Cost management device, cost management method, and cost management program

Abstract

To provide a cost management device, a cost management method, and a cost management program capable of automatically detecting projects where the occurrence ratio by project cost classification is abnormal, thereby allowing for rapid detection of projects suspected of fraud and assisting users until the fraud is discovered.SOLUTION: Abnormal projects are identified based on an abnormality determination definition master and project cost data. An abnormality determination result is obtained by setting the abnormal project, cost classification, cost ratio, and abnormality determination threshold in association with one another. Based on the abnormality determination result, a ratio analysis chart showing the cost ratio for each project cost classification is displayed, making it possible to identify abnormal states.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a cost management device, a cost management method, and a cost management program. [Background technology]

[0002] Patent Document 1 discloses a configuration for determining fraudulent manipulation of construction costs by combining a time series analysis of construction costs and a clustering analysis by construction, order cost, organization, and accounting year and month. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2024-13637 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the invention described in Patent Document 1 above has a problem in that it is not possible to detect projects in which fraudulent cost manipulation may have occurred from the perspective of the occurrence rate by project cost category.

[0005] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a cost management device, a cost management method, and a cost management program that can automatically detect projects with abnormal occurrence rates by project cost classification, thereby quickly detecting projects suspected of fraud and providing assistance to users until the fraud is discovered. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objectives, the cost management device of the present invention is a cost management device equipped with a memory unit and a control unit, wherein the memory unit comprises an abnormality determination definition master in which an abnormality determination algorithm is set, and a project management memory means for storing project cost data set by linking projects, cost classifications, and cost amounts, and the control unit comprises a result acquisition means for identifying abnormal projects based on the abnormality determination definition master and the project cost data, and acquiring abnormality determination results set by linking the abnormal projects, the cost classifications, cost ratios, and abnormality determination thresholds, and an analysis display means for displaying a ratio analysis chart showing the cost ratios for each cost classification of the project based on the abnormality determination results so that abnormal states can be identified.

[0007] In addition, in the cost management device of the present invention, the project management storage means further stores project data that is linked to the project's responsible business establishment, responsible department, person in charge, payee, supplier, type, period, and / or project amount, and the abnormality determination definition master further links and sets explanatory variables for clustering and the number of clusters, and the result acquisition means acquires clustering results that set clusters by clustering similar projects based on the abnormality determination definition master, the project cost data, and the project data, identifies the abnormal project based on the clustering results, the abnormality determination definition master, and the project cost data, identifies the abnormal cluster to which the abnormal project belongs, and acquires the abnormality determination result that is set by linking the projects that constitute the abnormal cluster, the cost classification, the abnormal cluster, the cost ratio, and the abnormality determination threshold.

[0008] In addition, in the cost management device of the present invention, the project cost data is further set with the recording date linked to it, the result acquisition means acquires the abnormality judgment results for each specified period based on the abnormality judgment definition master and the project cost data, and the analysis display means further displays a proportion trend diagram showing the cost proportions for each cost classification of the project in chronological order so that the abnormal state can be identified based on the abnormality judgment results.

[0009] In addition, in the cost management device of the present invention, the result acquisition means further acquires an abnormality judgment result message corresponding to the abnormality judgment result, and the analysis display means further displays the abnormality judgment result message.

[0010] In addition, in the cost management device of the present invention, the analysis display means is further characterized in that it displays an amount analysis diagram showing the cost amount for each cost classification of the project in a manner that allows the abnormal state to be identified based on the abnormality judgment result.

[0011] In addition, in the cost management device of the present invention, when a display switching instruction is set, the analysis display means displays the amount analysis diagram showing the cost amount for each cost classification of the project so that the abnormal state can be identified based on the abnormality determination result.

[0012] In addition, in the cost management device of the present invention, the analysis display means is characterized in that it displays the ratio analysis diagram showing the cost ratio for each cost classification of a predetermined number of projects in which the cost ratio of the abnormal cost classification included in the abnormal cluster is large, so that the abnormal state can be identified based on the abnormality determination result.

[0013] In addition, in the cost management device of the present invention, the analysis display means is further characterized in that it displays a cluster ratio diagram showing the cost ratio for each cost classification of the abnormal cluster so that the abnormal state can be identified based on the abnormality judgment result.

[0014] In addition, in the cost management device of the present invention, the analysis display means is further characterized in that it displays a cluster amount diagram showing the cost amount for each cost classification of the abnormal cluster so that the abnormal state can be identified based on the abnormality judgment result.

[0015] In addition, in the cost management device of the present invention, the analysis display means is further characterized in that it displays an amount trend diagram showing the cost amount for each cost classification of the project in chronological order so that the abnormal state can be identified based on the abnormality judgment result.

[0016] In addition, in the cost management device according to the present invention, the abnormality determination algorithm is an abnormal value detection algorithm that uses the interquartile range.

[0017] In the cost management device according to the present invention, the clustering is performed by the k-means method.

[0018] In addition, the cost management method of the present invention is a cost management method to be executed by a cost management device having a memory unit and a control unit, wherein the memory unit has a project management memory means for storing an abnormality determination definition master in which an abnormality determination algorithm is set, and project cost data set in association with a project, cost classification, and cost amount, and is characterized by including: a result acquisition step executed by the control unit, which identifies an abnormal project based on the abnormality determination definition master and the project cost data, and acquires an abnormality determination result set in association with the abnormal project, the cost classification, cost ratio, and an abnormality determination threshold; and an analysis display step, which displays a ratio analysis chart showing the cost ratio for each cost classification of the project based on the abnormality determination result so that abnormal states can be identified.

