Information processing device, information processing method, and information processing program

Abstract

To improve multiple operational metrics of an organization, it is necessary to identify which of the various health metrics of the organization's members should be prioritized for improvement. [Solution] The information processing device 10 acquires multiple health indicator values ​​for each of the multiple members belonging to the organization to be analyzed. Based on the improvement range of each of the multiple health indicator values ​​of the organization to be analyzed and the degree of correlation between each of the multiple health indicator values ​​of the organization and each of the multiple operational indicator values ​​of the organization, the information processing device 10 calculates the operational indicator value of the organization to be analyzed when each of the multiple health indicator values ​​of the organization to be analyzed changes in accordance with the improvement range. Based on the improvement range of the multiple operational indicator values, which represents the difference between the operational indicator value before the health indicator value is improved and the operational indicator value after the health indicator value is improved, the information processing device 10 calculates the priority of improvement for each of the multiple health indicator values.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and an information processing program. [Background Art]

[0002] Conventionally, there has been known a technique for visualizing the effect obtained by changing indicators related to the health of group members (see, for example, Patent Document 1). The prediction device disclosed in Patent Document 1 calculates a first predicted value of an outcome indicator for evaluating an effect obtained when any one of the plurality of health indicators is improved, based on target values of each of the plurality of health indicators related to the health of the plurality of members belonging to the group, accepts a change instruction to change the target value or values of one or more health indicators among the target values of each of the plurality of health indicators, changes the target value or values of the one or more health indicators based on the change instruction, thereby generating post-change target values of the one or more health indicators, and calculates a second predicted value of the outcome indicator based on the target values of health indicators other than the one or more health indicators among the target values of each of the plurality of health indicators, and the post-change target values of the one or more health indicators. Then, before the change instruction is accepted, the prediction device outputs first display data for displaying the target values of each of the plurality of health indicators and the first predicted value of the outcome indicator, and after the second predicted value of the outcome indicator is calculated, outputs second display data for displaying the second predicted value of the outcome indicator. [Prior Art Documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Unexamined Patent Publication No. 2023-061109 [Summary of the Invention] [Problem to be Solved by the Invention]

[0004] It is considered that an evaluation index related to the health status of members belonging to an organization such as a company (hereinafter, also simply referred to as a "health index") is related to an evaluation index related to the operation status of the organization (hereinafter, also simply referred to as an "operation index").

[0005] For this reason, it is considered that improving the health indices of the members belonging to the organization can improve the operation indices of the organization. In this case, in order to improve a plurality of operation indices of the organization, there are cases where it is desired to identify which of the plurality of health indices should be preferentially improved.

[0006] Although the above Patent Document 1 discloses calculating a predicted value of an outcome index for evaluating the effect when a health index is improved, it does not consider which of the plurality of health indices is preferably preferentially improved.

[0007] The present disclosure has been made in view of the above points, and an object of the present disclosure is to identify which health index among a plurality of health indices of members belonging to an organization should be preferentially improved in order to improve a plurality of operation indices of the organization. [Means for Solving the Problems]

[0008] To achieve the above objective, the information processing device relating to this disclosure includes: an acquisition unit that acquires a plurality of health indicator values ​​representing the health status of each of the plurality of members belonging to the organization to be analyzed; an improvement indicator calculation unit that calculates the operational indicator values ​​of the organization to be analyzed when the plurality of health indicator values ​​of the organization to be analyzed change in accordance with the improvement range, based on the improvement range of the plurality of health indicator values ​​of the organization to be analyzed and the degree of correlation between each of the plurality of health indicator values ​​of each of the plurality of organizations and each of the plurality of operational indicator values ​​representing the operational status of each of the plurality of organizations; and a priority calculation unit that calculates the priority of improvement for the plurality of health indicator values ​​based on the improvement range of a plurality of operational indicator values ​​representing the difference between the operational indicator values ​​of the organization to be analyzed before the health indicator values ​​are improved and the operational indicator values ​​of the organization to be analyzed after the health indicator values ​​are improved.

[0009] Furthermore, the information processing method disclosed herein is an information processing method in which a computer performs the following steps: obtains multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization under analysis; calculates the operational indicator values ​​of the organization under analysis when the multiple health indicator values ​​of the organization under analysis change in accordance with the improvement range, based on the improvement range of the operational indicator values ​​representing the difference between the operational indicator values ​​of the organization under analysis before the improvement and the operational indicator values ​​of the organization under analysis after the improvement, and calculates the priority for improvement of the multiple health indicator values.

[0010] Furthermore, the information processing program disclosed herein is an information processing program that causes a computer to execute the following processes: acquire multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization under analysis; calculate the operational indicator values ​​of the organization under analysis when the multiple health indicator values ​​of the organization under analysis change in accordance with the improvement range, based on the improvement range of the operational indicator values ​​representing the difference between the operational indicator values ​​of the organization under analysis before the improvement in the health indicator values ​​and the operational indicator values ​​of the organization under analysis after the improvement in the health indicator values. [Effects of the Invention]

[0011] According to this disclosure, in order to improve multiple operational indicators of an organization, it is possible to identify which of the multiple health indicators of the members belonging to that organization should be prioritized for improvement. [Brief explanation of the drawing]

[0012] [Figure 1] This is a diagram illustrating the health indicators of this embodiment. [Figure 2] This diagram illustrates the range of improvement in the health indicators of this embodiment. [Figure 3] This diagram illustrates the improvement in the operational indicators of this embodiment. [Figure 4] This figure shows the hardware configuration of the information processing device according to this embodiment. [Figure 5] This is a block diagram showing the schematic configuration of the information processing device of this embodiment. [Figure 6] This is a diagram illustrating the distribution of various types of corporations. [Figure 7] This figure shows an example of a table that correlates the improvement in multiple KPI values ​​with the improvement in multiple KGI values. [Figure 8]This figure shows an example of a table where KPIs, which have a high impact on the KGI, are sorted by priority. [Figure 9] This is a flowchart showing the information processing flow in this embodiment. [Figure 10] This is a flowchart showing the information processing flow in this embodiment. [Figure 11] This is a flowchart showing the information processing flow in this embodiment. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the present disclosure will be described with reference to the drawings. In this embodiment, an information processing device according to the present disclosure will be used as an example. In each drawing, the same or equivalent components and parts are given the same reference numerals. Also, the dimensions and proportions in the drawings are exaggerated for illustrative purposes and may differ from the actual proportions.

