JP4045835B2 - Decision support method - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an information providing system and method, and more particularly, to a system and method for supporting corporate decision making based on financial data.
[0002]
[Prior art]
A conventional decision support system is a management index that is an indicator of the management status and financial status of a company based on the items (hereinafter simply referred to as financial data) described in financial statements such as the balance sheet and profit and loss statement issued by the company. Data was generated, the generated management index data was compared with the industry average value, and management index data inferior to the industry average value was output.
[0003]
On the other hand, “Company Diagnosis and Management Decision Support System” disclosed in Japanese Patent Laid-Open No. 2001-125962 discloses internal environment data such as past financial data and external environment such as current or expected future interest rates. Based on the data and the management policy of the company to be diagnosed, the company value of the company at a future time is simulated, and the present value of the company obtained from the simulation result and the actual company value at the time of the simulation The company is diagnosed by comparing. At this time, if the former is less than the latter, the factor that caused the cause in the simulation, that is, the matter that needs to be improved is extracted and output to support the decision making of the company.
[0004]
[Problems to be solved by the invention]
As described above, the conventional technology compares the management index data of a company with the average value of each management index data, outputs management index data that is inferior to the average, and points out the factors that cause it.
[0005]
In addition, the “company diagnosis and management decision support system” mentioned above points out matters that need to be improved at present, taking into account the future corporate value of the company to be diagnosed.
[0006]
Companies can recognize the points to be improved by the above-mentioned conventional technology, but how to improve the pointed out matters remains as problems to be dealt with individually by each company. In this regard, the inventor of the present invention has found a problem that the formulation of countermeasures for the improvement items as described above is complicated because it requires specialized knowledge for reasons such as the checkability of each management index data. .
[0007]
SUMMARY OF THE INVENTION An object of the present invention is to make a decision support system and method for diagnosing the management status and financial status of a company, determining items to be improved, and generating and outputting information indicating improvement measures for each item for each company. Is to provide.
[0008]
[Means for Solving the Problems]
The objects of the invention are achieved by the method outlined below.
[0009]
That is, the management index data is generated from the financial data of the company, the industry ID indicating the industry of the company is referred to, and the average value of the management index items corresponding to the industry of the company from the industry average recording unit based on the industry ID For each index item, and if it is inferior, extract management problem information corresponding to the inferior index item . Further, scenario data corresponding to the management problem extracted from the scenario recording unit is output based on the extracted management problem.
[0010]
DETAILED DESCRIPTION OF THE INVENTION
Embodiments of the present invention will be described below with reference to the drawings.
[0011]
FIG. 1 is a block diagram illustrating an example of the system configuration of the present invention. In FIG. 1, the decision support apparatus 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. Further, 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.
[0012]
Hereinafter, each configuration of the decision support apparatus 10 will be described.
[0013]
The input / output unit 11 performs input / output of information with the terminal device 23 and the recording devices 15 to 20 connected via the network.
[0014]
The customer information recording device 20 is a storage device that stores customer information that is information relating to a company that uses a service realized by the implementation of the present invention. FIG. 2 is a diagram for explaining a specific example of the customer information recording apparatus 20. Customer information includes customer name, location, transaction history, etc., customer ID assigned to each customer company, industry ID of the customer company, industry ID set for each industry, number of employees, asset size In addition, it has information such as company size. In addition to the above, the customer information recording apparatus 20 stores “customer desired scenario”, “planned scenario”, and “rejection scenario”. These items will be described later.
[0015]
In FIG. 6, it can be seen that the customer “Ko Co., Ltd.” identified by the customer ID “C01” is a company that operates in Tokyo and has 50 employees.
[0016]
The financial data recording device 15 is a recording unit that stores the customer ID and financial data of each customer in association with each other, and the financial data is stored in time series for each issue date. FIG. 3 is a diagram for explaining a specific example of the customer-specific financial data recording apparatus 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 appropriation proposals may be included, and the present invention is not limited to the information items exemplified here. In the present embodiment, for the sake of simplicity, a decision support system and method using financial data for the current period and the previous period will be described below. However, the financial data does not necessarily have to be once a period, and may be monthly or weekly. Good.
