JP7678732B2 - Inventory fraud detection support device, inventory fraud detection support method, and inventory fraud detection support program - Google Patents

Description

The present invention relates to an inventory fraud detection support device, an inventory fraud detection support method, and an inventory fraud detection support program.

Patent document 1 discloses an inventory transition processing method, inventory transition processing program, and inventory transition processing device, the main purpose of which is to efficiently support the early detection of products that are abnormally in stock by presenting inventory transitions (see paragraphs 0001 and 0008 of patent document 1).

JP 2009-187449 A

In this way, in fields where abnormal inventory and inventory fraud are discovered, the indicator known as inventory turnover is often used. Inventory turnover is calculated, for example, using the formula: cost of sales / average monthly inventory value. If the inventory turnover rate is low, it is possible that inventory fraud, such as inflated end-of-period inventory, is occurring, so it is important to carefully check the increase or decrease in inventory turnover rate.

However, to calculate inventory turnover, the person in charge must collect the necessary data by referencing sales data to obtain the cost of sales and inventory data to obtain the monthly average inventory value. This creates the problem that discovering inventory fraud takes time and effort.

The present invention has been made in consideration of the above problems, and aims to provide an inventory fraud detection support device, an inventory fraud detection support method, and an inventory fraud detection support program that can support the task of detecting inventory fraud by automatically calculating inventory turnover and detecting abnormally low values in the calculated inventory turnover.

In order to solve the above-mentioned problems and achieve the object, the inventory fraud detection business support device according to the present invention is characterized in that it is an inventory fraud detection business support device comprising a control unit and a storage unit, the storage unit stores inventory data including a period, product identification data, and an average inventory value or average inventory quantity, and sales data including a period, product identification data, and a cost of sales or total number of shipments, the control unit comprises inventory turnover calculation means for calculating an inventory turnover rate for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory value in the inventory data or by dividing the total number of shipments in the sales data by the average inventory quantity in the inventory data, an abnormal value detection means for detecting, according to a predetermined statistical method, an inventory turnover rate calculated by the inventory turnover rate calculation means that is below a lower limit as an abnormal value, and an abnormality display means for displaying an inventory turnover rate detected as an abnormal value by the abnormal value detection means and a combination of the period and the product identification data corresponding to the detected inventory turnover rate.

In addition, in the inventory fraud detection business support device according to the present invention, the anomaly display means also displays the specified statistical method and the lower limit value.

The inventory fraud detection business support device according to the present invention is also characterized in that the predetermined statistical method is a method using the interquartile range, and the lower limit is a value calculated by subtracting 1.5 times the interquartile range from the first quartile.

In addition, in the inventory fraud detection business support device according to the present invention, the control unit is further characterized by having an inventory turnover trend graph display means for displaying an inventory turnover trend graph, which is a graph with the inventory turnover calculated by the inventory turnover calculation means on one axis and the period on the other axis, for checking the trend of the inventory turnover over the period, for the number of products.

The inventory fraud detection business support device according to the present invention is also characterized in that the inventory turnover trend graph is a line graph with the inventory turnover on the vertical axis and the period on the horizontal axis.

In addition, in the inventory fraud detection business support device according to the present invention, the inventory data further includes an end-of-month inventory amount, and the control unit further includes a month-end inventory amount trend graph display means for displaying, for products with an abnormality in the inventory turnover rate, a month-end inventory amount trend graph, which has on one axis the month-end inventory amount in the inventory data linked to the product identification data corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection means and on the other axis the period, and which is a graph for checking the trend of the month-end inventory amount per period.

In addition, in the inventory fraud detection business support device according to the present invention, the control unit is further characterized by comprising a cost of sales-average inventory value comparison graph display means for displaying a graph corresponding to the cost of sales in the sales data linked to the combination of the period and the product identification data corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection means, and a graph corresponding to the average inventory value in the inventory data linked to the combination, in a manner that allows the two graphs to be compared, for the period and product in which the inventory turnover rate was abnormal.

The inventory fraud detection business support method according to the present invention is executed by an information processing device having a control unit and a storage unit, and the storage unit stores inventory data including a period, product identification data, and an average inventory value or average inventory quantity, and sales data including a period, product identification data, and a cost of sales or total inventory quantity. The method includes an inventory turnover calculation step executed by the control unit to calculate an inventory turnover rate for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory value in the inventory data or by dividing the total inventory quantity in the sales data by the average inventory quantity in the inventory data, an abnormal value detection step to detect, according to a predetermined statistical method, an inventory turnover rate calculated in the inventory turnover calculation step that is below a lower limit as an abnormal value, and an abnormality display step to display the inventory turnover rate detected as an abnormal value in the abnormal value detection step and the combination of the period and the product identification data corresponding to the detected inventory turnover rate.

The inventory fraud detection business support program according to the present invention is a program for supporting inventory fraud detection to be executed by an information processing device having a control unit and a storage unit, the storage unit storing inventory data including a period, product identification data, and an average inventory value or average inventory quantity, and sales data including a period, product identification data, and a cost of sales or total inventory quantity, and the program includes an inventory turnover calculation step for causing the control unit to execute the program, the inventory turnover calculation step calculating an inventory turnover rate for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory value in the inventory data, or by dividing the total inventory quantity in the sales data by the average inventory quantity in the inventory data, an abnormal value detection step detecting, according to a predetermined statistical method, an inventory turnover rate calculated in the inventory turnover calculation step that is below a lower limit as an abnormal value, and an abnormality display step displaying the inventory turnover rate detected as an abnormal value in the abnormal value detection step and the combination of the period and the product identification data corresponding to the detected inventory turnover rate.

The present invention has the effect of supporting the detection of inventory fraud by automatically calculating inventory turnover and detecting abnormally low values in the calculated inventory turnover.

FIG. 1 is a block diagram showing an example of the configuration of an inventory fraud detection operation support device. FIG. 2 is a diagram showing an example of an image of the profit operation. FIG. 3 is a diagram showing an example of an image of an income statement before and after profit manipulation. FIG. 4 is a diagram showing an example of the initial analysis screen. FIG. 5 is a diagram showing an example of the analysis screen. FIG. 6 is a diagram showing an example of the automatic detection execution schedule data and the acquisition range condition data. FIG. 7 is a diagram showing an example of parameters used to acquire the automatic detection execution schedule data. FIG. 8 is a diagram showing an example of parameters used for acquiring data from the inventory turnover calculation result data. FIG. 9 is a diagram showing an example of the inventory data, the sales data, and the inventory turnover calculation result data. FIG. 10 is a diagram showing an example of abnormal value detection using the interquartile range. FIG. 11 is a diagram showing an example of abnormality determination result data, abnormality determination result message data, and abnormality determination result message detail data. FIG. 12 is a diagram showing an example of parameters used to acquire the abnormality determination result message data. FIG. 13 is a diagram showing an example of a summary message displayed on the initial analysis screen. FIG. 14 is a diagram showing an example of a set of base dates on the initial analysis screen. FIG. 15 is a diagram showing an example of a detailed message displayed on the analysis screen. FIG. 16 is a diagram showing an example of analysis data acquisition range conditions. FIG. 17 is a diagram showing an example of extraction condition initial values. FIG. 18 is a diagram showing an example of a set of a period start date and a period end date on the analysis screen. FIG. 19 is a diagram showing an example of parameters used to acquire data that is the basis for generating a graph. FIG. 20 is a diagram showing an example of inventory data, monthly average inventory amount calculation result data, sales data, and inventory turnover rate calculation result data. FIG. 21 is a diagram showing an example of a graph (Graph 1) showing a change in inventory turnover rate. FIG. 22 is a diagram showing an example of a graph (graph 2) showing the transition of the end-of-month inventory amount. FIG. 23 is a diagram showing an example of a graph (graph 3) comparing the cost of sales and the average inventory amount. FIG. 24 is a diagram showing an example of an analysis screen on which graphs 1 to 3 are displayed. FIG. 25 is a diagram showing an example of coloring parameters that need to be set when highlighting a graph by coloring. FIG. 26 is a diagram showing an example of a selected portion of graph 1 when switching the accounting year and month. FIG. 27 is a diagram showing an example of parameters in which the accounting year and month have been switched. FIG. 28 is a diagram showing an example of graph 3 generated based on the post-switching accounting year and month (2021/02). FIG. 29 is a diagram showing an example of Graph 3 before and after switching of the accounting year and month in a case where inventory fraud has occurred through inventory padding. FIG. 30 is a diagram showing an example of Graph 3 before and after switching of the accounting year and month in a case where inventory fraud has occurred through manipulation of the cost of sales. FIG. 31 is a diagram showing an example of graph 3 before and after switching of the accounting year and month in the case where slow-moving inventory exists. FIG. 32 is a diagram showing an example of a selected portion of graph 1 when switching products. FIG. 33 is a diagram showing an example of parameters when a product is switched. FIG. 34 is a diagram showing an example of graph 2 and graph 3 generated based on the post-switching product (product B). FIG. 35 is a diagram showing an example of designation of a department on the analysis screen. FIG. 36 is a diagram showing an example of parameters in which a department is newly designated. FIG. 37 is a diagram showing an example of inventory data, monthly average inventory amount calculation result data, sales data, and inventory turnover calculation result data. FIG. 38 is a diagram showing an example of graph 1 generated based on a specified department (department A). FIG. 39 is a diagram showing an example of graph 2 generated based on a specified department (department A). FIG. 40 is a diagram showing an example of a graph 3 generated based on a specified department (department A). FIG. 41 is a diagram showing an example of graphs 1 to 3 (without department designation) before switching and graphs 1 to 3 (with department designation) after switching. FIG. 42 is a diagram showing an example of graphs 1 to 3 (specifying product type 1) before switching and graphs 1 to 3 (specifying product type 2) after switching.