[0014] Figure 1 is a diagram illustrating the health indicators used in this embodiment. Figure 1 shows KPIs (Key Performance Indicators) as examples of health indicators for members belonging to an organization (for example, a corporation or other organization).

[0015] As shown in Figure 1, the KPIs of this embodiment include evaluation indicators that represent an individual's state (labeled "Individual State" in Figure 1) and evaluation indicators that an individual uses when evaluating an organization (labeled "Organizational State" in Figure 1). Note that the KPIs may also include evaluation indicators that represent an individual's state other than those shown in Figure 1, such as indicators related to an individual's health or physical condition. Other KPIs may also include indicators related to the relationship between the individual and the organization, such as trust in the organization, satisfaction with the evaluation, or understanding of the organization.

[0016] Furthermore, as shown in Figure 1, "individual status" includes "objective assessment of health status" and "subjective assessment of one's own physical condition." Similarly, "organizational status" includes "evaluation of work" and "evaluation of the organization to which one belongs."

[0017] The "objective assessment of health status" shown in Figure 1 is, for example, data obtained through health checkups. The "subjective assessment of one's own physical condition," "assessment of work performance," and "assessment of the organization to which one belongs" shown in Figure 1 are, for example, data obtained from stress checks (e.g., interviews) conducted on members.

[0018] On the other hand, Key Goal Indicators (KGIs) are known as operational indicators that represent the operational status of an organization. Examples of KGIs include absenteeism, presenteeism, and work engagement.

[0019] Absenteeism is an indicator related to opportunity losses, such as long-term absences due to health reasons among members of an organization. For example, if a member is unable to perform their duties due to poor physical or mental health, such as being late, leaving early, being absent due to difficulty working, or taking leave of absence, then the level of absenteeism is considered high.

[0020] Presenteeism, on the other hand, is an indicator of decreased productivity due to health reasons among members of an organization. For example, if members of an organization are engaged in work but their work efficiency is reduced due to their physical or mental health problems, then presenteeism is considered low.

[0021] Furthermore, work engagement is an indicator that represents, for example, the feelings that members have towards the organization. For instance, if the degree of attachment members have to the organization or the degree of passion members have for their work is high, then work engagement can be said to be at a high level.

[0022] In addition to the above, there are other indicators that can be considered as KGIs. For example, indicators related to the reduction of medical expenses can also be considered as KGIs.

[0023] KPIs and KGIs are related, and KPIs can be said to be factors of KGIs. Therefore, in this embodiment, the degree of correlation between KPIs and KGIs is calculated, the extent to which KGIs improve when KPIs are improved is identified, and the priority of KPIs to be improved is calculated. This makes it possible to identify which KPIs, which are examples of multiple health indicators for members of an organization, should be prioritized for improvement in order to improve multiple KGIs, which are examples of multiple operational indicators for an organization. In the following explanation, we will use the case where the organization is a corporation and its members are employees as an example.

[0024] Figure 2 is a diagram illustrating this embodiment. The table in Figure 2 shows the following columns: "KPI Category," "KPI," "Comparable Companies," "Performance of the Analyzed Company," and "Difference from the Comparable Companies."

[0025] In Figure 2, the "Comparable Companies" column shows, for example, the KPI values ​​of a group of companies considered to be high-performing. The "Analyzed Company's Performance" column shows, for example, the KPI values ​​of the analyzed company. The "Difference from Comparable Companies" column shows the difference between the KPI values ​​in the "Comparable Companies" column and the KPI values ​​in the "Analyzed Company's Performance" column.

[0026] As shown in Figure 2, among the multiple KPI values, "blood glucose risk" is "-5". This "blood glucose risk" is generally an indicator influenced by age, diet, exercise, obesity, and stress, and it is expected that employees belonging to the analyzed company have better diet and exercise habits than employees belonging to the comparison group of companies.

[0027] Furthermore, the "Difference from Comparable Companies" column in Figure 2 shows the difference between the KPI values ​​in the "Comparable Companies" column and the KPI values ​​in the "Analysis Target Company's Performance" column. In this embodiment, for each of the multiple KPIs, the difference between the analysis target company's KPI value and the comparison company's KPI value is set as the improvement range. Then, in this embodiment, by improving each KPI value for which an improvement range has been set, the improvement range of the KGI when each KPI value reaches the target value is calculated.

[0028] Figure 3 is a diagram illustrating the improvement range of KGIs. Figure 3 shows the improvement ranges for several KGIs, namely absenteeism (physical), absenteeism (mental), and presenteeism, when each KPI value is improved to its target value. Absenteeism (physical) is physical absenteeism, and is an indicator where improvement is most significant through improvements in physical complaints, including sleep disorders, gastrointestinal issues, and musculoskeletal and sensory organ problems among employees. Absenteeism (mental) is mental absenteeism, and is an indicator where improvement is most significant through improvements in employee autonomy in their work, growth opportunities, and support from supervisors. Presenteeism is an indicator where improvement is most significant through improvements in employee suitability for their work, clarity of target roles, and reduced feelings of depression. Note that in Figure 3, the improvement range of KGI values ​​is converted into monetary values. As shown in Figure 3, it can be seen that the improvement range for presenteeism is greater than the improvement range for absenteeism. In this embodiment, in order to improve the above-mentioned multiple presenteeisms, a priority is calculated to show which of the multiple KPI values ​​should be prioritized for improvement. The following provides a detailed explanation.

