Business strategy evaluation device and business strategy 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 for assisting in the design and improvement of various operations such as security operations, and a business policy evaluation method.

Background Art

[0002] In recent years, the business environment has been changing rapidly, and in various industries, there is a demand for reviewing and improving business designs according to the business environment. For example, in security operations such as infrastructure, railways, industrial equipment, and medical equipment, it is necessary to continuously perform maintenance such as inspections and repairs in order to operate assets (equipment) safely and stably. In such maintenance, organizational systems and work standards are designed in consideration of the operating status of assets, operating characteristics, and the status of maintenance resources such as workers and tools. However, when the business environment changes, it is necessary to take some business measures and review the security business design that suits the environment.

[0003] As a conventional technique for supporting the construction of such business measures, a business policy construction support system described in Patent Document 1 is known. For example, in the abstract of Patent Document 1, "a business simulator unit that generates a virtual business history by executing a simulation on a service business performed by a service provider under a predetermined business policy, and the business simulator unit is sequentially executed under different business policies. An acquisition unit that acquires virtual business histories corresponding to respective business policies by performing simulations, and when an evaluation value calculated based on the virtual business history acquired by the acquisition unit satisfies a predetermined condition, the business policy corresponding to the virtual business history is output in association with the evaluation value." A business policy construction support system is disclosed.

[0004] Furthermore, paragraph 0091 of the same document states, "If the evaluation value of the business measures entered into the business simulator unit 621 does not meet the predetermined conditions, the content of the items specified by the change item information will be changed sequentially. The example in Figure 8(b) shows that the content of the item specified by the change item information (item number = 1) is changed from P1 → P1' → P1''." Paragraph 0094 states, "In this way, according to the business measures construction support system 131, the business measures will be changed sequentially until the calculated evaluation value meets the predetermined conditions." [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2017-208035 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] In recent years, in various business sectors, diverse values ​​such as the environment, resilience, and human rights have become increasingly important, in addition to traditional values ​​such as sales and profits. Furthermore, the values ​​that are valued are changing rapidly. Therefore, businesses need to flexibly and continuously transform their business and operational designs in response to the evolving values ​​demanded by the times.

[0007] As mentioned above, Patent Document 1 describes a method for sequentially calculating the evaluation value of each business measure through simulation and presenting business measures that satisfy predetermined conditions. This method requires sequentially setting possible business measures in advance and simulating each of them, but the options for business measures are diverse. For example, these include performance improvement through asset renewal, changes to maintenance policies, increasing the number of workers, adding bases, and introducing IoT solutions. Furthermore, business measures that combine these can also be considered, so there are countless options for business measures.

[0008] Therefore, simulating and evaluating all of the countless options for business measures sequentially using the technology described in Patent Document 1 would require an enormous amount of time. In particular, since highly accurate simulations require a long evaluation time per case, it was extremely difficult to evaluate many business measures through simulation.

[0009] Therefore, the present invention aims to provide a business strategy evaluation device and a business strategy evaluation method that can rationally and quickly find suitable business strategies that satisfy diverse values ​​from among countless existing business strategies. [Means for solving the problem]

[0010] To solve the above problems, a representative example of the present invention is a business measure evaluation device comprising: an indicator designation unit that designates a target indicator from a plurality of pre-prepared indicators; a business measure extraction unit that extracts business measures related to the target indicator; an evaluation unit that evaluates the impact of the extracted business measures on the target indicator; and an output unit that outputs the evaluation results of the evaluation unit. [Effects of the Invention]

