JP2023181537A - Information processing device and program - Google Patents

Abstract

To provide a technique capable of easily setting a numerical target for each selection step in human resource recruitment.SOLUTION: An information processing device 1 comprises a storage part 12 for storing result data including passage rates of a plurality of selection steps performed for recruiting human resources in association with job types hired, an input part 111 for receiving inputs of job types of recruitment objects, and a calculation part 112 for calculating each predicted passage rate in each selection step to be performed for the job types of the recruitment objects based on a relation between the inputted job types and the job types in association with the result data.SELECTED DRAWING: Figure 2

Description

The present invention relates to technology for supporting human resource recruitment.

Various proposals have been made to support human resource recruitment in companies by using IT technology. An example is the human resources recruitment support system described in Patent Document 1.

JP2019-185638A

In the human resource recruitment support system disclosed in Patent Document 1, by appropriately updating the recruitment conditions at the time of recruitment, it is possible to adjust the number of applicants and efficiently recruit the desired human resources.
However, in general, when hiring human resources, multiple selection processes (document screening, written examination, first interview, second interview, etc.) are carried out to narrow down applicants in stages. On the other hand, hiring companies have limited resources for hiring personnel (such as securing interviewers, document reviewers, etc.). In this way, in order to make effective use of limited resources on the hiring side, recruitment is conducted from the perspective of how to narrow down applicants through a series of screening steps, such as setting the number of successful applicants at each screening step. It is desired to optimize the entire plan.
Regarding this point, for example, the technology described in Patent Document 1 only has the perspective of adjusting the number of applicants, but does not have the perspective of optimizing the entire series of selection steps from recruitment to employment.

The present invention aims to support the formulation of a human resource recruitment plan that narrows down applicants in stages.

An information processing apparatus according to a first aspect of the present invention includes a storage means for storing performance data including passing rates of a plurality of selection steps carried out for recruiting human resources in association with the type of job employed; An input means that accepts input of the target job type, and a predicted pass rate in each screening step to be performed for the target job type based on the relationship between the input job type and the job type associated with the performance data. and calculation means for calculating the respective values.
According to this, the predicted passing rate for each selection step is calculated, so it is possible to grasp the numerical target for each selection step.

In a preferred embodiment, the input means further receives an input of a target value for at least one of the number of people to be finally hired, the number of applicants, and the number of people who reach the plurality of selection steps, and the input means is an estimate of the number of applicants, an estimate of the number of people who will finally be hired, and an estimate of the number of people who will reach each selection step, corresponding to the input target value, based on the calculated predicted pass rate. Calculate the value.
According to this, it is possible to grasp the number of people passing through each selection step.

In a preferred embodiment, the device further includes a display unit that displays the estimated number of people passing through each of the plurality of selection steps in an order corresponding to the scheduled implementation time of each of the plurality of selection steps.
According to this, numerical targets for each selection step can be visualized.

In a preferred embodiment, the input means includes at least a target value for the number of people to be finally hired, a target value for the number of applicants, a target value for the number of people who will reach each selection step, and a predicted passing rate for each selection step. Accepting a change in any one, the calculation means:
Based on the received changed values and the performance data, an estimated value of the number of applicants, an estimated value of the number of people who will be finally hired, and an estimated value of the number of people who will reach each selection step are recalculated.
According to this, it is possible to simulate the results of correcting the number of people and the passing rate for each selection step.

In a preferred embodiment, at least one of the following: a target number of people who will eventually be hired, a target number of applicants, a target number of people who will reach each selection step, and a predicted passing rate at each selection step. The calculation means further includes a setting section for setting conditions for
The predicted passing rate of each screening step related to the track record data is corrected according to a predetermined algorithm so as to satisfy the set conditions.
According to this, the predicted passage rate can be corrected to an appropriate value so as to reach the desired target value (number of people).

In a preferred embodiment, the performance data is further associated with cost information including information regarding the hiring side's human resources or expenses invested in recruitment activities in addition to the job type for which the recruitment target is applied, and the input means The calculation means further receives input of the cost information related to the recruitment target, and further calculates the calculation means based on the relationship between the cost information input by the input means and the cost information associated with the performance data. The predicted passage rate is calculated.
According to this, it is possible to calculate a predicted pass rate that matches the cost of recruitment activities.

In a preferred embodiment, the input means outputs warning information in accordance with a difference between the input target value and a corresponding value of the actual performance data.
According to this, if the input target value deviates from the actual data or if there is a possibility of incorrect input, it is possible to notify the user to that effect.

In a preferred embodiment, the performance data is further associated with implementation timing information regarding when the plurality of selection steps were implemented, and the input means inputs implementation timing information regarding the scheduled implementation time of the selection steps related to the recruitment target. Further, the calculation means calculates the predicted pass rate based on the relationship between the implementation timing information inputted by the input means and the implementation timing information associated with the performance data. .
According to this, it is possible to calculate an appropriate predicted pass rate depending on the timing of recruitment.
In another aspect, the present invention provides a step of storing in a computer performance data including passing rates of a plurality of selection steps carried out for recruiting human resources in association with the hired job type; a step of receiving an input, and a predicted pass rate in each screening step to be performed for the job type to be recruited based on the relationship between the input job type and the job type associated with the stored performance data. Provides a program for executing each calculation step.

FIG. 1 is a block diagram showing a hardware configuration of an information processing device according to an embodiment. FIG. 1 is a block diagram showing a functional configuration of an information processing device according to an embodiment. The figure which showed the example of a structure of the track record data table based on embodiment. FIG. 3 is a diagram showing a flowchart of processing in the information processing device according to the embodiment. The figure which showed the example of a display of the input screen based on embodiment. The figure which showed the display example of the display screen of the predicted passage rate and estimated number of people who passed according to an embodiment. The figure showing another example of a display of an input screen concerning an embodiment, and a display screen of a predicted passage rate and an estimated number of people passing. The figure showing another example of a display of an input screen concerning an embodiment, and a display screen of a predicted passage rate and an estimated number of people passing. The figure which showed the example of a display when a change input is performed on the display screen of the predicted passage rate and estimated number of people who passed according to an embodiment. FIG. 7 is a diagram illustrating another display example when a change input is made on the display screen of the predicted passage rate and estimated number of people passing according to the embodiment. FIG. 7 is a diagram illustrating another display example when a change input is made on the display screen of the predicted passage rate and estimated number of people passing according to the embodiment. FIG. 7 is a diagram illustrating another display example when a change input is made on the display screen of the predicted passage rate and estimated number of people passing according to the embodiment. FIG. 3 is a block diagram showing a functional configuration of an information processing device according to a modification. The figure which showed the example of a display of the input screen concerning a modification. The figure which showed the display example of the display screen of the predicted passage rate and estimated number of people who passed according to a modification. FIG. 7 is a diagram illustrating another display example of an input screen according to a modification. The figure which showed another display example of the display screen of the predicted passing rate and the estimated number of people passing according to a modification. The figure which showed the display example of the display screen of the performance data table, predicted passage rate, and estimated number of people who passed according to a modification. FIG. 7 is a diagram showing a warning display on an input screen according to a modification. The figure which showed the example of a structure of the track record data table based on a modification. The figure which showed the example of a display of the input screen concerning a modification. The figure which showed the display example of the display screen of the predicted passage rate and estimated number of people who passed according to a modification.

