Assignment selection support device, assignment selection support method, and program

Abstract

To reduce burdens on a human resources person in personnel relocation.SOLUTION: A work placement selection support device 10 includes: a selection information generation unit 11 that in a case where personnel information on a target person of personnel relocation corresponds to any of a plurality of selection conditions set in advance, calculates an index value for the corresponding selection condition and generates selection information that specifies work placement related to the calculated index value, an identifier of the target person, and the corresponding selection condition; and a work placement candidate selection unit 12 that selects a work placement candidate for the target person on the basis of the selection information. The selection conditions include a condition that restricts a job content in which a specific target person can engage.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to an assignment destination selection support device and an assignment destination selection support method for assisting in the selection of an assignment destination for personnel, and further relates to a program for realizing these.

Background Art

[0002] Conventionally, in some organizations, personnel transfers are frequently carried out for the purpose of preventing adhesion to business partners and equalizing services. In such organizations, for example, personnel transfers are carried out for each person approximately every three years. However, if personnel transfers are carried out frequently, the burden on the personnel department will increase. For this reason, although it is conceivable to entrust the personnel transfer business outside the organization, since personal information needs to be handled in the personnel transfer business, entrusting it outside is not realistic.

[0003] For this reason, for example, Patent Document 1 discloses a personnel placement support system that automatically arranges optimal personnel in each of a plurality of departments. The personnel placement support system disclosed in Patent Document 1 first creates appropriate placement information that specifies the job ranks and numbers of personnel required for each department by using reference information that associates the business content, the job ranks and numbers of personnel required for that business, and business information including the business content of each department.

[0004] Subsequently, the personnel placement support system disclosed in Patent Document 1 uses the created appropriate placement information and actual placement information that specifies the job ranks and numbers of personnel actually placed in each department to extract the surplus and shortage of personnel in each department, and creates personnel information indicating the extraction results. By using the created personnel information, the personnel department can easily grasp the surplus and shortage of personnel in each department, so the burden on the personnel department is reduced compared to the past.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] However, the personnel allocation system disclosed in Patent Document 1 only has the function of showing surpluses and vacancies in each department, and the burden on HR personnel remains heavy in order to carry out appropriate personnel transfers. For example, in personnel transfers, personnel with little work experience are sometimes transferred so that they can gain experience in as many different tasks as possible, but the personnel allocation system disclosed in Patent Document 1 does not take such considerations into account, and HR personnel must take such considerations into account.

[0007] One example of the purpose of this disclosure is to reduce the burden on human resources personnel in personnel transfers. [Means for solving the problem]

[0008] To achieve the above objective, the assignment selection support device in one aspect of this disclosure is: A selection information generation unit generates selection information that, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, calculates an indicator value for the relevant selection criteria, identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria. Based on the aforementioned selection information, the assignment candidate selection department selects potential assignment locations for the aforementioned individuals. Equipped with, The aforementioned selection criteria include conditions that restrict the types of work that a particular person can engage in. It is characterized by the following:

[0009] Furthermore, in order to achieve the above objectives, the method for supporting the selection of assignment locations in one aspect of this disclosure is: A selection information generation step, in which, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, an indicator value is calculated for the relevant selection criteria, and selection information is generated that identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria, Based on the aforementioned selection information, a candidate placement step is made to select a candidate placement for the aforementioned person, It has, The aforementioned selection criteria include conditions that restrict the types of work that a particular person can engage in. It is characterized by the following:

[0010] Furthermore, in order to achieve the above objectives, the program in one aspect of this disclosure is On the computer, A selection information generation step, in which, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, an indicator value is calculated for the relevant selection criteria, and selection information is generated that identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria, Based on the aforementioned selection information, a candidate placement step is made to select a candidate placement for the aforementioned person, Make it run, The aforementioned selection criteria include conditions that restrict the types of work that a particular person can engage in. [Effects of the Invention]

[0011] As described above, this disclosure can reduce the burden on personnel personnel in personnel transfers. [Brief explanation of the drawing]

