JP2003296539A - Decision-making support method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a decision-making support system for diagnosing the business or financial conditions of companies to decide items to be improved, and generating and outputting information showing an improvement plan for the items for every company. <P>SOLUTION: Business index data of a company is generated from its financial data, an industry ID showing the kind of industry of the company is referred to, and the average value of a business index item corresponding to the industry of the company is extracted from an industry average storage part based on the ID. Whether the business index data of the company are inferior to the extracted average value or not is judged every index item, and when they are inferior, business subject information corresponding to the inferior index item is extracted. Further, scenario data corresponding to the extracted business subject are outputted from a scenario storage part based on the extracted business subject. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an information providing system and method, and more particularly to a system and method for supporting decision making of a company based on financial data.

[0002]

2. Description of the Related Art A conventional decision support system indicates a business condition or financial condition of a company from items (hereinafter simply referred to as financial data) described in financial statements such as balance sheets and income statements issued by the company. The management index data, which is an index, is generated, the generated management index data is compared with the industry average value, and the management index data inferior to the industry average value is output.

On the other hand, the "corporate diagnosis and management decision-making support system" disclosed in Japanese Patent Laid-Open No. 2001-125962 discloses internal environmental data such as past financial data and current or expected future interest rates. Based on the external environmental data and the management policy of the company to be diagnosed, the corporate value of the company at a certain point in the future is simulated, and the present value of the company obtained from the result of the simulation and the actual state at the time of execution of the simulation. The enterprise is diagnosed by comparing the enterprise value of the enterprise. At this time, if the former is less than the latter, the factors that caused it in the simulation, that is, the items requiring improvement are extracted and output,
It supports corporate decision making.

[0004]

As described above, the conventional technology compares the management index data of a company with the average value of each management index data, outputs the management index data that is inferior to the average, and causes the factors. There was something to point out.

Further, the above-mentioned "corporate diagnosis and management decision support system" points out the matters that need to be improved in the present situation, in consideration of the future corporate value of the enterprise to be diagnosed when diagnosing the enterprise.

[0006] Although the companies can recognize the points to be improved by the above-mentioned conventional techniques, how to improve the points pointed out remains as a problem to be addressed by each company individually. . In this regard, the inventor of the present invention is
We have found that the formulation of countermeasures against the above-mentioned improvement matters is complicated because it requires specialized knowledge for reasons such as the balance of each management index data.

An object of the present invention is to support decision making by diagnosing the business condition and financial condition of a company, determining items to be improved, and generating and outputting information indicating improvement measures for those items for each company. A system and method are provided.

[0008]

The objects of the invention are realized by the methods outlined below.

That is, the management index data is generated from the financial data of the company, the business type ID indicating the business type of the company is referred to, and the business index item corresponding to the business type of the company from the industry average recording section based on the business type ID. The average value of is extracted, and it is determined whether or not the management index data of the company is inferior to the extracted average value for each index item, and if it is inferior, management problem information corresponding to the inferior index item To extract. Further, based on the extracted management problem, the scenario data corresponding to the management problem extracted from the scenario recording unit is output.

[0010]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram illustrating an example of the system configuration of the present invention. In FIG. 1, the decision support device 10 includes an input / output unit 11, a processing unit 12, and a recording unit 13, and the recording unit 13 stores a management index calculation function 132, a management problem extraction function 133, and a customer-specific promotion scenario generation function 134. is doing. Recording devices 15 to 20 are connected to the decision support device 10, and the decision support device 10 refers to and updates these recording devices 15 to 20 as necessary.

Each configuration of the decision support device 10 will be described below.

The input / output unit 11 inputs / outputs information to / from the terminal device 23 and the recording devices 15 to 20 connected via the network.

