Technical Field
[0001]
The present invention relates to a business management system.
Background Art
[0002]
A system product, such as a control panel, includes a plurality of control panels and various units and is a make-to-order product whose configuration is designed according to the customer’s order and which is manufactured, tested, and shipped.
[0003]
In the management of the production cost of the make-to-order product, it is important to detect that the cost exceeds the estimated cost early and to take measures, in order to maintain the estimated cost of each product.
[0004]
For example, with a change in design specifications, the design is corrected and design costs are added. As such, additional costs associated with the occurrence of various risks cause the cost to exceed the estimated cost.
2
[0005]
Therefore, in order to detect that the cost exceeds the estimated cost, it is necessary to appropriately predict the occurrence of each risk, to appropriately estimate additional costs when the risk occurs, and to take measures to risks that have a great influence on production.
[0006]
In addition, risks occur independently and have the relationship therebetween in which risks in the previous process are propagated to risks in the next process. For example, with a change in design specifications, a manufacturing process is corrected. For this reason, it is necessary to consider indirect influence caused by propagation, in addition to the direct influence of each risk.
[0007]
PTL 1 discloses a method which expresses a risk propagation relationship in a network in advance and calculates the occurrence probability or the degree of indirect influence of each risk according to the occurrence situations of other risks.
Citation List
Patent Literature
[0008]
PTL 1: JP-A-2013-61694
Summary of Invention
3
Technical Problem
[0009]
However, for example, in a case in which a risk, such as a change in design specifications, occurs in the first half of a design process, the number of items to be corrected and additional costs are less than those in a case in which a risk occurs in the second half of the design process. As such, the occurrence probability or the degree of propagation of the risk and the degree of direct influence of the risk vary depending on a production situation.
[0010]
In the method disclosed in PTL 1, in a case in which the occurrence situations of other risks are the same, the occurrence probability or the degree of direct influence of the risk is constant. Therefore, PTL 1 does not consider that the occurrence probability or the degree of propagation of the risk and the degree of direct influence of the risk vary depending on a production situation.
[0011]
For this reason, in the method disclosed in PTL 1, it is difficult to accurately detect whether the cost exceeds the estimated cost, to appropriately predict the degree of influence of each risk on business indexes, and to determine risks to be managed.
[0012]
4
An object of the invention is to provide a business management system that appropriately predicts the degree of influence of each risk on business indexes according to a production situation and determines risks to be managed.
Solution to Problem
[0013]
A business management system according to an aspect of the invention includes a server terminal that includes an output unit, a storage unit, and a processing unit. The storage unit stores a risk propagation model master information table that stores risk propagation model master information for managing a causal relationship between a plurality of possible risks and a risk information table that stores risk information for managing an occurrence situation of the risk and a degree of influence indicating a magnitude of influence of the risk on a business index for each product. The processing unit includes: a risk correction unit that corrects the risk propagation model master information and the risk information for each product, using at least one of a degree of progress which is an index indicating progress of each process and a degree of pressure which is an index indicating a pressure level of workload on work capacity in work resources required to produce the product; and a risk score calculation unit that calculates a risk score which is a value obtained by adding indirect influence caused by propagation of the risk to direct
5
influence of the risk, on the basis of the corrected risk propagation model master information and the corrected risk information obtained by the risk correction unit. The output unit outputs the risk score calculated by the risk score calculation unit for each product in each process to a screen.
Advantageous Effects of Invention
[0014]
According to an aspect of the invention, it is possible to appropriately predict the degree of influence of each risk on business indexes according to a production situation and to determine risks to be managed.
Brief Description of Drawings
[0015]
[Fig. 1] Fig. 1 is a diagram illustrating an example of a process flow in Embodiment 1.
[Fig. 2] Fig. 2 is a block diagram illustrating the overall configuration of a system according to Embodiment 1.
[Fig. 3] Fig. 3 is a diagram illustrating an example of risk propagation model master information.
[Fig. 4] Fig. 4 is a diagram illustrating an example of the network expression of a risk propagation model.
[Fig. 5] Fig. 5 is a diagram illustrating an example of risk information.
[Fig. 6] Fig. 6 is a diagram illustrating an example of risk correction master information.
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[Fig. 7] Fig. 7 is a diagram illustrating an example of estimated cost information.