[0017]
The industry average value recording device 16 is a recording device that stores the average values of a plurality of types of management indexes calculated by calculation based on predetermined financial data for each industry. FIG. 2 is a diagram for explaining 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) for identifying each management index item for a plurality of types of management indices and the average of each index in the industry The values are stored in association with each other. In Figure 2, examples of management index items include indicators such as capital adequacy ratio, return on equity, and gross profit margin, but are not limited to these. In the following, these various management index items are simply referred to as index items.
[0018]
The management problem recording device 17 is a storage device that stores a management problem that causes deterioration for each index item. FIG. 4 is a diagram for explaining a specific example of the management problem recording device 17. The management problem recording device 17 stores, for each management index, the management index ID and a factor (one or more) that deteriorates the management index in association with each other. Each management issue is identified with an ID. In Figure 4, for example, the increase in uncollectible receivables (P10) and the prolonged collection of payments (P11) are the causes of the decline in the turnover ratio of sales receivables. Increase (P02).
[0019]
The scenario recording device 19 is a storage device that stores a management issue ID of a management issue that is a factor that deteriorates a management index and a scenario that is a solution to each management issue in association with each other. FIG. 5 is a diagram for explaining a specific example of the scenario recording device 19. The scenario recording device 19 has a table for each type of industry ID, and stores one or more scenarios for each problem according to 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).
[0020]
This scenario means an action that solves a certain management problem. For example, purchasing financial products (for example, risk hedging by options and futures transactions), introducing information systems (settlement systems, etc.), reducing personnel, etc. Is not limited. Also, here, the case where scenarios are stored by industry type and company size is described, but classification based on the number of employees or the like may be used. Each scenario is assigned a scenario ID. In FIG. 5, the scenario ID is stored, but actual scenario data may be stored instead of the scenario ID.
[0021]
When storing scenario IDs as shown in FIG. 5, a recording device that manages and saves the association between each scenario ID and scenario data is separately required, but this is not shown in this embodiment.
[0022]
By storing each scenario in association with each company attribute range, it is possible to propose a more effective scenario for the company.
[0023]
Furthermore, in order to show the standard of the effect about a scenario, it is also possible to store the number of times each scenario is applied and the hit rate calculated from the number of times the effect is obtained in association with each other. The hit rate is not limited to the simple calculation as described above, and any calculation method may be used as long as the information indicates the effect of each scenario.
[0024]
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 clarity of 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 industry ID for each industry, and this recording device is referred to when storing customer information in the customer information recording device 20. The business type ID is stored as an item of customer information.
[0025]
A control function 131 is resident in the processing unit 12 and controls input / output of information with the terminal device 23 and activation and termination of an index calculation function 132, an assignment extraction function 133, and a scenario generation function 134 described below.
[0026]
As described above, the recording unit 13 stores the index calculation function 132, the task extraction function 133, and the scenario generation function 134.
[0027]
The index calculation function 132 is a program that retrieves customer financial data from the customer-specific financial data recording device, calculates a plurality of types of management indices based on the retrieved financial data, and generates management index data.
[0028]
The task extraction function 133 searches the average value of each management index in the customer's industry from the industry average recording device 16 and compares the searched management index average value with each management index data generated by the management index function 132. The management problem recording device 17 searches for and outputs a corresponding management problem for the management index data that is inferior to the average.
[0029]
When outputting management issues, it is also possible to sequentially output management issue data corresponding to management indicator data having a large difference from the corresponding management indicator average value in the comparison described above. You may make it output preferentially the management subject which has high commonality or the management subject which has commonality irrespective of a customer's company scale and a business type.
[0030]
The scenario generation function 134 searches for and outputs a promotion scenario for each management problem based on the management problem extracted from the problem-specific promotion scenario recording device 19 by the problem extraction function 13.
[0031]
Further, the scenario generation function 134 extracts a customer rejection item stored for each customer in the customer information recording device 20 when outputting the scenario, and if the rejection item matches one of the searched promotion scenario items, the matched promotion is extracted. The scenario is not output. The customer rejection item is a promotion scenario that the customer refuses to adopt, and is stored in the customer information recording device 20 in advance.
[0032]
The terminal device 23 is a terminal used by the user. The user transmits / receives information to / from the decision support apparatus 10 via the terminal device 23. The terminal device 23 includes a transmission / reception unit (not shown) and an input / output device.
[0033]
In FIG. 1, only one terminal is shown for convenience of explanation, but it goes without saying that this does not mean that the number of terminals is limited.