[0029] <Information Processing Device 10>

[0030] Figure 4 is a block diagram showing the hardware configuration of the information processing device 10 according to this embodiment. As shown in Figure 4, the information processing device 10 includes a CPU (Central Processing Unit) 42, memory 44, storage device 46, input / output I / F (Interface) 48, storage medium reader 50, and communication I / F 52. Each component is connected to the others via a bus 54 so as to be able to communicate with each other.

[0031] The storage device 46 stores programs for executing the processes described later. The CPU 42 is a central processing unit that executes various programs and controls each component. Specifically, the CPU 42 reads programs from the storage device 46 and executes them using memory 44 as a workspace. The CPU 42 controls each component and performs various calculations according to the programs stored in the storage device 46.

[0032] Memory 44 consists of RAM (Random Access Memory) and temporarily stores programs and data as a working area. Storage device 46 consists of ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and stores various programs including the operating system and various data.

[0033] The I / F48 is an interface for inputting data from and outputting data to external devices. It may also be connected to various input devices, such as keyboards and mice, and output devices, such as displays and printers, for outputting various types of information. A touch panel display may also function as an input device by being used as an output device.

[0034] The storage medium reader 50 reads data stored on various storage media such as CD (Compact Disc)-ROM, DVD (Digital Versatile Disc)-ROM, Blu-ray disc, and USB (Universal Serial Bus) memory, and writes data to the storage media.

[0035] Communication I / F52 is an interface for communicating with other devices, and standards such as Ethernet®, FDDI, and Wi-Fi® are used.

[0036] Next, the functional configuration of the information processing device 10 will be described. As shown in Figure 5, the information processing device 10 functionally includes a processing unit 24, an acquisition unit 26, an improvement indicator calculation unit 28, a priority calculation unit 30, and an output unit 32. In addition, a corporate data storage unit 20 and a processing data storage unit 22 are provided in a predetermined storage area of ​​the information processing device 10. Each functional configuration is realized by the CPU 42 reading each program stored in the storage device 46, expanding it into memory 44, and executing it.

[0037] The corporate data storage unit 20 stores multiple KPI values ​​obtained from multiple employees belonging to each of multiple corporations. The corporate data storage unit 20 also stores multiple KGI values ​​for each of the multiple corporations. Each corporation is associated with its own KPI and KGI values, and this data is used to generate the statistical model described later.

[0038] The processing data storage unit 22 stores the data obtained from each of the processes described later.

[0039] The processing unit 24 calculates the degree of association between KPI values and KGI values of a plurality of corporations stored in the corporate data storage unit 20 using a multiple regression analysis model, which is an example of a statistical model. In the present embodiment, a case where the degree of association between KPI values and KGI values of a plurality of corporations is calculated using a multiple regression analysis model will be described as an example, but the present invention is not limited thereto. For example, the degree of association between KPI values and KGI values of a plurality of corporations may be calculated using a generalized linear model or a nonlinear regression model based on machine learning.

[0040] The following formula is an example of a formula of a multiple regression analysis model where Y1, Y2, Y3, which are KGI values, are used as objective variables and a plurality of KPI values X1, X2, X3, ... are used as explanatory variables. Each of Y1, Y2, Y3 is, for example, a value representing presenteeism, a value representing absenteeism, and a value representing work engagement. Further, X1, X2, X3, ... are, for example, blood pressure risk values, blood glucose risk values, lipid risk values, etc., which are KPI values. In order to uniformly handle differences in dimensions and differences in numerical ranges between variables, X1, X2, X3, ... may be normalized to have a mean of 0 and a variance of 1 so that risk values can be compared

[0041] Y1=β 1,0 +β 1,1 X1+β 1,2 X2+β 1,3 X3+··· Y2=β 2,0 +β 2,1 X1+β 2,2 X2+β 2,3 X3+··· Y3=β 3,0 +β 3,1 X1+β 3,2 X2+β 3,3 X3+···

[0042] In the present embodiment, as the degree of association between KPI values and KGI values of a plurality of corporations, the above β 1,0 ,β 1,1 ,β 1,2 ,β 1,3 ···β 2,0 ,β 2,1 ,β2,2 ,β 2,3 ,β 3,0 ,β 3,1 ,β 3,2 ,β 3,3 ... (hereinafter simply referred to as "β") is calculated.

[0043] Therefore, the processing unit 24 uses a multiple regression analysis model, which is an example of a statistical model, to calculate the correlation β between each of the multiple KPI values ​​of each of the multiple corporations and each of the multiple KGI values ​​of each of the multiple corporations. The processing unit 24 then stores the correlation β in the processing data storage unit 22. This correlation β will be used in the processing described later. The correlation β is also a value that represents the change in the KGI value in relation to the change in each of the multiple KPI values ​​of the multiple corporations.

[0044] Furthermore, the processing unit 24 calculates the average value and standard deviation for each KPI value among multiple corporations. Generally, multiple employees belong to one corporation. Therefore, the average value of each KPI of multiple employees belonging to one corporation can serve as a representative value of the KPI value representing that corporation. Accordingly, in this embodiment, the average value of the KPI values ​​obtained from each of the multiple employees belonging to one corporation is defined as the representative value of the KPI value for that one corporation.

[0045] Figure 6 illustrates the calculation of the mean and standard deviation of KPI values ​​across multiple corporations. The distribution D of KPI values ​​for the multiple corporations shown in Figure 6 represents the distribution of KPI values ​​for all employees belonging to each of the multiple corporations. On the other hand, distributions D1, D2, D3, D4, and D5 shown in Figure 6 represent the distribution of KPI values ​​for employees belonging to one of the multiple corporations. In this case, the variance σ of the distribution D of KPI values ​​for the multiple corporations is... all 2 And the variance σ of the distribution of KPI values ​​for one company (e.g., D4) A 2 And the variance σ of the distribution of KPI values ​​between corporations B 2 These can be defined as follows. The relationship between these variances is expressed by the following equation.