[Fig. 8] Fig. 8 is a diagram illustrating an example of production planning information.
[Fig. 9] Fig. 9 is a diagram illustrating an example of production result information.
[Fig. 10] Fig. 10 is a diagram illustrating an example of corrected risk propagation model information.
[Fig. 11] Fig. 11 is a diagram illustrating an example of corrected risk information.
[Fig. 12] Fig. 12 is a diagram illustrating an example of risk score information.
[Fig. 13] Fig. 13 is a diagram illustrating an example of contribution degree information.
[Fig. 14] Fig. 14 is a diagram illustrating an example of predicted cost information.
[Fig. 15] Fig. 15 is a diagram illustrating an example of the calculation result of the degree of progress in Step S110 of the process flow illustrated in Fig. 1.
[Fig. 16] Fig. 16 is a diagram illustrating an example of a detailed process flow in Step S120 of the process flow illustrated in Fig. 1.
[Fig. 17] Fig. 17 is a diagram illustrating an example of a process of correcting a risk propagation model in Step S120 of the process flow illustrated in Fig. 1.
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[Fig. 18] Fig. 18 is a diagram illustrating an example of a process of correcting the amount of propagation in Step S120 of the process flow illustrated in Fig. 1.
[Fig. 19] Fig. 19 is a diagram illustrating an example of an occurrence probability calculation process in Step S130 of the process flow illustrated in Fig. 1.
[Fig. 20] Fig. 20 is a diagram illustrating an example of a risk score calculation process in Step S140 of the process flow illustrated in Fig. 1.
[Fig. 21] Fig. 21 is a diagram illustrating an example of the corrected risk propagation model information in a case in which correction based on the degree of progress is not considered.
[Fig. 22] Fig. 22 is a diagram illustrating an example of the amount of propagation in a case in which correction based on the degree of progress is not considered.
[Fig. 23] Fig. 23 is a diagram illustrating an example of the risk information in a case in which correction based on the degree of progress is not considered.
[Fig. 24] Fig. 24 is a diagram illustrating an example of the occurrence probability calculation process in a case in which correction based on the degree of progress is not considered.
[Fig. 25] Fig. 25 is a diagram illustrating an example of risk score information in a case in which correction based
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on the degree of progress is not considered.
[Fig. 26] Fig. 26 is a diagram illustrating an example of the calculation result of the degree of contribution in Step S150 of the process flow illustrated in Fig. 1.
[Fig. 27] Fig. 27 is a diagram illustrating an example of predicted cost information in a case in which correction based on the degree of progress is not considered.
[Fig. 28] Fig. 28 is a diagram illustrating an example of an output screen in Embodiment 1.
[Fig. 29] Fig. 29 is a diagram illustrating an example of a process flow in Embodiment 2.
[Fig. 30] Fig. 30 is a block diagram illustrating the overall configuration of a system according to Embodiment 2.
[Fig. 31] Fig. 31 is a diagram illustrating an example of production planning information in Embodiment 2.
[Fig. 32] Fig. 32 is a diagram illustrating an example of work resource information in Embodiment 2.
[Fig. 33] Fig. 33 is a diagram illustrating an example of the calculation result of the degree of pressure in Step S1101 of the process flow illustrated in Fig. 29.
[Fig. 34] Fig. 34 is a diagram illustrating an example of a process flow in Embodiment 3.
[Fig. 35] Fig. 35 is a block diagram illustrating the overall configuration of a system according to Embodiment 3.
Description of Embodiments
9
[0016]
Hereinafter, embodiments will be described with reference to the drawings.
[Embodiment 1]
[0017]
Fig. 1 illustrates an example of a process flow in Embodiment 1 and Fig. 2 is a functional block diagram illustrating the configuration of a system according to Embodiment 1.
[0018]
In Fig. 2, a business management system 10 is an apparatus that includes a PC, such as a server or a terminal, and software installed in the PC and includes an input/output unit 100, a storage unit 200, and an arithmetic unit 300. Here, the arithmetic unit 300 is a processing unit that performs predetermined processes. In addition, the business management system 10 is connected to a production planning system 20 and a production result management system 30 through a network.