[0034]
In this embodiment, it is assumed that information is transmitted and received between the decision support apparatus 10 and the customer terminal via the Internet. In FIG. 1, the WWW server and the browser are described. It is not limited to.
[0035]
FIG. 7 is a flowchart for explaining processing of the decision support apparatus 10. In the decision making support of the present embodiment, the index calculation function 132, the task extraction function 133, and the scenario generation function 134 stored in the recording unit 13 of the decision support apparatus sequentially perform predetermined processing under the control of the control function 131. Implemented.
[0036]
FIG. 8 is a flowchart for explaining the processing of the index calculation function 132. Although not shown, when the operator logs in to the decision support apparatus 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 / herself, or may be a service provider who consults on proposals of financial products in accordance with the scenario provided by the decision support device of the present invention to the customer. Good.
[0037]
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 for the current period and the previous period (step 813).
[0038]
Based on the extracted financial data, the index calculation function 132 calculates a management index for each period (step 814), and stores it in a volatile memory (hereinafter simply referred to as a memory) (not shown) provided in the apparatus (step 815). An 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).
[0039]
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.
[0040]
FIG. 9 is a flowchart for explaining the processing of the task extraction function 133. The issue 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 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).
[0041]
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 item of the previous period and the index item of the current period, or the ratio of one to the other or other appropriate method, and is not particularly limited.
[0042]
Proceed to the next item if the index item for the current period has improved compared to the previous period.
[0043]
The task extraction function 133 determines whether or not the above comparison determination has been completed for all the index items (step 915). If “No”, the procedure returns to step 913. If “Yes”, go to the next step.
[0044]
Here, it is assumed that the classification is based on a set of index IDs stored in the memory as being inferior to the index items of the previous period. One category is information indicating index items that worsen from the previous term for a certain company.
[0045]
Next, the problem 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). As described above, industry average values are prepared for each index item for each industry ID.
[0046]
In step 917, the management index data for the current period and the industry average are compared and judged for each index item (step 918). If the management index for the current period is inferior to the industry average, the index ID of the index item is stored in the memory. Store (step 919).
[0047]
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 item for the current period and the industry average, or the ratio of the other to the other and other appropriate methods, and is not particularly limited. If the current index item is superior to the industry average, proceed to the next item.
[0048]
The task extraction function 133 determines whether or not the above comparison determination has been completed for all index items (step 920), and returns to step 918 if “no”. If “Yes”, go to the next step.
[0049]
Here, it is classified into two categories based on the set of index IDs stored in the memory as inferior to the industry average. The two categories are information indicating index items that are inferior to the average value of management indexes in the industry to which the company belongs.
[0050]
FIG. 10 is a flowchart for explaining processing of the task extraction function 133 following FIG. The task extraction function 133 compares one classification, which is a set of data items obtained in step 914, with two classifications, which is a set of data items obtained in step 919 (step 1011). Extract (step 1012). The set of extracted index IDs is stored in the memory as three classifications (step 1013). The three categories are information indicating index items that are worse than the previous period and inferior to the industry average among index items of a certain company. If there is no matching index ID, the process proceeds to step 1020.
[0051]
Next, the three classifications extracted in step 1012 are compared with one classification, and a mismatched index ID is extracted (step 1015). If there is no mismatch ID, the process proceeds to step 1017. If there is a mismatch ID, the mismatch ID is stored in the memory and classified into four categories (step 1016). The four categories are index items with values that are superior to the industry average, and are information that shows deterioration from the previous term.
[0052]
Subsequently, the task extraction function compares the two classifications with the three classifications (steps 1017 and 1018), and extracts the discrepancy index ID (step 1019). If there is no mismatch ID, the process proceeds to step 1111 (see FIG. 11). If there is a mismatch ID, the mismatch ID is stored in the memory and classified into 5 categories. The five categories are index items that are inferior to the industry average and are information that shows an improvement over the previous term.
[0053]
If there is no matching ID in step 1012, in step 1020, the task extraction function re-stores 1 classification as 4 classifications and 2 classifications as 5 classifications.