FIG. 1 is a block diagram showing the hardware configuration of an information processing device 1 according to an embodiment of the present invention. The information processing device 1 includes a processor 11, a storage section 12, an operation section 13, a display section 14,
The computer includes a communication section 15 and is configured such that these components can communicate with each other via a bus 16.

The processor 11 controls each part of the information processing device 1 by reading out and executing a computer program (hereinafter simply referred to as a program) stored in the storage unit 12 .
The processor 11 is, for example, a CPU (Central Processing Unit).

The storage unit 12 is a memory or a storage device that stores an operating system, various programs, data, etc. that are read into the processor 11. The storage unit 12 includes a main storage device and an auxiliary storage device. The main storage device includes, for example, RAM (Random Access Memory) and ROM (Read Only Memory). Auxiliary storage devices include solid state drives or hard disk drives.

The operation unit 13 is composed of a keyboard, a mouse, etc., and inputs various information according to the user's input operation. The display unit 14 is composed of a display device and displays characters and images. The communication unit 15 controls communication with other terminal devices (not shown) via the network.

FIG. 2 is a block diagram showing the functional configuration of the information processing device 1. As shown in FIG. In FIG. 2, the processor 11 of the information processing device 1 functions as an input unit 111, a calculation unit 112, and a display control unit 113 by reading and executing a program stored in the storage unit 12.

The storage unit 12 stores performance data including the passing rate of a plurality of selection steps carried out to hire human resources in association with the type of job for which the person was hired.

The input unit 111 receives input of the job type to be recruited through a user's operation using the operation unit 13 . Furthermore, the input unit 111 inputs a target value (a user input via the operation unit 13) for at least one of the number of people who will be finally hired for the job category to be recruited, the number of applicants, and the number of people who will reach the plurality of selection steps. It is also possible to accept the input (operation input by the user).

The calculation unit 112 calculates the recruitment target based on the relationship between the job type input from the operation unit 13 and accepted by the input unit 111 and the job type associated with the performance data stored in the storage unit 12. The predicted passing rate for each screening step performed for the job category is calculated.

Further, based on the calculated predicted pass rate, the calculation unit 112 calculates an estimated number of applicants, an estimated number of people who will be finally hired, and each selection step, corresponding to the target value input in the input unit 111. Calculate an estimate of the number of people who will reach .

The display control unit 113 causes the display unit 14 to display a screen for receiving input from the user via the input unit 111. Further, the display control unit 113 causes the display unit 14 to display various information, triggered by the calculation unit 112 calculating the predicted passing rate and estimated number of people in each selection step.

The input unit 111 may receive input of the job type to be recruited and the various target values described above from another terminal device connected via the network via the communication unit 15. In this case, the communication unit 15 receives data stored in the other terminal or data input by the user in the other terminal via the network.

FIG. 3 is a diagram showing an example of the configuration of a performance data table stored in the storage unit 12.
The performance data table is a collection of a plurality of data records whose data fields include job types, number of applicants, number of people who passed each screening step and their passing rate, number of people who joined the company, etc. in past recruitments.

Note that in this embodiment, the performance data stored in the performance data table is assumed to be actual data from past human resource recruitment carried out in the company, but there is no need to be limited to this. For example, performance data available in the market that has been subjected to statistical processing may be used. Also, when using your own company's performance data or other performance data, there is no need to use the actual numerical value of the data showing each number of people in the performance data, but it is estimated using AI (artificial intelligence) etc. You may use the value given.

Each data record has the following data fields: "Identification number" (in Figure 3,
No. 0101 etc.) is a number assigned to identify each recruitment. The "implementation time" is the time when recruitment was implemented, and is indicated by the start time and end time. “Recruitment type” indicates the type of job to be recruited. “Recruitment job type (classification)” indicates the classification name of the recruitment job type. "Applications" indicates the number of applicants who were selected. "Document screening" indicates the number of applicants who passed the document screening and the passing rate (ratio of applicants who passed the document screening).

"First selection", "Second selection", and "Final selection" are for those who pass the document screening, for example.
It shows the number of people who have passed the interview test, written test, etc., which are conducted in stages, and the passing rate. ``First screening'' refers to the number of people who passed the first screening and the passing rate (ratio of people who passed the first screening among those who passed the document screening), and ``Second screening'' refers to the number of people who passed the second screening. The number of people and the passing rate (the percentage of people who passed the second screening among those who passed the first screening), and the "final selection" are the number of people who passed the final screening and the passing rate (the percentage of those who passed the second screening). (percentage of people who passed the final selection).

"Notice of Offer" indicates the number of people who have been notified of a job offer and the passing rate (ratio of people who have been notified of a job offer among those who passed the final screening). ``Acceptance of a job offer'' indicates the number of people who accepted a job offer and the passing rate (ratio of people who accepted a job offer out of those who received a job offer notice). ``Joining'' shows the number of people who eventually joined the company and the passing rate (the percentage of people who joined the company among those who accepted a job offer).

For example, the data records of the performance data table described above are newly added and created between the time when a recruitment project occurs and the time when recruitment ends, and the data records are newly added and created from the time when a recruitment project occurs to the time when recruitment ends, and the data records are newly added and created at the time when recruitment is completed or at each selection step in the middle of recruitment. When the process is completed, data will be sequentially input and accumulated.

FIG. 4 is a diagram showing a flowchart of processing by the information processing device 1. When the processor 11 of the information processing device 1 reads and executes the program stored in the storage unit 12, the processing shown in FIG. 4 is executed.