The customer information recording device 20 is a storage device for storing customer information, which is information about a company that uses a service realized by implementing the present invention. FIG. 2 is a diagram illustrating a specific example of the customer information recording device 20. Customer information includes customer name, location, transaction history, etc., customer ID given to each customer company, industry ID of the customer company, and industry ID set for each industry, number of employees, asset size Also, it has information such as company size. In addition to the above, the customer information recording device 20 has a “customer desired scenario”, a “planned scenario”,
The “rejection scenario” is stored. These items will be described later.

In FIG. 6, it can be seen that the customer "Kou Co., Ltd." identified by the customer ID "C01" is a company that operates in Tokyo and has 50 employees.

The financial data recording device 15 is a customer ID of each customer.
The financial data is stored in chronological order for each issuing time. FIG. 3 is a diagram illustrating a specific example of the customer-specific financial data recording device 15. The financial data includes, for example, each account item on the balance sheet and its numerical value, and an item on the income statement and its numerical value. In addition, information items such as profit disposition plans may be included, and the information items are not limited to those illustrated here. In the present embodiment, for simplicity, a decision support system and method using financial data of the current period and the previous period will be described below.
The financial data does not necessarily have to be once a period, but may be monthly or weekly.

The industry average value recording device 16 is a recording device for storing the average value of a plurality of types of management indexes calculated by calculation based on predetermined financial data for each industry. Figure
2 is a diagram illustrating a specific example of the industry average recording device 16. In FIG. 2, the industry average recording unit 16 has a table for each industry ID, and in each table, an ID (index ID) that identifies each management index item for a plurality of types of management indexes and the average of each index in that industry. Values are stored in association with each other. In Fig. 2, indicators such as equity ratio, return on equity, and gross profit margin are listed as examples of management index items, but they are not limited to these. In addition, below, these various management index items are only called index items.

The management problem recording device 17 is a storage device for storing a management problem that causes deterioration for each index item. Figure 4
FIG. 6 is a diagram illustrating a specific example of the management task recording device 17.
The management problem recording device 17 stores, for each management index, the management index ID and a factor (1 or more) that deteriorates the management index in association with each other. In addition, each business problem is identified by being given an ID. In Fig. 4, for example, the increase in uncollectible loans (P1
0) and long-term payment collection (P11), etc. are cited, and as a cause of the deterioration of the equity ratio, capital decrease (P03) and increase of other people's capital (P02) are mentioned.

The scenario recording device 19 is a storage device that stores the management problem ID of the management problem that is a factor that deteriorates the management index and the scenario that is a solution to each management problem in association with each other. FIG. 5 is a diagram illustrating a specific example of the scenario recording device 19. The scenario recording device 19 has a table for each business type ID, and stores one or more scenarios for each problem depending on the scale of the company having the problem. For example, in FIG. 5, S01 to S04 are stored in association with the problem (P01) that there are too many assets (= the balance sheet is too large).

The scenario here means an action to solve a certain management problem, such as purchase of financial products (for example, risk hedging by options or futures trading), introduction of information system (settlement system, etc.), personnel reduction, etc. The aspect is not particularly limited. Further, here, the case where the scenario is stored according to the type of industry and the size of the company is described, but the classification according to the number of employees may be used. A scenario ID is attached to each scenario. Although the scenario ID is stored in FIG. 5, actual scenario data may be stored instead of the scenario ID.

When storing the scenario ID as shown in FIG. 5, a recording device for managing and storing the association between each scenario ID and the scenario data is required, but this is not shown in the present embodiment.

By storing each scenario in association with each range of company attributes, a more effective scenario proposal can be made to the company.

Further, in order to show the guideline of the effect of each scenario, the hit rate calculated from the number of times each scenario is applied and the number of times the effect is obtained can be stored in association with each other. The hit rate is not limited to the above simple calculation, and any calculation method may be used as long as it is information indicating the effect of each scenario.

The storage device connected to the decision support device 10 has been described above. Although these storage devices are described here as independent devices, this is for clarifying the description and does not require an independent storage device in actual implementation. Although not shown, the decision support device 10 is further connected to a recording device that stores the business type ID for each type of business. When the customer information is stored in the customer information recording device 20, the recording device is referred to. Industry ID is stored as one item of customer information.

