System for outputting information related to human resources

Abstract

To provide a system for supporting appropriate description for human resource clusters.SOLUTION: A system for outputting information related to human resources is configured to obtain, as input, human resource datasets including information on a plurality of human resources, the human resource datasets being formed, for each of human resources, by associating information identifying a cluster that the human resources belong to, the number of years of experience of the human resources, and a value related to an attribute item other than the number of years of experience of the human resources. The system outputs, for each cluster, an average value or a central value of the numbers of years of experience of human resources belonging to the cluster. Regarding each combination of the cluster and the attribute item, when a ratio of human resources belonging to the cluster for which values related to the attribute items satisfy a predetermined criterion is equal to or larger than a predetermined threshold, the system outputs the attribute item as a high-ratio attribute item for the cluster.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a system for outputting information relating to human resources. [Background technology]

[0002] In organizations such as companies, analysis is performed by clustering a large number of human resources. For example, Patent Document 1 describes a system that determines whether to hire a job seeker based on the clusters of human resources. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2004-21908 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, with conventional technology, there was a problem that it was difficult to give appropriate meaning to a talent cluster. Meaning is important for determining appropriate names for clusters, for example. For example, if it is understood that a certain cluster "contains many talents with short years of experience," it is possible to give the cluster the name "young talent cluster" to make it easier to understand intuitively.

[0005] Conventional technology can classify a large number of people into clusters, but it is difficult to automatically determine the characteristics of the generated clusters in the context of the entire population of people.

[0006] For this reason, managers need to assign meaning to each cluster, but in order to assign appropriate meaning, it is necessary to conduct a detailed analysis of the characteristics of the personnel belonging to the cluster, which is not an easy task.

[0007] The present invention has been made to solve such problems, and has an object to provide a system that supports appropriate meaning assignment for talent clusters. [Means for solving the problem]

[0008] In one example of a system for outputting information related to human resources according to the present invention, The system receives as input a talent dataset that includes information relating to a plurality of talents; The human resource data set associates, for each human resource, information for identifying a cluster to which the human resource belongs, the human resource's years of experience, and values ​​related to attribute items other than the human resource's years of experience; The system outputs, for each cluster, an average or median value of years of experience of personnel belonging to the cluster; For each combination of a cluster and an attribute item, if the proportion of people belonging to the cluster whose values ​​for the attribute item satisfy a specified criterion is equal to or greater than a specified threshold, the system outputs the attribute item as a high-ratio attribute item for the cluster.

[0009] In one example, The talent data set further associates, for each talent, a price for the talent; The system outputs information regarding the prices of human resources belonging to a cluster in which the high ratio attribute item does not exist.

[0010] In one example, The talent data set further associates, for each talent, a price for the talent; The system outputs information regarding the prices of human resources belonging to the cluster in which the high ratio attribute item exists.

[0011] In one example, The talent data set further associates, for each talent, a price for the talent; The system acquires information designating any one of the attribute items as a designated attribute item, The system outputs information regarding the prices of human resources belonging to a cluster in which the specified attribute item is a high ratio attribute item.

[0012] In one example, the system outputs high ratio attribute items for each cluster and then accepts input of a semantic expression associated with each cluster. Effect of the Invention

[0013] The system according to the present invention can assist in making appropriate sense of talent clusters. [Brief description of the drawings]

[0014] [Figure 1] 1 is a diagram showing the configuration and processing contents of a human resources management system 100 according to a first embodiment of the present invention. [Diagram 2] 2 is an overview of the processing of the clustering device 10 of FIG. 1. [Diagram 3] An example of cluster feature information 42 in FIG. [Figure 4] 4 is an example of a semantic expression determined based on the cluster feature information 42 of FIG. 3. [Diagram 5] Part of the process related to cluster price analysis. [Figure 6] An example of price analysis results 43 in Figure 1. [Figure 7] Another example of the price analysis results 43 in Figure 1. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. [Embodiment 1] 1 is a diagram showing the configuration and processing contents of a human resources management system 100 according to a first embodiment of the present invention. The human resources management system 100 is a system that outputs information related to human resources. The human resources management system 100 includes a clustering device 10, a cluster feature analysis device 20, and a human resources data set price analysis device 30.

[0016] The clustering device 10, the cluster feature analysis device 20, and the human resources data set price analysis device 30 each have a hardware configuration as a known computer, and include, for example, a calculation means and a storage means. The calculation means includes, for example, a processor, and the storage means includes, for example, a storage medium such as a semiconductor memory device and a magnetic disk device. A part or all of the storage medium may be a non-transitory storage medium.

[0017] Furthermore, the clustering device 10, the cluster feature analysis device 20, and the human resources data set price analysis device 30 each include an input means and an output means. The input means includes input devices such as a keyboard and a mouse. The output means includes output devices such as a display and a printer. The input means and the output means may also include a communication device such as a network interface.