Below, an embodiment of an inventory fraud detection business support device, an inventory fraud detection business support method, and an inventory fraud detection business support program according to the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to the present embodiment.

[1. Overview]
In this section, the background, problems, overview and effects of the present invention will be explained in order.

[1-1. background]
In recent years, corporate fraud and scandals such as embezzlement by employees and executives (hereinafter collectively referred to as "fraud") have been on the rise. In addition, due to the impact of COVID-19, such fraud is on the rise. As a result, fraud is exceeding the range of what can be detected by humans, and there is a demand for a system that can detect and deal with it early using non-human means.

There are various types of fraud. First, there is fraud related to sales records. This fraud inflates records by falsifying sales figures. Second, there is fraud related to purchasing records. This fraud involves collusion with business partners through fictitious payments. Third, there is fraud related to inventory. This fraud involves profit manipulation by inflating end-of-period inventory values, etc. This embodiment deals with the third of these, fraud related to inventory (hereinafter referred to as "inventory fraud").

There are two ways that inventory fraud can be committed. The first, as shown in Figure 2, is to inflate ending inventory. The second is to reduce cost of sales. By committing inventory fraud, as shown in Figure 3, it is possible to manipulate gross profit on the income statement. Inventory fraud is particularly prevalent in subsidiaries, and parent companies are required to take measures to prevent fraud.

One indicator for discovering this type of inventory fraud is the inventory turnover rate. For example, the inventory turnover rate can be calculated using the formula "cost of sales / average monthly inventory value." If end-of-period inventory is inflated, the average monthly inventory value will increase, resulting in a small inventory turnover rate. For this reason, it is possible for a person in charge to manually discover fraud by paying attention to increases and decreases in the inventory turnover rate.

[1-2. Issues]
However, when it comes to manually detecting inventory fraud, there are the following main problems (1) and (2) and, moreover, a secondary problem (3).

(1) First, one of the main problems was that it took a lot of time to check whether the inventory value had changed. In other words, to check whether the inventory value had changed, it was necessary to trace the history of price changes for all products managed by the company. It was possible to check whether the value had changed by checking the system registration history of each product one by one, but this required checking a huge amount of information, which was time-consuming and costly.

(2) The second major problem was that it took a lot of time to check whether the inventory value was reasonable. In other words, to check the appropriateness of the inventory value, it was necessary to calculate the inventory turnover rate for all products managed by the company by referring to past transaction records, and then use the calculated result as an index to compare with the set amount. This required checking the past transaction records of each product one by one and calculating an index to check the appropriateness of the amount, but it was necessary to calculate and check the index information from a huge amount of information, which was time-consuming and costly.

(3) And, as a secondary problem, there was a problem that only checking the indicator of inventory turnover may include information corresponding to operational issues (i.e., issues that are not fraudulent) such as slow inventory. Information that can be confirmed from the indicator of inventory turnover includes, for example, unauthorized increases in inventory and slow inventory. Regarding unauthorized increases in inventory, since only the inventory is manipulated and the sales results are not manipulated, the inventory amount at the end of the period increases and the cost of sales in the sales results does not change. Regarding slow inventory, since demand falls and sales do not increase, and inventory remains, the inventory amount increases and the cost of sales in the sales results decreases. In short, merely observing a decrease in inventory turnover may detect not only unauthorized increases in inventory, but also the occurrence of slow inventory, which is not fraudulent but is merely an operational issue. For this reason, there was a problem that further analysis from a perspective other than inventory turnover is required.

[1-3. Overview and Effects]
Therefore, in this embodiment, for example, the following functions (1) and (2) are implemented. The following function (1) is intended to solve, for example, the above-mentioned problems (1) and (2), and the following function (2) is intended to solve, for example, the above-mentioned problem (3).

(1) It is now possible to automatically detect anomalies due to trend changes in inventory turnover and quickly inform the user of the presence or absence of anomalies. This makes it possible to reduce the time and cost involved in calculating an indicator such as inventory turnover.

(2) It is now possible to visualize and compare the reasons for the detection of products in which an abnormality in inventory turnover was detected, as well as the monthly inventory value and cost of sales information. In other words, it is now possible to analyze three pieces of information - inventory turnover, inventory value, and cost of sales - on a single screen. This makes it possible, for example, when an abnormal inventory turnover is discovered, to simultaneously check the trends in inventory value and cost of sales, making it possible to determine with a single check whether the abnormality in inventory turnover is due to an unauthorized increase in inventory, or a non-illegal business issue such as the occurrence of delayed inventory. The specific configuration and operation are explained below.

2. Configuration
An example of the configuration of the inventory fraud detection work support device 100 according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of the inventory fraud detection work support device 100.

The inventory fraud detection business support device 100 is a commercially available desktop personal computer. Note that the inventory fraud detection business support device 100 is not limited to a stationary information processing device such as a desktop personal computer, but may be a portable information processing device such as a commercially available notebook personal computer, PDA (Personal Digital Assistant), smartphone, or tablet personal computer.

The inventory fraud detection business support device 100 includes a control unit 102, a communication interface unit 104, a memory unit 106, and an input/output interface unit 108. Each unit included in the inventory fraud detection business support device 100 is connected to each other so that they can communicate with each other via any communication path.

The communication interface unit 104 communicatively connects the inventory fraud detection business support 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 the communication line. Here, the network 300 has a function of connecting the inventory fraud detection business support 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). Note that data such as various masters described below may be stored in the server 200, for example.

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 home television), a speaker, or a printer. The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that works with a mouse to provide a pointing device function. In the following, the output device 114 may be referred to as the monitor 114, and the input device 112 may be referred to as the keyboard 112 or the mouse 112.

The storage unit 106 stores various databases, tables, files, and the like. The storage unit 106 records computer programs that work in cooperation with the OS (Operating System) to give instructions to the CPU (Central Processing Unit) to perform various processes. The storage unit 106 can 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, an optical disk, or the like.

The memory unit 106 includes, for example, automatic detection execution schedule data 106a, acquisition range condition data 106b, inventory data 106c, sales data 106d, inventory turnover calculation result data 106e, anomaly determination result data 106f, anomaly determination result message data 106g, and anomaly determination result message detail data 106h.