[0019]
The input/output unit 100 acquires data required for the processes of the arithmetic unit 300 and displays processing results. For example, the input/output unit 100 includes an input device, such as a keyboard or a mouse, a communication device that communicates with the outside, a recording and
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reproduction device for a disk-type storage medium, and an output device such as a CRT or liquid crystal monitor.
[0020]
The storage unit 200 has input information 210 that is used for the processes of the arithmetic unit 300 and output information 220 that stores processing results and is a storage device such as a hard disk drive or a memory. Here, each of the input information 210 and the output information 220 is stored in the form of a table in the storage unit 200.
[0021]
The input information 210 includes risk propagation model master information 211, risk information 212, risk correction information 213, and estimated cost information 214. These information items will be described below.
[0022]
The risk propagation model master information 211 is information for managing a causal relationship between possible risks and includes, for example, a risk ID, a process ID, a parent risk ID, a parent risk occurrence situation, and a conditional occurrence probability as illustrated in Fig. 3.
[0023]
The risk is an event that reduces business indexes such as cost, inventories, and cash flow. Examples of the risk include “a change in product specifications” and “repurchase
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of components”. The process ID indicates a process in which a risk is likely to occur. For example, in the risk propagation model master information 211 illustrated in Fig. 3, a risk ID “R-01” indicates that a risk is likely to occur in a process ID “P-01”.
[0024]
The risks not only sporadically occur but also have the causal relationship therebetween in which a risk follows another risk. For two risks having the causal relationship therebetween, a risk corresponding to a cause is referred to as a parent risk, a risk corresponding to a result is referred to as a child risk, and the causal relationship is referred to as risk propagation from the parent risk to the child risk. The risk propagation from the parent risk to the child risk means that the occurrence probability of the child risk depends on the occurrence situation of the parent risk.
[0025]
That is, the occurrence probability of the child risk is represented by conditional probability based on the occurrence situation of the parent risk (hereinafter, referred to as conditional occurrence probability). A model indicating the type of risk that is likely to occur and a risk propagation relationship (a parent-child relationship and the conditional occurrence probability) is referred to as a risk propagation model.
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[0026]
The value of the conditional occurrence probability of the risk propagation model is corrected according to the production progress situation of each product, which will be described below. In Embodiment 1, the risk propagation model master information 211 (Fig. 3) common to a plurality of products is used as an initial value.
[0027]
As can be seen from the risk propagation model master information 211 illustrated in Fig. 3, for example, the parent risk of a risk ID “R-02” is a risk ID “R-01” and the conditional occurrence probability of “R-02” is “0.4” in a case in which “R-01” occurs and is “0.2” in a case in which “R-01” does not occur. In Fig. 3, only one row is related to the risk ID “R-01” and the parent risk ID and the parent risk occurrence situation for the risk ID “R-01” are represented by “―”, which indicates that there is no parent risk and the occurrence probability of “R-01” is not changed by the occurrence situation of other risks.
[0028]
In the risk propagation model, the relationship between the parent risk and the child risk can be illustrated as a Bayesian network. Fig. 4 illustrates a network indicating the parent-and-child relationship between risks in the risk propagation model master information 211 illustrated in Fig.
13
3. A node 410 indicates the risk ID “R-01” and a link 420 indicates the causal relationship between the risk ID “R-01” and the risk ID “R-02”.
[0029]
Here, in a case in which the parent risk does not occur or in a case in which there is no parent risk, the conditional occurrence probability of a risk is referred to as natural occurrence probability which is the probability that the risk will occur, without being caused by other risks.
[0030]
An increase in the occurrence probability that the child risk will occur due to the occurrence of the parent risk is referred to as the amount of propagation from the parent risk to the child risk. The amount of propagation is calculated from the difference between the conditional occurrence probability of the child risk in a case in which the parent risk occurs and the conditional occurrence probability of the child risk in a case in which the parent risk does not occur.
[0031]
The amount of propagation from each parent risk to the child risk in the risk propagation model master information 211 illustrated in Fig. 3 is represented by a link in Fig. 4. For example, in Fig. 4, the amount of propagation from the parent risk “R-01” to the child risk “R-02” is “0.2”, which indicates that the conditional occurrence probability of
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“R-02” is increased by “0.2” (from 0.2 to 0.4) with the occurrence of “R-01” in the risk propagation model master information illustrated in Fig. 3.