[0018] The storage means of the clustering device 10, the cluster feature analysis device 20, and the human resources dataset price analysis device 30 may store a program. The processors of the clustering device 10, the cluster feature analysis device 20, and the human resources dataset price analysis device 30 may execute the program, causing the clustering device 10, the cluster feature analysis device 20, and the human resources dataset price analysis device 30 to perform the functions described in this embodiment.

[0019] In the clustering device 10, the calculation means constitutes an information processing unit 11, and the output means constitutes an output unit 13. Also, the storage means constitutes a human resources information storage unit 14 and a human resources data set storage unit 15.

[0020] In the cluster feature analysis device 20, the calculation means constitutes an information processing unit 21, the input means constitutes an input unit 22, and the output means constitutes an output unit 23. In addition, the storage means constitutes a human resources data set storage unit 25, a parameter storage unit 26, and a cluster feature information storage unit 27.

[0021] In the human resources data set price analysis device 30, the calculation means constitutes an information processing unit 31, the input means constitutes an input unit 32, and the output means constitutes an output unit 33. In addition, the storage means constitutes a human resources data set storage unit 35, a parameter storage unit 36, and an analysis result storage unit 38.

[0022] An overview of the processing of the clustering device 10 is shown in Fig. 2. The operation of the clustering device 10 will be described below with reference to Figs. 1 and 2. The clustering device 10 is configured to be able to communicate with an external environment. The external environment is, for example, another computer or a storage medium. The clustering device 10 copies and acquires human resource information 41 from the external environment, and stores it in the human resource information storage unit 14.

[0023] The human resource information 41 associates, for each of a plurality of human resources, the years of experience of the human resource with values ​​related to attribute items other than the years of experience of the human resource. In this embodiment, the human resource information 41 further associates, for each of the human resources, the price of the human resource. The price may be expressed in any format, for example, as an hourly rate.

[0024] Here, the number of attribute items is one or more, and the contents can be appropriately defined by a person skilled in the art, but in this embodiment, they include language used, database used, role, work field, and work process. The format of the value defined for each attribute item is arbitrary, but for example, attribute items such as "work field-test / verification" and "work field-system development" may be defined, and flags may be specified as attribute values ​​for these attribute items. Alternatively, for example, an attribute item called "work field" may be defined, and one or more attribute values ​​such as "test / verification" and "system development" may be specified for each attribute item.

[0025] The information processing unit 11 of the clustering device 10 acquires the talent information from the talent information storage unit 14, clusters the talent based on the attribute items, and generates a plurality of talent clusters. The specific clustering process can be appropriately designed by a person skilled in the art based on known techniques, and for example, the k-means method can be used. Although a total of five clusters, cluster 0 to cluster 4, are illustrated in FIG. 2, the number of clusters to be generated is not limited to this, and may be arbitrarily designed, or may be automatically determined by the clustering device 10.

[0026] As a result of the clustering process, a talent dataset is generated. In addition to what is included in the talent information 41, the talent dataset further associates, for each talent, information that identifies the cluster to which the talent belongs. That is, the talent dataset storage unit 15 includes cluster information and talent information. Note that in this embodiment, the talent dataset associates, for each talent, the price of the talent, similar to the above-mentioned talent information 41. The information processing unit 11 stores the generated talent dataset in the talent dataset storage unit 15.

[0027] Next, the operation of the cluster feature analysis device 20 will be described. The input unit 22 acquires a talent dataset (including cluster information and talent information) as an input from the output unit 13 of the clustering device 10, and stores it in the talent dataset storage unit 25. The information processing unit 21 generates cluster feature information 42 for each cluster based on the talent dataset and threshold information stored in the parameter storage unit 26, and stores it in the cluster feature information storage unit 27.

[0028] FIG. 3 shows an example of cluster characteristic information 42. Cluster characteristic information 42 includes the average value of years of experience of human resources belonging to each cluster. Instead of or in addition to the average value, a median value may be included. Cluster characteristic information 42 also includes high-ratio attribute items for each cluster. In this specification, a "high-ratio attribute item" is a concept defined for a combination of a cluster and an attribute item, and an attribute item is considered to be a high-ratio attribute item when the proportion of human resources belonging to the cluster whose values ​​for that attribute item meet a predetermined standard is equal to or greater than a predetermined threshold.

[0029] This predetermined criterion can be arbitrarily defined, and for example, when the value (attribute value) related to the attribute item is a flag, it may be defined as the flag being on. When the attribute value is a character string, it may be defined as the attribute value including a specific character string defined in advance. Furthermore, when the attribute value is a numerical value, it may be defined as the attribute value being within a predetermined range. The predetermined threshold value of the human resource ratio can also be arbitrarily defined, and may be, for example, 70%.