Before going into detailed explanations of the contents of each data, this paragraph will provide an overview and description of each data. Automatic detection execution schedule data 106a and acquisition range condition data 106b belong to "data for executing anomaly detection" and are pre-set data for performing anomaly detection. Inventory data 106c and sales data 106d belong to "business data" and are assumed data accumulated during business operations. Anomaly determination result data 106f, anomaly determination result message data 106g, and anomaly determination result message detail data 106h belong to "anomaly determination result data" and are data that store the determination results of anomaly detection execution.

As shown in FIG. 6, the automatic detection execution schedule data 106a includes, for example, a detection ID, a schedule ID, execution conditions, and execution time.

The acquisition range condition data 106b is data for setting the range when acquiring data from, for example, the inventory data 106c, the sales data 106d, and the inventory turnover calculation result data 106e. As shown in FIG. 6, the acquisition range condition data 106b includes, for example, the detection ID, the schedule ID, the target column, a FROM condition, a TO condition, and the like.

As shown in FIG. 9 and FIG. 37, the inventory data 106c includes, for example, a period (fiscal year and month), department identification data (department), product identification data (product), end-of-month inventory amount, average inventory amount, etc. Although not shown, the inventory data 106c may further include, for example, end-of-month inventory quantity, average inventory quantity, etc.

As shown in FIG. 9 and FIG. 37, the sales data 106d includes, for example, the period (fiscal year and month), the department identification data (department), the product identification data (product), and cost of sales. The cost of sales is, for example, the total amount of shipments within a specified period. Although not shown, the sales data 106d may further include, for example, the total number of shipments. The total number of shipments is, for example, the total number of shipments within a specified period.

As shown in Figures 9 and 37, the inventory turnover calculation result data 106e includes, for example, the period (fiscal year and month), business establishment identification data (business establishment), department identification data (department), product classification, product type, the product identification data (product), the inventory turnover calculated by the inventory turnover calculation unit 102a described below, the end-of-month inventory amount, the average inventory amount, the cost of sales, etc.

As shown in FIG. 11 etc., the abnormality determination result data 106f includes, for example, an abnormality detection ID (detection ID), which is the detection ID that detected an abnormality in the inventory turnover rate (hereinafter referred to as "anomaly detection" in this paragraph), an abnormality detection JOB ID (JOBID), which is an ID for identifying the JOB in which the abnormality was detected, a message ID, which is an ID for identifying the message to be output when the abnormality is detected, the period (fiscal year and month) in which the abnormality was detected, the product identification data (product) for the product in which the abnormality was detected, the abnormal inventory turnover rate that was detected, and a lower limit value calculated using a specified statistical method.

As shown in FIG. 11 etc., the abnormality determination result message data 106g includes, for example, the abnormality detection ID (detection ID), the abnormality detection JOBID (JOBID), the message ID, a category (anomaly level) for identifying the presence or absence of an abnormality, a definition name specified by the abnormality detection JOBID, an overview, the detected content (detection target), etc. The detected content is the detected abnormal inventory turnover rate and the corresponding combination of the period (fiscal year and month) and the product identification data (product).

As shown in FIG. 11, etc., the abnormality determination result message detail data 106h includes, for example, the abnormality detection ID (detection ID), the abnormality detection JOBID (JOBID), the message ID, the specified statistical method (detection method), a threshold value, a determination method, the lower limit value, etc.

The control unit 102 is a CPU or the like that provides overall control of the inventory fraud detection business support 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.

The control unit 102 has, in terms of its functional concept, for example, (1) an inventory turnover calculation unit 102a as an inventory turnover calculation means for calculating an inventory turnover for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory amount in the inventory data, or by dividing the total number of shipments in the sales data by the average inventory number in the inventory data, for each combination of the period and the product identification data; and (2) a lower limit of the inventory turnover calculated by the inventory turnover calculation means according to a predetermined statistical method. (3) an abnormality display unit 102c serving as an abnormality display means for displaying the inventory turnover ratio detected as an abnormal value by the abnormality detection means and the combination of the period and the commodity identification data corresponding to the detected inventory turnover ratio; and (4) an inventory turnover ratio transition graph, which is a graph having the inventory turnover ratio calculated by the inventory turnover ratio calculation means on one axis and the period on the other axis and is used to confirm the transition of the inventory turnover ratio per period, displayed on a commodity display unit 102b. (5) a month-end inventory amount trend graph display unit 102e as a month-end inventory amount trend graph display means that displays a month-end inventory amount trend graph, which is a graph with one axis being the month-end inventory amount in the inventory data linked to the product identification data corresponding to the inventory turnover detected as an abnormal value by the abnormal value detection means and the other axis being the period, for checking the trend of the month-end inventory amount per period, for products with an abnormal inventory turnover; and (6) a cost of sales-average inventory amount comparison graph display unit 102f as a cost of sales-average inventory amount comparison graph display means that displays, for a period and product with an abnormal inventory turnover, a graph corresponding to the cost of sales in the sales data linked to the combination of the period and the product identification data corresponding to the inventory turnover detected as an abnormal value by the abnormal value detection means, and a graph corresponding to the average inventory amount in the inventory data linked to the combination, in a manner that allows both graphs to be compared.

When performing a calculation focusing on "amount", the inventory turnover calculation unit 102a calculates the inventory turnover for each combination of the fiscal year/month and the product by dividing the cost of sales in the sales data 106d by the average inventory amount in the inventory data 106c. When performing a calculation focusing on "quantity", the inventory turnover calculation unit 102a calculates the inventory turnover for each combination of the fiscal year/month and the product by dividing the total number of items shipped in the sales data 106d by the average number of items in the inventory data 106c.

The abnormal value detection unit 102b detects, according to a predetermined statistical method, any inventory turnover ratio calculated by the inventory turnover ratio calculation unit 102a that is below a lower limit as an abnormal value. The predetermined statistical method is not particularly limited, but is, for example, a method using the interquartile range. When the interquartile range is used, the lower limit is, for example, a value calculated by subtracting a value 1.5 times the interquartile range from the first quartile.

The abnormality display unit 102c displays the inventory turnover rate detected as an abnormal value by the abnormal value detection unit 102b, as well as the accounting year/month and the combination of the product corresponding to the detected inventory turnover rate. At this time, the abnormality display unit 102c may also display the specified statistical method and the lower limit value.

The inventory turnover trend graph display unit 102d displays an inventory turnover trend graph (Graph 1), which is a graph with the inventory turnover calculated by the inventory turnover calculation unit 102a on one axis and the accounting year/month on the other axis, for checking the trend of the inventory turnover on an accounting year/month basis, for the number of products (see Figures 21 and 24, etc.). Since the inventory turnover trend graph is displayed for the number of products, from the viewpoint of ease of viewing the graph, it is preferable that the graph is a line graph with the inventory turnover on the vertical axis and the period on the horizontal axis (see Figures 21 and 24, etc.).

The end-of-month inventory amount trend graph display unit 102e displays a month-end inventory amount trend graph (Graph 2) for a product with an abnormal inventory turnover rate detected as an abnormal value by the abnormal value detection unit 102b, which has the end-of-month inventory amount in the inventory data 106c linked to the product on one axis (e.g., vertical axis) and the accounting year and month on the other axis (e.g., horizontal axis), for checking the trend in the end-of-month inventory amount by accounting year and month, for the product with an abnormal inventory turnover rate (see Figures 22 and 24, etc.).

The cost of sales - average inventory value comparison graph display unit 102f displays a graph (e.g., a bar graph) corresponding to the cost of sales in the sales data 106d linked to the accounting year/month and the product combination corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection unit 102b, and a graph (e.g., a bar graph) corresponding to the average inventory value in the inventory data 106c linked to that combination, in a manner that allows both graphs to be compared (e.g., side-by-side), for the accounting year/month and the product in which the inventory turnover rate was abnormal (see Figures 23 and 24, etc.).

[3. Processing Overview and Screen Configuration]
[3-1. Processing Overview]
In this embodiment, "information that an abnormality in inventory turnover has been detected" is output. Furthermore, in this embodiment, "business data related to the detected information" is also output. Information regarding a detected abnormality is highlighted by changing the color or font size so that the abnormality can be recognized at a glance. The data to be detected is inventory turnover for each month and product. Data in which the inventory turnover rate is abnormally low compared to past performance, based on the timing of the implementation of the abnormality detection process, is detected as abnormal data.