[0046] σ all 2 =σ A 2 +σ B 2

[0047] In this embodiment, in order to compare the KPI values ​​of the company under analysis with the KPI values ​​of the group of companies being compared, the variance σ described above is used. all 2 ,σ A 2 ,σ B 2 The calculation of the mean and standard deviation σ for each KPI value among multiple companies is required. B The processing unit 24 then calculates the mean and standard deviation σ for each KPI value among multiple companies. B The data is stored in the processing data storage unit 22. The mean and standard deviation σ for each KPI value among these multiple companies. B This will be used in the process described later.

[0048] As described above, the correlation β and the mean and standard deviation σ for each KPI value across multiple corporations. B Once this is calculated, it becomes possible to calculate the improvement in the KGI value of the company being analyzed.

[0049] The acquisition unit 26 acquires multiple KPI values ​​for each of the multiple employees belonging to the company under analysis from the corporate data storage unit 20. The acquisition unit 26 also acquires multiple KPI values ​​for each of the multiple employees belonging to the group of companies being compared from the corporate data storage unit 20.

[0050] The improvement indicator calculation unit 28 calculates the KGI value of the analyzed corporation when each of the multiple KPI values ​​of the analyzed corporation is improved to its target value by changing in accordance with the improvement range of each of the multiple KPI values ​​of the analyzed corporation, based on the improvement range of each of the multiple KPI values ​​of the analyzed corporation acquired by the acquisition unit 26 and the correlation β stored in the processing data storage unit 22.

[0051] Specifically, the improvement indicator calculation unit 28 first calculates the mean and standard deviation of each of the multiple KPI values ​​of the company under analysis. At this time, the mean and standard deviation of the KPI values ​​of multiple employees belonging to the company under analysis are calculated.

[0052] Next, the improvement indicator calculation unit 28 calculates the average value and standard deviation for each KPI value of the target corporation, and the average value and standard deviation σ for each KPI value among multiple corporations. B Based on this, each of the multiple KPI values ​​of the company being analyzed is converted into a first-degree standard score.

[0053] The mean and standard deviation σ for each KPI value across multiple corporations. B Since the data is stored in the processing data storage unit 22, the improvement indicator calculation unit 28 calculates the average value and standard deviation σ for each KPI value among multiple corporations from the processing data storage unit 22. B The data is read out. Then, the improvement indicator calculation unit 28 calculates the average value for each KPI value of the target corporation, and the average value and standard deviation σ for each KPI value among multiple corporations. B Based on this, a known standard score calculation method is used to convert each of the multiple KPI values ​​of the company being analyzed into a first standard score.

[0054] For example, the KPI values ​​shown in "Performance of the Analyzed Company" in Figure 2 are converted into standard scores, and the values ​​in each row shown in "Performance of the Analyzed Company" are examples of the first standard score.

[0055] Next, the improvement indicator calculation unit 28 calculates the mean and standard deviation of each of the multiple KPI values ​​of the comparison group of companies.

[0056] The group of comparable companies is selected in advance based on, for example, the attributes of each company. For example, the group of comparable companies may be selected based on factors such as the type of business, the percentage of female employees, the rate of non-regular employment, the average age of employees, the number of employees, the average monthly overtime hours per employee, the industry to which the company belongs, or whether or not the company is considered a high-performing company. For example, the group of comparable companies may consist of companies that are considered high-performing companies. Alternatively, the group of comparable companies may consist of multiple other companies belonging to the same industry as the company being analyzed. For example, if the group of comparable companies consists of companies corresponding to distribution D1 and distribution D2 in Figure 6, the mean and standard deviation of the KPI values ​​for those groups of companies are calculated.

[0057] Next, the improvement indicator calculation unit 28 calculates the mean and standard deviation for each KPI value of the comparison group of corporations, and the mean and standard deviation σ for each KPI value among multiple corporations. B Based on this, a known standardization method is used to convert each of the multiple KPI values ​​of the comparison group of companies into a second standard score.

[0058] For example, the KPI values ​​shown in the "Comparable Companies Group" in Figure 2 are converted to standard scores, and the values ​​in each row shown in the "Comparable Companies Group" are examples of the second standard score.

[0059] Next, the improvement indicator calculation unit 28 sets the second standard score as the target value and calculates the standard score improvement range, which represents the difference between the second standard score and the first standard score. The standard score improvement range is an example of the improvement range for each of the multiple KPI values ​​of the company under analysis.

[0060] Next, the improvement indicator calculation unit 28 calculates the KGI value of the analyzed company after improvement, assuming that the first standard score of the analyzed company improves to the target value, by multiplying the improvement range of the standard score by the correlation β stored in the processing data storage unit 22.

[0061] The improvement indicator calculation unit 28 may also be configured to correct the correlation β. In this case, the improvement indicator calculation unit 28 adjusts the variance σ for each KPI value among multiple companies. B 2 The variance σ for each KPI value of multiple corporationsall 2 The corrected correlation β' is calculated by multiplying the correlation β by the correction value obtained by dividing by β.

[0062] As mentioned above, the correlation β obtained by multiple regression analysis is calculated using the KPI values ​​of all employees belonging to each of the multiple corporations. However, in this embodiment, the target is the KPI values ​​of each corporation, and it can be said that it is not appropriate to use the correlation β calculated using the KPI values ​​of all employees belonging to each of the multiple corporations when calculating the first standard score of the corporation under analysis and the second standard score of the group of corporations being compared.

[0063] Therefore, the improvement indicator calculation unit 28 calculates the variance σ for each KPI value among multiple corporations. B 2 The variance σ for each KPI value of multiple corporations all 2 The corrected correlation β' is calculated by multiplying the correlation β by the correction value obtained by dividing by the first deviation score. The improvement indicator calculation unit 28 then calculates the improved KPI value of the analyzed company after improvement, assuming the first deviation score has improved to the target value, by multiplying the improvement range of the KPI value, which represents the difference between the second deviation score and the first deviation score, by the corrected correlation β'.