First, the display control unit 113 of the information processing device 1 causes the display unit 14 to display an input screen (step S201). The input screen is a screen for assisting the user in inputting information regarding recruitment using the operation unit 13.

Subsequently, after the input screen is displayed, the input unit 111 of the information processing device 1 waits for the user to input data using the operation unit 13 (step S202). If there is no input (step S202: NO), the waiting state continues in step S202.

FIG. 5 is a diagram showing a display example of the input screen. In FIG. 5, the input screen 30 displays a plurality of input columns as described below. The identification number input field 301 is a field for inputting the identification number of the recruitment. When entering a new recruitment, an identification number is automatically assigned and displayed in this column. If you wish to display the input contents of recruitment input in the past, you can do so by inputting the identification number. Recruitment period input fields 302 and 303 are fields for inputting the start date and end date of the recruitment implementation period, respectively. If the end date is not determined, you may enter only the start date.

The job type input field 304 is a field for inputting the type of job to be recruited. The job classification input field 305 is a field for inputting the classification name of the job to be recruited. In the example of FIG. 5, the job category to be recruited is "front-end engineer," and the classification name of the job category to be recruited is input as "engineer." Input to the recruitment job type input field 304 and job type input field 305 may be performed by displaying a pull-down menu and making a selection. The number of employees to be hired input column 306 is a column for inputting the number of people who will ultimately be hired and join the company in this recruitment. In the example of FIG. 5, 20 people are input.

The target input field 307 is a field for inputting numerical targets for this recruitment. The target input field 307 consists of a step name display field 311 and a target number of people input field 312. The user inputs the target number of people corresponding to each step of application, document screening, first selection, second selection, final selection, informal offer, acceptance of informal offer, and joining the company displayed in the step name display field 311. In the example of FIG. 5, a target number of 20 people (the same number of people hired as described above) is input in "Joining".

Returning to FIG. 4, if an input is made while the input screen as shown in FIG. 5 is displayed (step S202: YES), the input unit 111 accepts the input data (step S203). That is, as shown in the example of FIG. accept the data.

Subsequently, the calculation unit 112 calculates past recruitment performance data for occupations that are highly related to the recruitment target occupation entered in the recruitment occupation input field 304 from among the input data received by the input unit 111, as shown in FIG. is extracted from the performance data table shown in (step S204).

In FIG. 5, "front-end engineer" is entered in the recruitment job type input field 304. In FIG. Data record 0102 is performance data regarding recruitment of front-end engineers in the same job category. Therefore, the calculation unit 112 calculates No. The data record of 0102 is extracted and read from the storage unit 12.

If there is no performance data on recruitment for the same occupation, extract performance data on recruitment for similar occupations. For example, since the input content in the job classification input column 305 in FIG. 5 is "engineer," data records whose recruitment job classification stored in the track record data table is "engineer" are extracted. If there are multiple extracted data records, the data record of the job type that is highly related to the front-end engineer who is currently recruiting is extracted.

Subsequently, the calculation unit 112 calculates the predicted passing rate for each screening step to be performed for the current recruitment target job type (step S205). The predicted passage rate is calculated based on each passage rate in the extracted data records. When the number of extracted data records is one (data record No. 0102 in FIG. 3), the value of the passage rate of that data record is directly adopted as the predicted passage rate. If multiple data records are extracted, calculate the average value of the passing rates of those data records for each selection step (or the value obtained by performing other statistical processing on the extracted multiple passing rates), and Let it be the predicted passing rate of the selection step.

Subsequently, the calculation unit 112 calculates an estimated value of the number of applicants, an estimated value of the number of people who passed each selection step, and an estimated value of the number of employees joining the company, based on the calculated predicted passing rate (step S206). Subsequently, the display control unit 113 causes the display unit 14 to display the predicted passage rate and estimated number of people calculated as described above (step S207).

FIG. 6 is a diagram showing a display example of the predicted passage rate and estimated number of people. In FIG. 6, the predicted passage rate/estimated number of people display screen 32 displays a plurality of display columns as shown below. The identification number display field 321 includes the identification number input field 301, recruitment period input field 302,
The data input in 303 is displayed.

The job type display field 322 displays the data input in the job type input field 304 on the input screen of FIG. 5. The number of hires display field 323 displays the data input in the number of hires input field 306 on the input screen of FIG. 5.

The estimated value display column 324 is a column that displays the predicted passage rate (%) calculated by the calculation unit 112 and the estimated number of people. The estimated value display field 324 includes a step name display field 331, an estimated number of people display field 332, and a predicted passage rate display field 333.

In the step name display field 331, a plurality of step names in recruitment are displayed, similar to the step name display field 311 of the input screen in FIG. In this embodiment, the displayed step name is
Application, document screening, first selection, second selection, final selection, offer, acceptance of offer, and joining the company. The predicted pass rate display column 333 displays the predicted pass rate for each selection step calculated by the calculation unit 112. The estimated number of people display field 332 displays the estimated number of people who will reach each selection step, calculated by the calculation unit 112.

The estimated value display column 324 displays the estimated number of people who will reach each screening step in the same order as the order in which each screening step is performed, from “application” to “joining”, and the predicted passing rate at each screening step. be done. In this embodiment, the predicted pass rate for each selection step displayed in the predicted pass rate display column 333 is the No. 1 of the actual data table in FIG. The same numerical value as the passing rate in the data record 0102 is displayed.

The estimated number of people displayed in FIG. 6 is calculated by the calculation unit 112 as follows.
On the input screen in Figure 5, the number of people entered in the number of people to hire field (that is, the number of people who should eventually join the company) is 20, so ``20'' is displayed in the step name of ``Join'' in the estimated number of people in Figure 6. Ru. The predicted passing rate for "Joining" is 86%, so the estimated number of people for "Informal Offer Acceptance", which is the step before "Joining", is 20 people if 86% of the people pass, that is, 23 people. becomes. Next, since the predicted passing rate for ``acceptance of a job offer'' is 21%, the estimated number of people who will receive a ``office offer,'' which is the step before ``acceptance of a job offer,'' is 110.

As described above, the calculation unit 112 can calculate the estimated number of people at the previous step from the estimated number of people at each step and the predicted passing rate. In this way, the calculation unit 112 calculates the estimated number of people at each step from "application" to "offer offer acceptance".

In FIG. 6, among the estimated number of people displayed in the estimated value display field 324, the number of people input as the target number of people on the input screen of FIG. 5 is displayed in a different display method from the other estimated number of people. In FIG. 6, the estimated number of "joining employees", 20, is the number entered on the input screen of FIG. 5, so the display control unit 113 performs control to display this number surrounded by a square.