[0019] In addition, the cost management program of the present invention is a cost management program to be executed by a cost management device having a memory unit and a control unit, wherein the memory unit comprises an abnormality determination definition master in which an abnormality determination algorithm is set, and a project management memory means for storing project cost data set by linking projects, cost classifications, and cost amounts, and the control unit executes a result acquisition step of identifying an abnormal project based on the abnormality determination definition master and the project cost data, and acquiring an abnormality determination result set by linking the abnormal project, the cost classification, cost ratio, and an abnormality determination threshold, and an analysis display step of displaying a ratio analysis chart showing the cost ratio for each cost classification of the project based on the abnormality determination result so that abnormal states can be identified. [Effects of the Invention]

[0020] The present invention has the effect of enabling even personnel without knowledge of statistics or data analysis to detect anomalies in construction industry costs and strengthen control. Furthermore, the present invention has the effect of automatically monitoring construction costs, quickly identifying cost anomalies, and notifying users. Furthermore, the present invention has the effect of utilizing the perspective of cost manipulation (showing a trend of cost occurrence that deviates from the expected occurrence rate by cost category) to identify, detect, and notify construction in which the occurrence rate by cost type (cost category: material cost, subcontract cost, labor cost, and overhead) is abnormal, prompting investigation and providing support until fraud is discovered. Furthermore, the present invention has the effect of enabling information on noteworthy points to be obtained without being aware of the data reference range, and enabling even members without specialized knowledge to identify problems and pass the information on to knowledgeable members or those responsible for managing the data, enabling fact-finding and corrective action. The present invention also enables periodic cost status checks, which would be difficult to achieve manually, and allows for the rapid identification of cost issues, allowing resources to be allocated to the consideration, implementation, and implementation of improvements and corrections. Furthermore, the present invention automatically classifies, aggregates, and calculates data in a system, reducing operational costs without relying on human experience, facilitating the visualization and analysis of cost status. It also facilitates the comparison of cost classification rates among similar projects, visualizing projects with clearly exceptionally high or low costs and the corresponding cost classifications, thereby presenting key information and encouraging corrective action. The present invention also achieves the effect of automatically grouping similar projects using clustering and performing statistical analysis of the cost percentages for each cost classification within each group, thereby enabling the detection of anomalies while taking into account the characteristics of the projects. The present invention also achieves the effect of identifying projects with abnormally high cost percentages for specific cost classifications by comparing them with other projects in the same group.Furthermore, to understand the causes of cost fraud, it is necessary to visualize and confirm the accounting personnel, accounting amounts, voucher details, and transaction details from the actual recorded data. To determine the validity of the clustering results themselves, information on the items that served as the basis for clustering and the characteristics of each group must be provided. Therefore, the present invention has the effect of detecting and visualizing information so that this information can be confirmed. Furthermore, the present invention has the effect of detecting construction projects in which the cost ratio of a specific cost category is abnormally high. Furthermore, the present invention has the effect of providing users with information on abnormal factors such as unplanned cost recording (e.g., recording subcontracting costs that are not included in the budget), cost reassignment between construction projects (e.g., recording costs that should be recorded under Construction A as Construction B for the purpose of profit manipulation), and incorrect cost classification selection (e.g., erroneously recording costs that should be recorded under material costs as subcontracting costs). [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a cost management device according to this embodiment. [Figure 2] FIG. 2 is a diagram showing an example of the abnormality determination definition master in this embodiment. [Figure 3] FIG. 3 is a diagram showing an example of a clustering result in this embodiment. [Figure 4] FIG. 4 is a diagram showing an example of an abnormality determination result in this embodiment. [Figure 5] FIG. 5 is a diagram showing an example of an abnormality determination result message in this embodiment. [Figure 6] FIG. 6 is a diagram showing an example of construction data in this embodiment. [Figure 7] FIG. 7 is a diagram showing an example of construction cost data in this embodiment. [Figure 8] FIG. 8 is a flowchart showing an example of processing performed by the cost management device in this embodiment. [Figure 9]FIG. 9 is a diagram showing an example of the cost management process in this embodiment. [Figure 10] FIG. 10 is a diagram showing an example of the cost management process in this embodiment. [Figure 11] FIG. 11 is a diagram showing an example of the cost management process in this embodiment. [Figure 12] FIG. 12 is a diagram showing an example of the cost management process in this embodiment. [Figure 13] FIG. 13 is a diagram showing an example of the cost management process in this embodiment. [Figure 14] FIG. 14 is a diagram showing an example of the cost management process in this embodiment. [Figure 15] FIG. 15 is a diagram showing an example of the cost management process in this embodiment. [Figure 16] FIG. 16 is a diagram showing an example of the cost management process in this embodiment. [Figure 17] FIG. 17 is a diagram showing an example of the cost management process in this embodiment. [Figure 18] FIG. 18 is a diagram showing an example of the cost management process in this embodiment. [Figure 19] FIG. 19 is a diagram showing an example of the cost management process in this embodiment. [Figure 20] FIG. 20 is a diagram showing an example of the cost management process in this embodiment. [Figure 21] FIG. 21 is a diagram showing an example of the cost management process in this embodiment. [Figure 22] FIG. 22 is a diagram showing an example of the cost management process in this embodiment. [Figure 23] FIG. 23 is a diagram showing an example of the cost management process in this embodiment. [Figure 24] FIG. 24 is a diagram showing an example of the cost management process in this embodiment. [Figure 25] FIG. 25 is a diagram showing an example of the cost management process in this embodiment. [Figure 26] FIG. 26 is a diagram showing an example of the cost management process in this embodiment. [Figure 27] FIG. 27 is a diagram showing an example of the cost management process in this embodiment. [Figure 28] FIG. 28 is a diagram showing an example of the cost management process in this embodiment. [Figure 29] FIG. 29 is a diagram showing an example of a business use image in this embodiment. [Figure 30] FIG. 30 is a diagram showing an example of a business use image in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0022] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to this embodiment.