[3-2. Screen configuration]
In this embodiment, the displayed screen changes in the following order: (1)→(2).

(1) Display of anomaly detection results First, as shown by A in the example screen of FIG. 4, the results of detecting an anomaly in stock turnover are displayed at the summary message level. The area shown by A in FIG. 4 is an area for displaying messages related to the anomaly detection process. Specifically, this area displays the timing at which the anomaly was detected, the product, and the stock turnover. Note that if there are multiple pieces of information detected as an anomaly, the multiple pieces of information are displayed vertically, as shown by A in FIG. 4. This area only displays the anomaly detection results on a summary basis, so if you want to see the anomaly detection results on a detailed basis, you need to select the summary message and switch screens.

(2) Display of business data related to the detected abnormal information Next, detailed messages and graphs necessary for analyzing the detected abnormal information are displayed.

The area shown in the lower half of A in Figure 5 is an area that displays detailed messages regarding the anomaly detection results. This area displays information such as the "detection method used to detect the anomaly" and "key information of the data in which the anomaly was detected." If multiple pieces of information are detected as an anomaly, a summary message will be displayed for each anomaly, but a detailed message will only be displayed for the one selected from the displayed summary messages. After the detailed message is displayed, the following graphs B to D are displayed.

The graph shown in area B of Figure 5 is a graph showing the inventory turnover rate for each product. In other words, it is a graph for checking "how inventory turnover rates are changing on a monthly and product-by-product basis." This graph makes it possible to compare the inventory turnover rate for the month in which an abnormality was detected with the inventory turnover rates for previous months.

The graph shown in area C of Figure 5 is a graph showing the end-of-month inventory amount of a product. In other words, it is a graph for checking the "end-of-month inventory amount of one product for each month." This graph makes it possible to compare the end-of-month inventory amount of the month in which an abnormality was detected with the end-of-month inventory amount of previous months, while only looking at the inventory amount.

The graph shown in area D of Figure 5 is a graph showing the cost of sales and average inventory value of a product within a month. In other words, it is a graph for checking the "cost of sales and average inventory value of one product within a month." This graph allows you to compare the cost of sales and the average inventory value and determine whether the abnormality in inventory turnover is caused by a cost abnormality, an inventory abnormality, or inventory fluctuations associated with sales.

Area E in Figure 5 is an area for setting extraction conditions for data extraction. In other words, this area is used when you want to display the results of data extraction with different conditions for the graphs displayed in areas B to D in Figure 5. For example, if you want to switch and analyze output data at the organizational level, the business establishment and department, etc., would be specified in area E in Figure 5. Also, if you want to switch and analyze output data at the product level, the product classification, product type, item number, product, etc. would be specified in area E in Figure 5.

4. Specific examples of processing
In this section, a specific example of the processing according to this embodiment will be described. In [4-1], the processing (anomaly detection processing) of automatically detecting products with an abnormally low inventory turnover rate by checking the contents of the inventory data 106c in the business data will be described. In [4-2], the processing (analysis screen display processing) of displaying the detected abnormal data and data related to the abnormal data that was referenced for the detection on the analysis screen will be described.

[4-1: Abnormality detection process]
(1) Pre-setting As the pre-setting, information required for anomaly detection is stored in advance in a table. In this example, it is assumed that the automatic detection execution schedule data 106a and the acquisition range condition data 106b are set and stored as shown in FIG.

(2) Automatic Execution (2-1) Acquisition of Automatic Detection Execution Schedule Data 106a First, the automatic detection execution schedule data 106a set in (1) is automatically acquired. Specifically, if "detection ID: AB001, schedule ID: SH001" is set as parameters as shown in Fig. 7, the automatic detection execution schedule data 106a shown in Fig. 6 is automatically acquired as data having the detection ID and schedule ID.

(2-2) Determining whether it is the timing to detect an anomaly Next, it is determined whether the timing automatically acquired in (1) is the timing to detect an anomaly. Specifically, it is assumed that the timing automatically acquired in (1) is "2021/04/07 (the fifth business day of April)." It is possible to determine holidays and business days by referencing the calendar master in the business database, and it is assumed that the calendar master is always updated based on the sales business calendar.

Now, referring to the automatic detection execution schedule data 106a in FIG. 6 that was automatically acquired in (1), the execution condition is "the fifth business day of every month." In this way, if the timing at which the automatic detection execution schedule data 106a was automatically acquired matches the timing defined by the execution condition set in the automatic detection execution schedule data 106a, the execution decision result will be "execute," and the processing from (2-3) onwards will be executed. On the other hand, if there is no match, the execution decision result will be "do not execute," and the processing will end.

(2-3) Acquisition of range conditions of data for detecting anomalies Next, the acquisition range condition data 106b set in (1) is automatically acquired. Specifically, the acquisition range condition data 106b shown in FIG. 6 is automatically acquired as data having the parameters "detection ID: AB001, schedule ID: SH001" set in (2-1).

(2-4) Detection of products with abnormally low stock turnover Finally, products with abnormally low stock turnover are detected by referring to the stock data 106c and sales data 106d in the business data. In this example, the stock data 106c and sales data 106d are set and saved as shown in FIG.

(2-4-1) Calculation of inventory turnover The inventory turnover calculation unit 102a calculates the inventory turnover for each combination of accounting month and product by dividing the cost of sales in the sales data 106d in Fig. 9 by the average inventory amount in the inventory data 106c in Fig. 9. The inventory turnover calculation unit 102a saves the calculated inventory turnover for each combination of accounting month and product as inventory turnover calculation result data 106e shown in Fig. 9.

As shown in FIG. 8, the parameters are set as follows: "Extraction condition column: accounting year/month, FROM condition: 2020/04, TO condition: 2021/03." In this case, the inventory turnover calculation unit 102a obtains the inventory turnover associated with the accounting years/months 2020/04 to 2021/03 from the inventory turnover calculation result data 106e in FIG. 9.

(2-4-2) Detection of Abnormal Values Next, the upper and lower limits are obtained using the "quartile range," which is one of the statistical methods, and abnormal values are detected based on the lower limit. FIG. 10 is a diagram showing how to obtain the upper limit (1.66) and the lower limit (0.91) when focusing on product E in the inventory turnover calculation result data 106e in FIG. 9. As shown in FIG. 10, when the inventory turnover of product E is plotted in a box-and-whisker plot, the inventory turnover of 0.85 in March 2021 falls below the lower limit of 0.91. Therefore, the abnormal value detection unit 102b detects the inventory turnover of 0.85 in March 2021 as an abnormal value.

Here, three representative statistical methods will be described. First, standard deviation is a method of taking the distribution of values at a certain point, setting a confidence interval, and judging normality/abnormality. Standard deviation is an analysis that does not include the concept of a time axis, so it is not suitable for cross-sectional data analysis of "tendencies from the past". Secondly, the interquartile range used in this example is a method of determining the analysis range of data, aggregating it on the vertical axis, determining the quartile range, and specifying the range magnification that can be judged as normal. The interquartile range is compatible with cross-sectional data analysis because it treats both time series data and point data as vertical axis elements. Thirdly, moving average + outlier is a method of determining the range of the average value and calculating it for each time series. Although moving average + outlier requires a threshold value to be set in advance to judge abnormality from the average value, it is possible to detect abnormal increases and decreases in the transition of the time series. In this embodiment, the interquartile range and moving average + outlier are raised as candidates, but for moving average + outlier, the user needs to set a threshold value of the average value to judge abnormality. For this reason, in this embodiment, it is preferable to use the interquartile range, which can automatically calculate a threshold and determine abnormality.

(2-4-3) Storage of Abnormal Values Finally, the abnormal value detection unit 102b stores information such as the abnormality determination result (accounting year and month: 2021/03, product: product E, inventory turnover rate: 0.85) in a table, and at the same time, stores a message to be displayed on the analysis screen. FIG. 11 shows abnormality determination result data 106f, abnormality determination result message data 106g, and abnormality determination result message detail data 106h generated by storing the abnormality determination result.