[0012] [Figure 1] Figure 1 is a schematic diagram showing the general configuration of the assignment selection support device in an embodiment. [Figure 2] Figure 2 is a diagram specifically showing the configuration of the assignment selection support device in the embodiment. [Figure 3]FIG. 3 is a diagram showing an example of selection conditions used in the embodiment. [Figure 4] FIG. 4 is a diagram showing an example of selection information generated in the embodiment. [Figure 5] FIG. 5 is a diagram showing an example of assignee candidates selected in the embodiment. [Figure 6] FIG. 6 is a flowchart showing the operation of the assignee selection support device in the embodiment. [Figure 7] FIG. 7 is a block diagram showing an example of a computer that realizes the assignee selection support device in the embodiment. [[ID=!4]] BEST MODE FOR CARRYING OUT THE INVENTION

[0013] (Embodiment) Hereinafter, an assignee selection support device, an assignee selection support method, and a program in the embodiment will be described with reference to FIGS. 1 to 7.

[0014] [Device Configuration] First, the schematic configuration of the assignee selection support device in the embodiment will be described with reference to FIG. 1. FIG. 1 is a configuration diagram showing the schematic configuration of the assignee selection support device in the embodiment.

[0015] The assignee selection support device 10 in the embodiment shown in FIG. 1 is a device that supports the selection of the assignee of the organization's personnel. As shown in FIG. 1, the assignee selection support device 10 includes a selection information generation unit 11 and an assignee candidate selection unit 12.

[0016] The selection information generation unit 11 calculates an index value for a corresponding selection condition when the personal information of the person subject to personnel transfer corresponds to any of a plurality of preset selection conditions. The plurality of selection conditions include conditions that limit the work content that a specific subject can engage in.

[0017] Furthermore, the selection information generation unit 11 generates selection information that identifies the calculated indicator values, the subject's identifier, and the assignment destinations associated with the relevant selection conditions. The assignment destination candidate selection unit 12 selects candidate assignment destinations for the subject based on the generated selection information.

[0018] In this way, the assignment selection support device 10 sets assignments based on selection criteria that match the personnel transfer target. The selection criteria also include conditions set for the job content, for example, conditions that allow personnel with little work experience to gain experience in a wide range of tasks. Therefore, the assignment selection support device 10 can reduce the number of items that HR personnel need to consider, thereby reducing the burden on HR personnel in personnel transfers.

[0019] Next, the configuration and functions of the assignment selection support device 10 in the embodiment will be specifically described using Figures 2 to 5. Figure 2 is a configuration diagram specifically showing the configuration of the assignment selection support device in the embodiment. Figure 3 is a diagram showing an example of the selection conditions used in the embodiment. Figure 4 is a diagram showing an example of the selection information generated in the embodiment. Figure 5 is a diagram showing an example of the assignment candidate selected in the embodiment.

[0020] As shown in Figure 2, in this embodiment, the assignment selection support device 10 includes, in addition to the selection information generation unit 11 and the assignment candidate selection unit 12 described above, a data acquisition unit 13, a target person identification unit 14, a machine learning model 15, a rule determination unit 16, and an output information generation unit 17. Also, as shown in Figure 2, an input device 20 and an output device 30 are connected to the assignment selection support device 10.

[0021] The input device 20 is a device for inputting personnel information of an organization's members into the assignment selection support device 10. Examples of input devices for the input device 20 include touch panels, keyboards, and mice. Alternatively, the input device 20 may be a device other than these input devices, such as a terminal device connected to the assignment selection support device 10 via a network. Examples of terminal devices include general-purpose PCs (Personal Computers), smartphones, and tablet devices.

[0022] The output device 30 displays candidate assignments for the subject based on the output information generated by the output information generation unit 17. Examples of output devices 30 include display devices such as liquid crystal displays and printing devices. Alternatively, the output device 30 may be a device other than those mentioned above, such as a terminal device connected to the assignment selection support device 10 via a network. In this case, the input device 20 and the output device 30 may be the same terminal device. The output information will be described later.