[0023] [1. Overview] First, an outline of the present invention will be described.

[0024] Previously, corporate internal control standards have been revised, driving increased demand for strengthened controls and monitoring. However, fraud in corporate management continues to increase. Combined with the aging and turnover of the workforce, data checks have become a necessity even for unskilled personnel. This has become a particularly pressing issue in the construction industry, where companies are faced with a need to ensure efficient and regular data monitoring, even amid labor shortages. Furthermore, cost manipulation has traditionally been a common form of fraud in the construction industry, often aimed at profit manipulation or concealing losses. These manipulations are often hidden among multiple cost statements or transferred to the cost of projects with significant profits, making them difficult for people to detect. Therefore, detecting cost anomalies requires multifaceted analysis, which is not practical while dealing with labor shortages. In addition, in the past, when the fraudulent manipulation of construction costs became an issue in the construction industry, it was necessary to analyze construction costs in order to strengthen internal controls.However, analyzing construction costs requires a multifaceted perspective, and there were issues such as labor shortages and the personalization of work.

[0025] Therefore, in the past, determining the data required for cost compilation and analysis required specialized understanding, and the process tended to be personalized. Analysis required processing a huge amount of data, making it unrealistic to manually review it. In order to compare the proportion of construction costs categorized by cost between projects, it was effective to compare similar projects, but the task of determining similar projects itself required specialized knowledge, and the process tended to be personalized.

[0026] Furthermore, (1) conventionally, checking for cost anomalies for each project was highly unlikely to be detected by simply checking the amounts, requiring a percentage comparison, which required the perspective of "checking the cost status = checking the percentage status and changes for each cost classification / checking the associated details." Performing the required calculations for each required calculation each time required a large number of details, which took too much time and made manual work difficult. In other words, it was difficult to simply look at the cost details or aggregate the entire data and detect data anomalies. In many cases, cost anomalies were only discovered after complex analysis, such as aggregation by cost classification and fluctuations over time, and then checking the detailed information at a specific point in time in the past. However, the large number of details involved made it difficult to manually calculate and visualize such calculations from multiple perspectives on a regular basis.

[0027] Therefore, in this embodiment, the proportion of each cost category and the changes over time are visualized, detailed information at each point in time in the past is linked as data and stored in the system, and a mechanism is provided that allows the data linked to the entire construction project or a specific point in the past to be selected and quickly output.

[0028] In addition, (2) conventionally, because similar types of work tend to have similar trends in the proportions of costs incurred, abnormalities can only be detected by narrowing down the scope of similar work and comparing the proportions. However, classifying similar types of work requires know-how, such as rules of thumb, and is prone to personalization. Furthermore, because it is necessary to address the labor cost issues described in (1), regular manual checks have not been practical. Traditionally, construction costs have been divided into material costs, subcontracting costs, labor costs, and overhead. Since the trends in the occurrence of these costs are often similar between similar types of work, cost analysis is the optimal method for identifying cost anomalies when classifying similar types of work. However, in reality, judgments about similar types of work are based on human rules of thumb. Experienced personnel are limited, and analytical know-how is also required. This creates significant barriers to personalization and labor costs, making regular checks impractical.

[0029] Therefore, in this embodiment, a system is provided that automatically classifies similar projects based on the contract amount, construction period, and type of work, which are commonly used as criteria for determining similar projects by humans, automatically calculates the cost ratio within the same project, and visualizes the cost occurrence status and trends. In this embodiment, the contract amount and construction period are used as parameters that represent the "scale" of the project, and the type of work is used as a parameter that represents the "contents of the project." Furthermore, in this embodiment, projects of the same scale and type often have similar required materials (material costs), outsourced work (subcontract costs), project personnel (labor costs), and other costs (expenses). In other words, the occurrence trends for each cost category in similar projects are often similar. Therefore, by utilizing this characteristic, a system is provided that compares the cost occurrence ratio for each similar project and each cost category, and detects and visualizes deviations from the trend (cost categories in which costs are significantly higher or lower than those of other projects) as anomalies.

