Work measure evaluation device and work measure evaluation method

Abstract

To provide a work measure evaluation device that reasonably and promptly finds out appropriate work measures which satisfy various concepts of values among a vast number of work measures.SOLUTION: A work measure measurement device includes: an index designating unit that designates a target index from multiple indexs prepared in advance; a work measure extracting unit that extracts work measures relating to the target index; an evaluating unit that evaluates an effect of the extracted work measures to the target index; and an output unit that outputs an evaluation result by the evaluating unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a business policy evaluation device and a business policy evaluation method that support the design and improvement of various businesses such as maintenance businesses. [Background technology]

[0002] In recent years, the business environment has changed rapidly, and various industries are required to review and improve their business operations in response to the business environment. For example, in the maintenance of infrastructure, railways, industrial equipment, medical equipment, etc., it is necessary to continuously carry out maintenance such as inspection and repair to ensure the safe and stable operation of assets (facilities). In such maintenance, organizational structures and work standards are designed taking into account the operating status and operational characteristics of assets, and the status of maintenance resources such as workers and tools. However, when the business environment changes, it is necessary to take some kind of business measures and review the maintenance operation design to suit the environment.

[0003] A known prior art for supporting the construction of such business measures is a business measure construction support system described in Patent Document 1. For example, the abstract of Patent Document 1 discloses a business measure construction support system including "a business simulator unit that generates a virtual business history by executing a simulation of a service operation performed by a service provider under a predetermined business measure, an acquisition unit that acquires a virtual business history corresponding to each business measure by having the business simulator unit execute simulations sequentially under different business measures, and an output unit that, when an evaluation value calculated based on the virtual business history acquired by the acquisition unit satisfies a predetermined condition, outputs the business measure corresponding to the virtual business history in association with the evaluation value."

[0004] Furthermore, paragraph 0091 of the same document states, "When the evaluation value of the business measure input to the business simulator unit 621 does not satisfy a predetermined condition, the contents of the item identified by the change item information are changed sequentially. The example of Figure 8(b) shows that the contents of the item identified by the change item information (item number = 1) are changed from P1 to P1' to P1'', and paragraph 0094 states, "In this way, according to the business measure construction support system 131, the business measures are changed sequentially until the calculated evaluation value satisfies the predetermined condition." [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2017-208035 A Summary of the Invention [Problem to be solved by the invention]

[0006] In recent years, in various business fields, in addition to traditional values ​​such as sales and profits, a variety of values ​​such as the environment, resilience, and human rights are being emphasized. Furthermore, the values ​​that are emphasized are also changing rapidly. Therefore, business operators need to flexibly and continuously transform their business and operational designs in response to the changes in values ​​required by the times.

[0007] As described above, Patent Document 1 describes a method of sequentially calculating the evaluation value of each business measure by simulation and presenting the business measure whose evaluation value satisfies a predetermined condition. In this method, it is necessary to sequentially set possible business measures in advance and simulate each business measure, but the options for business measures are wide-ranging. For example, performance improvement by updating assets, changes in maintenance policies, increasing the number of workers, adding bases, introduction of IoT solutions, etc. Furthermore, business measures that combine these are also possible, so the options for business measures are countless.

[0008] Therefore, it takes a huge amount of time to sequentially simulate and evaluate all of the countless options for business measures using the technology of Patent Document 1. In particular, a highly accurate simulation requires a long evaluation time per case, making it extremely difficult to evaluate many cases of business measures through simulation.

[0009] Therefore, an object of the present invention is to provide a business policy evaluation device and a business policy evaluation method that can rationally and quickly find suitable business policies that satisfy diverse values ​​from among the countless business policies that exist. [Means for solving the problem]

[0010] In order to solve the above problems, a representative embodiment of the present invention is a business policy evaluation device that includes an index designation unit that designates a target index from a plurality of pre-prepared indexes, a business policy extraction unit that extracts business policies related to the target index, an evaluation unit that evaluates the influence of the extracted business policies on the target index, and an output unit that outputs the evaluation result of the evaluation unit. Effect of the Invention

[0011] According to the business policy evaluation device or the business policy evaluation method of the present invention, it is possible to find an appropriate solution rationally and quickly by narrowing down and evaluating suitable policies that improve a target index from among countless business policy options. Note that the problems, configurations and effects other than those described above will be made clear by the explanation of the following examples. [Brief description of the drawings]