Since the estimated number of people in each selection step is displayed on the display screen as shown in FIG. 6, the numerical target can be grasped. For example, according to FIG. 6, it can be seen that in order to make the final number of employees 20, it is estimated that 1469 people will need to apply.

FIG. 7 is a diagram showing another example of the input screen and the display screen for the predicted passing rate and estimated number of people. FIG. 7(A) shows a case where 900 people are input in "Application" as the target number of people to be input in the target input field 307 of the input screen 30 in FIG.

FIG. 7(B) shows that 900 people, which is the target number of people for "Application", were input in the estimated value display field 324 of the predicted pass rate/estimated number of people display screen 32 in FIG. A state in which the calculation unit 112 calculates the estimated number of people who have passed through each step up to “joining the company” based on the predicted passing rate of each step is displayed.
According to FIG. 7B, it can be seen that if the target number of applicants is 900, the final number of employees will be 12, which is estimated to fall short of the target number of hires of 20.

FIG. 8 is a diagram showing another example of the input screen and the display screen for the predicted passing rate and estimated number of people passing.
FIG. 8(A) shows a case where 80 people are input in the "final selection" as the target number of people to be input in the target input field 307 of the input screen 30 in FIG.

FIG. 8(B) shows that in the estimated value display column 324 of the display screen 32 of predicted passing rate/estimated number of passing persons in FIG. The figure shows a state in which the calculation unit 112 calculates the estimated number of people who will pass through each step from '' to ``joining the company'' based on the predicted passing rate of each step.
According to FIG. 8(B), if the target number of people for the final selection is 80 people, the final number of people joining the company will be 13 people, which is estimated to fall short of the target number of hires of 20 people.

In addition, in the examples of FIGS. 5 to 8, the number of people for "joining", "applying", and "final selection" is input as the target number of people to be input in the target input field 307 of the input screen 30. It is also possible to input the number of people in other steps such as "document screening," "first screening," "secondary screening," "official offer," and "official offer acceptance." Even in that case, the calculation unit 112 can calculate the estimated number of people passing through each step in the same manner as in the examples of FIGS. 5 to 8.

Returning to FIG. 4, after the display screen as shown in FIG. 6 is displayed, the input unit 111 waits for the user to input changes to the displayed estimated number of people or predicted passage rate (step S208 ). Further, if there is no change input within the predetermined time (step S208: NO),
The input unit 111 determines whether there is an operation to instruct the end of the process (step S209).

If an instruction to end the process is input (step S209: YES), the calculation unit 112 stores the calculated data and the data input by the input unit 111 in the storage unit 12 (step S210). Then, the process ends. If the instruction to end the process is not input (step S209: NO), the process returns to step S208, and the input unit 111 enters an input waiting state.

If the estimated number of people or predicted passage rate is input to change (step S208: YES),
The calculation unit 112 recalculates the estimated number of people based on the changed input numerical value (step S211). Then, the display control unit 113 causes the display unit 14 to re-display the result of the recalculation by the calculation unit 112 (step S212). After that, the process returns to step S208, and the input unit 111 enters an input waiting state.

FIG. 9 is a diagram showing an example of a display when a change input is made on the display screen of the predicted passing rate and estimated number of people. FIG. 9(A) shows that the user changes the estimated number of people for "apply" from 1469 (see FIG. 6) to 700 in the estimated value display column 324 of the predicted passing rate/estimated number of people display screen 32 of FIG. Shows the display when the operation is performed. In this case, the display control unit 113 performs control to display the input numerical value "700" surrounded by a square.

Figure 9 (B) shows the estimated number of people who passed through each step from "document screening" to "joining" for the estimated number of 700 people who applied for the change, and the predicted passing rate for each step. This shows a state in which the calculation unit 112 displays the results calculated again based on the calculation unit 112.

According to Figure 9 (B), if the number of applicants is changed from 1,469 to 700, which is less than half, the estimated number of people who pass through each step will also be less than half, and the final number of new employees will be 20,000 compared to the original target. It can be seen that it is estimated that there are 9 people per person.

FIG. 10 is a diagram showing another display example when a change input is made on the display screen of the predicted passing rate/estimated number of people. FIG. 10(A) shows that in the estimated value display field 324 of the predicted passing rate/estimated number of people display screen 32 in FIG. This shows the display when performing an operation to change to a person. In this case, the display control unit 113 performs control to display the input numeric value "15" surrounded by a square.

Figure 10 (B) shows the estimated number of people who passed through each step from ``application'' to ``acceptance of job offer'' for the changed estimated number of ``joining companies'' of 15 people, and the predicted passing rate for each step. This shows a state in which the calculation unit 112 displays the results calculated again based on the calculation unit 112.
According to FIG. 10(B), it can be seen that if the final number of employees is reduced to 15, it is estimated that 1077 applicants will need to be secured.

FIG. 11 is a diagram showing another display example when a change input is made on the display screen of the predicted passing rate/estimated number of people. FIG. 11(A) shows that in the estimated value display field 324 of the predicted passing rate/estimated number of people display screen 32 in FIG. This shows the display when performing a change operation. In this case, the display control unit 113
Control is performed to display the input numerical value "300" surrounded by a square.

Figure 11 (B) shows the estimated number of people who applied for 300 people, which is the estimated number of people for the changed ``first selection,'' and the number of people from ``document screening'' and ``secondary selection'' to ``joining the company.'' The figure shows a state in which the calculation unit 112 recalculates the estimated number of people passing through each step based on the predicted passing rate of each step.
According to Figure 11 (B), if the number of people who pass the first screening is reduced to 300, it is estimated that 688 people will apply, but the final number of people to join the company will only be 9. It can be seen that this is estimated.

FIG. 12 is a diagram showing another display example when a change input is made on the display screen of the predicted passing rate/estimated number of people. FIG. 12(A) shows that 43% of the predicted passing rate for "secondary selection" (see FIG. 11 ) is shown when the user performs an operation to change it to 80%. in this case,
The display control unit 113 performs control to display the input numerical value "80" surrounded by a square.

Figure 12 (B) shows the estimated number of people who pass through each step from "secondary selection" to "joining the company," based on the predicted passing rate of 80% for the changed "secondary selection." This shows a state in which the calculation unit 112 displays the results calculated again based on the predicted passage rate. In this case, the estimated number of people in the steps from "application" to "first selection" does not change, so there is no need to calculate it again.