[0023] The data acquisition unit 13 acquires personnel information entered by the input device 20. In this embodiment, the personnel information is personal information of each employee, and includes, for example, age, gender, department, position, commute route, relatives, transfer history, long-term sick leave, number of employees in the department, characteristics of the department, transfer requests, qualifications, etc. However, the personnel information is not limited to the information described above. Furthermore, the personnel information may be created automatically or manually.

[0024] The target identification unit 14 inputs the personnel information of each target person acquired by the data acquisition unit 13 into the machine learning model 15 to identify the target persons for personnel transfers. The machine learning model 15 is a model constructed using machine learning, with the personnel information of past personnel transfer targets and the results of past personnel transfers used as training data. The results of personnel transfers are represented by whether or not the target person was transferred.

[0025] Machine learning model 15 is a model generated using, for example, AI discriminant analysis (supervised learning). Specifically, machine learning model 15 is a model generated using predictive decision trees, random forests, LightGBM, etc. Machine learning model 15 is actually implemented by a machine learning program that runs on a computer. An example of such a computer is the computer that builds the placement selection support device 10 (described later). Note that machine learning model 15 may also be implemented on a device (computer) separate from the placement selection support device 10.

[0026] In this embodiment, the selection information generation unit 11 first determines whether the personnel information acquired by the data acquisition unit 13 for the target person identified by the target person identification unit 14 falls under any of the pre-set selection conditions. Then, based on the determination result, the selection information generation unit 11 identifies the applicable selection conditions for each target person and calculates an index value for the identified selection conditions. Subsequently, the selection information generation unit 11 generates selection information using the calculated index value. The processing by the selection information generation unit 11 will be described in detail below.

[0027] As shown in Figure 3, in this embodiment, each selection criterion is a condition for selecting personnel to work in the corresponding job category, and is associated with "job category" and "indicator." "Job category" is information that represents the type of work performed at the personnel's workplace, and indicates the type of work to which each selection criterion applies. The "job category" column contains the corresponding specific job category, such as "Job Category A" and "Job Category B." If it is marked as "Common," all job categories apply.

[0028] Furthermore, among the selection criteria shown in Figure 3, selection criterion "No. 12" is a condition that restricts the types of work that a specific person can engage in. Selection criterion "No. 12" requires that the job group to which the new assignment belongs is different from any job group to which the previous assignment belonged. A job group is a group set up according to the content of the work. In this embodiment, the work within the organization is classified into multiple systems in advance, and organizations and work content within the same system are defined as one job group. Specifically, for example, a job group for the counter service system that handles counter services, a job group for the planning system that handles planning work, and so on are set up.

[0029] Thus, with selection criterion No. 12 in place, personnel will be preferentially assigned to job groups different from their current job group. In other words, job rotation will be more easily achieved. Through job rotation, personnel can gain experience in a variety of tasks. As a result, personnel can achieve the development of a broad career, and it becomes easier for HR personnel to assess personnel suitability.

[0030] Furthermore, in Figure 3, the "indicator" is information used to calculate the indicator value for the relevant selection criterion, and consists of "points" and "weights." Each selection criterion has pre-set points for "points" and points for "weights." The values ​​of the points are determined based on, for example, past experience and case studies.

[0031] The selection information generation unit 11 compares the personnel information of the person subject to personnel transfer with the selection information shown in Figure 2 to identify the applicable selection criteria. Then, for each applicable selection criterion, the selection information generation unit 11 calculates an index value by summing the "bonus points" and "weight" points shown in Figure 2.

[0032] Specifically, if the personnel information of the person subject to personnel transfer matches the selection criteria for "No. 1" in Figure 2, the selection information generation unit 11 calculates an index value of "4 (=1 × 4)" using the bonus point "1" and the weight "4".

[0033] Furthermore, if selection criteria for a specific job category and selection criteria for all job categories are specified, the selection information generation unit 11 adds up the sum of the "additional points" and "weight" points calculated for each selection criterion, and uses the resulting value as the index value. For example, in Figure 2, suppose that in addition to the selection criteria for "No. 1", the selection criteria for "No. 7" and "No. 12" also match. In this case, the selection information generation unit 11 adds up the index value of "No. 1" (5), the index value of "No. 7" (4), and the index value of "No. 12" (6), and uses the resulting value 15 (= 5 + 4 + 6) as the index value for the target person.