[0012] [Figure 1] FIG. 2 is a functional block diagram of the business policy evaluation device according to the first embodiment. [Diagram 2] 3 is a process flowchart of the business policy evaluation device according to the first embodiment. [Diagram 3] 13 is an example of an index setting screen according to the first embodiment. [Figure 4] 4 is an example of a business policy list according to the first embodiment. [Diagram 5] 4 is an example of causal relationship structure information according to the first embodiment. [Figure 6A] 3 shows an example of a business policy extraction process according to the first embodiment. [Figure 6B] 3 shows an example of a business policy extraction process according to the first embodiment. [Figure 6C] 3 shows an example of a business policy extraction process according to the first embodiment. [Figure 7] FIG. 11 is a functional block diagram of a business policy evaluation device according to a second embodiment. [Figure 8] 11 is a process flowchart of a business policy evaluation device according to the second embodiment. [Figure 9] 13 is an example of an output screen of an evaluation result according to the second embodiment. [Figure 10] FIG. 11 is a functional block diagram of a business policy evaluation device according to a third embodiment. [Figure 11] 11 is a process flowchart of a business policy evaluation device according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, an embodiment of the business policy evaluation device of the present invention will be described with reference to the drawings. In each embodiment, maintenance work is exemplified as a business to which the present invention is applied, but the present invention can also be applied to service industries such as finance and transportation, manufacturing industries such as chemicals and automobiles, and agriculture. Maintenance work includes various tasks such as inspection, repair, and maintenance, and assets that are the subject of maintenance work include various items such as facilities, equipment, machinery, and products. EXAMPLES

[0014] FIG. 1 is a functional block diagram of a business policy evaluation device 1 according to a first embodiment of the present invention. As shown in FIG. 1, the business policy evaluation device 1 of the present embodiment includes an index designation unit 11, a business policy extraction unit 12, an evaluation unit 13, an output unit 14, and an information storage unit 15. The information storage unit 15 stores a business policy list 15a, causal relationship structure data 15b, and evaluation results 15c. Specifically, the business policy evaluation device 1 is a computer equipped with hardware such as a calculation unit such as a CPU, a storage unit such as a semiconductor memory, and a communication unit. The calculation unit executes a desired program to realize each function of the above-mentioned index designation unit 11, etc., but the following description will be given while omitting such well-known techniques as appropriate.

[0015] In addition, the business policy evaluation device 1 is connected to an input device 2 such as a keyboard, mouse, or touch panel, and an output device 3 such as an LCD display, allowing a user to input commands to the business policy evaluation device 1 and for the business policy evaluation device 1 to present information to the user.

[0016] Next, the processes executed by the business policy evaluation device 1 will be described in sequence according to the flowchart of FIG.

[0017] <Step S1> First, in step S1, the index designation unit 11 designates indexes that the user considers important in conducting business, according to a user selection described later. An index is also called a KPI (Key Performance Indicator), and is a value that measures the progress of business achievements and the performance of activities related to business. These indexes can be classified into social issue indexes related to society as a whole, management indexes related to the business status of a company, and activity indexes that represent the status of assets, organizations, resources, etc. in business activities. From the viewpoint of improving the user's understanding, it is even better if the activity indexes are expressed as quantitative numerical values.

[0018] Social issue indicators include, for example, stable energy supply, resilience, green, recycling, human rights, and QoL (Quality of Life). The indicators that are emphasized among these social issue indicators change rapidly depending on the social situation. Management indicators include, for example, profits, sales, free cash flow, stock prices, labor costs, operating costs, parts costs, travel costs, penalties, and employee satisfaction. Activity indicators are diverse and include, for example, operating rate, downtime, number of bases, number of workers, CO2 emissions, number of spares, number of warehouses, and number of maintenance tasks.

[0019] FIG. 3 is an example of a target KPI setting screen displayed on the output device 3 (liquid crystal display) when the user selects a target indicator (target KPI) that the user places emphasis on from a plurality of indicators (KPIs) prepared in advance prior to step S1. This example shows a state in which the user operates the input device 2 (e.g., a mouse) to select an operating rate, which is one of the activity indicators, as the target indicator. Note that only one target indicator is selected here, but if there are multiple indicators that the user wishes to place emphasis on, the user can select any multiple target indicators. Note that the target indicator is an indicator (KPI) that the user places emphasis on and selects from a plurality of indicators (KPIs) prepared in advance.