[4-2: Analysis screen display process]
(1) Display of Summary Message Next, information that is automatically detected as an anomaly from the inventory data 106c and sales data 106d in the business data is output to the initial analysis screen. The acquisition and display of the initial analysis screen will be described in detail below in (1-1) to (1-3).

(1-1) Acquisition of a result message for detecting an abnormality First, assume that "JOBID: inventory rotation alert" is set as a parameter, as shown in Fig. 12. Note that, in order to display the detected information in a list, the detection ID is set to an unset state, as shown in Fig. 12. In this case, the abnormality display unit 102c acquires the abnormality determination result data 106f, abnormality determination result message data 106g, and abnormality determination result message detail data 106h in Fig. 11, which were generated in (2-4-3), as data having "JOBID: inventory rotation alert".

(1-2) Displaying on screen a message resulting from the detection of an anomaly Next, the anomaly display unit 102c displays the degree of anomaly "X", the definition name "Inventory rotation alert", the summary "Product E detected", and the detection target "2021/03 Product E 0.85" in the anomaly determination result message data 106g of Fig. 11 acquired in (1-1) on the initial analysis screen shown in Fig. 13. The displayed degree of anomaly, definition name, summary, and detection target are collectively referred to as a summary message.

This allows the operator to know that there is a product with an abnormally low inventory turnover rate, and what that product is (product E in this example). At this time, the operator can also know the accounting month and year in which the abnormality occurred (2021/03 in this example) and the inventory turnover rate value that was detected as an abnormal value (0.85 in this example).

(1-3) Setting the reference date After the abnormality is displayed, the date when the initial analysis screen was launched (2021/04/08) is set as the reference date in the extraction condition area on the initial analysis screen, as shown in Figure 14.

(2) Display of detailed message and graphs Next, the detailed message and three graphs are output to the analysis screen. The display of the detailed message and the acquisition and display of the three graphs will be explained below in (2-1) to (2-4).

(2-1) Displaying detailed messages and securing output area for graphs 1. First, when the summary message displayed in (1-2) is selected, an analysis screen is launched. The anomaly display unit 102c displays the detection method "interquartile range," the threshold value "1.5 times normal range," and the lower limit value "0.91" in the anomaly determination result message detailed data 106h acquired in (1-2) on the analysis screen shown in Fig. 15. The displayed detection method, normal range, and lower limit value are collectively referred to as a detailed message.

In this way, by displaying the summary message explained in (1-2) and the detailed message explained in this section (2-1), the operator can understand the target (product) for which an anomaly was detected, the detection method (calculation method), the benchmark value (inventory turnover) for determining that an anomaly occurred, etc. This allows the operator to perform an analysis while knowing which products have an abnormal inventory turnover rate. The inventory turnover rate of products with an abnormal inventory turnover rate and the past performance that is the basis for determining that an anomaly occurred can be analyzed using the three graphs explained below.

2. Next, an output area for the graphs is secured. In this embodiment, three graphs are output, so an output area for three graphs is secured, as shown by the three blank spaces on the analysis screen in FIG. 15.

(2-2) Setting Extraction Conditions Next, in order to output a graph, it is necessary to extract the data to be output from the analysis data, such as the inventory data 106c, the sales data 106d, and the inventory turnover calculation result data 106e. Prior to the extraction, the extraction conditions are set by the method described in this section (2-2).

1. First, the acquisition range of the analysis data is obtained based on the accounting year and month (2021/03) held in the anomaly determination result data 106f in FIG. 11 that is linked to the summary message displayed in (1-2).

(i) Specifically, the accounting year and month within a range of one year before and after the accounting year and month in which the anomaly was detected (2021/03) is acquired as the acquisition range. In other words, the accounting year and month one year ago is 2020/03, and the accounting year and month one year later is 2022/03.

(ii) Based on the acquired acquisition range (2020/03 to 2022/03), the analysis data acquisition range condition is set by narrowing down the range in which the analysis data exists. In this example, it is assumed that the analysis data exists only in the range of 2020/04 to 2021/03. In this case, the analysis data acquisition range condition is as follows, as shown in FIG.
・Extraction condition column: Fiscal year and month ・FROM condition: 2020/04 (taken from the month after 2020/03, which is the fiscal year and month one year ago)
・TO condition: 2021/03 (hiring from the month before 2022/03, which is the fiscal year one year later)
As mentioned above, the TO condition is set to March 2021 because the only data available for analysis is up to that date.

2. Next, the analysis data acquisition range conditions set in "1." are set as initial values, which become the extraction condition initial values (base date, period start, and period end). The extraction condition initial values are shown in FIG. 17. These extraction condition initial values are then set in the extraction condition area of the analysis screen, as shown in FIG. 18. Note that the period start date and period end date set in this area can be manually changed by the operator.

(2-3) Obtaining the data that will be used to generate the graphs Next, the data for analysis is obtained based on the extraction conditions set in (2-2). The parameters used for the acquisition are shown in Figure 19. Hereinafter, the graph showing the monthly inventory turnover trend will be referred to as "Graph 1", the graph showing the month-end inventory value trend will be referred to as "Graph 2", and the graph comparing the cost of sales and average inventory value will be referred to as "Graph 3".

As shown in the parameter table in Figure 19, the start of the period (2020/04) and the end of the period (2021/03) are parameters used to obtain the data that will be used to generate Graphs 1 and 2, the designated product (Product E) is a parameter used to obtain the data that will be used to generate Graphs 2 and 3, and the designated accounting year and month (2021/03) is a parameter used to obtain the data that will be used to generate Graph 3. As will be explained in detail in (2-4) below, when generating Graphs 2 and 3, only information related to data that detected an abnormality in inventory turnover is obtained. Below, we will explain how to obtain the data that will be used to generate Graphs 1 to 3.

The inventory turnover trend graph display unit 102d obtains the products and inventory turnover linked to the period start 2020/04 to period end 2021/03 in the parameters of FIG. 19 from the inventory turnover calculation result data 106e of FIG. 20. In other words, it obtains the data that is the basis for generating Graph 1.

The end-of-month inventory amount trend graph display unit 102e obtains, from the inventory data 106c in FIG. 20, the end-of-month inventory amount that is linked to the period start 2020/04 to period end 2021/03 in the parameters in FIG. 19 and is also linked to the specified product E (the product for which an abnormality in inventory turnover was detected) in the parameters in FIG. 19. In other words, it obtains the data that is the basis for generating Graph 2.

The cost of sales-average inventory amount comparison graph display unit 102f obtains the average inventory amount linked to the specified accounting month 2021/03 (accounting month when an abnormality in inventory turnover was detected) in the parameters of FIG. 19 and to the specified product E (product when an abnormality in inventory turnover was detected) in the parameters of FIG. 19 from the monthly average inventory amount calculation result data in FIG. 20. In addition, the cost of sales-average inventory amount comparison graph display unit 102f obtains the cost of sales linked to the specified accounting month 2021/03 (accounting month when an abnormality in inventory turnover was detected) in the parameters of FIG. 19 and to the specified product E (product when an abnormality in inventory turnover was detected) in the parameters of FIG. 19 from the sales data 106d in FIG. 20. In other words, the data that is the basis for generating Graph 3 is obtained.

(2-4) Graph output (display)
Next, the data obtained in (2-3) is bound to the graph.

1. Data binding information used when binding data to graph 1 and the generated graph 1 are shown in FIG. 21. Based on the data acquired in (2-3), the inventory turnover trend graph display unit 102d generates a line graph for each of products A to F, with inventory turnover on the vertical axis and accounting year and month on the horizontal axis, as shown in FIG. 21. By looking at the graph, the operator can compare it with past inventory turnover, which provides the basis for determining an abnormality. In this example, by comparing the inventory turnover in March 2021 with the inventory turnover before February 2021, the operator can determine that the inventory turnover in March 2021 has indeed abnormally declined.