[0064] Next, the priority calculation unit 30 calculates the priority of improvement for each of the multiple KPI values ​​based on the improvement in KGI values, which represents the difference between the KGI value of the analyzed company before the KPI value was improved and the KGI value of the analyzed company after the KPI value was improved. At this time, the priority calculation unit 30 converts the improvement in KGI values ​​into monetary value. Therefore, the priority calculation unit 30 calculates the priority of improvement for each of the multiple KGI values ​​according to the monetary value representing the improvement in KGI values. Each value shown in Figure 3 corresponds to the improvement in KGI values ​​converted into monetary value.

[0065] Any method can be used to convert to a monetary value. For example, the priority calculation unit 30 may calculate the monetary value representing the improvement in the KGI value by referring to a table that associates the improvement in the KGI value with a monetary value. Alternatively, for example, the priority calculation unit 30 may calculate the monetary value representing the improvement in the KGI value according to the following formula. Note that ΔKGI represents the improvement in the KGI value, ΔKPI represents the improvement in the KPI value, and ΔLoss Amount is the amount of loss that may occur due to the change in KGI.

[0066] The monetary value representing the improvement in KGI value = (ΔLoss Amount / ΔKGI) × (ΔKGI / ΔKPI × ΔKPI)

[0067] The monetary value representing the improvement in presenteeism can be calculated, for example, using the following formula:

[0068] A monetary value representing the improvement in presenteeism. = Total salary × (Δ productivity / Δ standard score × improvement of standard score by 1)

[0069] The total salary for all employees [yen] is calculated from the data of the analyzed companies. Additionally, the productivity improvement [%] for a 1-point improvement in a KPI is calculated using the formula (Δproductivity / Δstandard score × improvement of 1 standard score). This productivity improvement [%] is calculated from the data of each of the multiple companies.

[0070] Furthermore, the monetary value representing the improvement in absenteeism can be calculated using the following formula.

[0071] Monetary value representing the improvement in absenteeism = Average annual income × Average number of working days per year for employees on leave × (Δ Number of employees on leave / Δ Standard score × Improvement of standard score by 1)

[0072] The average total loss for employees on leave [yen] is calculated by (average annual income × average number of working days per year for employees on leave). This average total loss for employees on leave [yen] is calculated from the data of the companies being analyzed. In addition, the improvement in the number of employees on leave [people] when the KPI improves by 1 point is calculated by (Δ number of employees on leave / Δ standard score × improvement of standard score by 1). This improvement in the number of employees on leave [people] is calculated from the data of each of the multiple companies.

[0073] The priority calculation unit 30 then calculates the priority of improvement for each of the multiple KPI values ​​based on the monetary value representing the improvement range of the KGI value. For example, the priority calculation unit 30 calculates the priority of improvement for each of the multiple KPI values ​​based on the sum of the monetary values ​​representing the improvement range of the multiple KGI values.

[0074] Figure 7 is a table that associates the improvement ranges of multiple KPI values ​​with the improvement ranges of multiple KGI values. As shown in Figure 7, each improvement range of a multiple KPI value is associated with the improvement range (in monetary terms) of each of the multiple KGI values. For example, the priority calculation unit 30 calculates the sum of the improvement ranges (in monetary terms) of each of the multiple KGI values ​​for each KPI value. The priority calculation unit 30 then uses the order of the sums of the improvement ranges (in monetary terms) of each KGI value as the priority for improving the KPI values.

[0075] For example, as shown in Figure 7, the KPI "Work Autonomy" has a higher sum of improvements in absenteeism (physical), absenteeism (mental), and presenteeism than other KPIs, making it the KPI that should have the highest priority for improvement. Also, as shown in Figure 7, the KPI "Fatigue" has a higher sum of improvements in absenteeism (physical), absenteeism (mental), and presenteeism than the KPI "Stress Response," making it the KPI that should have the second highest priority for improvement after the KPI "Stress Response."

[0076] Figure 8 is a table showing KPIs with a high impact on the KGI, sorted by priority. In the example shown in Figure 8, among the multiple KPIs, "autonomy in work" has the highest overall impact on the KGI and therefore has the highest priority.

[0077] The output unit 32 outputs the improvement priority for each of the multiple KPI values ​​calculated by the priority calculation unit 30. For example, the output unit 32 may output the improvement priority for each of the multiple KPI values ​​in the format shown in Figures 7 and 8.

[0078] Next, the operation of the information processing device 10 according to this embodiment will be described.

[0079] When the information processing device 10 receives a predetermined instruction signal, the CPU 42 of the information processing device 10 reads the information processing program from the storage device 46, loads it into memory 44, and executes it. As a result, the CPU 42 functions as each of the functional configurations of the information processing device 10, and the information processing shown in Figure 9 is executed.

[0080] In step S100, the processing unit 24 retrieves KPI value data and KGI value data for multiple corporations stored in the corporate data storage unit 20.

[0081] In step S102, the processing unit 24 performs known processing on the missing data contained in the KPI value data and KGI value data of multiple corporations obtained in step S100, thereby converting it into data that does not contain missing data. For example, the processing unit 24 removes missing data from the KPI value data and KGI value data of multiple corporations.

[0082] In step S104, the KPI values ​​of multiple companies obtained in step S102 are normalized so that the mean is 0 and the variance is 1. This normalizes the KPI values, which contain a variety of values.

[0083] In step S106, the processing unit 24 uses a multiple regression analysis model, which is an example of a statistical model, to calculate the correlation β between the KPI values ​​and KGI values ​​of multiple corporations.

[0084] In step S108, the processing unit 24 stores the correlation β obtained in step S106 in the processing data storage unit 22.