[0032]
The structure of the risk propagation model is predetermined, considering the causal relationship between risks, or is determined by, for example, a method which learns the structure of the Bayesian network.
[0033]
The risk information 212 is information for managing the occurrence situation of each risk and the degree of influence of each risk for each product and includes, for example, a product ID, a risk ID, an occurrence situation, and the degree of influence as illustrated in Fig. 5. The degree of influence means the magnitude of influence on business indexes, such as cost, inventories, and cash flow, associated with the occurrence of the risk. In Embodiment 1, the business index is cost and an increase in cost, that is, additional cost is used as the degree of influence.
[0034]
As can be seen from the risk information 212 illustrated in Fig. 5, for example, for a product ID “A-01”, the risk ID “R-01” has not occurred and the degree of influence (additional cost) in a case in which the risk ID “R-01” has occurred is “1000”.
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[0035]
The risk correction master information 213 is information for correcting the risk propagation model master information 211 or the risk information 212 for each product according to the degree of progress of the process in which each risk is likely to occur. The degree of progress of the process is an index indicating the progress level of the process. A detailed method for calculating the degree of progress will be described below. In addition, for simplicity, hereinafter, the degree of progress of the process in which each risk is likely to occur is referred to as the degree of progress of the risk.
[0036]
As illustrated in Fig. 6, the risk correction master information 213 includes, for example, a risk ID, a reference risk, the degree of progress, an influence degree correction expression, a natural occurrence probability correction expression, and a propagation amount correction expression.
[0037]
In Embodiment 1, in the following description, it is assumed that the degree of influence and the natural occurrence probability change depending on the degree of progress of the risk and the amount of propagation (from the parent risk) changes depending on the degree of progress of the parent risk. In Fig. 6, a row in which the reference risk is “itself” is
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information for performing correction according to the degree of progress of the risk and a row in which the reference risk is “parent (parent risk ID)” is information for performing correction according to the degree of progress of the parent risk. In addition, it is assumed that the parent risk ID is matched with the risk propagation model master information 211 illustrated in Fig. 3.
[0038]
As can be seen from the risk correction master information 213 illustrated in Fig. 6, for example, the amount of propagation from the parent risk to the risk ID “R-02” is increased by “0.15” in a case in which the degree of progress of a parent risk ID “R-01” is “equal to or greater than 0.8 and equal to or less than 1 ([0.8, 1])”. In addition, in a case in which the degree of progress of the risk ID “R-02” is “equal to or greater than 0.5 and less than 0.8 ([0.5, 0.8))”, the degree of influence is increased by 20% and the natural occurrence probability is reduced by 0.15.
[0039]
The estimated cost information 214 is information for managing estimated cost for each product and includes, for example, a product ID, a month, estimated cost, and reserve capacity for risks as illustrated in Fig. 7. The estimated cost includes reserve capacity estimated considering additional cost for risks, in addition to the minimum cost
17
required in a case in which manufacture progresses satisfactorily, and the reserve capacity is referred to as reserve capacity for risks.
[0040]
As can be seen from the estimated cost information 214 illustrated in Fig. 7, for example, the estimated cost of a product with a product ID “A-01” in “January” is “2000 (k¥)” and “725 (k¥)” is reserve capacity for risks. In addition, in a row in which “total” is written in a field “month”, the estimated cost and reserve capacity for risks in each month indicate the total estimated cost and the total reserve capacity for risks for the entire period, respectively. For the product ID “A-01”, “4000 (k¥)” obtained by adding up the estimated cost for the period from January to March is the total estimated cost and “1735 (k¥)” obtained by adding up reserve capacity for risks is the total reserve capacity for risks. In Embodiment 1, the cost is managed monthly. However, the cost may be managed daily or quarterly, or only the total cost may be managed.
[0041]
In addition to the above-mentioned input information 210, the business management system 10 acquires production planning information 231 and production result information 232 from the production planning system 20 and the production result management system 30, respectively.
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[0042]
The production planning information 231 is information for managing production plans for each product and each process and includes, for example, a product ID, a process ID, a scheduled work time, a scheduled start date, and a scheduled end date as illustrated in Fig. 8. As can be seen from the production planning information 231 illustrated in Fig. 8, for example, work corresponding to a product ID “A-01” and a process ID “P-01” is scheduled to be performed for a work time “120 (h)”, to start on “1/5”, and to end on “2/15”.