[0030] Furthermore, (3) the data required for cost aggregation and analysis traditionally required specialized knowledge and was highly dependent on the individual who could verify it. Traditionally, cost analysis required collecting all the relevant information, but focusing on construction costs required understanding the information collected, such as material costs, subcontracting costs, labor costs, and overhead costs. Furthermore, it was necessary to define a verification axis, such as whether to conduct a time-series analysis or a direct analysis of actual results at a specific point in time in the past, and this required know-how for an effective analytical perspective. For this reason, traditionally, few companies had many personnel with this knowledge and understanding, leading to a tendency for the process to be highly dependent on the individual. This created a risk that the process would become unmanageable if specialized personnel were replaced.

[0031] Therefore, in this embodiment, the system automatically collects and compiles information related to construction costs, visualizes the cost situation at each point in time in the past, and provides a mechanism to encourage users to analyze it.

[0032] [2. Configuration] An example of the configuration of a cost management device 100 according to this embodiment will be described with reference to Figures 1 to 7. Figure 1 is a block diagram showing an example of the configuration of a cost management device 100 according to this embodiment.

[0033] 1, the cost management device 100 is a commercially available desktop personal computer. Note that the cost management device 100 is not limited to a stationary information processing device such as a desktop personal computer, but may also be a portable information processing device such as a commercially available notebook personal computer, PDA (Personal Digital Assistant), smartphone, or tablet personal computer.

[0034] The cost management device 100 comprises a control unit 102, a communication interface unit 104, a memory unit 106, and an input / output interface unit 108. Each unit of the cost management device 100 is connected to each other so as to be able to communicate with each other via any communication path.

[0035] The communication interface unit 104 communicatively connects the cost management device 100 to the network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 has a function of communicating data with other devices via a communication line. Here, the network 300 has a function of connecting the cost management device 100 and the server 200 so that they can communicate with each other, and is, for example, the Internet or a LAN (Local Area Network).

[0036] An input device 112 and an output device 114 are connected to the input / output interface unit 108. The output device 114 may be a monitor (including a touch panel), a speaker, or a printer. The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that cooperates with a mouse to achieve a pointing device function. In the following, the output device 114 may be referred to as the monitor 114 or the printer 114, and the input device 112 may be referred to as the keyboard 112 or the mouse 112.

[0037] The storage unit 106 stores various databases, tables, files, etc. The storage unit 106 stores computer programs that work in conjunction with an OS (Operating System) to issue commands to a CPU (Central Processing Unit) to perform various processes. The storage unit 106 may be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, or an optical disk. The storage unit 106 includes a project management database 106a.

[0038] The project management database 106a stores project management data. The project management database 106a may store an anomaly detection definition master in which an anomaly detection algorithm is set, as well as project cost data in which the project, cost classification, and cost amount are linked. The project management database 106a may also store project data in which the project's responsible business establishment, responsible department, person in charge, payee, supplier, type, period, and / or project amount are linked. The anomaly detection definition master may also be linked to the clustering explanatory variables and the number of clusters. The project cost data may also be linked to the accounting date. The anomaly detection algorithm may be an anomaly detection algorithm using the interquartile range. The clustering may be performed using the k-means method. The project management database 106a may also store clustering results, anomaly detection results, and anomaly detection result messages.

[0039] An example of the abnormality determination definition master in this embodiment will now be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the abnormality determination definition master in this embodiment.

[0040] As shown in FIG. 2, the abnormality determination definition master table in this embodiment is a table for managing predefined information required when performing abnormality determination.

[0041] In this embodiment, there are two algorithms for detecting anomalies: (1) clustering (grouping similar constructions together), and (2) an anomaly detection algorithm (focusing on the cost ratio for each group and cost category, and detecting constructions with an abnormally high cost ratio for a certain cost category), and each algorithm has required settings. As shown in Figure 2, the required settings for (1) clustering include the "clustering explanatory variables (quantitative variables)" that set the items that form the basis for clustering constructions, the "clustering explanatory variables (qualitative variables)" that set the items that form the basis for clustering constructions, and the "number of clusters" that sets the number of groups into which constructions are to be grouped. As shown in Figure 2, the required settings for the (2) anomaly detection algorithm include the "anomaly detection algorithm," which sets the algorithm (interquartile range) used to detect construction projects with abnormally high cost ratios; the "quartile multiplier," a parameter required when employing the interquartile range algorithm; the larger the value, the wider the range of values ​​considered normal; the "judgment item," which determines which item the interquartile range algorithm is applied to; and the "group unit," which determines the unit of data within which anomalies are detected. The quartile multiplier is set to a certain value during system operation and may be subsequently adjusted (tuned) based on the detection results. Also, as shown in Figure 2, if "cluster, cost classification" is set for "group unit," anomalies may be detected by comparing values ​​within records with the same cluster and cost classification.

[0042] Furthermore, as shown in FIG. 2, in this embodiment, as an example that can be used as a standard in the analysis of construction work, the clustering explanatory variables (quantitative variables) are "contract amount, construction period" and the clustering explanatory variable (qualitative variable) is "type of construction," and these can be additionally set according to the purpose. Furthermore, in this embodiment, if the construction work ordered by the national government has characteristics, the clustering explanatory variables (quantitative variables) may be "contract amount, construction period" and the clustering explanatory variable (qualitative variable) may be "type of construction, public / private classification." Furthermore, in this embodiment, if the location where the construction work was carried out has characteristics, the clustering explanatory variables (quantitative variables) may be "contract amount, construction period" and the clustering explanatory variables (qualitative variables) may be "type of construction, construction location code."