[0011] According to the business strategy evaluation device or business strategy evaluation method of the present invention, it is possible to find an appropriate solution rationally and quickly by narrowing down and evaluating suitable measures that improve a target indicator from among countless business strategy options. Other issues, configurations, and effects will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0012] [Figure 1] Functional block diagram of the business strategy evaluation device according to Example 1. [Figure 2] A processing flowchart of the business strategy evaluation device according to Example 1. [Figure 3] An example of the indicator setting screen for Example 1. [Figure 4] An example of a list of business measures related to Example 1. [Figure 5] An example of causal relationship structure information according to Embodiment 1. [Figure 6A] An example of an operation policy extraction process according to Embodiment 1. [Figure 6B] An example of an operation policy extraction process according to Embodiment 1. [Figure 6C] An example of an operation policy extraction process according to Embodiment 1. [Figure 7] Functional block diagram of an operation policy evaluation device according to Embodiment 2. [Figure 8] Process flowchart of an operation policy evaluation device according to Embodiment 2. [Figure 9] An example of an output screen of an evaluation result according to Embodiment 2. [Figure 10] Functional block diagram of an operation policy evaluation device according to Embodiment 3. [Figure 11] Process flowchart of an operation policy evaluation device according to Embodiment 3.

Modes for Carrying Out the Invention

[0013] Hereinafter, embodiments of the operation policy evaluation device of the present invention will be described with reference to the drawings. In each embodiment, the security operation is exemplified as the operation to which the present invention is applied, but the present invention can also be applied to service industries such as the financial industry and the transportation industry, manufacturing industries such as chemical and automobile industries, or agriculture. In addition, the security operation includes various operations such as inspection, repair, and maintenance, and the assets subject to the security operation include various items such as equipment, devices, machines, and products.

Embodiment

[0014] Figure 1 is a functional block diagram of the business policy evaluation device 1 according to Embodiment 1 of the present invention. As shown here, the business policy evaluation device 1 of this 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. Further, in the information storage unit 15, a business policy list 15a, causal relationship structure data 15b, and evaluation results 15c are stored. Specifically, the business policy evaluation device 1 is a computer equipped with hardware such as an arithmetic device such as a CPU, a storage device such as a semiconductor memory, and a communication device. Then, by executing a desired program by the arithmetic device, each function such as the above-mentioned index designation unit 11 is realized. Hereinafter, such well-known techniques will be omitted as appropriate for explanation.

[0015] In addition, an input device 2 such as a keyboard, a mouse, and a touch panel and an output device 3 such as a liquid crystal display are connected to the business policy evaluation device 1, so that a user can input a command to the business policy evaluation device 1 or the business policy evaluation device 1 can present information to the user.

[0016] Next, the processing executed by the business policy evaluation device 1 will be sequentially described according to the flowchart of FIG. 2.

[0017] <Step S1> First, in step S1, the index designation unit 11 designates an index that the user values in conducting business according to the user's selection described later. The index is also called a KPI (Key Performance Indicator) and is a value that measures the achievement status of a business or the performance of activities related to the business. This index can be classified into social issue indexes related to society as a whole, management indexes related to the management status of a company, and activity indexes representing the status of assets, organizations, resources, etc. in corporate activities. From the perspective of improving the user's understanding, it is better if the activity index is represented by a quantitative numerical value.

[0018] Social issue indicators include, for example, energy security, resilience, green infrastructure, 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, profit, sales, free cash flow, stock price, labor costs, operating costs, parts costs, travel costs, penalties, and employee satisfaction. Activity indicators are also diverse, including, for example, utilization rate, downtime, number of locations, number of workers, CO2 emissions, number of spare parts, number of warehouses, and number of maintenance operations.

[0019] Figure 3 shows an example of the target KPI setting screen displayed on the output device 3 (LCD display) when the user selects a target indicator (target KPI) that they consider important from a set of pre-prepared indicators (KPIs) prior to step S1. In this example, the user has selected the utilization rate, one of the activity indicators, as the target indicator by operating the input device 2 (for example, a mouse). Although only one target indicator is selected here, if there are multiple indicators that the user wants to emphasize, the user can select any number of target indicators. A target indicator is an indicator (KPI) that the user considers important and has selected from a set of pre-prepared indicators (KPIs).

[0020] <Step S2> In step S2, the business strategy extraction unit 12 selects a business strategy pattern to be evaluated from the business strategy list 15a stored in the information storage unit 15. Here, a business strategy pattern refers to any change to a design parameter that can be intentionally altered from the current business design. The scope of business design is diverse, including assets, organization, resources, and technology (IoT solutions).