[0054]
FIG. 11 is a flowchart for explaining the processing of the task extraction function following FIG. The issue extraction function searches for management issues based on the index ID from the management issue recording device 17 for each of the 3 categories, 4 categories, and 5 categories, extracts the corresponding issue ID, and stores the 3 category issues and 4 respectively in the memory. It memorize | stores as a classification task and 5 classification tasks (steps 1111-1116). Here, the 3 classification task, the 4 classification task, and the 5 classification task are information indicating management problems, which are events that cause deterioration of the index IDs included in the 3 classification, 4 classification, and 5 classification, respectively.
[0055]
Next, in step 1117, the 3 classification task and the 4 classification task are compared, and it is analyzed whether or not the 4 classification task includes a task that conflicts with the 3 classification task. If “No”, the process proceeds to Step 1119.
[0056]
Specifically, for example, when 3 classification tasks have (return on equity) and 4 classifications have ER (equity ratio), the 3 classification tasks include "low profit", "tax and public dues" and "large capital" The four classification issues include “large assets”, “large liabilities” and “undercapital”. Of these, “capital adequacy” related to the three classification tasks and “undercapitalization” related to the four classification tasks are contradictory to each other, so they were extracted by the analysis in step 1117, and from the four classification tasks in step 1118 “Capital shortage” is deleted.
[0057]
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 conflicts with the 3 classification task. If “No”, the process proceeds to Step 1121. If “yes”, the task extracted by the analysis in step 1119 is deleted from the five classification tasks (step 1120).
[0058]
By this process, important issues (3 classification issues) can be prioritized over other issues (4 classification and 5 classification issues), and matters to be started with priority can be clarified. In this case, the conflicting tasks are deleted. However, for example, a new task classification is newly provided and placed at the lower level of the four classification tasks or the five classification tasks, or priority described later at the stage of output to the terminal device 23. Processing such as lowering the order is also possible.
[0059]
The assignment extraction function 133 ends the above-described series of processing, and outputs an end notification to the control function 131 (step 1121). In step 1101, the control function 131 inputs an end notification and activates the scenario generation function 134.
[0060]
FIG. 12 is a flowchart for explaining the processing of the scenario generation function 134. The scenario generation function 134 prepares a scenario prepared as a measure for solving or improving the management problem indicated by the individual problem ID from the scenario recording device 19 based on the problem ID included in the 3 classification problem, the 4 classification problem, and the 5 classification problem. Search and extract corresponding scenarios.
[0061]
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, and the attribute information is stored in the attribute information. Search for matching scenarios. That is, the scenario storage device 19 searches and extracts scenario data having proposal condition information that is satisfied by the attribute information of the company. This process can provide more effective scenario data for each company.
[0062]
The extracted scenario IDs are stored in the memory as 3 classification scenarios for 3 classification tasks, 4 classification scenarios for 4 classification problems, and 5 classification scenarios for 5 classification problems (steps 1212 to 1217).
[0063]
FIG. 13 is a flowchart for explaining the processing of the scenario generation function 134 following FIG. The scenario generation function 134 generates proposed scenario information for each of the above-described three classification scenarios, four classification scenarios, and five classification scenarios, stores it in the memory (step 1311), and outputs an end notification to the control function 131 (step 1312). ).
[0064]
As shown in FIG. 13, the proposed scenario information indicates a top priority improvement item, an improvement required item, and an improvement proposal in association with an action for improving each item. Here, the top priority improvement items are the 3 classification issues, the improvement required items are the 5 classification issues, and the improvement proposals are the management issues shown in the 4 classification issues. Scenario data is shown. Further, in FIG. 13, the management problem is one item each, but there may be a plurality of management issues.
[0065]
For example, when a plurality of three classification tasks are extracted, there are a plurality of top priority improvement items in the proposed scenario information. Further, among the top priority improvement items, important items may be placed higher than other items. “Important” means that when “Degree of Deterioration” and “Difference” are stored in the memory in Step 913 and Step 918, the management index data for the previous fiscal year or the difference from the industry average was large. It may be possible to display management issues corresponding to It is also possible to preferentially output management issues common to the scale, type of business, and business type of the target company based on the attribute information of the target company.
[0066]
In FIG. 13, a plurality of scenario data is shown for each management problem. The display order of these scenario data may be based on the expected effect and the like.
[0067]
For example, when the hit rate is stored in the scenario recording device 19, the scenario generation function 134 may output the hit rate in descending order based on the hit rate corresponding to each proposed scenario.