2. The data binding information used when binding data to graph 2 and the generated graph 2 are shown in FIG. 22. Based on the data acquired in (2-3), the end-of-month inventory value trend graph display unit 102e generates a bar graph for product E, for which an abnormality in inventory turnover was detected, with the end-of-month inventory value on the vertical axis and the accounting year and month on the horizontal axis, as shown in FIG. 22. By looking at the graph, the operator can confirm the inventory value generation situation by month. In this example, the operator can see that the inventory value for March 2021 is clearly higher than the other months.

3. The data binding information used when binding data to graph 3 and the generated graph 3 are shown in FIG. 23. The cost of sales-average inventory value comparison graph display unit 102f generates a bar graph showing the cost of sales and the average inventory value for product E in which an abnormality in inventory turnover was detected and for the accounting year and month 2021/03 in which an abnormality in inventory turnover was detected, based on the data acquired in (2-3). By looking at the graph, the operator can confirm the cost of sales and the average inventory value for the month used to calculate the inventory turnover. Note that at the initial display stage, the amount can be confirmed only for the accounting year and month in which the abnormality occurred (actual amount confirmation), but as explained below in (3), the relationship between the cost of sales and the average inventory value for the month can be analyzed by switching the accounting year and month.

4. The generated graphs 1 to 3 are displayed in a list on the analysis screen, as shown in Figure 24.

5. When displaying Graph 1, the graph of the product (product E) for which an abnormality in stock turnover was detected may be highlighted (for example, by changing the color). As shown in FIG. 25, if the product in the legend and the product for which an abnormality was detected are the same, the color of the abnormality flag in the legend will be applied when it is True, and in contrast, the color of the abnormality flag in the legend will be applied when it is False for legends other than the product for which an abnormality was detected. By setting the coloring parameters, the graph can be highlighted by coloring.

(3) Switching the Graph Display Finally, switching the graph display for analysis will be explained in the following (3-1) to (3-2).

(3-1) Switching the graph display by changing the accounting year and month or product 1. For example, if you want to switch the accounting year and month that is the key information output in graph 3 (the product remains product E) and analyze the relationship between the cost of sales and the inventory amount of the month, the accounting year and month (in this example, 2021/02) that you want to output in graph 3 is selected from graph 1. This selection refers to, for example, the operator clicking on the 2021/02 part in graph 1, as shown in FIG. 26. When the selection is made, the specified accounting year and month 2021/03 in the parameters in FIG. 19 is automatically changed to 2021/02, as shown in FIG. 27.

2. Next, based on the selected changed accounting year/month 2021/02, the data that will be the basis for generating graphs 1 to 3 is obtained. The method of obtaining the data is the same as in (2-3), but the data obtained only for graph 3 is different from that in (2-3), so it will be explained again. The cost of sales-average inventory amount comparison graph display unit 102f obtains the data that will be the basis for generating graph 3. That is, the cost of sales-average inventory amount comparison graph display unit 102f obtains the average inventory amount linked to the changed accounting year/month 2021/02 and linked to product E from the monthly average inventory amount calculation result data in FIG. 20. In addition, the cost of sales-average inventory amount comparison graph display unit 102f obtains the cost of sales linked to the changed accounting year/month 2021/02 and linked to product E from the sales data 106d in FIG. 20.

3. Next, the data acquired in "2." is bound to graphs 1 to 3. The generation method itself is the same as "1." to "3." in (2-4), but the content of the graph generated for graph 3 differs from "3." in (2-4), so it will be explained again. Figure 28 shows the data binding information used when binding data to graph 3 and the generated graph 3. The cost of sales - average inventory amount comparison graph display unit 102f generates a bar graph showing the cost of sales and a bar graph showing the average inventory amount for product E and the changed accounting year and month 2021/02, as shown in Figure 28, based on the data acquired in "2.".

4. The generated graphs 1 to 3 are displayed in a list on the analysis screen (not shown).

5. When displaying graph 1, the graph for product E may be highlighted (for example, by changing the color) in the same way as in "5." in (2-4).

6. Regarding Graph 3, by comparing the graph before the switch (graph for Product E in March 2021) with the graph after the switch (graph for Product E in February 2021) as shown in Figure 29, the following can be considered. That is, when comparing the graphs before and after the switch in Figure 29, the relationship between the cost of sales and the average inventory amount has reversed, but the amount of cost of sales has hardly changed. In other words, it can be seen that an increase in inventory unrelated to sales occurred in March 2021, which suggests the possibility of inventory fraud due to inventory padding.

In this example, we have explained a pattern of inventory fraud where only the average inventory value increases, but by switching the graph, it is also possible to discover patterns of inventory fraud where only the cost of sales decreases and patterns where backlogged inventory occurs, as described below.

Figure 30 shows the graphs before and after the switch for the inventory fraud pattern in which only the cost of sales decreases. Comparing the graphs before and after the switch in Figure 30, the inventory amount has remained almost unchanged (just under 10,000 yen), but the cost of sales has decreased significantly in September 2020. In other words, since we can see that inventory has not changed despite a decrease in sales, it is possible that inventory fraud has occurred due to the manipulation of the cost of sales.

Figure 31 shows graphs of patterns of slow inventory occurrence before and after the switch. Comparing the graphs before and after the switch in Figure 31, the cost of sales decreased in the month when the abnormality was detected (March 2021), and the inventory amount increased in the month when the abnormality was detected (March 2021). In other words, it can be seen that the inventory amount increases at the same time as sales decrease, which means that an increase in inventory occurs due to a decrease in sales, and it can be inferred that slow inventory may have occurred. Note that the occurrence of slow inventory is not due to inventory fraud.

In addition, in this example, an example of switching the accounting year and month of graph 3 from 2021/03 to 2021/02 has been described, but products can also be switched. That is, when it is desired to switch the products to be output in graphs 2 and 3 for analysis, the product to be output in graphs 2 and 3 (product B in this example) is selected from graph 1. This selection refers to, for example, the operator clicking on the horizontal bar for each product (the horizontal bar for product B in this example) at the top of the line graph in graph 1, as shown in FIG. 32. When the selection is made, product E, which is the designated product in the parameters of FIG. 19, is automatically changed to product B, as shown in FIG. 33. Note that the designated accounting year and month remains 2021/03, as shown in FIG. 33.

The method of obtaining the data that is the basis for generating the graphs and the method of displaying the graphs after switching products are similar to (2-3) and (2-4), respectively, so a detailed explanation is omitted. However, graphs 2 and 3 generated after switching to product B are shown in Figure 34.

(3-2) Switching the graph display by specifying an organization or product 1. By specifying an organization or product in the extraction conditions area of the analysis screen, it is possible to perform analysis by the specified organization or product. For example, as shown in Fig. 35, when the department code "Department A" is specified as the department in the extraction conditions area of the analysis screen, "Department A" is set in the department that was left blank in the parameters of Fig. 19, as shown in Fig. 36.

2. Next, the data that will be used to generate graphs 1 to 3 is obtained based on the specified department code "Department A". In this example, it is assumed that "Department A" handles three products: Product A, Product D, and Product F. Figure 37 shows inventory data 106c, sales data 106d, and inventory turnover calculation result data 106e used in the explanation of this example. Note that inventory turnover calculation result data 106e only contains information about product A, product D, or product F, which are products handled by "Department A". The acquisition process will be explained in detail below.

The inventory turnover trend graph display unit 102d obtains, from the inventory turnover calculation result data 106e in FIG. 37, the inventory turnover that is linked to the period start 2020/04 to period end 2021/03 in the parameters in FIG. 36 and is linked to product A, product D, or product F that is handled by "department A" in the parameters in FIG. 36. In other words, the data that is the basis for generating graph 1 is obtained.

The end-of-month inventory amount trend graph display unit 102e obtains, from the inventory data 106c in FIG. 37, the end-of-month inventory amount that is linked to the period start 2020/04 to period end 2021/03 in the parameters in FIG. 36 and is linked to product A, product D, or product F that is handled by "department A" in the parameters in FIG. 36. In other words, it obtains the data that is the basis for generating graph 2.