A control function 131 is resident in the processing unit 12,
It controls the input / output of information with the terminal device 23 and the activation and termination of the index calculation function 132, the task extraction function 133, and the scenario generation function 134 described below.

The recording unit 13 has the index calculating function 13 as described above.
2, a task extraction function 133, and a scenario generation function 134 are stored.

The index calculation function 132 is a program for retrieving the financial data of a customer from the financial data recording device for each customer, calculating a plurality of types of management indices based on the retrieved financial data, and generating management index data. .

The task extraction function 133 retrieves the average value of each management index in the industry of the customer from the industry average recording device 16 and the retrieved management index average value and each management index data generated by the management index function 132. Of the management index data that is inferior to the average of the management index data
7, the corresponding management problem is retrieved and output.

When outputting the management problem, it is also possible to sequentially output the management problem data corresponding to the management index data having a large difference from the corresponding management index average value in the above-described comparison. It is also possible to preferentially output a management task having a high degree of commonality, or a management task having a degree of commonality, irrespective of the company size or type of business of the customer.

The scenario generation function 134 retrieves and outputs a promotion scenario for each business task based on the business task extracted from the task-specific promotion scenario recording device 19 by the task extraction function 13.

Further, the scenario generation function 134 extracts a customer refusal item stored for each customer in the customer information recording device 20 when outputting a scenario, and if the refusal item matches any of the retrieved promotion scenario items, The matching promotion scenario is not output. The customer refusal item is a promotion scenario in which the customer refuses to be employed, and is stored in the customer information recording device 20 in advance.

The terminal device 23 is a terminal used by the user. The user uses the terminal device 23 to make a decision support device 1
Sends and receives information to and from 0. The terminal device 23 includes a transmission / reception unit (not shown) and an input / output device.

In FIG. 1, the number of terminals is 1 for convenience of explanation.
Only the units are shown, but needless to say, this does not mean that the number of terminals is limited.

Further, in the present embodiment, it is assumed that information is transmitted and received between the decision support device 10 and the customer terminal via the Internet, and a WWW server and a browser are described in FIG. It is not limited to such a method.

FIG. 7 is a flow chart for explaining the processing of the decision support device 10. The decision support of this embodiment is performed by the index calculation function 132, the task extraction function 133, and the scenario generation function 1 stored in the recording unit 13 of the decision support apparatus.
Under the control of the control function 131, the control unit 34 sequentially executes a predetermined process to be executed.

FIG. 8 is a flow chart for explaining the processing of the index calculation function 132. Although not shown, when the operator logs in to the decision support device 10 from the terminal device 23, inputs the customer ID of the customer and instructs the customer to output a scenario, the control function 131 activates the index calculation function 132 (step 801) The operator may be the customer himself or may be a service provider or the like that provides the customer with consulting such as suggestion of a financial product in accordance with the scenario provided by the decision support apparatus of the present invention. Good.

The index calculation function 132 retrieves the financial data of the customer from the financial data recording device 15 based on the customer ID (step 812) and extracts the financial data of the current period and the previous period (step 813).

The index calculation function 132 calculates the management index for each period based on the extracted financial data (step 814) and stores it in a volatile memory (not shown) (not shown) provided in the apparatus (step). 81
5) The end notification is output to the control function 131 (step 816). The control function 131 that has input the end notification (step 802) then activates the task extraction function 133 (step 901, next figure).

The management index data is stored for each index item in association with the index ID. Further, the calculated management index data may be stored in the customer information recording device 20 in association with the customer ID and used for generation of scenario information from the next time.

FIG. 9 is a flow chart for explaining the processing of the task extraction function 133. The problem extraction function 133 compares the management index for the current period and the previous period calculated by the index calculation function 132 for each index item (step 912), and determines whether the index item for the current period is inferior to the index item for the previous period (step 912). Step 913). If the index item of the current period is inferior to the index item of the previous period, the index ID of the index item is stored in the memory (step 914).