[0021] Figure 4 shows an example of a business initiative list 15a. As illustrated here, the business initiative list 15a in this embodiment is defined by a combination of business initiative patterns and initiative parameters. For example, the initiative parameters for business initiative pattern #1, "Replacing aging equipment," include multiple parameters that affect the evaluation results of "Replacing aging equipment," such as the number of pieces of equipment to be replaced (replacement scope), the performance improvement amount due to equipment replacement, and the replacement cost.

[0022] Thus, even for a single business measure pattern, numerous measure parameters can be considered, resulting in a vast number of business measures that need to be considered for business improvement. Furthermore, since a composite measure combining multiple business measure patterns can also be considered a single measure, there are countless options for business measures. It should also be noted that measures that do not change the business design can be considered a status quo measure and thus a single business measure.

[0023] <Step S3> In step S3, the business strategy extraction unit 12 uses causal relationship structure data 15b, which defines the causal relationships between business strategies and indicators, as well as the causal relationships between indicators themselves, to select business strategy patterns that are related to the target indicators specified in step S1. Below, we will first explain the structure of the causal relationship structure data 15b, and then explain the business strategy extraction process.

[0024] Figure 5 shows an example of the structure of causal relationship structure data 15b. On the left side of the figure, various business-related indicators (KPIs) are listed in boxes, arranged hierarchically from left to right 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 positive causal relationships and dashed arrows indicating negative causal relationships. For example, a solid arrow connects the indicator "maintenance work time" to the indicator "downtime," indicating a causal relationship where an increase in maintenance work time leads to an increase in downtime. Similarly, a dashed arrow connects the indicator "downtime" to the indicator "utilization rate," indicating a causal relationship where an increase in downtime leads to a decrease in the utilization rate.

[0025] In Figure 5, each indicator is connected by a causal arrow to all of the downstream indicators that are affected when the indicator changes. As the number of indicators increases, the causal structure becomes more complex, making it difficult for a person to grasp all the causal relationships. Generally, these causal structures often involve causal relationships from activity indicators to management indicators and social issue indicators, and have a hierarchical tree structure. By hierarchically organizing this causal structure data 15b and representing it as a KPI tree, it becomes easier to understand the causal relationships between numerous indicators. In addition, the example in Figure 5 includes indicators from multiple stakeholders, such as the operations department and the maintenance department.

[0026] Typically, social issue indicators are common across stakeholders. On the other hand, management indicators and activity indicators differ for each stakeholder, and indicators can influence 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 more complex business structures. For example, it is possible to identify cases of conflicts of interest where an indicator for one stakeholder improves while an indicator for another stakeholder deteriorates. It is also possible to identify cases of synergistic effects where an improvement in an indicator for one stakeholder leads to an improvement in the indicators of another stakeholder.

[0027] Furthermore, on the right side of Figure 5, the business initiative patterns included in business initiative list 15a (see Figure 4) are listed, and each business initiative is connected to an activity indicator by an arrow. The arrows indicate the causal relationship between the business initiative and the indicator affected by that business initiative. For example, in the case of the business initiative pattern "Replacement of aging equipment," since performance improvement and a reduction in failure frequency can be expected due to equipment replacement, solid arrows are connected to the indicator "equipment performance" and the indicator "failure frequency." Since business initiatives involve changing the current business design and altering a part of business activities, the impact of business initiatives is generally linked to activity indicators.

[0028] The causal relationships described above are generally constructed based on human experience and know-how. If experienced individuals define the causal relationships, a more accurate causal structure model can be constructed. On the other hand, for unfamiliar business strategies, the lack of past performance data carries a risk of errors or oversights in causal relationships. It should be noted that causal relationships can also be automatically generated from documents and internet information using natural language processing and machine learning.