The cost of sales-average inventory amount comparison graph display unit 102f obtains the average inventory amount linked to the specified accounting month 2021/03 (the accounting month when the inventory turnover abnormality was detected) in the parameters of FIG. 36 and linked to product A, product D, or product F handled by "department A" in the parameters of FIG. 36 from the monthly average inventory amount calculation result data in FIG. 37. In addition, the cost of sales-average inventory amount comparison graph display unit 102f obtains the cost of sales linked to the specified accounting month 2021/03 (the accounting month when the inventory turnover abnormality was detected) in the parameters of FIG. 36 and linked to product A, product D, or product F handled by "department A" in the parameters of FIG. 36 from the sales data 106d in FIG. 37. In other words, the data that is the basis for generating graph 3 is obtained.

3. Next, the data obtained in step 2 is bound to the graph.

Figure 38 shows the data binding information used when binding data to graph 1 and the graph 1 that is generated. Based on the data acquired in "2.", the inventory turnover trend graph display unit 102d generates line graphs with inventory turnover on the vertical axis and accounting year and month on the horizontal axis, as shown in Figure 38, for each of products A, D, and F handled by "department A".

The data binding information used to bind data to graph 2 and the generated graph 2 are shown in FIG. 39. Based on the data acquired in "2.", the end-of-month inventory value trend graph display unit 102e generates a bar graph for product A with the end-of-month inventory value on the vertical axis and the accounting year and month on the horizontal axis, as shown in FIG. 39. Note that information on product E, which is the specified product in the parameters in FIG. 36, is not included in the inventory turnover calculation result data 106e in FIG. 37, as explained in the first paragraph of "2.", so in this case, the end-of-month inventory value trend graph display unit 102e displays only the end-of-month inventory value for product A, which is the product at the top of the inventory turnover calculation result data 106e in FIG. 37, as a graph, as shown in FIG. 39, out of the end-of-month inventory values of product A, product D, and product F acquired in "2.".

The data binding information used to bind data to graph 3 and the generated graph 3 are shown in FIG. 40. The cost of sales-average inventory value comparison graph display unit 102f generates a bar graph showing the cost of sales and a bar graph showing the average inventory value for product A and the accounting month and year 2021/03 in which an abnormality in inventory turnover was detected, based on the data acquired in "2.". Note that information on product E, which is the specified product in the parameters of FIG. 36, is not included in the inventory turnover calculation result data 106e of FIG. 37 as described in the first paragraph of "2.". In this case, the cost of sales-average inventory value comparison graph display unit 102f displays only the average inventory value and cost of sales for product A, which is the product at the top of the inventory turnover calculation result data 106e of FIG. 37, out of the average inventory values and cost of sales for products A, D, and F acquired in "2.", as a graph as shown in FIG. 40.

4. The generated graphs 1 to 3 are displayed in a list on the analysis screen (not shown).

5. As shown in FIG. 41, by comparing the graphs before switching (graphs based on products A to F) and after switching (graphs based on products A, D, and F handled by department A), the following considerations can be made regarding graphs 1 to 3. That is, if there is a product showing a tendency to be abnormal other than the product for which an anomaly has been detected, the person in charge can suspect that the product in question may have already been detected as a separate anomaly detection result, or that an anomaly may be detected at a later date. In addition, if a graph is generated specifying another department (e.g., department B), and there is a product for which an anomaly has been detected only in a specific department (e.g., department A), the person in charge can suspect that fraud may be occurring in that specific department (e.g., department A).

In this example (3-2), an example was described in which a specific department was specified as the extraction condition, but a specific product group (product classification, product type, and product number, etc.) can also be specified as the extraction condition. For example, for graphs 1 to 3, the following considerations can be made by comparing the graph before the switch (graph of products A, D, and E belonging to product type 1) with the graph after the switch (graph of products B and F belonging to product type 2) as shown in FIG. 42. That is, in the graph before the switch that reflects product type 1, there is a product (product A) with an abnormally low stock turnover rate, whereas in the graph after the switch that reflects product type 2, the trend of stock turnover rate is constant, so the person in charge can suspect the possibility that fraud using the characteristics of product type 1 is taking place. A more specific consideration is as shown in the next paragraph.

<When the inventory turnover rate of other products in the same product range (product group) is also abnormally low>
Because there is a possibility of fraud involving the misuse of the characteristics of a variety, it is necessary to check whether the products being targeted are those for which "inventory management is not meticulously managed" or those for which "generic code management" has been applied due to frequent temporary transactions.
<When there is no abnormal decline in inventory turnover rate for other products of the same type (product group)>
It is necessary to trace the inventory management history of the product for which an anomaly was detected. There is a possibility that a registration error occurred.
<For companies that manage inventory revaluation products>
It is necessary to check whether any abnormalities have been detected in the products that are the subject of inventory revaluation. There is a possibility that the inventory revaluation has not been processed.

[5. Summary of the present embodiment]
In this way, according to the inventory fraud detection support device 100 of this embodiment, as mainly described in [4-1], it is possible to support the inventory fraud detection work by automatically calculating the inventory turnover rate and detecting abnormally low values among the calculated inventory turnover rates.

In addition, according to the inventory fraud detection business support device 100 of this embodiment, as mainly explained in [4-2], by referring to the three graphs displayed on the analysis screen, it is also possible to analyze the cause of the abnormality in the inventory turnover rate.

Inventory fraud is often only discovered during end-of-period stocktaking, as the inventory investigation itself is costly. Previously, in order to discover inventory fraud, it was necessary to gather sales and inventory data from business data, calculate and confirm. This created the problem of it taking a long time to discover fraud. Also, when inventory fraud is discovered using an indicator called inventory turnover, it is necessary to conduct in-depth data analysis for each sales and inventory, which creates the problem of a large scope and amount of data that must be checked before the cause can be identified.

In this embodiment, for example, fraudulently registered data can be automatically detected and notified within transaction data in sales operations. At this time, abnormalities can be detected using the actual inventory turnover rate, which is one of the indicators used in accounting audit investigations. In other words, inventory data and sales data within the business data are accessed to calculate an indicator value, and products with abnormal indicator values can be automatically detected based on the data for executing anomaly detection. Also, in this embodiment, for example, a screen can be output that enables analysis of the cost of sales and inventory amount, which are the base data for inventory turnover rate. In other words, analysis information regarding products with abnormal values can be displayed.

As a result, this embodiment provides the following effects, for example. First, since anomalies are detected using an index (inventory turnover) used in accounting audit investigations, it is now possible to detect fraud from an audit perspective. In addition, since anomalies are detected periodically, inventory investigations, which were previously costly, can now be performed periodically and accurately. And, since there is a screen for analyzing and confirming anomalies and their reasons, it is now possible to detect anomalies with high reliability.

[6. 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 making it possible to contribute to goals 8 and 9 of the SDGs.

In addition, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, which can contribute to the achievement of SDGs goals 12, 13, and 15.

In addition, this embodiment can contribute to strengthening control and governance, making it possible to contribute to Goal 16 of the SDGs.

7. Other embodiments
The present invention may be embodied in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

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 a known method.

In addition, the processing procedures, control procedures, specific names, registered data for each process, information including search conditions and other parameters, screen examples, and database configurations shown in this specification and drawings may be changed as desired unless otherwise specified.

In addition, with regard to the inventory fraud detection business support device 100, each component shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure.

For example, the processing functions of the inventory fraud detection business support device 100, particularly the processing functions performed by the control unit, may be realized in whole or in part by a CPU and a program interpreted and executed by the CPU, or may be realized as hardware using wired logic. The program is recorded on a non-transient computer-readable recording medium that contains programmed instructions for causing the information processing device to execute the processes described in this embodiment, and is mechanically read by the inventory fraud detection business support device 100 as necessary. That is, a computer program is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive) to work with the OS to give instructions to the CPU and perform various processes. This computer program is executed by being loaded into RAM, and works with the CPU to form the control unit.

In addition, this computer program may be stored in an application program server connected to the inventory fraud detection business support device 100 via any network, and it is also possible to download all or part of it as needed.