[0020] <Step S2> In step S2, the business policy extraction unit 12 selects a business policy pattern to be evaluated from the business policy list 15a stored in the information storage unit 15. Here, a business policy pattern refers to a change in some design parameter that can be intentionally changed from the current business design. The targets of business design are diverse, including assets, organizations, resources, and technologies (IoT solutions).

[0021] 4 is an example of the business policy list 15a. As shown in the example, the business policy list 15a of this embodiment is defined by a combination of business policy patterns and policy parameters. For example, as policy parameters for "update (replacement) of aging equipment" of business policy pattern #1, multiple parameters that affect the evaluation result of "update (replacement) of aging equipment" such as the number of equipment to be updated (update range), the extent of performance improvement by equipment update, and replacement cost are registered.

[0022] In this way, since there are many possible parameter settings for one business measure pattern, the number of business measures that need to be considered for business improvement is enormous. In addition, a composite measure that combines multiple business measure patterns can also be considered as one measure, so there are countless options for business measures. Furthermore, measures that do not change the business design can also be considered as a business measure, as they are measures that maintain the status quo.

[0023] <Step S3> In step S3, the business measure extraction unit 12 selects business measure patterns that are related to the target indicators specified in step S1, using the causal relationship structure data 15b that defines the causal relationships between business measures and indicators and the causal relationships between indicators. In the following, we will first explain the structure of the causal relationship structure data 15b, and then explain the process of extracting business measures.

[0024] FIG. 5 is an example of the structure of the causal relationship structure data 15b. On the left side of the figure, various indicators (KPIs) related to the business are written in boxes, and are arranged hierarchically from the left in the order of social issue indicators, management indicators, and activity indicators. The arrows in the figure indicate causal relationships between indicators, with solid arrows indicating causal relationships of positive effects and dashed arrows indicating causal relationships of negative effects. For example, a solid arrow connects the indicator "maintenance work time" to the indicator "downtime," indicating a causal relationship in which an increase in maintenance work time also increases downtime. Also, a dashed arrow connects the indicator "downtime" to the indicator "operation rate," indicating a causal relationship in which an increase in downtime decreases operation rate.

[0025] Each index in FIG. 5 is connected by causal arrows to all the indexes one level downstream that are affected when the index changes. As the number of indexes increases, the causal structure also becomes more complex, making it difficult for people to grasp all the causal relationships. In general, these causal structures are often causal from activity indexes to management indexes and social issue indexes, and have a hierarchical tree structure. By hierarchically organizing such causal structure data 15b and expressing it as a KPI tree, it becomes easier to understand the causal relationships between a large number of indexes. In addition, in the example of FIG. 5, indexes of multiple stakeholders such as the operation department and the maintenance department are also listed.

[0026] Typically, social issue indicators are common to all stakeholders. On the other hand, management indicators and activity indicators differ for each stakeholder, and indicators may affect each other across stakeholders. By creating causal relationship structure data that includes multiple stakeholders, it is possible to understand the causal relationships between indicators in a more complex business structure. For example, it is possible to understand cases where there is a conflict of interest, such as when one stakeholder's indicator improves while another stakeholder's indicator worsens. It is also possible to understand cases of synergy, where an improvement in one stakeholder's indicator also improves the indicators of the other stakeholder.

[0027] Moreover, on the right side of Fig. 5, the business measure patterns (see Fig. 4) included in the business measure list 15a are listed, and each business measure is connected to an activity indicator by an arrow. The arrow indicates the causal relationship with the indicator that is affected by the business measure in question. For example, in the case of the business measure pattern "update (replacement) of aging equipment," since it is expected that performance will improve and the frequency of failures will decrease by updating the equipment, a solid arrow is connected between the indicator "equipment performance" and the indicator "failure frequency." Since business measures involve changing part of business activities by modifying the current business design, the impact of business measures is generally often linked to activity indicators.

[0028] The causal relationships described above are generally constructed based on human experience and know-how. If experienced people define the causal relationships, a more accurate causal relationship structure model can be constructed. On the other hand, when it comes to inexperienced business measures, there is a risk of mistakes or oversights in the causal relationships because there is no past track record. Note that causal relationships can also be automatically created from documents and internet information using natural language processing and machine learning.

[0029] 6A to 6C are examples of the process of extracting business measure patterns when the user selects "operating rate" as the target index (see FIG. 3). Note that, in order to clearly show the procedure of this process, information (indexes, business measure patterns) that is not necessary for explaining the extraction process is omitted in FIG. 6A to 6C, but the causal relationship structure data 15b in each figure actually holds information equivalent to that in FIG. 5.