[0034] Once the selection information generation unit 11 calculates the indicator values ​​in this manner, it uses the calculated indicator values ​​to generate the selection information shown in Figure 4. In the example in Figure 4, the selection information associates each target person (personnel identification number) with the assignment destination related to the corresponding selection conditions and the indicator values ​​calculated from those selection conditions. In other words, the selection information is information that associates indicator values ​​for each combination of personnel transfer target person and assignment destination, and is composed of a scoring table represented by a matrix (n x n) using personnel and assignment destination.

[0035] For example, in the case of an individual with identification number "4589," their personnel information meets the selection criteria for "Job Category A," "Job Category B," and "Job Category C." Therefore, the indicator value will be assigned to departments related to "Job Category A," "Job Category B," and "Job Category C," but not to "Job Category D." Note that the indicator value is assigned only to departments (e.g., ●● Department, ◇◇ Department) where there are vacant positions in the relevant job category. The availability of vacant positions is registered in advance by manual means.

[0036] As described above, when the selection information generation unit 11 calculates indicator values ​​for each target person and generates selection information for each target person, the assignment candidate selection unit 12 selects a target person's assignment candidate based on the generated selection information. In this embodiment, the assignment candidate selection unit 12 sets each target person's assignment candidate so that the sum of each target person's indicator values ​​is maximized.

[0037] Specifically, the assignment candidate selection unit 12 first obtains selection information from the selection information generation unit 11. Next, the assignment candidate selection unit 12 inputs the obtained selection information into, for example, an optimization engine.

[0038] An example of an optimization engine is a combinatorial optimization engine that employs the Hungarian algorithm. In this case, the assignment candidate selection unit 12 sets the index value of departments with no vacant positions to "0 (zero)" in the selection information shown in Figure 4, and processes this selection information into an n x n matrix. The assignment candidate selection unit 12 then inputs the resulting matrix into the optimization engine.

[0039] As a result, the optimization engine outputs a combinatorially optimal solution that maximizes the sum of each individual's indicator values. Then, as shown in Figure 5, the assignment candidate selection unit 12 selects a candidate assignment for each individual subject to personnel transfer based on the outputted optimal solution. Subsequently, the assignment candidate selection unit 12 passes the information identifying each individual's candidate assignment to the rule determination unit 16.

[0040] The rule determination unit 16 determines whether the selected candidate assignment for each individual conforms to the pre-set rules. Specifically, the rule determination unit 16 checks the individual's personnel information and the personnel information of the personnel belonging to the candidate assignment against the rules to make a determination. Examples of rules include the following: (1) A specific person, such as a relative of the person concerned, is present at the workplace of the candidate for assignment. (2) The subjects are replaced one-to-one. (3) The departments to which the applicant previously belonged are listed as potential assignment locations. (4) Two or more people will be transferred to the department to which you are a candidate for assignment at the same time. (5) If the person in question has been with the company for 10 years or less, the number of departments they have worked in must not be two or less.

[0041] If the assignment candidate selection unit 12 determines that there are individuals who do not conform to the rules by the rule determination unit 16, it performs the assignment candidate selection process again. On the other hand, if the assignment candidate selection unit 12 determines that there are no individuals who do not conform to the rules by the rule determination unit 16, it passes information identifying the assignment candidates for each individual to the output information generation unit 17.

[0042] When the output information generation unit 17 receives information from the assignment candidate selection unit 12, it generates output information to present the personnel personnel with the assignment candidates for each person subject to personnel transfer. Subsequently, the output information generation unit 17 outputs the output information to the output device 30.

[0043] Specifically, the output information is for displaying the potential assignment locations for each subject shown in Figure 5 on the screen of the output device 30. The output information includes the identification number, potential assignment locations, and the sum of the indicator values ​​for each subject.