At this time, the degree of deterioration may be stored together. The degree of deterioration may be the difference between the numerical values shown in the index items of the previous period and the index item of the current period, the ratio of the other to one, or another appropriate method, and is not particularly limited.

When the index item for the current period is improved compared to the previous period, the process proceeds to the next item.

The task extraction function 133 judges whether or not the above comparison judgment has been completed for all index items (step 91).
5) If "No", the process returns to step 913. If yes, go to the next step.

Here, it is classified as one classification by a set of index IDs stored in the memory as inferior to the index items of the previous term. 1
The classification is information indicating an index item that is worse than the previous term for a certain company.

Next, the task extraction function 133 searches the industry average recording device 16 and extracts the industry average value which is the average value of the management index in the customer's industry based on the customer's industry ID (step 916). The industry average value is prepared for each index item for each industry ID as described above.

In step 917, the management index data for the current period and the industry average value are compared and judged for each index item (step 918). If the management index for the current period is inferior to the industry average value, the index ID of the index item. Is stored in the memory (step 919).

At this time, the difference between the two may be stored together. The difference between the two may be the difference between the numerical values shown in the index items for this period and the industry average value, the ratio of the other to one, or another appropriate method, and is not particularly limited. If the index items for the current period are superior to the industry average, proceed to the next item.

The task extraction function 133 judges whether or not the above comparison judgment has been completed for all index items (step 92).
0), in the case of "no", the process returns to step 918. If yes, go to the next step.

Here, it is classified into two categories with a set of index IDs stored in the memory as being inferior to the industry average value. The two classifications are information indicating an index item that is inferior to the average value of the management index in the industry to which the company belongs.

FIG. 10 is a flowchart of FIG. 9 followed by the task extraction function 133.
5 is a flowchart illustrating the processing of step S1. The task extraction function 133 compares one classification, which is the set of data items obtained in step 914, with two classifications, which is the set of data items obtained in step 919 (step 1011), and determines the index IDs that match both. Extract (step 1012). The set of extracted index IDs is stored in the memory as three categories (step 101).
3). The three categories are information indicating the index items of a certain company that are worse than the previous term and are inferior to the industry average. If there is no matching index ID, the process proceeds to step 1020.

Next, the three categories extracted in step 1012 are compared with the one category, and a mismatching index ID is extracted (step 1015). If there is no mismatch ID, the process proceeds to step 1017. If there is a mismatch ID, store the mismatch ID in memory,
There are four categories (step 1016). The four categories are index items that are superior to the industry average value, and are information that show worse than the previous term.

Subsequently, the task extraction function compares the two categories with the three categories (steps 1017, 1018) and extracts the index IDs of disagreement (step 1019). If there is no mismatch ID, step 1111
(See FIG. 11). If there is a discrepancy ID, the discrepancy ID is stored in the memory and classified into 5 categories. The five categories are index items that are inferior to the industry average value, and are information that show improvements over the previous term.

If there is no matching ID in step 1012,
In step 1020, the task extraction function sets 1 classification to 4 classifications and 2 classifications to 5 classifications.
Re-store as classification.

FIG. 11 is a flow chart for explaining the processing of the task extraction function following FIG. The task extraction function searches the business task recording device 17 for the business task based on the index ID for each of the three, four, and five categories, extracts the corresponding task ID, and stores the three-class task in the memory, four tasks, respectively. It is stored as a classification task and 5 classification tasks (steps 1111-111).
6). Here, the three-classification task, the four-classification task, and the five-classification task are information indicating management problems that are events that cause deterioration of the index IDs included in the third classification, the fourth classification, and the fifth classification, respectively.

Next, in step 1117, the three-classification task and the four-classification task are compared, and it is analyzed whether or not the four-classification task includes a task that conflicts with the three-classification task. If no, step 11
Proceed to 19.