[0085] Next, when the information processing device 10 receives a predetermined instruction signal, the information processing device 10 executes the process shown in Figure 10.

[0086] In step S200, the processing unit 24 retrieves KPI value data and KGI value data for multiple corporations stored in the corporate data storage unit 20.

[0087] In step S202, the processing unit 24, similar to step S102, performs known processing on the missing data contained in the KPI value data and KGI value data of multiple corporations obtained in step S200, thereby converting them into data that does not contain missing data.

[0088] In step S204, the processing unit 24 calculates the average value for each KPI value for each employee belonging to each of the multiple corporations.

[0089] In step S206, the processing unit 24 calculates the average value and standard deviation σ for each KPI value among multiple corporations based on the average value (representative value) for each KPI value obtained in step S204. B Calculate.

[0090] In step S208, the processing unit 24 calculates the mean and standard deviation σ for each KPI value among the multiple companies obtained in step S206. B This is stored in the processing data storage unit 22.

[0091] Next, when the information processing device 10 receives a predetermined instruction signal, the information processing device 10 executes the process shown in Figure 11.

[0092] In step S300, the acquisition unit 26 acquires multiple KPI values ​​for each of the multiple employees belonging to the company under analysis from the company data storage unit 20. The acquisition unit 26 also acquires multiple KPI values ​​for each of the multiple employees belonging to the group of companies being compared from the company data storage unit 20.

[0093] In step S302, the improvement indicator calculation unit 28 calculates the mean and standard deviation of each of the multiple KPI values ​​of the target company obtained in step S300.

[0094] In step S304, the improvement indicator calculation unit 28 calculates the mean and standard deviation of each of the multiple KPI values ​​of the comparison group of companies obtained in step S300.

[0095] In step S306, the improvement indicator calculation unit 28 calculates the average value and standard deviation σ for each KPI value among multiple companies, which are stored in the processing data storage unit 22. B Obtain it.

[0096] In step S308, the improvement indicator calculation unit 28 calculates the average value and standard deviation for each KPI value of the analyzed corporation obtained in step S302, and the average value and standard deviation σ for each KPI value among multiple corporations obtained in step S306. B Based on this, a known standard score calculation method is used to convert each of the multiple KPI values ​​of the company being analyzed into a first standard score.

[0097] In step S310, the improvement indicator calculation unit 28 calculates the mean and standard deviation for each KPI value of the comparison group of corporations obtained in step S304, and the mean and standard deviation σ for each KPI value among multiple corporations obtained in step S306. B Based on this, a known standardization method is used to convert each of the multiple KPI values ​​of the comparison group of companies into a second standard score.

[0098] In step S312, the improvement index calculation unit 28 sets the second standard score obtained in step S310 as the target value and calculates the improvement range of the standard score, which represents the difference between the second standard score and the first standard score.

[0099] In step S314, the improvement index calculation unit 28 obtains the relevance β stored in the processing data storage unit 22.

[0100] In step S316, the improvement indicator calculation unit 28 calculates the variance σ for each KPI value among multiple corporations. B 2 The variance σ for each KPI value of multiple companies all 2 The corrected correlation β' is calculated by multiplying the correlation β obtained in step S314 by the correction value obtained by division.

[0101] In step S318, the improvement indicator calculation unit 28 calculates the KGI value of the analyzed company after improvement, assuming the first standard score is improved to the target value, by multiplying the improvement range of the KPI value, which represents the difference between the second standard score and the first standard score, by the corrected correlation β' obtained in step S316. In step S318, the improvement indicator calculation unit 28 also estimates the improvement range of the KGI value, which represents the difference between the KGI value of the analyzed company before the KPI value is improved and the KGI value of the analyzed company after the KPI value is improved.

[0102] In step S320, the priority calculation unit 30 calculates a priority and a monetary value according to the improvement in the KGI value obtained in step S318.

[0103] In step S322, the output unit 32 outputs the improvement priority and monetary value for each of the multiple KPI values ​​calculated by the priority calculation unit 30.

[0104] As described above, the information processing device according to this embodiment acquires multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization under analysis. The information processing device also calculates the operational indicator values ​​of the organization under analysis when the multiple health indicator values ​​of the organization under analysis change in accordance with the improvement range, based on the improvement range of the multiple health indicator values ​​of the organization under analysis and the degree of correlation between each of the multiple health indicator values ​​of the organization and each of the multiple operational indicator values ​​representing the operational status of each of the organizations. The information processing device calculates the priority of improvement for the multiple health indicator values ​​based on the improvement range of the multiple operational indicator values, which represents the difference between the operational indicator values ​​of the organization under analysis before the improvement in health indicator values ​​and the operational indicator values ​​of the organization under analysis after the improvement in health indicator values. This makes it possible to identify which of the multiple health indicators of the members belonging to the organization should be prioritized for improvement in order to improve the organization's multiple operational indicators. In particular, it is possible to identify which health indicators should be prioritized for improvement in order to improve each of multiple operational indicators, rather than just one specific operational indicator. For example, as shown in Figure 7, by considering the improvement margin for each of the multiple operational indicators, it is possible to identify which health indicators should be prioritized for improvement.

[0105] This disclosure is not limited to the embodiments and examples described above, and various modifications and applications are possible without departing from the spirit of the invention.

[0106] For example, in the above embodiment, the calculation of priority was explained using only the improvement range of multiple health indicator values, but it is not limited to this. For example, each of the improvement ranges of multiple health indicator values ​​may be multiplied by a weight representing the degree of ease of improvement of each of the multiple health indicator values, and various calculations may be performed. For example, after multiplying each of the improvement ranges of multiple health indicator values ​​by a weight representing the degree of ease of improvement of each of the multiple health indicator values, the operational indicator value of the analyzed organization after improvement, assuming that the first standard score has improved to the target value, may be calculated. This makes it possible to calculate priority while also considering the ease of improvement of health indicators.

