. In a preferred example, the historical data includes human data and enterprise data involved in a third-order data system of the demand enterprise and the peer enterprise.
And step S104, calculating industry data matched with intention analysis data according to the industry model, and generating an evaluation value range according to the industry data. Specifically, firstly, in historical data, a peer enterprise is selected by utilizing the industry model; acquiring intention analysis data of the peer enterprise; and calculating an average value of the intention analysis data of the same-row enterprises as the industry data. If the intention analysis data is the first-year rate of separation, calculating industry data representing the average level of the first-year rate of separation of the same industry by using the first-year rate of separation in a three-order data system of the same enterprise contained in an industry model, and broadening the fluctuation range of the value of the industry data to form an evaluation value range, wherein if the first-year rate of separation of the industry data is 11%, the evaluation value range is formed according to the value of the industry data, and the first-year rate of separation is less than or equal to 15%. Specifically, when the intention analysis data represents different human resource data, the calculation processes for widening the evaluation value range according to the value of the industry data are different, and the calculation processes can be determined by a demand enterprise or a worker according to the condition of the demand enterprise, which is not described herein again.
In a preferred example, one or more personalized parameters may also be introduced prior to calculating the industry data. The personalized parameters are screening conditions set according to personalized characteristics of the enterprise per se, such as preference, behavior, relationship and the like. For example, if the obtained target enterprise includes a target enterprise with a poor relationship with the required enterprise, the enterprise may be deleted from the target enterprise; if the demand enterprise is in the preparation process of listing, the personalized feature is the company of listing, and the companies which are not the company of listing in the target enterprise are deleted; for example, if a demand enterprise wants to use an optimization scheme of a target enterprise with a smaller average employee age, the target enterprise is screened according to a preset value of the average employee age of the enterprise.
And screening the same-row enterprises contained in the industry model through personalized parameters before calculating the industry data, thereby reducing the number of the same-row enterprises in the industry data calculating process.
And S105, judging whether the intention analysis data exceeds the evaluation value range, if so, outputting a judgment result that the enterprise health degree is low, otherwise, outputting a judgment result that the enterprise health degree is good.
According to the above contents, the three-order data system is established, the selection and arrangement of the structured data are realized, so that the data coverage on the human resources of enterprises is realized on a plurality of fixed layers, the normalized arrangement and collection of the HR data of each enterprise are realized, when the human resource problem of the enterprise occurs, the data can be reflected on one or more human resources in the three-order data system, and the measurement of the health degree of the enterprise is realized by comparing the human resources data with the evaluation value range.
In another embodiment, the present application further provides an enterprise health measuring device, including:
the first data acquisition module is used for acquiring enterprise data;
the data processing module is used for establishing a three-order data system formed by combining a plurality of human resource data according to the enterprise data;
the second data acquisition module is used for acquiring intention analysis data and required enterprise data, wherein the intention analysis data is one or more of the human resource data;
the model building module is used for building an industry model according to the required enterprise data,
the data calculation module is used for calculating industry data matched with intention analysis data according to the industry model and generating an evaluation value range according to the industry data;
and the health degree judging module is used for outputting a judgment result to be low when the intention analysis data exceeds the evaluation value range.
The data calculation module includes:
the data selection unit is used for selecting the enterprises in the same row by using the industry model in historical data;
the data acquisition unit is used for acquiring intention analysis data of the same-row enterprise;
and the data calculation unit is used for calculating the average value of the intention analysis data of the same-row enterprise as the industry data.
Various changes and specific examples in the method are also applicable to each module in the enterprise health degree measuring device of the embodiment, and through the detailed description of the enterprise health degree measuring method, those skilled in the art can clearly know the implementation method of the enterprise health degree measuring device in the embodiment, and for the sake of brevity of the description, detailed description is not repeated here.
In order to better execute the program of the method, the embodiment of the invention provides an enterprise health measuring system, which comprises at least one memory and at least one processor.
Wherein the memory is operable to store an instruction, a program, code, a set of codes, or a set of instructions. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (e.g., building an industry model according to the required enterprise data, etc.), and instructions for implementing the enterprise health measure method provided by the above method, etc.; the storage data area can store data and the like involved in the method for improving the energy efficiency of the enterprise through big data.
A processor may include one or more processing cores. The processor executes or executes the instructions, programs, code sets, or instruction sets stored in the memory, calls data stored in the memory, performs various functions of the present invention, and processes the data. The processor may be at least one of an application specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It will be appreciated that the electronic devices used to implement the processor functions described above may be other devices, and embodiments of the present invention are not limited in particular.
An embodiment of the present invention further provides a computer-readable storage medium, including: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk. The computer readable storage medium stores a computer program that can be loaded by a processor and execute the above-mentioned method for measuring the health of an enterprise.