[0030] In the example of Figure 3, a high ratio attribute item of "Work field - Testing / Verification" is shown for Cluster 2. This means that among the personnel belonging to Cluster 2, the ratio of personnel who fall under the attribute item of "Work field - Testing / Verification" is high (for example, 70% or more), which means that many of the personnel belonging to Cluster 2 specialize in testing and verification as a work field.

[0031] As described above, the specific attribute items and the format of the attribute values ​​can be defined arbitrarily, and for example, if the attribute value for the attribute item "Work field - Test / Verification" is defined as a flag, it means that 70% or more of the people belonging to cluster 2 have this flag. Or, for example, if the attribute value for the attribute item "Work field" is defined as a character string, it means that 70% or more of the people belonging to cluster 2 have an attribute value including "Test / Verification" for "Work field".

[0032] The output unit 23 acquires this cluster feature information 42 from the cluster feature information storage unit 27 and outputs it. That is, the cluster feature analysis device 20 outputs, for each cluster, the average number of years of experience of the personnel belonging to that cluster and the high ratio attribute items for that cluster. This allows the user of the human resource management system 100 to view the cluster feature information 42 displayed in a format such as that shown in FIG.

[0033] The cluster feature analysis device 20 outputs cluster feature information 42 (including high-ratio attribute items for each cluster) and then accepts input of a semantic expression associated with each cluster. Here, a user of the human resource management system 100 can appropriately determine a semantic expression for each cluster by referring to the cluster feature information 42. The semantic expression is input, for example, as a name (label) for each cluster. In this way, the human resource management system 100 can support appropriate semantic assignment of human resource clusters.

[0034] 4 shows an example of a semantic expression (name) determined based on the cluster characteristic information 42 in FIG. 3. The user can refer to the average years of experience and, for example, name cluster 0, which has short years of experience, as "mid-level / young employee," and cluster 1, which has long years of experience, as "veteran." In addition, the user can name cluster 2, which has multiple high-ratio attribute items related to infrastructure, as "infrastructure manager," cluster 3, which has multiple high-ratio attribute items related to application development, as "application developer (general)," and cluster 4, which has more high-ratio attribute items related to application development than cluster 3, as "application developer (senior)."

[0035] Next, the operation of the talent dataset price analysis device 30 will be described. The input unit 32 acquires the talent dataset (including cluster information and talent information) as input from the output unit 13 of the clustering device 10, and stores it in the talent dataset storage unit 35. The information processing unit 31 performs price analysis of each cluster based on the talent dataset and the cluster group information stored in the parameter storage unit 36, and stores the results in the analysis result storage unit 38. The output unit 33 acquires the results from the analysis result storage unit 38, and outputs them as price analysis results 43.

[0036] A part of the process related to the price analysis of the clusters is shown in Fig. 5. The information processing unit 31 performs a grouping process of the clusters to classify each cluster into either a general-purpose human resources cluster group or a specialized human resources cluster group.

[0037] Here, the parameter storage unit 36 ​​stores information designating one of the attribute items as cluster group information. The attribute item designated in the cluster group information is referred to as a "designated attribute item" in this specification. The information processing unit 31 acquires this cluster group information, and classifies the clusters in which the designated attribute item is a high-ratio attribute item into a specialized talent cluster group, and the clusters in which the designated attribute item is not a high-ratio attribute item into a general-purpose talent cluster group. The format of the cluster group information can be appropriately designed by a person skilled in the art according to the definition format of the attribute item, etc.

[0038] The example in Figure 5 is when attribute items related to "work field" and "work process" are specified as the specified attribute items. As shown in Figure 3, attribute items related to "work field" and "work process" are high-ratio attribute items for cluster 2, cluster 3, and cluster 4, so these clusters are classified into the specialized talent cluster group. On the other hand, attribute items related to "work field" and "work process" are not high-ratio attribute items for cluster 0 and cluster 1, so these clusters are classified into the general-purpose talent cluster group.

[0039] Figure 6 shows an example of a price analysis result 43. Figure 6(a) is an example of the result for the general-purpose talent cluster group, and Figure 6(b) is an example of the result for the specialized talent cluster group. For each cluster in each cluster group, information on the price of talent belonging to that cluster is output.

[0040] The content and format of the information on the price of talent can be appropriately determined by those skilled in the art, but in this embodiment, the quartile range of the price of talent belonging to each cluster is included. For example, in the "Mid-level / Young" cluster (cluster 0), the 25% position from the lowest price (first quartile) is 5,689 yen, the 50% position from the lowest price (second quartile, i.e., median) is 6,682 yen, and the 75% position from the lowest price (third quartile) is 7,758 yen.