[0044] [Device operation] Next, the operation of the assignment selection support device 10 in the embodiment will be explained using Figure 6. Figure 6 is a flowchart showing the operation of the assignment selection support device in the embodiment. In the following explanation, Figures 1 to 5 will be referred to as appropriate. In the embodiment, the assignment selection support method is implemented by operating the assignment selection support device 10. Therefore, the explanation of the assignment selection support method in the embodiment will be replaced by the following explanation of the operation of the assignment selection support device 10.

[0045] As shown in Figure 6, first, the data acquisition unit 13 acquires personnel information for each member of the organization from the input device 20 (Step A1).

[0046] Next, the target identification unit 14 inputs the personnel information of each member of the organization obtained in step A1 into the machine learning model 15 to identify the individuals subject to personnel changes (step A2).

[0047] Next, the selection information generation unit 11 determines whether the personnel information obtained in step A1 for each person identified in step A2 falls under any of the pre-set selection criteria (see Figure 3) (step A3).

[0048] Next, the selection information generation unit 11 identifies the applicable selection criteria for each subject based on the results of the determination in step A3, and calculates an index value for the identified selection criteria (step A4). Furthermore, the selection information generation unit 11 generates selection information (see Figure 4) using the index values ​​for each subject calculated in step A4 (step A5).

[0049] Next, when the selection information is generated in step A5, the assignment candidate selection unit 12 selects an assignment candidate for each individual based on the generated selection information (step A6).

[0050] Next, the rule determination unit 16 determines whether the candidate placement for each individual selected in step A6 conforms to the pre-set rules (step A7).

[0051] After step A7 is completed, the assignment candidate selection unit 12 determines whether there are any individuals who were determined not to conform to the rules in step A7 (step A8).

[0052] If, as a result of the determination in step A8, there are individuals who were determined not to conform to the rules in step A7 (step A8: Yes), the assignment candidate selection unit 12 excludes assignments that do not conform to the rules and executes step A6 again.

[0053] On the other hand, if the result of the determination in step A8 is that there are no individuals who were determined not to conform to the rules in step A7 (step A8: No), then the assignment candidate selection unit 12 will: each Information identifying potential assignment locations for the individuals is passed to the output information generation unit 17. The output information generation unit 17 then generates output information to present potential assignment locations for each individual subject to personnel transfer to the personnel manager (Step A9).

[0054] Subsequently, the output information generation unit 17 outputs the output information to the output device 30 (step A10). As a result, the screen of the output device 30 displays the possible assignment destinations for each of the personnel transfer recipients.

[0055] [Effects in the embodiment] As described above, the assignment selection support device 10 can automatically generate candidate assignments for employees subject to personnel transfers and present them to HR personnel, allowing HR personnel to efficiently select assignments. Therefore, the burden on HR personnel can be reduced.

[0056] Furthermore, the selection criteria include, for example, conditions that allow personnel with limited work experience to gain experience in a wide range of tasks, thus enabling the formation of career plans and the creation of personnel placement proposals to assess personnel suitability. The assignment selection support device 10 reduces the number of items that HR personnel need to consider, thereby easing the burden on HR personnel in personnel transfers. In addition, the assignment selection support device 10 can also eliminate assignment candidates that violate certain rules, further reducing the burden on HR personnel.

[0057] [program] The program in this embodiment can be any program that causes a computer to execute steps A1 to A10 shown in Figure 6. By installing and running this program on a computer, the assignment selection support device 10 and the assignment selection support method in this embodiment can be realized. In this case, the computer's processor functions as the selection information generation unit 11, the assignment candidate selection unit 12, the data acquisition unit 13, the target person identification unit 14, the machine learning model 15, the rule determination unit 16, and the output information generation unit 17, and performs the processing. In addition to general-purpose computers (including server devices), smartphones and tablet terminal devices can also be used as computers.

[0058] Furthermore, the program in this embodiment may be executed by a computer system constructed from multiple computers. In this case, for example, each computer may function as one of the following: a selection information generation unit 11, a placement candidate selection unit 12, a data acquisition unit 13, a target person identification unit 14, a machine learning model 15, a rule determination unit 16, or an output information generation unit 17.