[0043] An example of a clustering result in this embodiment will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of a clustering result in this embodiment.

[0044] As shown in FIG. 3, the clustering result table in this embodiment is a table for managing the clustering results.

[0045] An example of an abnormality determination result in this embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of an abnormality determination result in this embodiment.

[0046] As shown in FIG. 4, the abnormality determination result table in this embodiment is a table for managing the results of abnormality determination.

[0047] An example of an abnormality determination result message in this embodiment will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of an abnormality determination result message in this embodiment.

[0048] As shown in FIG. 5, the abnormality determination result message table in this embodiment is a table for managing information in which the abnormality determination result has been processed into language that is easy for people to understand.

[0049] An example of construction data in this embodiment will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of construction data in this embodiment.

[0050] As shown in FIG. 6, the construction data table in this embodiment is a table in which data relating to construction is managed.

[0051] An example of construction cost data in this embodiment will be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of construction cost data in this embodiment.

[0052] As shown in FIG. 7, the construction cost data table in this embodiment is a table in which costs recorded for construction are managed.

[0053] Returning to Figure 1, the control unit 102 is a CPU or the like that comprehensively controls the cost management device 100. The control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing operations based on these stored programs. Functionally, the control unit 102 conceptually includes a result acquisition unit 102a and an analysis display unit 102b.

[0054] The result acquiring unit 102a acquires abnormality determination results for abnormal projects. Here, the result acquiring unit 102a may identify abnormal projects based on the abnormality determination definition master and the project cost data, and acquire abnormality determination results that are set by linking the abnormal projects, cost classifications, cost ratios, and abnormality determination thresholds. The result acquiring unit 102a may also acquire clustering results that set clusters by clustering similar projects based on the abnormality determination definition master, the project cost data, and the project data, identify abnormal projects based on the clustering results, the abnormality determination definition master, and the project cost data, identify abnormal clusters to which the abnormal projects belong, and acquire abnormality determination results that are set by linking the projects, cost classifications, abnormal clusters, cost ratios, and abnormality determination thresholds that constitute the abnormal clusters. The result acquiring unit 102a may also acquire abnormality determination results for each predetermined period based on the abnormality determination definition master and the project cost data. The result acquiring unit 102a may also acquire abnormality determination result messages corresponding to the abnormality determination results.

[0055] The analysis display unit 102b displays an analysis screen of the project so that abnormal conditions can be identified. Here, the analysis display unit 102b may display a proportion analysis chart showing the cost proportion of each cost category of the project so that abnormal conditions can be identified, based on the abnormality determination result. Furthermore, the analysis display unit 102b may display a proportion transition chart showing the cost proportion of each cost category of the project in time series so that abnormal conditions can be identified, based on the abnormality determination result. Furthermore, the analysis display unit 102b may display an abnormality determination result message. Furthermore, the analysis display unit 102b may display an amount analysis chart showing the cost amount of each cost category of the project so that abnormal conditions can be identified, based on the abnormality determination result. Furthermore, when a display switching instruction is set, the analysis display unit 102b may display an amount analysis chart showing the cost amount of each cost category of the project so that abnormal conditions can be identified, based on the abnormality determination result. Furthermore, the analysis display unit 102b may display a ratio analysis diagram showing the cost ratio for each cost category of a predetermined number of projects in which the cost ratio of the abnormal cost category included in the abnormal cluster is large, so as to identify abnormal conditions, based on the abnormality determination result. Furthermore, the analysis display unit 102b may display a cluster ratio diagram showing the cost ratio for each cost category of the abnormal cluster, so as to identify abnormal conditions, based on the abnormality determination result. Furthermore, the analysis display unit 102b may display a cluster amount diagram showing the cost amount for each cost category of the abnormal cluster, so as to identify abnormal conditions, based on the abnormality determination result. Furthermore, the analysis display unit 102b may display an amount trend diagram showing the cost amount for each cost category of the project in chronological order, so as to identify abnormal conditions, based on the abnormality determination result.

[0056] [3. Specific Examples] A specific example of this embodiment will be described with reference to FIGS.

[0057] [Cost Management Processing] An example of the cost management process in this embodiment will now be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the process of the cost management device 100 in this embodiment.

[0058] As shown in FIG. 8, the result acquiring unit 102a acquires a clustering result in which clusters are set by clustering similar projects based on the abnormality determination definition master, project cost data, and project data (step SA-1).

[0059] Then, the result acquisition unit 102a identifies abnormal projects based on the clustering results, the abnormality determination definition master, and the project cost data, identifies abnormal clusters to which the abnormal projects belong, and acquires abnormality determination results that are set by linking the projects, cost classifications, abnormal clusters, cost ratios, and abnormality determination thresholds that make up the abnormal clusters (step SA-2).

[0060] Then, based on the abnormality determination result, the analysis display unit 102b displays on the output device 114 an analysis screen that sets up a cluster ratio diagram showing the cost ratio for each cost classification of the abnormal cluster so that the abnormal state can be identified (step SA-3).

[0061] Then, the analysis display unit 102b determines whether or not a display switching instruction has been set by the user via the input device 112 (step SA-4).