[0029] Figures 6A to 6C show an example of the process for extracting business strategy patterns when the user selects "utilization rate" as the target indicator (see Figure 3). Note that, in order to clearly illustrate the steps of this process, Figures 6A to 6C omit illustrations of information unnecessary for explaining the extraction process (indicators, business strategy patterns). However, the causal relationship structure data 15b in each figure actually holds the same information as in Figure 5.

[0030] Figure 6A shows the process by which the evaluation unit 13 sets "utilization rate," the indicator specified in step S1, as the target indicator.

[0031] Figure 6B shows the process by which the evaluation unit 13 extracts all other indicators that affect the target indicator "occupancy rate" by tracing back the causal relationship arrows, regardless of whether they are solid or dashed lines, starting from the target indicator "occupancy rate". Through this process, all indicators that are upstream of the target indicator "occupancy rate," such as "operating hours" and "downtime," are extracted.

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

[0033] Thus, 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 could influence the target indicator.

[0034] Each of the causal relationship arrows mentioned above can be assigned a weight coefficient. By tracing back the causal relationships while considering these weight coefficients, it is possible to evaluate the magnitude of the impact of the extracted business measures and rank the extracted business measures by their impact. By prioritizing the extraction and evaluation of business measures with higher ranks and greater impact, it is possible to efficiently identify the most effective business measures.

[0035] Note that while Figures 3 and 6A-6C illustrate a situation where only one indicator is set as a target indicator, multiple indicators may be set as target indicators. In that case, the process corresponding to Figures 6A-6C is performed for each target indicator to find the business measure patterns related to each target indicator, and then the business measure patterns common to all target indicators are identified, thereby extracting the business measure patterns that affect all target indicators.

[0036] <Step S4> In step S4, the evaluation unit 13 evaluates the effects and impacts on each indicator when the business measure pattern selected in step S3 is implemented, that is, to what extent the indicator changes from the current state.

[0037] One evaluation method in this step is agent simulation, which replicates real-world operations. In agent simulation, data for each asset in the simulation world is generated for each management unit, such as a real-world asset or an asset component. These assets then autonomously operate and fail, replicating the behavior of real-world assets. The generated data is called an agent, and in the case of an asset, it is called an asset agent. Similarly, agents are generated for each maintenance worker in the simulation world and are called maintenance worker agents. These agents perform actions such as waiting, moving, executing tasks, and resting in response to work instructions. Furthermore, agents corresponding to real-world characters are generated, such as operation agents that handle asset operation and request fault response when problems occur, and assignment agents that assign tasks to maintenance workers.

[0038] Using such agent simulations, complex business activities involving diverse real-world stakeholders 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 effects and impacts on indicators when implementing business measures, the situation in which the business measures are virtually implemented can be reproduced through simulation, the indicators in that situation can be predicted and evaluated, and then compared with the indicators in the current business design by taking the difference. Note that the simulation method is not limited to agent simulation; for example, machine learning models generated from past performance data can also be used.

[0039] <Step S5> In step S5, the evaluation unit 13 saves the evaluation result 15c simulated in step S4 to the information storage unit 15. The output unit 14 then 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 evaluation result presentation screen will be explained in Embodiment 2.

[0040] The business strategy evaluation device of this embodiment, as described above, can narrow down and evaluate the most suitable measures for improving target indicators from among countless options for business strategies. Furthermore, the business strategy evaluation device of this embodiment can prioritize the evaluation of the effectiveness of business strategies, starting with those that have a significant impact on target indicators, enabling the rapid and efficient selection of promising business strategies. Moreover, even in complex situations such as when there are multiple target indicators or multiple stakeholders, the business strategy evaluation device of this embodiment can rationally narrow down effective business strategies based on the causal relationships between indicators. [Examples]

[0041] Next, the business policy evaluation device 1 according to Embodiment 2 of the present invention will be described using Figures 7 to 9. Note that similarities with Embodiment 1 will not be explained again, and only the differences will be described.