[0107] Furthermore, the health indicator value used as the basis for calculating the improvement in health indicator values ​​may be the average health indicator value of all data. Alternatively, as mentioned above, it may be the average health indicator value of a specific category (for example, the industry to which the analyzed company belongs), or some weight may be added to them. Also, if the improvement range is negative, the improvement range may be set to zero, or that health indicator may be excluded from the improvement target. In addition, the degree of association between health indicator values ​​and operational indicators exists for each combination of health indicator values ​​and operational indicators. For this reason, the association may be calculated using other statistical processing methods or machine learning, not just the multiple regression analysis model described above. Furthermore, the degree of association may be changed depending on the operational indicator.

[0108] Furthermore, although the above embodiment was described using the example of setting an improvement range for each (or all) of the multiple health indicator values, it is not limited to this. For example, an improvement range may be set for at least some of the multiple health indicator values, and various calculation processes may be performed to calculate the priority of improvement for the multiple health indicator values. Alternatively, an improvement range may be set for each of the multiple health indicator values, but the value of some of the improvement ranges may be set to zero, thereby effectively considering the improvement of only some of the health indicator values ​​when performing various calculation processes and calculating the priority of improvement for the multiple health indicator values.

[0109] Furthermore, in the above embodiment, each process that the CPU reads and executes software (programs) may be executed by various processors other than the CPU. Examples of such processors include PLDs (Programmable Logic Devices) such as FPGAs (Field-Programmable Gate Arrays) whose circuit configuration can be changed after manufacturing, and dedicated electrical circuits that are processors with circuit configurations specifically designed to execute specific processes, such as ASICs (Application Specific Integrated Circuits). Each process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (for example, multiple FPGAs, and a combination of a CPU and an FPGA). More specifically, the hardware structure of these various processors is an electrical circuit that combines circuit elements such as semiconductor elements.

[0110] Furthermore, although the above embodiment describes a configuration in which each program is pre-stored (installed) on a storage device, the invention is not limited to this configuration. Programs may be provided in a form stored on a storage medium such as a CD-ROM, DVD-ROM, Blu-ray disc, or USB memory. Programs may also be provided in a form that can be downloaded from an external device via a network.

[0111] (Note) The following is an addendum regarding the nature of this disclosure.

[0112] (Note 1) An acquisition unit that acquires multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization being analyzed, An improvement indicator calculation unit calculates the operational indicator values ​​of the organization under analysis when the health indicator values ​​of the organization under analysis change in accordance with the improvement range, based on the improvement range of the multiple health indicator values ​​of the organization under analysis and the degree of correlation between each of the multiple health indicator values ​​of each of the multiple organizations and each of the multiple operational indicator values ​​representing the operational status of each of the multiple organizations. A priority calculation unit calculates the priority of improvement for a plurality of health indicators based on the improvement range of a plurality of operational indicators, which represents the difference between the operational indicators of the organization under analysis before the health indicators are improved and the operational indicators of the organization under analysis after the health indicators are improved. Information processing device including (Note 2) The aforementioned correlation is a value that represents the change in the operational indicator value in relation to the change in each of the aforementioned health indicator values ​​of the multiple organizations. The aforementioned improvement indicator calculation unit is: For each of the aforementioned health indicator values ​​of the organization being analyzed, the average value of the health indicator value is calculated. Based on the average value for each health indicator value of the group under analysis, and the average value and standard deviation for each health indicator value across multiple groups, each of the multiple health indicator values ​​of the group under analysis is converted into a first standard score. For each of the aforementioned health indicator values ​​of the group of organizations being compared, the mean and standard deviation of the health indicator value are calculated. Based on the mean and standard deviation of each health indicator value for the group of organizations being compared, and the mean and standard deviation of each health indicator value across multiple organizations, each of the multiple health indicator values ​​for the group of organizations being compared is converted into a second standard score. The aforementioned second standard score is set as the target value, By multiplying the deviation score improvement range, which represents the difference between the second deviation score and the first deviation score, by the correlation coefficient, the improved operational indicator value of the analyzed organization is calculated when the first deviation score is improved to the target value. The information processing device described in Appendix 1. (Note 3) The aforementioned improvement indicator calculation unit is: The corrected correlation is calculated by multiplying the correlation by a correction value obtained by dividing the variance for each health indicator value among the multiple organizations by the variance for each health indicator value of the multiple organizations, By multiplying the improvement range of the health indicator value, which represents the difference between the second standard score and the first standard score, by the corrected correlation, the improved operational indicator value of the organization under analysis, when the first standard score is improved to the target value, is calculated. The information processing device described in Appendix 2. (Note 4) After multiplying the improvement range of multiple health indicator values ​​by a weight representing the degree of ease of improvement of the multiple health indicator values, the operational indicator value of the analyzed organization after improvement, assuming that the first standard score improves to the target value, is calculated. The information processing device described in Appendix 2 or Appendix 3. (Note 5) The improvement in each of the aforementioned health indicator values ​​of the organization under analysis is calculated from the average values ​​of the aforementioned health indicator values ​​of several other organizations belonging to the same industry as the organization under analysis. An information processing device as described in any one of the items in Appendix 1 to Appendix 4. (Note 6) The priority calculation unit, The improvement in the aforementioned operational indicators is converted into monetary value, Based on the aforementioned monetary value, the priority for improvement of the aforementioned multiple health indicator values ​​is calculated. An information processing device as described in any one of the items 1 to 5 of the appendix. (Note 7) We obtained multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization being analyzed. Based on the improvement range of the multiple health indicator values ​​of the organization under analysis and the degree of correlation between each of the multiple health indicator values ​​of each of the multiple organizations and each of the multiple operational indicator values ​​representing the operational status of each of the multiple organizations, the operational indicator values ​​of the organization under analysis are calculated when the multiple health indicator values ​​of the organization under analysis change in accordance with the improvement range. Based on the improvement range of multiple operational indicators, which represents the difference between the operational indicators of the organization under analysis before the improvement of the health indicators and the operational indicators of the organization under analysis after the improvement of the health indicators, the priority for improvement of each of the multiple health indicators is calculated. An information processing method in which a computer performs the processing. (Note 8) We obtained multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization being analyzed. Based on the improvement range of the multiple health indicator values ​​of the organization under analysis and the degree of correlation between each of the multiple health indicator values ​​of each of the multiple organizations and each of the multiple operational indicator values ​​representing the operational status of each of the multiple organizations, the operational indicator values ​​of the organization under analysis are calculated when the multiple health indicator values ​​of the organization under analysis change in accordance with the improvement range. Based on the improvement range of multiple operational indicators, which represents the difference between the operational indicators of the organization under analysis before the improvement of the aforementioned health indicators and the operational indicators of the organization under analysis after the improvement of the aforementioned health indicators, the priority for improvement of the multiple health indicators is calculated. An information processing program that causes a computer to perform a task. [Explanation of Symbols]