[0068]
Further, the rejection scenario ID, the scheduled scenario ID, and the customer desired scenario ID are stored in the customer information recording device 20, and the proposed scenario information is generated based on these by referring to the customer information recording device 20 when the proposed scenario information is generated. You can also.
[0069]
The rejection scenario ID is an ID of a scenario that the customer refuses to adopt, 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 the present embodiment desires to propose to the customer, and is stored in advance. The customer desired scenario ID is an ID of a scenario that the customer desires to adopt, and is stored in advance.
[0070]
Using this information, it is also possible to lower or delete the scenario ranking that the customer refuses to adopt.
[0071]
Further, when the operator is a service provider, sales procedure information regarding each scenario data may be included in the scenario information. The business procedure information is information indicating procedures to be taken by the service provider, points to be noted, etc., regarding the execution of the corresponding scenario. For example, when the scenario is a predetermined financial product, the business procedure information may include, for example, important matters to be explained to the customer.
[0072]
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 process.
[0073]
【The invention's effect】
As described above, according to the present invention, it is possible to acquire information related to products and services suitable for each customer from the management index obtained from the financial data for each customer.
[Brief description of the 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 for explaining a specific example of a customer-specific financial data recording device 15;
FIG. 4 is a diagram for explaining a specific example of an industry average recording device 16;
FIG. 5 is a diagram for explaining a specific example of a management problem recording device 17;
6 is a diagram illustrating a specific example of a scenario recording device 19. FIG.
FIG. 7 is a flowchart for explaining processing of the decision support apparatus 10;
FIG. 8 is a flowchart for explaining processing of an index calculation function 132;
FIG. 9 is a flowchart for explaining processing of an assignment extraction function 133;
FIG. 10 is a flowchart for explaining processing of an assignment extraction function 133;
FIG. 11 is a flowchart for explaining processing of an assignment extraction function 133;
12 is a flowchart for explaining processing of a scenario generation function 134. FIG.
FIG. 13 is a flowchart for explaining processing of a scenario generation function 134;
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 10 ... Decision support apparatus 15 ... Financial data recording device 16 ... Industry average recording device 17 ... Management problem recording device 19 ... Scenario recording device 20 ... Customer information recording device 23 ...・ Terminal equipment

Claims (2)

  A storage device that stores the financial data of the company, the industry average value of the management index item of the industry to which the company belongs, and the management problem data, and a processing unit that identifies the management problem of the company based on the data stored in the storage device And a decision support device composed of a memory,
  The processor is
  Means for calculating the value of the first management index item based on the financial data for the current period of the company read from the storage device;
  Means for calculating the value of the second management index item based on the financial data for the previous period of the company read from the storage device;
  The value of the first management index item is compared with the value of the second management index item, and the value of the first management index item is inferior to the value of the second management index item. Means for extracting and storing the first classification information in the memory;
  The value of the first management index item read from the memory is compared with the industry average value of the management index item stored in the storage device, and the value of the first management index item is the value of the management index item. Means for extracting the second classification information which is a management index item inferior to the industry average value and storing it in the memory;
  The management index item of the first classification information read from the memory and the management index item of the second classification information read from the memory are compared, and the management index item of the first classification information and the second Means for extracting the third classification information which is the management index item that matches the management index item of the classification information and storing it in the memory;
  The management index item of the first classification information is compared with the management index item of the first classification information read from the memory and the management index item of the third classification information read from the memory. Means for extracting the fourth classification information which is the management index item that does not match the management index item of the classification information of 3 and storing it in the memory;
  The management index item of the second classification information is compared with the management index item of the second classification information read from the memory and the management index item of the third classification information read from the memory. Means for extracting the fifth classification information which is a management index item that does not match the management index item of the classification information of 3, and storing it in the memory,
  According to the importance set in the first to fifth classification information, it is possible to set the importance of the extracted management problem data by extracting the management problem data corresponding to the management index item of the classification information A decision support apparatus characterized by:   The decision support device according to claim 1,
  The processor is
  The management index item of the first classification information read from the memory and the management index item of the second classification information read from the memory are compared, and the management index item of the first classification information and the second If there is no management index item that matches the management index item of the classification information, the first classification information is classified into the fourth classification information, and the second classification information is classified into the fifth classification information. The decision support device further comprising means for classifying the data into the memory and storing it in the memory.

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