Specifically, for example, when the three classification tasks have (return on equity) and the four classifications have ER (equity ratio), the three classification tasks are “low profit”, “large tax and public dues”, and “large equity capital”. "
However, the four categories of tasks include "large assets,""largedebts," and "insufficient equity capital." Of these, “large equity capital” related to the 3 classification tasks and “insufficient equity capital” related to the 4 classification tasks are issues that are mutually contradictory. Therefore, they are extracted by the analysis in step 1117, and from the 4 classification tasks in step 1118 “Undercapitalization” is deleted.

Similarly, in step 1119, the 3 classification task and the 5 classification task are compared, and it is analyzed whether or not the 5 classification task includes a task that contradicts the 3 classification task. If no, step
Continue to 1121. In the case of “yes”, the problem extracted by the analysis in step 1119 is deleted from the five classification scenario (step 1120).

Important tasks by this processing (3 classification tasks)
Can be prioritized over other issues (4 categories and 5 categories), and the matters to be started with priority can be clarified. Although the conflicting scenarios are deleted here, for example, a new task classification is newly provided and placed at the lower rank of the four-class task and the five-class task, or the priority described later at the stage of output to the terminal device 23. Processing such as lowering the rank is also possible.

The task extraction function 133 ends the series of processes described above and outputs an end notification to the control function 131 (step 1121). The control function 131 inputs the end notification in step 1101 and activates the scenario generation function 134.

FIG. 12 is a flow chart for explaining the processing of the scenario generation function 134. Scenario generation function 13
4 is an individual task ID from the scenario recording device 19 based on the task IDs included in the 3-class task, 4-class task and 5-class task.
The scenario prepared as a measure for solving or improving the management problem indicated by is searched and the corresponding scenario is extracted.

When the scenario recording device 19 associates the proposal condition information with each scenario data, the customer information recording device 20 refers to the attribute information of the company to be proposed based on the company ID, Search for scenarios that match the attribute information. That is, the scenario storage device 19 searches for and extracts scenario data having proposal condition information that is satisfied by the attribute information of the company. By this processing, more effective scenario data can be provided for each company.

The extracted scenario IDs are stored in the memory as a 3-classification scenario for the 3-classification task, a 4-classification scenario for the 4-classification task, and a 5-classification scenario for the 5-classification task (steps 1212 to 1217).

FIG. 13 is a continuation of FIG. 12 and the scenario generation function 1
It is a flow chart explaining processing of 34. The scenario generation function 134 generates proposal scenario information for each of the above-mentioned three-classification scenario, four-classification scenario, and five-classification scenario, stores it in the memory (step 1311), and outputs an end notification to the control function 131 (step 1312). ).

The proposed scenario information is as shown in FIG.
The top priority improvement item, the improvement required item, and the improvement proposal are shown in association with the action for improving each item.
Here, the top-priority improvement items are the management issues shown in the three-category task, the improvement-requiring items are in the five-category task, and the improvement proposals are the management issues in the four-category task. Scenario data is shown. See also FIG.
In item 3, there is one management task for each item, but multiple items are acceptable.

For example, when a plurality of three classification problems are extracted, there are a plurality of highest priority improvement items in the proposed scenario information. Further, the most important improvement items may be placed higher than the other items. “Important” means that when the “deterioration degree” and “difference” are stored in the memory in steps 913 and 918, the management index data that has a large difference from the business index of the previous term or the industry average value. It is conceivable to display the management issues corresponding to the above in a higher order. It is also possible to preferentially output management issues common to the size, type of business, and business type of the target company based on the attribute information of the target company.

Although a plurality of scenario data are shown for each management problem in FIG. 13, the display order of these scenario data may be based on the expected effect or the like.

For example, when the hit ratio is stored in the scenario recording device 19, the scenario generation function 134 may output the hit ratios in descending order based on the hit ratio corresponding to each proposed scenario.