[0059] [Physical configuration] Here, a computer that implements the assignment selection support device 10 by executing the program in the embodiment will be described with reference to Figure 7. Figure 7 is a block diagram showing an example of a computer that implements the assignment selection support device in the embodiment.

[0060] As shown in Figure 7, the computer 110 comprises a CPU (Central Processing Unit) 111, main memory 112, storage device 113, input interface 114, display controller 115, data reader / writer 116, and communication interface 117. Each of these components is connected to the others via a bus 121, enabling data communication.

[0061] Furthermore, the computer 110 may include a GPU (Graphics Processing Unit) or an FPGA (Field-Programmable Gate Array) in addition to, or instead of, the CPU 111. In this embodiment, the GPU or FPGA can execute the program in the embodiment.

[0062] The CPU 111 loads the program in the embodiment, which consists of a set of codes stored in the storage device 113, into the main memory 112, and performs various calculations by executing each code in a predetermined order. The main memory 112 is typically a volatile storage device such as DRAM (Dynamic Random Access Memory).

[0063] Furthermore, the program in this embodiment is provided stored on a computer-readable recording medium 120. The program in this embodiment may also be distributed over the internet via a communication interface 117.

[0064] Specific examples of the storage device 113 include hard disk drives and semiconductor storage devices such as flash memory. The input interface 114 mediates data transmission between the CPU 111 and input devices 118 such as a keyboard and mouse. The display controller 115 is connected to the display device 119 and controls the display on the display device 119.

[0065] The data reader / writer 116 mediates data transmission between the CPU 111 and the recording medium 120, reads programs from the recording medium 120, and writes processing results from the computer 110 to the recording medium 120. The communication interface 117 mediates data transmission between the CPU 111 and other computers.

[0066] Furthermore, specific examples of the recording medium 120 include general-purpose semiconductor memory devices such as CF (Compact Flash®) and SD (Secure Digital), magnetic recording media such as Flexible Disks, or optical recording media such as CD-ROMs (Compact Disk Read Only Memory).

[0067] Furthermore, the assignment selection support device 10 in this embodiment can be implemented not by a computer with a program installed, but by using hardware corresponding to each part, such as electronic circuits. Moreover, the assignment selection support device 10 may be partially implemented by a program and the remaining part by hardware. In this embodiment, the computer is not limited to the computer shown in Figure 7.

[0068] Some or all of the embodiments described above can be expressed by (Appendix 1) to (Appendix 15) described below, but are not limited to the following descriptions.

[0069] (Note 1) A selection information generation unit generates selection information that, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, calculates an indicator value for the relevant selection criteria, identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria. Based on the aforementioned selection information, the assignment candidate selection department selects potential assignment locations for the aforementioned individuals. Equipped with, The aforementioned selection criteria include conditions that restrict the types of work that a particular person can engage in. A placement selection support device characterized by the following features.

[0070] (Note 2) Multiple job groups are pre-configured according to the nature of the work. The condition that restricts the types of work that the aforementioned specific individuals can engage in is that the job group to which their new assignment belongs is different from any job group to which their previous assignment belonged. The assignment selection support device described in Appendix 1.

[0071] (Note 3) The system further includes a rule determination unit that determines whether the selected candidate assignment conforms to pre-set rules. A placement selection support device as described in Appendix 1 or 2.

[0072] (Note 4) The system further includes a target identification unit that identifies individuals subject to personnel changes by inputting the personnel information of each member of the organization into a machine learning model constructed using machine learning, which is trained on personnel information of individuals subject to past personnel changes and the results of past personnel changes. The assignment selection support device described in Appendix 1.

[0073] (Note 5) There are multiple individuals who are subject to the aforementioned personnel changes. The selection information generation unit calculates the indicator value for each of the multiple target individuals and for each of the identified assignment locations, and generates the selection information. The assignment candidate selection unit sets the assignment candidates for each of the multiple target persons such that the sum of the indicator values ​​for each of the target persons is maximized. The assignment selection support device described in Appendix 1.