[0030] FIG. 6A shows the process in which the evaluation unit 13 sets the "operating rate", which is the index specified in step S1, as the target index.

[0031] 6B shows the process in which the evaluation unit 13 starts from the target indicator "availability" and traces the causal arrows, whether solid or dashed, backward to extract all other indicators that affect the "availability". Through this process, all indicators such as "operating time" and "downtime" that are upstream of the target indicator "availability" are extracted.

[0032] Fig. 6C shows the process in which the evaluation unit 13 extracts all business measure patterns linked to each indicator extracted in Fig. 6B. This process extracts each business measure pattern, such as "update of aging equipment" and "remote monitoring and diagnosis", which are located upstream of the target indicator "operation rate". As can be seen from a comparison of Fig. 5 and Fig. 6C, Fig. 6C also shows that business measure patterns, such as "hiring new workers" and "training workers using digital knowledge", were not extracted as business measure patterns that affect the target indicator "operation rate".

[0033] In this manner, in step S3, by tracing back the causal relationships in the causal relationship structure data 15b, it is possible to narrow down the business measures that may affect the target indicator.

[0034] A weighting factor can be set for each of the causal arrows described above. By tracing the causal relationships backwards while taking these weighting factors into consideration, it is possible to evaluate the magnitude of the impact of the extracted business measures and to rank the degree of impact of the extracted business measures. By prioritizing the extraction and evaluation of business measures with high impact rankings, it is possible to efficiently find business measures with high effects.

[0035] Although Fig. 3 and Fig. 6A to Fig. 6C show an example in which only one indicator is set as the target indicator, multiple indicators may be set as the target indicator. In that case, the process corresponding to Fig. 6A to Fig. 6C is performed for each target indicator to obtain the business measure pattern related to each target indicator, and then the business measure pattern common to all target indicators is identified, thereby making it possible to extract the business measure pattern that affects all target indicators.

[0036] <Step S4> In step S4, the evaluation unit 13 evaluates the effect and influence on each index when the business measure pattern selected in step S3 is implemented, that is, the degree to which the index changes from the current state.

[0037] One example of an evaluation method in this step is agent simulation, which reproduces real-world operations. In agent simulation, asset data corresponding to each asset in the simulation world is generated for each management unit, such as real-world assets or asset parts, and the behavior of the real-world assets is reproduced by having them operate and break down autonomously. The data generated at this time is called an agent, and in the case of assets, it is called an asset agent. Similarly, agents for maintenance personnel are generated in the simulation world one by one, and are called maintenance personnel agents. These perform actions such as waiting, moving, performing tasks, and resting in response to work instructions. In addition, agents corresponding to real-world characters are generated, such as an operation agent that operates assets and requests troubleshooting when problems occur, and an allocation agent that assigns tasks to maintenance personnel.

[0038] By using this type of agent simulation, complex business activities involving various real-world actors can be virtually reproduced in cyberspace, and activity indicators related to business activities can be quantitatively predicted and evaluated from the behavior of each agent. To evaluate the effect and impact on indicators when a business measure is implemented, a situation in which the business measure is virtually implemented is reproduced in a simulation, indicators in that situation are predicted and evaluated, and the difference is compared with the indicators in the current business design. The simulation method is not limited to agent simulation, and for example, a machine learning model generated from past performance data may be used.

[0039] <Step S5> In step S5, the evaluation unit 13 stores the evaluation result 15c simulated in step S4 in the information storage unit 15. In addition, the output unit 14 outputs the evaluation result 15c stored in the information storage unit 15 to the output device 3 (liquid crystal display) and presents the evaluation result 15c to the user. An example of the presentation screen for the evaluation result will be described in Example 2.

[0040] According to the business policy evaluation device of the present embodiment described above, it is possible to narrow down and evaluate suitable business policy measures that improve the target index from among countless business policy options. Also, according to the business policy evaluation device of the present embodiment, it is possible to evaluate the effects of business policies by prioritizing business policies that have a large impact on the target index, and it is possible to quickly and efficiently select promising business policies. Furthermore, according to the business policy evaluation device of the present embodiment, even in a complex situation such as when there are multiple target indexes or when there are multiple stakeholders, it is possible to narrow down rationally effective business policies based on the causal relationships between the indexes. EXAMPLES

[0041] Next, a business policy evaluation device 1 according to a second embodiment of the present invention will be described with reference to Fig. 7 to Fig. 9. Note that the points in common with the first embodiment will not be described again, and only the differences will be described.