[0113] 10 Information Processing Devices 20 Enterprise Data Storage Unit 22 Processing data storage unit 24 Processing Unit 26 Acquisition Department 28 Improvement index calculation section 30 Priority calculation unit 32 Output section

Claims

1. An acquisition unit that acquires multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization being analyzed, An improvement indicator calculation unit calculates the operational indicator values ​​of the organization under analysis when the health indicator values ​​of the organization under analysis change in accordance with the improvement range, based on the improvement range of the multiple health indicator values ​​of the organization under analysis and the degree of correlation between each of the multiple health indicator values ​​of each of the multiple organizations and each of the multiple operational indicator values ​​representing the operational status of each of the multiple organizations. A priority calculation unit calculates the priority of improvement for a plurality of health indicators based on the improvement range of a plurality of operational indicators, which represents the difference between the operational indicators of the organization under analysis before the health indicators are improved and the operational indicators of the organization under analysis after the health indicators are improved. Information processing device including

2. The aforementioned correlation is a value that represents the change in the operational indicator value in relation to the change in each of the aforementioned health indicator values ​​of the multiple organizations. The aforementioned improvement indicator calculation unit is: For each of the aforementioned health indicator values ​​of the organization being analyzed, the average value of the health indicator value is calculated. Based on the average value for each health indicator value of the organization under analysis, and the average value and standard deviation for each health indicator value across multiple organizations, each of the multiple health indicator values ​​of the organization under analysis is converted into a first standard score. For each of the aforementioned health indicator values ​​of the group of organizations being compared, the mean and standard deviation of the health indicator value are calculated. Based on the mean and standard deviation of each health indicator value for the group of organizations being compared, and the mean and standard deviation of each health indicator value across multiple organizations, each of the multiple health indicator values ​​for the group of organizations being compared is converted into a second standard score. The second standard score is set as the target value, By multiplying the deviation score improvement range, which represents the difference between the second deviation score and the first deviation score, by the correlation coefficient, the operational indicator value of the analyzed organization after improvement, when the first deviation score is improved to the target value, is calculated. The information processing apparatus according to claim 1.

3. The aforementioned improvement indicator calculation unit is: The corrected correlation is calculated by multiplying the correlation by a correction value obtained by dividing the variance for each health indicator value among the multiple organizations by the variance for each health indicator value of the multiple organizations, By multiplying the improvement range of the health indicator value, which represents the difference between the second standard score and the first standard score, by the corrected correlation, the improved operational indicator value of the organization under analysis is calculated when the first standard score improves to the target value. The information processing apparatus according to claim 2.

4. After multiplying the improvement range of the multiple health indicator values ​​by a weight representing the degree of ease of improvement of the multiple health indicator values, the operational indicator value of the analyzed organization after improvement, when the first standard score is improved to the target value, is calculated. The information processing apparatus according to claim 2.

5. The improvement in the aforementioned health indicator values ​​of the organization under analysis is calculated from the average values ​​of the aforementioned health indicator values ​​of several other organizations belonging to the same industry as the organization under analysis. The information processing apparatus according to claim 1 or claim 2.

6. The priority calculation unit, The improvement in the aforementioned operational indicators is converted into monetary value, Based on the aforementioned monetary value, the priority for improvement of the aforementioned multiple health indicator values ​​is calculated. The information processing apparatus according to claim 1 or claim 2.

7. We obtained multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization being analyzed. Based on the improvement range of the multiple health indicator values ​​of the organization under analysis and the degree of correlation between each of the multiple health indicator values ​​of each of the multiple organizations and each of the multiple operational indicator values ​​representing the operational status of each of the multiple organizations, the operational indicator values ​​of the organization under analysis are calculated when the multiple health indicator values ​​of the organization under analysis change in accordance with the improvement range. Based on the improvement range of multiple operational indicators, which represents the difference between the operational indicators of the organization under analysis before the improvement of the aforementioned health indicators and the operational indicators of the organization under analysis after the improvement of the aforementioned health indicators, the priority for improvement of the multiple health indicators is calculated. An information processing method in which a computer performs the processing.

8. We obtained multiple health indicator values ​​representing the health status of each of the multiple members belonging to the organization being analyzed. Based on the improvement range of the multiple health indicator values ​​of the organization under analysis and the degree of correlation between each of the multiple health indicator values ​​of each of the multiple organizations and each of the multiple operational indicator values ​​representing the operational status of each of the multiple organizations, the operational indicator values ​​of the organization under analysis are calculated when the multiple health indicator values ​​of the organization under analysis change in accordance with the improvement range. Based on the improvement range of multiple operational indicators, which represents the difference between the operational indicators of the organization under analysis before the improvement of the aforementioned health indicators and the operational indicators of the organization under analysis after the improvement of the aforementioned health indicators, the priority for improvement of the multiple health indicators is calculated. An information processing program that causes a computer to perform a task.

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