The present invention is not limited to the specific embodiments, but can be modified as required by those skilled in the art after reading the present specification without any inventive contribution to the present invention, and all of the modifications are protected by patent laws within the scope of the claims.
Claims (8)
1. A method for measuring the health degree of an enterprise is characterized by comprising the following steps:
acquiring enterprise data;
establishing a third-order data system formed by combining a plurality of human resource data according to enterprise data, wherein the third-order data system comprises:
first-order data including per-class per-capita income, per-capita profit, per-capita overtime, per-capita cost/income ratio, labor cost/expenditure ratio, absenteeism rate, manager/department absenteeism rate, periodic overtime cost, per-capita training cost, training efficiency, active leave-time rate, passive leave-time rate, talent loss rate, manager/department leave-time rate, spacious labor cost, personnel change cost, HR population percentage, HR cost percentage, and average lead-up period;
second-order data including an enrollment period, a recruitment period, direct recruitment cost, a recruitment channel, a first-year rate of leaving, a first-year turnover rate, a first-month turnover rate, recruitment satisfaction, candidate satisfaction, job applicant number, recruitment rate, total recruitment cost, offer acceptance rate, job vacancy rate, recruitment completion rate, recruitment conversion rate, channel recruitment cost, post adaptation cost and promotion rate of an organization hierarchy;
and third-order data comprising average age, average job age, retirement rate, average commute distance, employee employment degree, work satisfaction degree, rise range, comprehensive per-capita income, comprehensive per-capita profit and comprehensive per-capita cost of the general class;
acquiring intention analysis data and required enterprise data, and establishing an industry model according to the required enterprise data, wherein the intention analysis data is one or more of the human resource data;
calculating industry data matched with intention analysis data according to the industry model, and generating an evaluation value range according to the industry data;
and when the intention analysis data exceeds the evaluation value range, the enterprise health degree is low.
2. The method of claim 1, wherein calculating industry data matching an intent analysis data based on the industry model comprises:
in historical data, selecting a peer enterprise by using the industry model;
acquiring intention analysis data of the peer enterprise;
and calculating an average value of the intention analysis data of the same-row enterprises as the industry data.
3. The method of claim 2, wherein calculating industry data matching an intent analysis data based on the industry model further comprises:
and acquiring personalized parameters, and selecting the enterprises in the same row by using the industry model and the personalized parameters in historical data.
4. The method as claimed in claim 1, wherein the required enterprise data includes a domain, a location, and a size that are automatically extracted according to the required enterprise information.
5. The method as claimed in claim 1, wherein the required enterprise data includes at least one preset model parameter, and the industry model is established according to the model parameter.
6. An enterprise health measuring device, comprising:
the first data acquisition module is used for acquiring enterprise data;
the data processing module is used for establishing a third-order data system formed by combining a plurality of human resource data according to enterprise data, and the third-order data system comprises:
first-order data including per-class per-capita income, per-capita profit, per-capita overtime, per-capita cost/income ratio, labor cost/expenditure ratio, absenteeism rate, manager/department absenteeism rate, periodic overtime cost, per-capita training cost, training efficiency, active leave-time rate, passive leave-time rate, talent loss rate, manager/department leave-time rate, spacious labor cost, personnel change cost, HR population percentage, HR cost percentage, and average lead-up period;
second-order data including an enrollment period, a recruitment period, direct recruitment cost, a recruitment channel, a first-year rate of leaving, a first-year turnover rate, a first-month turnover rate, recruitment satisfaction, candidate satisfaction, job applicant number, recruitment rate, total recruitment cost, offer acceptance rate, job vacancy rate, recruitment completion rate, recruitment conversion rate, channel recruitment cost, post adaptation cost and promotion rate of an organization hierarchy;
and third-order data comprising average age, average job age, retirement rate, average commute distance, employee employment degree, work satisfaction degree, rise range, comprehensive per-capita income, comprehensive per-capita profit and comprehensive per-capita cost of the general class;
the second data acquisition module is used for acquiring intention analysis data and required enterprise data, wherein the intention analysis data is one or more of the human resource data;
the model building module is used for building an industry model according to the required enterprise data,
the data calculation module is used for calculating industry data matched with intention analysis data according to the industry model and generating an evaluation value range according to the industry data;
and the health degree judging module outputs a low judgment result when the intention analysis data exceeds the evaluation value range.
7. The device of claim 6, wherein the data calculation module comprises:
the data selection unit is used for selecting the enterprises in the same row by using the industry model in historical data;
the data acquisition unit is used for acquiring intention analysis data of the same-row enterprise;
and the data calculation unit is used for calculating the average value of the intention analysis data of the same-row enterprise as the industry data.
8. An enterprise health measurement system, comprising:
one or more memories for storing instructions;
one or more processors configured to receive and execute the instructions to cause performance of the method recited in any of claims 1-5.
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