[0042] Fig. 7 is a functional block diagram of a business policy evaluation device 1 according to a second embodiment. As can be seen from a comparison between Fig. 1 and Fig. 7, the business policy evaluation device 1 of this embodiment differs from the business policy evaluation device 1 of the first embodiment in the following points. That is, firstly, a statistical evaluation unit 16 is added to the business policy evaluation device 1 of this embodiment. Secondly, in the business policy evaluation device 1 of this embodiment, the simulation carried out by the evaluation unit 13 is a probabilistic event simulation. Thirdly, in the business policy evaluation device 1 of this embodiment, the output of the statistical evaluation unit 16 is stored in the information storage unit 15 as a statistical evaluation result 15c'.

[0043] The processes executed by the business policy evaluation device 1 of this embodiment, which differ from the first embodiment in the above points, will be described in sequence with reference to the flowchart of Fig. 8. Note that the description of the points in common with the flowchart of Fig. 2 will be omitted as appropriate.

[0044] The process contents from step S1 to step S3 are the same as those in the first embodiment.

[0045] In step S4a, the evaluation unit 13 executes a stochastic event simulation multiple times. In the case of a simulation of a maintenance operation, for example, the stochastic event simulation executed in this step is a simulation in which a failure probability of an asset or an asset component is set and failures occur randomly according to the failure probability. The failure probability is given by a distribution function such as the Weibull distribution. It is also possible to assign a distribution function to the work time of a worker or the lead time of part sharing so that the work time or lead time varies randomly. By executing a stochastic event simulation multiple times that takes into account such randomly changing uncertain events, it becomes possible to evaluate the risk of uncertain events.

[0046] In step S4b, the statistical evaluation unit 16 receives the evaluation results of the random event simulations performed multiple times in step S4a, and statistically processes the evaluation results of the multiple indexes. Examples of statistical processing include average, variance, standard deviation, and confidence interval.

[0047] In step S5, the statistical evaluation unit 16 stores the evaluation result of the index statistically processed in step S4b as the statistical evaluation result 15c' in the information storage unit 15. In addition, the output unit 14 outputs the statistical evaluation result 15c' stored in the information storage unit 15 to the output device 3 (liquid crystal display) and presents the evaluation result to the user.

[0048] FIG. 9 is an example of a presentation screen of the statistical evaluation result 15c' displayed on the output device 3 (liquid crystal display). In the upper part of the screen in this figure, the evaluation results of the target indicators are shown in a graph format. In the area in the upper part of the screen, it is possible to select the target indicators to be output to the graph, and in this example, "green" is selected as the target indicator KPI1, and "employee satisfaction" is selected as KPI2. In this case, the statistical results when a predetermined business measure pattern is executed are shown on a two-dimensional graph with KPI1 "green" on the horizontal axis and KPI2 "employee satisfaction" on the vertical axis.

[0049] In Fig. 9, in addition to the evaluation value of the target KPI when business measure A selected by the business measure extraction unit 12 is implemented, the result of the current business state is also plotted. This display method allows the user to intuitively recognize the difference between the two as the improvement effect by business measure A. Note that in Fig. 9, the evaluation results of multiple probability event simulations are plotted small, and their average value is plotted large. This makes it possible to visualize the results taking into account the randomness of uncertain events, grasp the range in which the KPI can fluctuate, and evaluate the risk.

[0050] Also, at the bottom of Fig. 9, the causal relationship structure data 15b (KPI tree) stored in the information storage unit 15 is displayed, and all indicators affected by business measure A (i.e., indicators downstream of business measure A) are highlighted. By displaying in this manner, the ripple effect of business measure A on various indicators can be visually grasped. Also, it is possible to prevent the user from overlooking effects on indicators that were not anticipated. EXAMPLES

[0051] Next, a business policy evaluation device 1 according to a third embodiment of the present invention will be described with reference to Fig. 10 and Fig. 11. Note that the points in common with the above-mentioned embodiments will not be described again, and only the differences will be described.