[0043]
The production result information 232 is information for managing the production results of each product and each process and includes, for example, a product ID, a process ID, an actual work time, an actual start date, and an actual end date as illustrated in Fig. 9. As can be seen from the production result information 232 illustrated in Fig. 9, for example, work corresponding to a product ID “A-01” and a process ID “P-01” starts on “1/5”, is performed for “100 (h)”, and has not ended.
[0044]
In addition, the output information 220 includes corrected risk propagation model information 221, corrected risk information 222, risk score information 223, contribution degree information 224, and predicted cost information 225,
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which will be described below.
[0045]
The corrected risk propagation model information 221 is information for managing the risk propagation model corrected according to the degree of progress of each process. The corrected risk propagation model information 221 includes, for example, a product ID, a risk ID, a parent risk occurrence situation, and an initial value, a parent risk progress degree effect, a risk progress degree effect, and a final value of conditional occurrence probability as illustrated in Fig. 10. The parent risk progress degree effect and the risk progress degree effect of the conditional occurrence probability indicate the amounts of correction of the conditional occurrence probability according to the degrees of progress of a parent risk and a target risk.
[0046]
As can be seen from Fig. 10, for example, in a case in which a parent risk occurs in a product ID “A-01”, the conditional occurrence probability of a child risk ID “R-02” has an initial value of “0.4”, is increased by “0.15” according to the degree of progress of the parent risk, is reduced by “0.15” according to its own degree of progress, and has a final value of “0.4”.
[0047]
The corrected risk information 222 is information for
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managing the magnitude of the influence of each risk corrected according to the degree of progress of each process and includes, for example, a product ID, a risk ID, and an initial value, a risk progress degree effect, and a final value of the degree of influence as illustrated in Fig. 11. As can be seen from the corrected risk information 222 illustrated in Fig. 11, for example, in a case in which a risk ID “R-01” occurs in a product ID “A-01”, the degree of influence has an initial value of “1000”, is increased by “500” according to the degree of progress, and has a final value of “1500”.
[0048]
The risk score information 223 is information for managing the information of a risk score which is a value obtained by adding indirect influence caused by risk propagation to the direct influence of each risk. A method for calculating the risk score will be described below.
[0049]
As illustrated in Fig. 12, the risk score information 223 includes, for example, a product ID, a risk ID, occurrence probability, the degree of influence, an expected value of the degree of influence, a risk score, and an expected value of the risk score. As can be seen from the risk score information 223 illustrated in Fig. 12, for example, in a case in which a risk “R-01” occurs in a product “A-01”, the degree of influence is “1500” and the risk score is “2340”. These values
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are multiplied by “0.3” that is the occurrence probability of the risk to obtain “450” and “702” that are the expected values of the degree of influence and the risk score, respectively.
[0050]
The contribution degree information 224 is information for managing the degree of contribution, which is the magnitude of the correction effect according to the degree of progress, to the expected value of each risk score. A method for calculating the degree of contribution will be described below.
[0051]
As illustrated in Fig. 13, the contribution degree information 224 includes, for example, a product ID, a risk ID, and the degree of contribution of the degree of progress. As can be seen from the contribution degree information 224 illustrated in Fig. 13, for example, for a risk “R-01” that occurs in a product “A-01”, the expected value of the risk score is increased by “7” according to the degree of progress.
[0052]
The predicted cost information 225 is information that is used to collect a change in the expected value of the degree of influence of each risk for each product and to manage the predicted cost and includes, for example, a product ID, a month, estimated cost, an expected value of the degree of influence, reserve capacity for risks, a cost increment, and predicted cost as illustrated in Fig. 14. As can be seen from the
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predicted cost information 225 illustrated in Fig. 14, for example, for a product ID “A-01”, in “January”, the expected value of the degree of influence of each risk is “869”, the reserve capacity for risks is “725 (k¥)”, the cost is predicted to be greater than the estimated cost by a cost increment “144” which is the difference between the expected value of the degree of influence and the reserve capacity for risks, and the predicted cost is “2144 (k¥)”.
[0053]
In addition, in a row in which “total” is written in the field “month”, the predicted cost indicates the total predicted cost obtained by adding up the predicted cost in each month for the entire period. For the product ID “A-01”, “4243 (k¥)” that is the total predicted cost from January to March is stored.