[0041] The human resources data set price analysis device 30 may output only the results for the general-purpose human resources cluster group as shown in Fig. 6(a), may output only the results for the specialized human resources cluster group as shown in Fig. 6(b), or may output both of these. That is, the human resources data set price analysis device 30 may output information on the prices of human resources belonging to a cluster whose designated attribute item is not a high-ratio attribute item, or may output information on the prices of human resources belonging to a cluster whose designated attribute item is a high-ratio attribute item, or may do both of these.

[0042] The general-purpose talent cluster group is a group of general-purpose talent that is not biased toward specific knowledge, skills, or work responsibilities, and analysis can determine the price range required for procuring talent for such a cluster. On the other hand, the specialized talent cluster group is a group of specialized talent that is well versed in specific knowledge, skills, or work responsibilities, and analysis can determine the price range required for procuring talent for such a cluster.

[0043] FIG. 7 shows another example of the price analysis result 43. Information on the prices of human resources belonging to each cluster is output as a box-and-whisker plot. The box-and-whisker plot shows the quartiles and outliers of the human resources prices for each cluster. In addition to the box-and-whisker plot, information indicating whether each cluster belongs to a general-purpose human resources cluster group or a specialized human resources cluster group is also shown. This output format allows the user of the human resources management system 100 to visually and easily grasp the price distribution.

[0044] In this way, the human resource management system 100 automatically associates clusters with price information and generates cluster groups. In particular, by automatically associating each cluster with price information and automatically classifying clusters into cluster groups, it becomes possible to simulate the case where human resources belonging to a specific cluster, such as infrastructure personnel, are procured from a human resource group according to the purpose of price estimation such as mid-term management plans, budget formulation, and project plans, and the case where human resources belonging to a specific cluster are replaced, for example, by replacing some mid-level / young personnel with veterans.

[0045] In this embodiment, the information processing unit 31 groups the clusters based on the cluster group information in the parameter storage unit 36, but in a modified example, the cluster group information may be omitted. In that case, the information processing unit 31 may classify a cluster having any high-ratio attribute item into a specialized talent cluster group, and classify a cluster having no high-ratio attribute item into a general-purpose talent cluster group. In such a modified example, the talent dataset price analysis device 30 outputs information regarding the price of talent belonging to a cluster having no high-ratio attribute item and / or a cluster having a high-ratio attribute item.

[0046] Those skilled in the art may arbitrarily add, modify or delete components within the scope of the present invention in the above-described embodiment 1. For example, in the human resource management system 100, any of the clustering device 10, the cluster feature analysis device 20 and the human resource data set price analysis device 30 may be divided into multiple computers, or two or more of the clustering device 10, the cluster feature analysis device 20 and the human resource data set price analysis device 30 may be configured by a single computer.

[0047] The clustering device 10 and the talent data set price analysis device 30 may be omitted. A known clustering device may be used instead of the clustering device 10. Even if a clustering device is not used, processing by the cluster feature analysis device 20 is possible if a talent data set in an appropriate format is prepared. [Explanation of symbols]

[0048] 10…Clustering device 11...Information processing section 13...Output section 14…Human Resources Information Storage Section 15…Human Resources Data Set Storage Section 20…Cluster feature analysis device 21...Information processing section 22...Input section 23...Output section 25…Human Resources Data Set Storage Section 26...Parameter storage section 27…Cluster feature information storage unit 30…Human Resources Data Set Price Analysis Device 31...Information processing section 32...Input section 33...Output section 35…Human Resources Data Set Storage Section 36...Parameter storage section 38…Analysis result storage unit 41. Human Resources Information 42…Cluster feature information 43...Price analysis results 100…Human Resources Management System

Claims

1. A system for outputting information related to human resources, The system receives as input a talent dataset that includes information relating to a plurality of talents; The human resource data set associates, for each human resource, information for identifying a cluster to which the human resource belongs, the human resource's years of experience, and values ​​related to attribute items other than the human resource's years of experience; The system outputs, for each cluster, an average or median value of years of experience of personnel belonging to the cluster; The system outputs, for each combination of a cluster and an attribute item, an attribute item as a high-ratio attribute item for the cluster when a ratio of human resources belonging to the cluster whose values ​​for the attribute item satisfy a predetermined criterion is equal to or greater than a predetermined threshold. system.

2. The talent data set further associates, for each talent, a price for the talent; The system outputs information regarding the price of human resources belonging to a cluster in which the high ratio attribute item does not exist. The system of claim 1 .

3. The talent data set further associates, for each talent, a price for the talent; The system outputs information regarding the price of human resources belonging to the cluster in which the high ratio attribute item exists. The system of claim 1 .

4. The talent data set further associates, for each talent, a price for the talent; The system acquires information designating any one of the attribute items as a designated attribute item, The system outputs information regarding the price of human resources belonging to a cluster in which the designated attribute item is a high ratio attribute item. The system of claim 1 .

5. The system of claim 1 , wherein the system outputs high ratio attribute items for each cluster and then accepts input of a semantic expression associated with each cluster.

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