[0042] Figure 7 is a functional block diagram of the business strategy evaluation device 1 according to Embodiment 2. As can be seen from the comparison between Figure 1 and Figure 7, the business strategy evaluation device 1 of this embodiment differs from the business strategy evaluation device 1 of Embodiment 1 in the following respects. Firstly, the business strategy evaluation device 1 of this embodiment has an additional statistical evaluation unit 16. Secondly, in the business strategy evaluation device 1 of this embodiment, the simulation performed by the evaluation unit 13 is a probabilistic event simulation. Thirdly, in the business strategy evaluation device 1 of this embodiment, the output of the statistical evaluation unit 16 is stored in the information storage unit 15 as the statistical evaluation result 15c'.

[0043] The processes performed by the business policy evaluation device 1 of this embodiment, which differs from Embodiment 1 in the points described above, will be explained sequentially according to the flowchart in Figure 8. Points that are common with the flowchart in Figure 2 will be omitted from explanation as appropriate.

[0044] The processing steps S1 to S3 are the same as those in Example 1.

[0045] In step S4a, the evaluation unit 13 performs numerous probabilistic event simulations. The probabilistic event simulation performed in this step is, for example, in the case of a maintenance operation simulation, a simulation in which the failure probability of an asset or an asset's 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 conceivable to apply a distribution function to the worker's working time or the lead time for sharing components, so that the working time or lead time varies randomly. By performing numerous probabilistic event simulations that take into account such randomly changing uncertain events, it becomes possible to assess the risk of uncertain events.

[0046] In step S4b, the statistical evaluation unit 16 receives the evaluation results of the numerous probability event simulations performed in step S4a and statistically processes the evaluation results of the numerous indicators. Examples of statistical processing include the mean, variance, standard deviation, and confidence interval.

[0047] In step S5, the statistical evaluation unit 16 stores the evaluation results of the indicators statistically processed in step S4b as statistical evaluation results 15c' in the information storage unit 15. The output unit 14 then outputs the statistical evaluation results 15c' stored in the information storage unit 15 to the output device 3 (liquid crystal display) to present the evaluation results to the user.

[0048] Figure 9 shows an example of the display screen for the statistical evaluation results 15c' shown on the output device 3 (liquid crystal display). In the upper part of this figure, the evaluation results of the target indicators are shown in graph format. In the upper part of the screen, it is possible to select the target indicators to be output to the graph. 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 implemented are shown on a two-dimensional graph with KPI1 "Green" on the horizontal axis and KPI2 "Employee Satisfaction" on the vertical axis.

[0049] Figure 9 plots not only the evaluation values ​​of the target KPI when business measure A, selected by the business measure extraction unit 12, is implemented, but also the results of the current business state. This display method allows users to intuitively recognize the difference between the two as the improvement effect of business measure A. In Figure 9, the evaluation results of multiple probability event simulations are plotted as small values, and their average values ​​are plotted as large values. This makes it possible to visualize the results even considering the randomness due to uncertain events, understand the range in which the KPI may fluctuate, and assess the risks.

[0050] Furthermore, the lower part of Figure 9 displays the causal relationship structure data 15b (KPI tree) stored in the information storage unit 15, with all indicators affected by business measure A (i.e., indicators downstream of business measure A) highlighted. This display allows for a visual understanding of the ripple effects of business measure A on various indicators. It also helps prevent users from overlooking impacts on indicators they did not anticipate. [Examples]

[0051] Next, the business policy evaluation device 1 according to Embodiment 3 of the present invention will be described using Figures 10 and 11. Note that similarities with the above embodiment will not be explained again, and only the differences will be described.

[0052] Figure 10 is a functional block diagram of the business strategy evaluation device 1 according to Embodiment 3. As can be seen from the comparison between Figure 1 and Figure 10, the business strategy evaluation device 1 of this embodiment is the same as that of Embodiment 1 with the addition of a causal relationship learning unit 17. The processing performed by the business strategy evaluation device 1 of this embodiment, which differs from Embodiment 1 in this respect, will be explained using the flowchart in Figure 11.

[0053] The processing steps S1 to S5 are the same as those in Example 1.