According to Figure 12 (B), in the example of Figure 11, it was estimated that if the number of people in the first selection was reduced to 300, the final number of people joining the company would be only 9, but the predicted passing rate in the second selection was It can be seen that by increasing the number of employees to 80%, it is estimated that the final number of employees will be 17.

In addition, in the examples of FIGS. 9 to 11, an example is shown in which the number of people for "apply", "join", and "first selection" is changed in the estimated value display column 324 of the display screen 32 for predicted passing rate/estimated number of people. As mentioned above, the estimated number of people for other steps such as "document screening," "secondary selection," "final selection," "official offer," and "official offer acceptance" may be changed. Even in that case, the calculation unit 112 can recalculate the estimated number of people passing through each step.

In addition, in the example of FIG. 12, an example was given in which the predicted passing rate of "secondary selection" is changed in the estimated value display field 324 of the predicted passing rate/estimated number of people display screen 32, but this is done in another step. The predicted passing rates for "document screening," "first screening," "final selection," "official offer," "acceptance of non-official offer," and "joining the company" may be changed. Even in that case, the calculation unit 112 can recalculate the estimated number of people passing through each step.

According to the above-described embodiment, it is possible to foresee the entire recruitment process, from application to job offer, in which applicants are narrowed down step by step. For example, a hiring manager belonging to the human resources department can verify the predicted results for each selection step based on the presented results, and decide whether or not to make adjustments, as well as the details of the adjustments. For example, if the final selection is an executive interview, and you determine that you cannot allocate time for the executive interview, you can simulate how reducing the number of finalists will affect other selection steps. . As a result, the person in charge of the previous process will be asked to raise the pass criteria, such as making the difficulty of the exam questions higher than in previous years, so that the recalculated pass rate for the previous process will be achieved. Possible responses include issuing a notice.
Furthermore, in general, the content and number of selection steps to be set and the degree of narrowing down in each selection step may change depending on the job type, position, and other work content to be recruited. Since the corresponding past selection results are reflected, the accuracy of prediction improves.

[Modified example]
The embodiments described above can be modified in various ways. Examples of those modifications are shown below. Note that the embodiments described above and the modifications shown below may be combined as appropriate.

(1) In the above-described embodiment, the target number of people for each selection step can be input on the input screen shown in FIG. 5, but the configuration of the input screen is not limited to this. For example, conditions may be set for one or more of the selection steps.

FIG. 13 is a block diagram showing the functional configuration of an information processing device 1A according to a modification. In FIG. 13, the components denoted by the same reference numerals as those in FIG. 2 have the same configuration as those in FIG. 2, and therefore the description thereof will be omitted. The processor 11A of the information processing device 1A functions as an input unit 111, a calculation unit 112, a display control unit 113, and a setting unit 114 by reading and executing a program stored in the storage unit 12.

The setting unit 114 sets at least one of the following: a target number of people who will be finally hired, a target number of applicants, a target number of people who will reach each selection step, and a predicted passing rate at each selection step. Set conditions for Then, the calculation unit 112 corrects the predicted passing rate of each selection step based on the performance data according to a predetermined algorithm so as to satisfy the conditions set by the setting unit 114.

FIG. 14 is a diagram showing an example of a screen for inputting conditions set by the setting unit 114 on the input screen 30A. In FIG. 14, the components denoted by the same reference numerals as those in FIG. 5 have the same configuration as in FIG. 5, and therefore the description thereof will be omitted.

The condition input field 308 is composed of a step name display field 311, a passer count input field 315, a pass rate input field 316, and a condition input field 317. The user enters the number of passes input field 315 and the pass rate input field 316 at the position corresponding to each step displayed in the step name display field 311.
You can input the set value for the number of people passing through and the set value for the passing rate.

In addition, the user can enter "upper limit", " You can input either the "lower limit value" or "fixed value" condition. FIG. 14 shows a state in which the upper limit of the number of people who pass the document screening is set to 500 people, and the fixed value of the number of people joining the company is set to 20 people.

In order to satisfy the conditions set by the setting unit 114 by receiving input from the user in this way, the calculation unit 112 calculates the estimated number of people at each step when the number of new employees is 20 as shown in FIG. Modify the predicted passage rate.

FIG. 15 is a diagram showing a display example of the estimated number of people and predicted passage rate corrected by the calculation unit 112. For example, when a setting is made to reduce the number of people who pass through a selection step, the calculation unit 112 makes a correction to lower the predicted pass rate of that selection step, and increases the number of people who pass through the selection step. If the setting is made to
Modifications will be made to increase the predicted passing rate of that selection step.

In the example in Figure 15, the estimated number of people who pass the document screening is set to be reduced to 500 compared to the estimated number of 815 in Figure 6, so the predicted passing rate for the document screening is 56% (Figure 6). By lowering the percentage to 34%, the estimated number of people who pass the document screening will be 500.

In addition, if the estimated number of people in the steps after a job offer is set to decrease or increase, or if it is set as a fixed value, the calculation unit 112 Adjust by increasing or decreasing the passage rate.

In the example of FIG. 15, the number of people joining the company is fixed at 20, and the estimated number of people who pass the document screening is set to be reduced to 500. Modifications will be made to increase the pass rate for either the next or final selection. In the case of the example in Figure 15, the predicted passing rate for the second screening is the lowest of the three screenings, at 43% (see Figure 6), so by increasing the predicted passing rate for the second screening to 70%, The estimated number of participants is 20.

In FIG. 15, the set values of the estimated number of people, 500 people for "document screening" and 20 people for "joining", are shown surrounded by squares. In addition, the corrected predicted passing rate values of 34 for "document screening" and 70 for "secondary screening" are displayed in squares.

FIG. 16 is a diagram showing another example of the input screen 30A, which is different from FIG. 14, for inputting conditions set by the setting unit 114. In FIG. 16, a state is shown in which the upper limit of the pass rate for the primary screening is set to 50%, and the fixed value of the number of employees to join the company is set to 20. In order to satisfy the conditions set in this way, the calculation unit 112 corrects the estimated number of people and predicted passing rate at each step when the number of new employees is 20 as shown in FIG.

FIG. 17 is a diagram illustrating a display example of the estimated number of people and predicted passage rate corrected by the calculation unit 112 so as to satisfy the conditions set in FIG. 16. For example, when a setting is made to lower the passing rate of a screening step, the calculation unit 112 makes corrections to increase the predicted passing rate of the screening step after that screening step, and When a setting is made to increase the pass rate, the calculation unit 112 makes a correction to lower the predicted pass rate of the screening step after that screening step.