Further, the customer information recording device 20 displays a rejection scenario.
It is also possible to store the ID, the planned scenario ID, and the customer desired scenario ID, refer to the customer information recording device 20 when generating the proposed scenario information, and generate the proposed scenario information based on these.

The refusal scenario ID is an ID of a scenario in which the customer rejects the hiring and is stored in advance. The scheduled scenario ID is an ID of a scenario that the person who operates the decision support apparatus 10 of this embodiment wants to propose to the customer,
It is stored in advance. The customer-desired scenario ID is an ID of a scenario that the customer wants to employ and is stored in advance.

By using these pieces of information, it is possible to lower the rank of the scenario in which the customer rejected the hire or delete it.

When the operator is the service provider, the business procedure information regarding each scenario data may be included in the scenario information. The business procedure information is information indicating procedures, points to be noted and the like that the service provider should take in executing the corresponding scenario. For example, when the scenario is a predetermined financial product, it is conceivable that the business procedure information indicates, for example, important items to be explained to the customer.

The control function 131 transmits the proposed scenario information stored in the memory from the transmission / reception unit 11 to the terminal device 23 and ends the processing.

[0073]

As described above, according to the present invention, it is possible to obtain information on products and services suitable for each customer from the management index obtained from the financial data for each customer.

[Brief description of drawings]

FIG. 1 is a block diagram illustrating an example of a system configuration of the present invention.

FIG. 2 is a diagram illustrating a specific example of a customer information recording device 20.

FIG. 3 is a diagram illustrating a specific example of a customer-specific financial data recording device 15.

FIG. 4 is a diagram illustrating a specific example of the industry average recording device 16.

FIG. 5 is a diagram illustrating a specific example of a management problem recording device 17.

FIG. 6 is a diagram illustrating a specific example of a scenario recording device 19.

FIG. 7 is a flowchart illustrating a process of the decision support device 10.

FIG. 8 is a flowchart illustrating a process of an index calculation function 132.

FIG. 9 is a flowchart illustrating a process of a task extraction function 133.

FIG. 10 is a flowchart illustrating a process of a task extraction function 133.

FIG. 11 is a flowchart illustrating a process of a task extraction function 133.

FIG. 12 is a flowchart illustrating a process of a scenario generation function 134.

FIG. 13 is a flowchart illustrating a process of a scenario generation function 134.

[Explanation of symbols]

10 ... Decision support device 15 ... Financial data recorder 16 ... Industry average recording device 17 ... Management problem recording device 19: Scenario recording device 20 ... Customer information recording device 23 ... Terminal device

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Tetsuya Kamoto             890 Kashimada, Sachi-ku, Kawasaki City, Kanagawa Stock             Hitachi, Ltd. Financial Systems Division

Claims (4)

[Claims] 1. A decision support method in a decision support device for generating management index data of a company based on the financial data of the company, the management index data being generated from the financial data of the company, and the type of business of the company. Is extracted, the average value of the management index items corresponding to the industry of the company is extracted from the industry average recording unit that stores the average value of the management index items for each industry based on the industry ID. For each index item, determine whether the management index data of the company is inferior to the averaged value, and if it is inferior, refer to the management problem recording section that stores information indicating the deterioration factor for each index item. Then, a decision support method characterized by extracting management problem information corresponding to the inferior index item. 2. The decision support method according to claim 1, further comprising: based on the extracted management task, a scenario recording unit that stores scenario data indicating a measure for the management task for each management task. A decision support method characterized by outputting scenario data corresponding to an extracted management problem. 3. The decision support method according to claim 2, wherein the extraction of the management task information includes a step of storing a difference between the inferior index item and the corresponding average value. Decision support device. 4. The decision support method according to claim 3, wherein the management task information is extracted between a plurality of management task information extracted from the management task information recording unit for each of the inferior index items. In the case where one management problem conflicts with another management problem in, the difference between the index value corresponding to each management problem and the stored average value is referred to A decision support method characterized by not outputting scenario data for management issues corresponding to items.