[0054]
The arithmetic unit 300 acquires data required for calculation from the input/output unit 100, the input information 210 of the storage unit 200, the production planning system 20, and the production result management system 30 and outputs the processing results to the output information 220 of the storage unit 200. The arithmetic unit 300 includes an arithmetic processing unit 320 that actually performs arithmetic processing and a memory unit 310 which is an arithmetic processing work area of the arithmetic processing
23
unit 320.
[0055]
The memory unit 310 temporarily stores the data acquired from the input/output unit 100, the input information 210 of the storage unit 200, the production planning system 20, and the production result management system 30 or the results processed by the arithmetic processing unit 320.
[0056]
The arithmetic processing unit 320 includes a data acquisition unit 321 that acquires data required for calculation from the input information 210, the production planning system 20, and the production result management system 30 and stores the acquired data in the memory unit 310. In addition, the arithmetic processing unit 320 includes a progress degree calculation unit 322 that calculates the degree of progress of the process in which each risk occurs from the production planning information 231 and the production result information 232.

[Claim 1]
A business management system comprising:
a server terminal that includes an output unit, a storage unit, and a processing unit,
wherein the storage unit stores a risk propagation model master information table that stores risk propagation model master information for managing a causal relationship between a plurality of possible risks and a risk information table that stores risk information for managing an occurrence situation of the risk and a degree of influence indicating a magnitude of influence of the risk on a business index for each product,
the processing unit includes:
a risk correction unit that corrects the risk propagation model master information and the risk information for each product, using at least one of a degree of progress which is an index indicating progress of each process and a degree of pressure which is an index indicating a pressure level of workload on work capacity in work resources required to produce the product; and
a risk score calculation unit that calculates a risk score which is a value obtained by adding indirect influence caused by propagation of the risk to direct influence of the risk, on the basis of the corrected risk propagation model
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master information and the corrected risk information obtained by the risk correction unit, and
the output unit outputs the risk score calculated by the risk score calculation unit for each product in each process to a screen.
[Claim 2]
The business management system according to claim 1,
wherein the processing unit further includes a risk occurrence probability calculation unit that calculates occurrence probability of the risk from the corrected risk propagation model information,
the risk score calculation unit calculates an expected value of the degree of influence of the risk and an expected value of the risk score, on the basis of the corrected risk propagation model information, the corrected risk information, and the occurrence probability of the risk, and
the output unit outputs the expected value of the degree of influence of the risk and the expected value of the risk score calculated by the risk score calculation unit to the screen.
[Claim 3]
The business management system according to claim 2,
wherein the output unit outputs at least the expected values of the degree of influence of the risk before and after the risk correction unit performs the correction to the screen.
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[Claim 4]
The business management system according to claim 1,
wherein the processing unit further includes a progress degree calculation unit that calculates the degree of progress from production planning information and production result information.
[Claim 5]
The business management system according to claim 1,
wherein the processing unit further includes a pressure degree calculation unit that calculates the degree of pressure from production planning information and work resource information.
[Claim 6]
The business management system according to claim 1,
wherein the processing unit further includes a predicted cost calculation unit that predicts the cost of each product and calculates predicted cost, on the basis of an expected value of the degree of influence of the risk and estimated cost, and
the input/output unit outputs the estimated cost and the predicted cost calculated by the predicted cost calculation unit to the screen in a form of a graph.
[Claim 7]
The business management system according to claim 2,
wherein the processing unit further includes a contribution degree calculation unit that calculates a degree
64
of contribution of the degree of progress to at least one of the degree of influence of the risk, the expected value of the degree of influence of the risk, the risk score, and the expected value of the risk score.
[Claim 8]
The business management system according to claim 1,
wherein the risk correction unit corrects the risk propagation model master information and the risk information for each product on the basis of the degree of progress of each process.
[Claim 9]
The business management system according to claim 1,
wherein the risk correction unit corrects the risk propagation model master information and the risk information for each product on the basis of the degree of pressure of the resources.
[Claim 10]
The business management system according to claim 1,
wherein the risk correction unit corrects the risk propagation model master information and the risk information for each product on the basis of the degree of progress of each process and the degree of pressure of the resources.