In the example of FIG. 17, the number of employees joining the company is fixed at 20, and the passing rate of the first screening is set to be lowered to 50%, so the calculation unit 112 calculates the Modifications will be made to increase the pass rate for either selection or final selection. In the case of the example in Figure 17, the predicted passing rate for the second screening is lower (43%) among the two screenings (see Figure 6), so by increasing the predicted passing rate for the second screening to 67%, The estimated number of people will be 20.

In FIG. 17, the set values of 50% for the predicted passing rate of the "first round" and 20 people for the estimated number of "joiners" are shown surrounded by squares. In addition, the corrected predicted passing rate value of 67% for the ``secondary selection'' is shown enclosed in a square.

Note that in the example of FIG. 14, the conditions for the number of people passing through "document screening" and "joining" are set in the condition input field 308 of the input screen 30A, but other steps "application", Conditions may be set for the number of people who pass the "first selection," "second selection," "final selection," "official offer," and "official offer acceptance." Even in that case, the calculation unit 112 can make adjustments to satisfy the set conditions by correcting the predicted passing rate of each step.

In addition, in the example of FIG. 16, the conditions for the passing rate of "first screening" and the number of people passing "joining" are set in the condition input field 308 of the input screen 30A. Instead of the pass rate, conditions for the pass rate of "secondary selection," "final selection," "offer offer," "acceptance of offer," or "joining the company" may be set. Even in that case, the calculation unit 112 can make adjustments to satisfy the set conditions by correcting the predicted passing rates of other steps.

(2) In the above-described embodiment, each data record in the performance data table shown in FIG. Cost information including information regarding human resources (human costs) and/or expenses (expense costs) of the employing side invested in the recruitment process may also be recorded.

The input unit 111 further receives input of cost information regarding recruitment, and the calculation unit 112 calculates each cost information based on the relationship between the cost information input by the input unit 111 and the cost information recorded in the performance data. The predicted passage rate in each step may be calculated.

Specifically, a data field for storing cost information is provided in the data record of the performance data table to record the data. For example, there is a payment amount column that records the amount paid to the recruitment media and agent used during recruitment, and a number of interviewers column that records the number of employees assigned to interviewers who interview applicants during recruitment selection. and record performance data.

The input screen 30 shown in FIG. 5 is provided with a column for inputting a target value of the cost that can be spent on recruitment. For example, the payment amount field for entering the target value (maximum amount) of the amount to be paid to the recruitment media and agent to be used, and the number of interviewers for inputting the target value (maximum number) for the number of interviewers to be allocated in the selection step. Provide a column. The input unit 111 receives data input into these columns.

The calculation unit 112 extracts performance data that is highly relevant to the payment amount or the number of interviewers received by the input unit 111. For example, among the data records in the performance data table, data records in which the value in the payment amount column or the number of interviewers column is close to the value of the payment amount or the number of interviewers accepted by the input unit 111 are extracted. If multiple data records are extracted, select the data record that is highly relevant to the job category for which the job is being offered. Conversely, if a plurality of data records are extracted that are highly relevant to the type of job to be recruited, data records with similar numerical values for payment amount or number of interviewers may be selected.

The calculation unit 112 uses the pass rate at each selection step of the data record of the performance data table selected in this way as the predicted pass rate. As described above, when a plurality of data records are extracted, the average value of the pass rates of those data records for each selection step may be calculated and used as the predicted pass rate for each selection step.

(3) In the above-described embodiment, in the display of the predicted passage rate and estimated number of people in FIG. 6 and the like, each numerical value is expressed as an integer, but it may be displayed as a decimal. That is, in the above embodiment, the numbers below the decimal point are rounded off and displayed, but these numbers may be rounded off to the second decimal place and displayed as numbers up to the first decimal place. Further, the estimated number of people may be displayed in a range such as "10-20".

FIG. 18 is a diagram showing an example in which numerical values are displayed as decimals or ranges. FIG. 18(A) is
No. of the actual data table shown in FIG. This is an example in which the passing rate (%) of the data record 0102 is expressed as a decimal. In this example, the second decimal place is rounded off.

FIG. 18(B) is an example in which the numerical values in the estimated number of people display field 332 and the predicted number of people display field 333 on the predicted passage rate/estimated number of people passing display screen 32 shown in FIG. 6 are displayed as decimals. The predicted passage rate display column 333 displays the same numerical value as the passage rate of the actual data in FIG. 18(A), and is displayed as a decimal to the first decimal place.

In the "Joining" column of the estimated number of people display column 332, 10 people are set and input as the number of employees. As in the case of FIG. 6, the estimated number of people at each step is calculated by the calculation unit 112 based on the predicted passing rate, but in FIG. 18(B), the estimated number of people is displayed as a decimal value. In this example, the value obtained by rounding off the calculated numerical value to the second decimal place is displayed.

FIG. 18(C) shows another example of the estimated number of people displayed in the estimated number of people display column 332,
The estimated number of people at each step is displayed in a range such as ``10-20''. The calculation of the lower limit and upper limit of the range is performed by the calculation unit 112, for example, as follows.

First, the calculation unit 112 calculates the estimated number of people in each step as shown in FIG. 18(B) (this value may be a decimal or an integer). The lower limit value is, for example, the number of people who are minus 20% of the calculated estimated number of people, that is, the range of the number of people after subtracting 20%. Then, for example, the number of people plus 20%, that is, the number of people added by 20%, is set as the upper limit value. With such calculation,
For example, since the calculated value in FIG. 18(B) is "61.9", the estimated number of people in the "final selection" step in FIG. ”.

Such range display may be performed only in some steps. That is, the step of displaying the estimated number of people as an integer or decimal number and the step of displaying the estimated number of people as a range may be mixed. Other methods may be used to calculate the range. For example, the calculated numerical value may be set as the center value, and the plus/minus range may be fixed by the number of people, such as plus/minus five people. The range of the number of people to be fixed may be determined according to the calculated number of people.

(4) In the above-described embodiment, in FIGS. 5 to 11, etc., when the input unit 111 receives the input of the target number of people for each step from the user, if it determines that the input numerical value is not appropriate, The display control unit 113 may be instructed to display a warning.

FIG. 19 is a diagram showing an example of a warning display. FIG. 19(A) shows a display similar to the target input field 307 shown in FIG. 7(A). A case is shown in which the user inputs a value of 6,000 people in "Apply" as the target number of people.