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

A "program" is a data processing method written in any language or description method, and may be in any format, such as source code or binary code. Note that a "program" is not necessarily limited to a single configuration, but also includes a distributed configuration consisting of multiple modules or libraries, and a program that works in conjunction with a separate program, such as an OS, to achieve its function. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known configurations and procedures.

The various databases stored in the memory unit are storage devices such as RAM, ROM, etc., 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.

The inventory fraud detection business support device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which any peripheral device is connected. The inventory fraud detection business support device 100 may be realized by implementing software (including programs or data, etc.) that causes the device to realize the processing described in this embodiment.

Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the figures, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various additions or functional loads. In other words, the above-mentioned embodiments can be implemented in any combination, or the embodiments can be implemented selectively.

The present invention is useful, for example, in any industry or business where inventory management occurs.

100 Inventory fraud detection business support device 102 Control unit
102a Inventory turnover calculation unit
102b Abnormal value detection unit
102c Abnormality display section
102d Inventory turnover rate transition graph display section
102e End of month inventory amount transition graph display section
102f Sales cost-average inventory amount comparison graph display unit 104 Communication interface unit 106 Storage unit
106a Automatic detection execution schedule data
106b Acquisition range condition data
106c Stock Data
106d Sales Data
106e Inventory turnover calculation result data
106f Abnormality determination result data
106g Abnormality determination result message data
106h Abnormality determination result message detail data 108 Input/output interface unit 112 Input device 114 Output device 200 Server 300 Network

Claims (10)

An inventory fraud detection business support device including a control unit and a storage unit,
The storage unit includes:
Inventory data including a period, product identification data, and an average inventory value or average inventory quantity;
Sales data including a period, product identification data, and cost of goods sold or total number of shipments;
is stored,
The control unit is
an inventory turnover calculation means for calculating an inventory turnover for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory amount in the inventory data, or by dividing the total number of shipments in the sales data by the average inventory number in the inventory data, for each combination of the period and the product identification data;
an abnormal value detection means for detecting, according to a predetermined statistical method, an inventory turnover ratio calculated by the inventory turnover ratio calculation means that is below a lower limit value as an abnormal value;
an abnormality display means for displaying an inventory turnover rate detected as an abnormal value by the abnormal value detection means and a combination of the period and the commodity identification data corresponding to the detected inventory turnover rate;
Equipped with
The predetermined statistical method is a method using an interquartile range,
the lower limit is calculated by subtracting 1.5 times the interquartile range from the first quartile;
The device for supporting the detection of inventory fraud is characterized by the above. An inventory fraud detection business support device including a control unit and a storage unit,
The storage unit includes:
Inventory data including a period, product identification data, and an average inventory value or average inventory quantity;
Sales data including a period, product identification data, and cost of goods sold or total number of shipments;
is stored,
The inventory data further includes end-of-month inventory amounts,
The control unit is
an inventory turnover calculation means for calculating an inventory turnover for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory amount in the inventory data, or by dividing the total number of shipments in the sales data by the average inventory number in the inventory data, for each combination of the period and the product identification data;
an abnormal value detection means for detecting, according to a predetermined statistical method, an inventory turnover ratio calculated by the inventory turnover ratio calculation means that is below a lower limit value as an abnormal value;
an abnormality display means for displaying an inventory turnover rate detected as an abnormal value by the abnormal value detection means and a combination of the period and the commodity identification data corresponding to the detected inventory turnover rate;
a month-end inventory amount trend graph display means for displaying a month-end inventory amount trend graph, the month-end inventory amount in the inventory data linked to the commodity identification data corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection means, on one axis and the period on the other axis, the month-end inventory amount trend graph being a graph for checking the trend of the month-end inventory amount per period, for commodities with an abnormality in the inventory turnover rate;
a cost of sales-average inventory value comparison graph display means for displaying a graph corresponding to the cost of sales in the sales data linked to the combination of the period and the commodity identification data corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection means, and a graph corresponding to the average inventory value in the inventory data linked to the combination, in a manner that allows comparison of both graphs, for a period and commodity in which an abnormality in the inventory turnover rate occurred;
To have
The device for supporting the detection of inventory fraud is characterized by the above. The abnormality display means is
Displaying the predetermined statistical method and the lower limit together;
3. The inventory fraud detection support device according to claim 1 or 2 , The predetermined statistical method is a method using an interquartile range,
the lower limit is calculated by subtracting 1.5 times the interquartile range from the first quartile;
3. The inventory fraud detection support device according to claim 2 , further comprising: The control unit is
an inventory turnover rate transition graph display means for displaying an inventory turnover rate transition graph, which is a graph having the inventory turnover rate calculated by the inventory turnover rate calculation means as one axis and the period as the other axis, for confirming the transition of the inventory turnover rate over the period, for the number of products;
3. The inventory fraud detection support device according to claim 1 or 2 , The inventory turnover rate transition graph is
A line graph with the inventory turnover rate on the vertical axis and the period on the horizontal axis;
6. The inventory fraud detection support device according to claim 5 , The inventory data further includes end-of-month inventory amounts,
The control unit is
a month-end inventory amount trend graph display means for displaying, for a commodity having an abnormality in the inventory turnover rate, a month-end inventory amount trend graph, the month-end inventory amount in the inventory data linked to the commodity identification data corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection means, on one axis and the period on the other axis, the month-end inventory amount trend graph being a graph for checking the trend of the month-end inventory amount per period;
2. The inventory fraud detection support device according to claim 1 , The control unit is
a cost of sales-average inventory value comparison graph display means for displaying a graph corresponding to the cost of sales in the sales data linked to the combination of the period and the commodity identification data corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection means, and a graph corresponding to the average inventory value in the inventory data linked to the combination, in a manner that allows comparison of both graphs, for a period and commodity in which an abnormality in the inventory turnover rate occurred;
2. The inventory fraud detection support device according to claim 1 , A method for supporting inventory fraud detection operations executed by an information processing device having a control unit and a storage unit,
The storage unit includes:
Inventory data including a period, product identification data, and an average inventory value or average inventory quantity;
Sales data including a period, product identification data, and cost of goods sold or total number of shipments;
is stored,
Executed by the control unit,
an inventory turnover calculation step of calculating an inventory turnover for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory amount in the inventory data, or by dividing the total number of shipments in the sales data by the average inventory number in the inventory data, for each combination of the period and the product identification data;
an abnormal value detection step of detecting, according to a predetermined statistical method, an inventory turnover ratio calculated in the inventory turnover ratio calculation step that is below a lower limit value as an abnormal value;
an abnormality display step of displaying the inventory turnover rate detected as an abnormal value in the abnormal value detection step and the combination of the period and the commodity identification data corresponding to the detected inventory turnover rate;
Including ,
The predetermined statistical method is a method using an interquartile range,
the lower limit is calculated by subtracting 1.5 times the interquartile range from the first quartile;
The method for supporting inventory fraud detection operations is characterized by the above. An inventory fraud detection business support program to be executed by an information processing device having a control unit and a storage unit,
The storage unit includes:
Inventory data including a period, product identification data, and an average inventory value or average inventory quantity;
Sales data including a period, product identification data, and cost of goods sold or total number of shipments;
is stored,
To cause the control unit to execute
an inventory turnover calculation step of calculating an inventory turnover for each combination of the period and the product identification data by dividing the cost of sales in the sales data by the average inventory amount in the inventory data, or by dividing the total number of shipments in the sales data by the average inventory number in the inventory data, for each combination of the period and the product identification data;
an abnormal value detection step of detecting, according to a predetermined statistical method, an inventory turnover ratio calculated in the inventory turnover ratio calculation step that is below a lower limit value as an abnormal value;
an abnormality display step of displaying the inventory turnover rate detected as an abnormal value in the abnormal value detection step and the combination of the period and the commodity identification data corresponding to the detected inventory turnover rate;
Including,
The predetermined statistical method is a method using an interquartile range,
the lower limit is calculated by subtracting 1.5 times the interquartile range from the first quartile;
This is a program that supports the detection of inventory fraud.

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