[0052] Fig. 10 is a functional block diagram of a business policy evaluation device 1 according to a third embodiment. As can be seen from a comparison between Fig. 1 and Fig. 10, the business policy evaluation device 1 of the present embodiment is obtained by adding a causal relationship learning unit 17 to the components of the first embodiment. The process executed by the business policy evaluation device 1 of the present embodiment, which differs from the first embodiment in this respect, will be described with reference to the flowchart of Fig. 11.

[0053] The process contents from step S1 to step S5 are the same as those in the first embodiment.

[0054] In step S6, the causal relationship learning unit 17 receives the causal relationship structure data 15b and the evaluation result 15c stored in the information storage unit 15, learns the causal relationship between the indicators from the evaluation result 15c, and if the learning result differs from the causal relationship structure data 15b, corrects and updates the causal relationship between the indicators in the causal relationship structure data 15b.

[0055] As described in the first embodiment, the causal relationship structure data 15b used in the present invention is basically a causal relationship structure model constructed by a person based on the person's experience and know-how, so it does not reflect business measures that people have no experience with, and there is a risk of errors or oversights in the causal relationships. Therefore, the causal relationship structure data 15b at the time of construction is often low in accuracy, and does not necessarily properly indicate the causal relationship structure between the business measures and indicators, or the causal relationship structure between indicators.

[0056] Therefore, according to the third embodiment, the impact and effect on indicators of inexperienced business measures can be virtually evaluated by business simulation. By learning the simulation evaluation results, the causal structure can be reflected in the impact of inexperienced business measures on each indicator, and mistakes and omissions in causal relationships can be improved.

[0057] The present invention is not limited to the above-described embodiments, and includes various modified examples. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the configurations described. It is also possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace a part of the configuration of each embodiment with another configuration. [Explanation of symbols]

[0058] 1...Business policy evaluation device, 11...Indicator specification section, 12...Business policy extraction department, 13...Evaluation section, 14...output section, 15...Information storage unit, 15a…Business Measures List, 15b…Causal structure data, 15c…Evaluation results, 15c'...Statistical evaluation results, 16…Statistical Evaluation Section, 17…Causal Learning Division, 2 input device, 3 Output Device

Claims

1. An information storage unit that stores causal relationship structure data that defines causal relationships between business measures and indicators and causal relationships between indicators; An index designation unit that designates a target index from a plurality of indexes prepared in advance; a business measure extraction unit that extracts business measures related to the target indicators based on the causal relationship structure data; an evaluation unit that evaluates an influence of the extracted business measures on the target indicator; an output unit that outputs an evaluation result of the evaluation unit; A business policy evaluation device comprising:

2. 2. The business policy evaluation device according to claim 1, The business policy evaluation device is characterized in that in the causal relationship structure data, each indicator is classified into one of a social issue indicator, a management indicator, and an activity indicator.

3. 2. The business policy evaluation device according to claim 1, The business policy evaluation device, wherein each index in the causal relationship structure data is classified into an index of any one of a plurality of stakeholders.

4. 2. The business policy evaluation device according to claim 1, The business policy evaluation device further comprises a causal relationship learning unit that learns causal relationships between indexes based on the evaluation results of the evaluation unit and updates the causal relationship structure data.

5. 2. The business policy evaluation device according to claim 1, The business policy evaluation device is characterized in that the output unit outputs all of the affected indicators based on the causal relationship structure data.

6. An index designation unit that designates a target index from a plurality of indices prepared in advance; a business measure extraction unit that extracts business measures related to the target indicator; an evaluation unit that executes a simulation including random probabilistic events multiple times to evaluate an index, statistically processes the index evaluated multiple times, and evaluates an influence of the extracted business measure on the target index; an output unit that outputs an evaluation result of the evaluation unit; A business policy evaluation device comprising:

7. An index designation step of designating a target index from a plurality of indexes prepared in advance; a business measure extraction step of extracting business measures related to the target indicators based on causal relationship structure data that defines causal relationships between business measures and indicators and causal relationships between indicators; an evaluation step of evaluating an impact on the target indicator when the extracted business measure is implemented; an output step of outputting an evaluation result of the evaluation step; A business policy evaluation method comprising:

8. An index designation step of designating a target index from a plurality of indexes prepared in advance; A business measure extraction step of extracting business measures related to the target indicator; an evaluation step of evaluating an index by executing a simulation including random probabilistic events a plurality of times, statistically processing the index evaluated a plurality of times, and evaluating an influence of the extracted business measure on the target index; an output step of outputting an evaluation result of the evaluation step; A business policy evaluation method comprising:

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