As described in the above embodiment, in the recruitment shown in FIG. 7A, the performance data extracted by the calculation unit 112 for calculating the predicted passage rate is No. 1 shown in FIG. This is the performance data record of 0102. No. In the performance data record 0102, the number of applicants is 438. Compared to the number of people in the corresponding step (“application”) of the extracted performance data record, the number of people inputted in FIG. 19(A), 6000, is very large.

If the input unit 111 compares the input number of people with the corresponding number of people in the performance data table extracted by the calculation unit 112 and the difference is greater than a predetermined range, the input unit 111 displays the input number on the display unit via the display control unit 113. 14, a warning display field 341 as shown in FIG. 19(A) is displayed as a pop-up. A pop-up display is displayed pointing to the entered numerical value. The case where the difference is more than a predetermined range can be determined as appropriate, such as when the input value is more than 10 times the value of the corresponding number of people in the actual data table, or when it is less than 1/10. Bye.

In the case of FIG. 19(A), the input number of people is more than 10 times the corresponding number of people in the actual data table, so the message "Is the number too large?" is displayed in the warning display field 341. do. Furthermore, selection buttons for "cancel" and "continue" are displayed in the warning display field 341.

The user looks at the warning display field 341 and clicks one of the selection buttons. When the "Cancel" button is clicked, the input unit 111 performs processing to clear the input numerical value "6000". If the "Continue" button is clicked, the input unit 111 validates the input value "6000" and performs the next process.

FIG. 19(B) shows a display similar to the target input field 307 shown in FIG. 8(A). A case is shown in which the user inputs a value of 3 people in the "final selection" as the target number of people. No. In the performance data record 0102, the number of people selected for the "final selection" is 37. Compared to the number of people in the corresponding step ("final selection") of the extracted performance data record, the value of three people input in FIG. 19(B) is less than 1/10, which is very small.

In this case, as in the case of FIG. 19(A), the input unit 111 causes the display unit 14 to pop-up display the warning display field 341 via the display control unit 113. In the case of FIG. 19(B), the value of the number of people input is less than 1/10 of the value of the corresponding number of people in the actual data table, so the message "Is the number too small?" is displayed in the warning display field 341. Display. Furthermore, selection buttons for "cancel" and "continue" are displayed in the warning display field 341. The processing after the selection button is clicked is the same as in the case of FIG. 19(A).

By displaying the warning display field 341 as described above, if the input target number of people deviates from the corresponding value in the actual data, or if there is a possibility of incorrect input, the user will be notified to that effect. It is possible to notify the user and encourage appropriate processing.
Note that the display of the warning display field 341 and subsequent processing may be performed also when the user inputs information to other fields. For example, when inputting to the number of hires input field 306 in FIG.
When changing the estimated number of people displayed in the estimated value display field 324 in FIGS. 9, 10, and 11,
It may be executed at the time of inputting a change to the predicted passing rate displayed in the estimated value display field 324 in FIG.

(5) In the embodiment described above, each data record in the performance data table shown in FIG. 3 has a data field that records the time when recruitment was implemented, but it also indicates the time when each selection step was performed. Implementation timing information may be recorded in addition to the number of people and passing rate for each screening step.

FIG. 20 is a diagram showing a configuration example of a performance data table according to a modification. The difference from the configuration example of the performance data table shown in FIG. 3 of the above-described embodiment is that in the example of FIG. 20, information on "implementation time" is stored in each data record. "Implementation timing" is related to each step ("Application", "Document screening", "First selection", "Second selection", "Final selection", "Notice of offer", "Acceptance of offer", "Joining"). , indicates the period in which each was implemented (for example, the period from the first implementation date to the last implementation date). If the implementation is completed in one day, only the date of that day is stored.

The input unit 111 further receives input of implementation timing information regarding the scheduled implementation time of each selection step, and the calculation unit 112 receives the implementation timing information input by the input unit 111 and the implementation timing information recorded in the performance data. The predicted passage rate at each step may be calculated based on the relationship with the .

FIG. 21 is a diagram showing a display example of an input screen according to a modification. The difference from the input screen shown in FIG. 5 of the above-described embodiment is that in the input screen 30B of FIG. 21, an implementation timing input field 318 is provided in the target input field 307B. The implementation time input field 318 is a field for the user to input the scheduled implementation time of each step displayed in the step name display field 311. The scheduled implementation time can be input as a range from a certain date to a certain date. It is also possible to input only either the start date or the end date. Also,
If the scheduled implementation date has not been determined, this field may be left blank. The input unit 111 accepts the data of the input scheduled implementation time information when input is made while the input screen as shown in FIG. 21 is displayed.

The calculation unit 112 extracts performance data that is highly relevant to the implementation schedule information received by the input unit 111. For example, among the data records of the performance data table, the recorded implementation timing information has a high degree of coincidence with the implementation timing information received by the input unit 111 (for example, many steps are performed in the same month). Extract records. If multiple data records are extracted, select the data record that is highly relevant to the job category for which the job is being offered. Conversely, when a plurality of data records that are highly relevant to the job category to be recruited are extracted, the data record that has a high degree of matching in implementation timing information may be selected.

The calculation unit 112 uses the pass rate at each selection step of the data record of the performance data table selected in this way as the predicted pass rate. As described above, when a plurality of data records are extracted, the average value of the pass rates of those data records for each selection step may be calculated and used as the predicted pass rate for each selection step.

FIG. 22 is a diagram illustrating a display example of a display screen of the predicted passage rate and estimated number of people passing according to the modification. The difference from the predicted passing rate/estimated number of passing people display screen 32 shown in FIG. A period display column 334 is provided. In the implementation time display field 334, each piece of data input in the implementation time input field 318 in FIG. 21 is displayed at the position of the corresponding step name in the step name display field 331.

Similar to FIG. 6, the predicted pass rate display field 333 displays the predicted pass rate for each selection step calculated by the calculation unit 112. The calculation unit 112, as in the above embodiment,
This is performed based on each passing rate stored in the data record extracted from the data table of performance data.
In this modification, the calculation unit 112 extracts data records from the data table based on the job type and implementation period. As shown in Figure 20, the performance data for the same recruitment position as "Front End Engineer", which is the current recruitment position, is No. 0094 and No. Assume that two records of 0102 are extracted.