[0062] If the analysis display unit 102b determines that a display switching instruction has not been set (step SA-4: No), it ends the process.

[0063] On the other hand, if the analysis display unit 102b determines that a display switching instruction has been set (step SA-4: Yes), it shifts the process to step SA-5.

[0064] Then, based on the abnormality determination result, the analysis display unit 102b displays on the output device 114 an analysis screen that sets up a cluster amount diagram showing the cost amount for each cost classification of the abnormal cluster so that the abnormal state can be identified (step SA-5), and ends the processing.

[0065] An example of the cost management process in this embodiment will now be described with reference to Figures 9 to 28. Figures 9 to 28 are diagrams showing an example of the cost management process in this embodiment.

[0066] As shown in FIG. 9, in this embodiment, <1> Obtain the master records required for anomaly detection from the "Abnormality Judgment Definition Master Table" in Figure 2, <2> Obtain the target data for anomaly detection from the "Construction Data Table" in Figure 6 and the "Construction Cost Data Table" in Figure 7, <3> The algorithm "clustering" is implemented to group similar projects, and the table is updated with the clustering results. As shown in Figure 9, this embodiment employs the k-means method, a clustering technique that automatically groups large amounts of data into similar groups and can be applied to "classifying similar projects." While the k-means method requires the specification of explanatory variables, this embodiment employs the contract amount, construction period (quantitative variables), and type of work (qualitative variables), which are considered effective criteria for determining whether projects are similar, as shown in Figure 2. This increases the effectiveness of determining whether projects are similar, thereby addressing issues such as "personalization" and "workload" in the task of grouping similar projects.

[0067] As shown in FIG. 10, in this embodiment, <4> The algorithm "Interquartile Range" set in the "Abnormality Determination Algorithm" of the "Abnormality Determination Definition Master Table" in Figure 2 is used to detect abnormal construction work. <5> The "abnormality determination result table" and "abnormality determination result message table" are updated based on the results of the abnormality determination. Here, as shown in Fig. 10, in this embodiment, the interquartile range, which is an algorithm that statistically extracts data with abnormally large (or small) values ​​from multiple data, is used to automatically detect construction projects with abnormally large occurrence rates for each cost classification for each construction project in the population of clusters (similar construction projects) obtained as a result of clustering, thereby addressing issues such as "dependence on the expertise of personnel" and "workload caused by handling large amounts of data."

[0068] As shown in FIG. 11, in this embodiment, <1> Based on the definition ID of the "Abnormality Determination Definition Master Table," the abnormality detection results are obtained from the "Abnormality Determination Result Table" and "Abnormality Determination Result Message Table," and the obtained detection results are displayed in the message field.

[0069] As shown in FIGS. 12 to 24, in this embodiment, <2> The extraction conditions you set, and <1> Based on the abnormality judgment results obtained in the above, the original data to be displayed on the abnormality detection result screen (analysis screen) is obtained from the "construction data table" and "construction cost data table," and the data is processed for the initial display on the analysis screen, and six charts and graphs are displayed.

[0070] 12 and 13, in this embodiment, the cost ratio of the detected construction work at the time of construction completion is displayed as Chart (1), and the cost ratios of other construction work in the cluster to which the detected construction work belongs and the cost ratio value that serves as the threshold for abnormality judgment are also displayed as reference information. Also, as shown in Fig. 13, in this embodiment, the cost amount is tallied for each construction work and for each cost classification, and the "cost ratio (%) = (total cost amount within construction work of a certain cost classification / total cost amount within construction work) * 100" for each cost classification for each construction work is calculated.

[0071] 14 and 15, in this embodiment, the cost ratio of the detected construction work at each past point in time is displayed as Chart (2). Here, as shown in Fig. 15, in this embodiment, the cumulative total of the cost amount up to the past accounting month and year is tallied for each cost classification, and the "cost ratio (%) = (total cost amount within the accounting month for a certain cost classification / total cost amount within the accounting month) * 100" for each cost classification in each accounting month is calculated.

[0072] As shown in FIGS. 16 and 17, in this embodiment, chart (3) displays the details of costs recorded within a single month for the detected construction work.

[0073] 18 and 19, in this embodiment, the cost ratio for each grouped cluster and cost category is displayed as Chart (4). Here, as shown in Fig. 19, in this embodiment, the cost amount is tallied for each cluster and each cost category, and "cost ratio (%) = (total cost amount within a cluster of a certain cost category / total cost amount within the cluster) * 100" for each cost category in each cluster is calculated.

[0074] 20 and 21, in this embodiment, a list of the construction works belonging to one cluster is displayed as Chart (5), and reference information is also displayed, including the items that served as the basis for grouping the construction works by clustering, and the cost amount and cost ratio of the detected cost classification. Here, as shown in FIG. 21, in this embodiment, for the cost classification in which an abnormality was detected, the cost amount is tallied for each construction work, and for the cost classification in which an abnormality was detected, "cost ratio (%) = (total cost amount within the construction work of the cost classification in which an abnormality was detected / total cost amount within the construction work) * 100" is calculated.

[0075] 22 and 23, in this embodiment, a list of the breakdown of cost percentages and cost amounts for each cost category is displayed for one cluster as Chart (6). Here, as shown in Fig. 23, in this embodiment, the cost amount is tallied for each cost category, and "cost percentage (%) = (total cost amount of a cost category / total cost amount within a cluster) * 100" is calculated for each cost category.