[0074] (Note 6) A selection information generation step, in which, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, an indicator value is calculated for the relevant selection criteria, and selection information is generated that identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria, Based on the aforementioned selection information, a candidate placement step is made to select a candidate placement for the aforementioned person, It has, The aforementioned selection criteria include conditions that restrict the types of work that a particular person can engage in. A method for supporting the selection of a work assignment, characterized by the following features.

[0075] (Note 7) Multiple job groups are pre-configured according to the nature of the work. The condition that restricts the types of work that the aforementioned specific individuals can engage in is that the job group to which their new assignment belongs is different from any job group to which their previous assignment belonged. The method for supporting the selection of assignment locations, as described in Appendix 6.

[0076] (Note 8) The system further includes a rule determination step that determines whether the selected candidate assignment conforms to pre-set rules. The method for supporting the selection of assignment locations as described in Appendix 6 or 7.

[0077] (Note 9) The system further includes a target identification step, in which the personnel information of each member of the organization is input into a machine learning model, which is built using machine learning with personnel information of past personnel transfer targets and the results of past personnel transfers as training data, in order to identify the aforementioned personnel transfer targets. The method for supporting the selection of assignment locations, as described in Appendix 6.

[0078] (Note 10) There are multiple individuals who are subject to the aforementioned personnel changes. In the selection information generation step, for each of the multiple target individuals, the indicator value is calculated for each of the identified assignment locations, and the selection information is generated. In the aforementioned step of selecting a candidate assignment, the candidate assignment for each of the multiple individuals is set such that the sum of the respective indicator values ​​for each individual is maximized. The method for supporting the selection of assignment locations, as described in Appendix 6.

[0079] (Note 11) On the computer, A selection information generation step, in which, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, an indicator value is calculated for the relevant selection criteria, and selection information is generated that identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria, Based on the aforementioned selection information, a candidate placement step is made to select a candidate placement for the aforementioned person, Make it run, A program in which the aforementioned selection criteria include conditions that restrict the types of work that specific individuals can engage in.

[0080] (Note 12) Multiple job groups are pre-configured according to the nature of the work. The condition that restricts the types of work that the aforementioned specific individuals can engage in is that the job group to which their new assignment belongs is different from any job group to which their previous assignment belonged. The program described in Appendix 11.

[0081] (Note 13) To the aforementioned computer, The selected candidate assignment location will then be subjected to a rule determination step to determine whether it conforms to pre-set rules. The program described in Appendix 11 or 12.

[0082] (Note 14) To the aforementioned computer, A machine learning model, built using training data consisting of personnel information of past personnel transfer targets and the results of past personnel transfers, is then further executed by inputting the personnel information of each individual in the organization to identify the individuals to be transferred. The program described in Appendix 11.

[0083] (Note 15) There are multiple individuals who are subject to the aforementioned personnel changes. In the selection information generation step, for each of the multiple target individuals, the indicator value is calculated for each of the identified assignment locations, and the selection information is generated. In the aforementioned step of selecting a candidate assignment, the candidate assignment for each of the multiple individuals is set such that the sum of the respective indicator values ​​for each individual is maximized. The program described in Appendix 11. [Industrial applicability]

[0084] As described above, this disclosure can reduce the burden on HR personnel in personnel transfers. This disclosure is useful in fields where HR work is required. [Explanation of symbols]

[0085] 10 Assignment selection support device 11 Selection information generation section 12. Department for Selecting Candidates for Placement 13. Data Acquisition Unit 14. Department for Identifying Target Individuals 15 Machine Learning Models 16 Rule Determination Unit 17 Output Information Generation Unit 20 Input devices 30 Output device 110 Computer 111 CPU 112 Main Memory 113 Storage device 114 Input Interface 115 Display Controller 116 Data Readers / Writers 117 Communication Interface 118 Input devices 119 Display device 120 recording media 121 Bus

Claims

1. A selection information generation unit generates selection information that, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, calculates an indicator value for the relevant selection criteria, identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria. Based on the aforementioned selection information, the assignment candidate selection department selects potential assignment locations for the aforementioned individuals. A rule determination unit determines whether the selected candidate assignment conforms to pre-set rules, Equipped with, The aforementioned selection criteria include conditions that restrict the types of work that a particular person can engage in. A placement selection support device characterized by the following features.