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

[0055] As explained in Example 1, the causal relationship structure data 15b used in the present invention is basically a causal relationship structure model constructed by humans based on human experience and know-how. Therefore, it does not reflect business measures that humans have not experienced, and there is a risk of errors or omissions in causal relationships. Consequently, the causal relationship structure data 15b at the time of construction is often inaccurate and does not necessarily appropriately show the causal relationship structure between business measures and indicators, or the causal relationship structure between indicators.

[0056] Therefore, according to Example 3, even for business measures that are unfamiliar, the impact and effect on indicators can be virtually evaluated through business simulation. By learning from the simulation evaluation results, the causal structure of the impact of business measures that are unfamiliar to humans on each indicator can be reflected, and errors and oversights in causal relationships can be improved.

[0057] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations. [Explanation of Symbols]

[0058] 1... Business policy evaluation device, 11...Indicator specification section, 12...Business policy extraction department, 13…Evaluation Department, 14…Output section, 15... Information storage unit, 15a… Business initiative list, 15b…Causal relationship structure data, 15c...Evaluation results, 15c'...Statistical evaluation results, 16…Statistical Evaluation Department, 17…Causal Relationship Learning Department, 2 Input devices, 3. Output device

Claims

1. An information storage unit that stores causal relationship structure data defining the causal relationships between business measures and indicators, and the causal relationships between indicators, An indicator specification unit that specifies a target indicator from a set of pre-defined indicators, Based on the aforementioned causal relationship structure data, a business measure extraction unit extracts business measures related to the aforementioned target indicator, An evaluation unit that evaluates the impact of the extracted business measures on the target indicators, An output unit that outputs the evaluation results of the evaluation unit, A business strategy evaluation device characterized by being equipped with the following features.

2. In the business policy evaluation device described in claim 1, The aforementioned causal relationship structure data is characterized in that each indicator is classified into one of the following categories: social issue indicator, management indicator, or activity indicator. This is a business policy evaluation device.

3. In the business policy evaluation device described in claim 1, The aforementioned causal relationship structure data is characterized in that each indicator is classified into one of the indicators of multiple stakeholders, and is used as a business policy evaluation device.

4. In the business policy evaluation device described in claim 1, Furthermore, the business policy evaluation device has a causal relationship learning unit that learns the causal relationships between indicators based on the evaluation results of the evaluation unit and updates the causal relationship structure data.

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

6. An indicator specification unit that specifies a target indicator from a set of pre-defined indicators, A business strategy extraction unit extracts business strategies related to the aforementioned target indicators, An evaluation unit that performs multiple simulations including random probability events to evaluate the indicators, statistically processes the indicators evaluated multiple times, and evaluates the impact of the extracted business measures on the target indicators, An output unit that outputs the evaluation results of the evaluation unit, Equipped with, The business strategy evaluation device is characterized in that the simulation performed by the evaluation unit is a maintenance operation simulation in which the failure probability of an asset or a component of an asset is set using a Weibull distribution, and failures occur randomly according to the failure probability.

7. A method for evaluating business measures performed by computer, A step to specify a target indicator from a set of pre-defined indicators, A business strategy extraction step that extracts business strategies related to the target indicator based on causal relationship structure data that defines the causal relationship between business strategies and indicators, and the causal relationship between indicators themselves, An evaluation step to evaluate the impact on the target indicators when the extracted business measures are implemented, An output step that outputs the evaluation result of the evaluation step, A method for evaluating business measures, characterized by comprising the following features.

8. A method for evaluating business measures performed by a computer, A step to specify a target indicator from a set of pre-defined indicators, A business strategy extraction step for extracting business strategies related to the aforementioned target indicators, An evaluation step involves running simulations that include random probability events multiple times to evaluate the indicators, statistically processing the indicators evaluated multiple times, and evaluating the impact of the extracted business measures on the target indicators. An output step that outputs the evaluation result of the evaluation step, Equipped with, The business strategy evaluation method is characterized in that the simulation performed in the evaluation step is a maintenance operation simulation in which the failure probability of an asset or a component of an asset is set using a Weibull distribution, and failures occur randomly according to the failure probability.

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