The calculation unit 112 selects No. 1 with a high degree of coincidence in implementation timing. Select data record 0094. For example, the scheduled period for "application" for this recruitment is from January 15th to March 31st (see Figure 21), and No. The implementation period of "Application" in the data record 0094 is from January 10th to March 14th, which has a high degree of coincidence as a period of one year. Therefore, the calculation unit 112 calculates No. The passing rate value of data record 0094 is directly adopted as the predicted passing rate. As a result, the predicted passage rate display field 333 in FIG. 22 shows No. 2 in FIG. The passing rate data of data record 0094 is displayed as is.

Subsequently, the calculation unit 112 calculates an estimated value of the number of applicants, an estimated value of the number of people who passed each selection step, and an estimated value of the number of employees joining the company, based on the calculated predicted pass rate, as in the above-described embodiment. Then, the display control unit 113 causes the display unit 14 to display the calculated predicted passing rate and estimated number of people.

In general, the number of applicants and the quality of applicants (vocational ability; which can affect the pass rate in the subsequent selection process) are affected by timing requirements such as the end of the fiscal year, economic trends and other external factors, and other recruitment timing (selection period). ). According to this modification, it is expected that the accuracy of estimating the passing rate will be improved by reflecting the timing requirements in the estimation of the passing rate.

(6) In the above embodiment, the performance data table is stored in the storage unit 12 in the information processing device 1, but it is connected to the network via the communication unit 15 of the information processing device 1. Performance data stored in an external information processing device (server, etc.) may be read and used.

(7) In the above embodiment, the input unit 111 accepts input from the operation unit 13, but it also accepts input from an external information processing device connected to the network via the communication unit 15. Good too. In this case, the display control of various display screens by the display control unit 113 will also be executed so that they can be displayed on an external information processing device via the network.
In short, in the information processing system according to the present invention, there are two steps: storing performance data including the passing rate of a plurality of screening steps carried out to recruit human resources in association with the hired job type; Based on the step of accepting input and the relationship between the input job type and the job type associated with the stored performance data, the predicted pass rate in each screening step to be performed for the job type of the recruitment target is calculated respectively. It is only necessary that the step of calculating is executed.

1, 1A...information processing device, 11, 11A...processor, 12...storage unit, 13...operation unit, 14...display unit, 15...communication unit, 16...bus, 30, 30A, 30B...input screen, 32...prediction Display screen for passing rate/estimated number of people passing, 111...Input section, 112...Calculation section, 113...Display control section, 114...Setting section, 301...Identification number input field, 302, 303...Recruitment period input field, 304...Recruitment Occupation type input field, 305...Occupation classification input field, 306...Number of employees to be hired, 307, 307B...Target input field, 308...Condition input field, 311...Step name display field, 312...Target number of people input field, 315...Number of people who passed Input field, 316...Pass rate input field, 317...Condition input field, 318...Implementation date input field, 321...Identification number display field, 322...Recruitment type display field, 323...Number of employees display field, 324, 324B...Estimated value Display column, 331... Step name display column, 332... Estimated number of people display column, 333... Predicted passing rate display column, 334... Implementation time display column, 341... Warning display column.

Claims (12)

a storage means for storing performance data indicating selection results in a plurality of selection steps carried out to hire human resources;
Input means that accepts input of a target value for at least one of the number of people who will eventually be hired, the number of applicants, and the number of people who will reach any one of the plurality of selection steps;
Based on the performance data, calculate an estimated value for at least one of the number of applicants, the number of people who will be finally hired, and the number of people who will reach any of the selection steps, corresponding to the input target value. An information processing device comprising: calculation means for calculating; The performance data includes at least one of the following: cost information including information on the type of job hired, human resources or costs on the hiring side invested in recruitment activities, and implementation timing information regarding when the plurality of selection steps were performed. It is stored in association with information,
The input means further receives input of at least one of the job type of the recruitment target, the cost information related to the recruitment target, or implementation timing information regarding the scheduled implementation time of the selection step related to the recruitment target,
The calculation means calculates the estimated value based on the relationship between the input information and the information associated with the performance data.
The information processing device according to claim 1. The information according to claim 2, wherein the calculation means calculates the estimated value using the passage rate described in the performance data associated with the hired job type that is the same as or similar to the job type to be recruited. Processing equipment. The performance data includes a pass rate in at least one of the plurality of selection steps,
The information processing device according to claim 1, wherein the calculation means calculates a predicted passing rate in at least one of the plurality of selection steps. further comprising a display unit that displays the estimated number of people passing through each of the plurality of selection steps in an order corresponding to the scheduled implementation time of each of the plurality of selection steps;
The information processing device according to any one of claims 1 to 4. The input means includes at least one of the following: a target number of people who will be finally hired, a target number of applicants, a target number of people who will reach each selection step, and a predicted passing rate at each selection step. We accept changes regarding
The calculation means re-calculates the estimated number of applicants, the estimated number of people who will be finally hired, and the estimated number of people who will reach each selection step, based on the received changed value and the performance data. The information processing device according to claim 4. Set conditions for at least one of the following: the target number of people who will eventually be hired, the target number of applicants, the target number of people who will reach each selection step, and the predicted passing rate at each selection step. further comprising a setting section to
The information processing device according to claim 4, wherein the calculation means corrects the predicted pass rate of each selection step related to the performance data according to a predetermined algorithm so as to satisfy the set condition. The calculation means calculates a difference between the input target value and a corresponding value of the actual performance data.
The information processing device according to claim 1. The information processing device according to claim 1, wherein the content or number of the plurality of selection steps is set according to the type of job to be recruited. The information processing device according to claim 1, wherein the performance data is performance data available in the market. to the computer,
a step of storing performance data indicating selection performance in a plurality of selection steps performed for hiring human resources;
Receiving input of a target value for at least one of the number of people who will be finally hired, the number of applicants, and the number of people who will reach the plurality of selection steps;
Based on the performance data, calculate an estimated value for at least one of the number of applicants, the number of people who will be finally hired, and the number of people who will reach any of the selection steps, corresponding to the input target value. A program to execute the steps and . a step in which the information processing device stores performance data indicating selection results in a plurality of selection steps performed for hiring human resources;
a step in which the information processing device receives an input of a target value for at least one of the number of people who will be finally hired, the number of applicants, and the number of people who will reach the plurality of selection steps;
Based on the performance data, the information processing device determines at least one of the number of applicants, the number of people who will be finally hired, and the number of people who will reach any of the selection steps corresponding to the input target value. a step of calculating an estimate for

How to have.

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