2. Multiple job groups are pre-configured according to the nature of the work. The condition that restricts the types of work that the aforementioned specific individuals can engage in is that the job group to which their new assignment belongs is different from any job group to which their previous assignment belonged. The assignment selection support device according to claim 1.

3. The system further includes a target identification unit that identifies individuals subject to personnel changes by inputting the personnel information of each member of the organization into a machine learning model constructed using machine learning, which is trained on personnel information of individuals subject to past personnel changes and the results of past personnel changes. The assignment selection support device according to claim 1.

4. There are multiple individuals who are subject to the aforementioned personnel changes. The selection information generation unit calculates the indicator value for each of the multiple target individuals and for each of the identified assignment locations, and generates the selection information. The assignment candidate selection unit sets the assignment candidates for each of the multiple target persons such that the sum of the indicator values ​​for each of the target persons is maximized. The assignment selection support device according to claim 1.

5. A method performed by a computer, A selection information generation step, in which, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, an indicator value is calculated for the relevant selection criteria, and selection information is generated that identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria, Based on the aforementioned selection information, a candidate placement step is made to select a candidate placement for the aforementioned person, A rule determination step to determine whether the selected candidate assignment conforms to pre-established rules, It has, The aforementioned selection criteria include conditions that restrict the types of work that a particular person can engage in. A method for supporting the selection of a work assignment, characterized by the following features.

6. Multiple job groups are pre-configured according to the nature of the work. The condition that restricts the types of work that the aforementioned specific individuals can engage in is that the job group to which their new assignment belongs is different from any job group to which their previous assignment belonged. The method for supporting the selection of a department / workplace according to claim 5.

7. The system further includes a target identification step, in which the personnel information of each member of the organization is input into a machine learning model, which is built using machine learning with personnel information of past personnel transfer targets and the results of past personnel transfers as training data, in order to identify the aforementioned personnel transfer targets. The method for supporting the selection of a department / workplace according to claim 5.

8. There are multiple individuals who are subject to the aforementioned personnel changes. In the selection information generation step, for each of the multiple target individuals, the indicator value is calculated for each of the identified assignment locations, and the selection information is generated. In the aforementioned step of selecting a candidate assignment, the candidate assignment for each of the multiple individuals is set such that the sum of the respective indicator values ​​for each individual is maximized. The method for supporting the selection of a department / workplace according to claim 5.

9. On the computer, A selection information generation step, in which, when the personnel information of a person subject to personnel transfer meets any of a set of predefined selection criteria, an indicator value is calculated for the relevant selection criteria, and selection information is generated that identifies the calculated indicator value, the identifier of the person subject, and the assignment destination associated with the relevant selection criteria, Based on the aforementioned selection information, a candidate placement step is made to select a candidate placement for the aforementioned person, A rule determination step to determine whether the selected candidate assignment conforms to pre-established rules, Make it run, A program in which the aforementioned selection criteria include conditions that restrict the types of work that specific individuals can engage in.

10. Multiple job groups are pre-configured according to the nature of the work. The condition that restricts the types of work that the aforementioned specific individuals can engage in is that the job group to which their new assignment belongs is different from any job group to which their previous assignment belonged. The program according to claim 9.

11. To the aforementioned computer, A machine learning model, built using training data consisting of personnel information of past personnel transfer targets and the results of past personnel transfers, is then further executed by inputting the personnel information of each individual in the organization to identify the individuals to be transferred. The program according to claim 9.

12. There are multiple individuals who are subject to the aforementioned personnel changes. In the selection information generation step, for each of the multiple target individuals, the indicator value is calculated for each of the identified assignment locations, and the selection information is generated. In the aforementioned step of selecting a candidate assignment, the candidate assignment for each of the multiple individuals is set such that the sum of the respective indicator values ​​for each individual is maximized. The program according to claim 9.

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