[0076] As shown in FIG. 24, in this embodiment, these charts (1) to (6) can be displayed on the analysis screen. This allows the user to analyze the detected construction work and determine at what point in the past the cost ratio abnormality occurred. In other words, charts (1) to (3) allow the user to analyze from the perspective of "how abnormal the detected construction work is," "at what point in the past the cost ratio became abnormal," or "the specific details of the recorded cost details." By additionally referring to chart (4), the user can also analyze from the perspective of "which cluster's cost ratio the detected construction work's cost ratio is closest to." Furthermore, this embodiment allows the user to perform analysis without aggregating data that requires specialized knowledge, and eliminates the need to check a huge number of cost details (reducing downtime).

[0077] As shown in Figures 25 to 28, in this embodiment, the actual cost amounts incurred for the detected construction projects can also be analyzed. As shown in Figure 25, in this embodiment, the extraction condition screen (dialog) has an item called "Display Switch (Percentage / Amount)," and this extraction condition allows the graphs in Chart (1), Chart (2), and Chart (4) to be switched between percentage display and cost amount display. Furthermore, as shown in Figures 26 to 28, the percentage display displays the cost composition within a construction project as a percentage (total 100%), allowing the cost composition within the construction project to be compared on the same scale. Meanwhile, the amount display displays the actual cost amounts incurred, allowing for a comparison of the magnitude of amounts. This allows the user to compare cost amounts between construction projects and analyze the transition of cost amounts within a single construction project.

[0078] An example of a business use image in this embodiment will be described with reference to Figures 29 and 30. Figures 29 and 30 are diagrams showing an example of a business use image in this embodiment.

[0079] As shown in Figure 29, in this embodiment, the proportion of outsourcing costs for Construction A in Cluster A is abnormally high, and by focusing only on the proportion of outsourcing costs, it is possible to clearly see that the proportion of outsourcing costs for Construction A is close to the proportion for Cluster B, making it possible for the user to become aware that there may be some problem with the accounting of outsourcing costs for Construction A.

[0080] Furthermore, as shown in Figure 30, in this embodiment, when checking the list of recorded cost details, it is possible to clearly indicate that there are items with high outsourcing costs compared to other details, which allows the user to realize questions such as, "Have unexpected high outsourcing costs been incurred?", "Since a different person entered the data, have outsourcing costs for another construction project been mistakenly recorded in construction A?", "Since construction projects in cluster A tend to have a high proportion of material costs and labor costs, perhaps the wrong cost category was selected for the outsourcing costs?", or "Could these costs have been fraudulently transferred from another construction project?"

[0081] [4. Contribution to the United Nations-led Sustainable Development Goals (SDGs)] This embodiment can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby contributing to the achievement of SDGs Goals 8 and 9.

[0082] Furthermore, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to the achievement of SDGs Goals 12, 13, and 15.

[0083] Furthermore, this embodiment can contribute to strengthening control and governance, which can contribute to the achievement of Goal 16 of the SDGs.

[0084] 5. Other Embodiments The present invention may be implemented in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

[0085] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0086] Furthermore, the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions for each process, screen examples, and database configurations shown in this specification and drawings can be changed as desired unless otherwise specified.

[0087] Furthermore, with regard to the cost management device 100, the components shown in the figures are functional concepts, and do not necessarily have to be physically configured as shown in the figures.

[0088] For example, all or any part of the processing functions of the cost management device 100, particularly those performed by the control unit 102, may be implemented by a CPU and a program interpreted and executed by the CPU, or by hardware using wired logic. The program is stored on a non-transitory, computer-readable recording medium containing programmed instructions for causing the information processing device to execute the processes described in this embodiment, and is mechanically read by the cost management device 100 as needed. In other words, a computer program for providing instructions to the CPU in cooperation with the OS and performing various processes is stored in a storage unit such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM and cooperates with the CPU to form the control unit.

[0089] In addition, this computer program may be stored in an application program server connected to the cost management device 100 via any network, and all or part of it may be downloaded as needed.

[0090] Furthermore, the program for executing the processes described in this embodiment may be stored in a non-transitory computer-readable recording medium or configured as a program product. Here, the term "recording medium" includes any "portable physical medium" such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM (registered trademark)), a Compact Disk Read Only Memory (CD-ROM), a Magneto-Optical disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray (registered trademark) disc.

[0091] Furthermore, a "program" is a data processing method written in any language or description method, and does not matter whether it is in the form of source code or binary code. Note that a "program" is not necessarily limited to a single structure, but also includes a structure that is distributed as multiple modules or libraries, or a structure that achieves its function by cooperating with a separate program, such as an OS. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in this embodiment, as well as the installation procedure after reading, can use well-known configurations and procedures.

[0092] The various databases stored in the memory unit 106 are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

[0093] The cost management device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as an information processing device connected to any peripheral device. The cost management device 100 may also be realized by installing software (including programs, data, etc.) that causes the device to perform the processing described in this embodiment.

[0094] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be configured by functionally or physically distributing and integrating them in any unit depending on various additions or functional loads. In other words, the above-described embodiments can be implemented in any combination, or embodiments can be implemented selectively. [Industrial Applicability]

[0095] The present invention is useful in industries that require project or case management, such as the construction industry, engineering, IT industry, media industry, and real estate industry. [Explanation of symbols]

[0096] 100 Cost Management Device 102 Control section 102a Result acquisition part 102b Analysis display section 104 Communication interface unit 106 Storage section 106a Project Management Database 108 Input / Output Interface Section 112 Input Device 114 Output Device 200 servers 300 Network

Claims

1. A cost management device comprising a memory unit and a control unit, The storage unit a project management storage means for storing an abnormality determination definition master in which an abnormality determination algorithm is set, and project cost data in which projects, cost categories, and cost amounts are linked together; Equipped with The control unit a result acquisition means for identifying an abnormal project based on the abnormality determination definition master and the project cost data, and acquiring an abnormality determination result set by linking the abnormal project, the cost classification, the cost ratio, and an abnormality determination threshold; an analysis display means for displaying a proportion analysis chart showing the cost proportion for each cost classification of the project in a manner that enables identification of abnormal states based on the abnormality determination result; A cost management device comprising:

2. The project management storage means Furthermore, the project data is stored by linking the responsible business establishment, responsible department, person in charge, payee, supplier, type, period, and / or project amount of the project, The abnormality determination definition master includes: Furthermore, the explanatory variables for clustering and the number of clusters are linked and set. The result acquisition means The cost management device of claim 1, characterized in that it acquires clustering results in which similar projects are clustered based on the abnormality determination definition master, the project cost data, and the project data, identifies the abnormal projects based on the clustering results, the abnormality determination definition master, and the project cost data, identifies the abnormal cluster to which the abnormal projects belong, and acquires the abnormality determination results that are set by linking the projects that constitute the abnormal cluster, the cost classification, the abnormal cluster, the cost ratio, and the abnormality determination threshold.

3. The project cost data is Furthermore, the accounting date is linked and set, The result acquisition means The abnormality determination result is obtained for each predetermined period based on the abnormality determination definition master and the project cost data. The analysis and display means The cost management device of claim 1 further displays a ratio trend diagram showing the cost ratio for each cost classification of the project in chronological order so that the abnormal state can be identified based on the abnormality judgment result.

4. The result acquisition means Furthermore, an abnormality determination result message corresponding to the abnormality determination result is obtained, The analysis and display means 2. The cost management device according to claim 1, further comprising a display of the abnormality determination result message.

5. The analysis and display means The cost management device according to claim 1, further comprising: displaying an amount analysis chart showing the cost amounts for each cost classification of the project in a manner that enables the abnormal state to be identified based on the abnormality determination result.

6. The analysis and display means A cost management device as described in claim 5, characterized in that when a display switching instruction is set, the amount analysis diagram showing the cost amount for each cost classification of the project is displayed so that the abnormal state can be identified based on the abnormality judgment result.

7. The analysis and display means The cost management device of claim 2, characterized in that the ratio analysis diagram showing the cost ratio for each cost classification of a predetermined number of projects in which the cost ratio of the abnormal cost classification included in the abnormal cluster is large is displayed so that the abnormal state can be identified based on the abnormality judgment result.

8. The analysis and display means The cost management device of claim 2 further displays a cluster ratio diagram showing the cost ratio for each cost classification of the abnormal cluster so that the abnormal state can be identified based on the abnormality judgment result.

9. The analysis and display means The cost management device of claim 2 further displays a cluster amount diagram showing the cost amount for each cost classification of the abnormal cluster so that the abnormal state can be identified based on the abnormality judgment result.

10. The analysis and display means The cost management device according to claim 3, further comprising a cost trend diagram that displays the cost amounts for each cost classification of the project in chronological order so that the abnormal state can be identified based on the abnormality determination result.

11. The abnormality determination algorithm 11. The cost management device according to claim 1, wherein the algorithm for detecting abnormal values ​​uses the interquartile range.

12. The clustering 3. The cost management device according to claim 2, wherein the cost management method is a k-means method.

13. A cost management method to be executed by a cost management device having a memory unit and a control unit, The storage unit a project management storage means for storing an abnormality determination definition master in which an abnormality determination algorithm is set, and project cost data in which projects, cost categories, and cost amounts are linked together; Equipped with Executed in the control unit: a result acquisition step of identifying an abnormal project based on the abnormality determination definition master and the project cost data, and acquiring an abnormality determination result set by linking the abnormal project, the cost classification, the cost ratio, and an abnormality determination threshold; an analysis display step of displaying a proportion analysis chart showing the cost proportion for each cost category of the project in a manner that enables identification of abnormal states based on the abnormality determination result; A cost management method comprising:

14. A cost management program to be executed by a cost management device having a memory unit and a control unit, The storage unit a project management storage means for storing an abnormality determination definition master in which an abnormality determination algorithm is set, and project cost data in which projects, cost categories, and cost amounts are linked together; Equipped with In the control unit, a result acquisition step of identifying an abnormal project based on the abnormality determination definition master and the project cost data, and acquiring an abnormality determination result set by linking the abnormal project, the cost classification, the cost ratio, and an abnormality determination threshold; an analysis display step of displaying a proportion analysis chart showing the cost proportion for each cost category of the project in a manner that enables identification of abnormal states based on the abnormality determination result; Cost management program to implement the above.

Images