Inventory fraud detection support device, inventory fraud detection support method, and inventory fraud detection support program

Abstract

To provide a stock fraud detection work support device or the like capable of supporting stock fraud detection work by automatically calculating stock turnover rates and automatically detecting a stock turnover rate whose numeric value abnormally drops in the calculated turnover rates.SOLUTION: In an embodiment, (1) stock turnover rates of each combination of a fiscal year and commodities are calculated by dividing a sales cost in sales data by an average stock amount in stock data in each combination of the fiscal year and the commodities on the basis of the stock data including the fiscal year, the commodities and the average stock amount, and the sales data including the fiscal year, the commodities and the sales cost, (2) stock turnover rates less than a lower limit among the calculated stock turnover rates are detected as abnormal values according to a predetermined statistical method, and (3) the stock turnover rates detected as abnormal values (0.85 in Figure 13) and a combination of the fiscal year (2021 / 03 in Figure 13) and the commodities (commodities E in Figure 13) corresponding to the detected stock turnover rate are displayed.SELECTED DRAWING: Figure 13

Description

[Technical field]

[0001] The present invention relates to an inventory fraud detection support device, an inventory fraud detection support method, and an inventory fraud detection support program. [Background technology]

[0002] Patent document 1 discloses an inventory trend processing method, an inventory trend processing program, and an inventory trend processing device, the main purpose of which is to efficiently support the early detection of products that have abnormal inventory by presenting inventory trends (see paragraphs 0001 and 0008 of Patent document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2009-187449 A Summary of the Invention [Problem to be solved by the invention]

[0004] In this way, in the field of discovering abnormal inventory and inventory fraud, an indicator called inventory turnover is often used. Inventory turnover is calculated, for example, by the formula: Cost of sales / average monthly inventory amount. If the inventory turnover rate is low, there is a possibility that inventory fraud such as padding of end-of-period inventory is occurring, so it is important to carefully check the increase or decrease in inventory turnover rate.

[0005] However, to calculate the inventory turnover rate, 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 amount, which poses the problem that discovering inventory fraud takes time and effort.

[0006] The present invention has been made in consideration of the above problems, and aims to provide an inventory fraud detection work support device, an inventory fraud detection work support method, and an inventory fraud detection work support program that can support the work of detecting inventory fraud by automatically calculating inventory turnover rates and detecting abnormally low values ​​among the calculated inventory turnover rates. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the object, the inventory fraud detection work support device of the present invention is characterized in that it comprises a control unit and a memory unit, wherein the memory 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 a total number of shipments, and the control unit comprises 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 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 calculated by the inventory turnover calculation means that is below a lower limit as an abnormal value, and an abnormality display means for displaying the inventory turnover detected as an abnormal value by the abnormal value detection means and the combination of the period and the product identification data corresponding to the detected inventory turnover.

[0008] In the inventory fraud detection work support device according to the present invention, the abnormality display means also displays the predetermined statistical method and the lower limit value.

[0009] In addition, in the inventory fraud detection work support device of the present invention, the specified statistical method is a method using the interquartile range, and the lower limit value is a value calculated by subtracting a value 1.5 times the interquartile range from the first quartile.

[0010] In addition, in the inventory fraud detection business support device of 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 per period, as many times as the number of products.

[0011] In addition, in the inventory fraud detection work support device according to the present invention, the inventory turnover trend graph is a line graph with the inventory turnover on the vertical axis and the period on the horizontal axis.

[0012] In addition, in the inventory fraud detection business support device of the present invention, the inventory data further includes an end-of-month inventory amount, and the control unit further comprises a month-end inventory amount trend graph display means for displaying, for products that have 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 in the month-end inventory amount per period.

[0013] In addition, in the inventory fraud detection business support device of the present invention, the control unit is further characterized by having 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 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 both graphs to be compared, for the period and product in which the inventory turnover was abnormal.

[0014] In addition, an inventory fraud detection work support method according to the present invention is executed by an information processing device having a control unit and a memory unit, wherein the memory 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 a total number of shipments, and the method includes an inventory turnover calculation step executed by the control unit to calculate 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 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 step to detect, according to a predetermined statistical method, an inventory turnover 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 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.

[0015] In addition, an inventory fraud detection business support program according to the present invention is an inventory fraud detection business support program to be executed by an information processing device having a control unit and a memory unit, wherein the memory 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 a total number of shipments, and the program includes an inventory turnover calculation step to be executed by the control unit, the inventory turnover calculation step 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 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 step detecting, according to a predetermined statistical method, an inventory turnover 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 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. Effect of the Invention

[0016] According to the present invention, by automatically calculating inventory turnover and detecting abnormally low values ​​in the calculated inventory turnover, it is possible to assist in the detection of inventory fraud. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an inventory fraud detection operation support device. [Diagram 2] FIG. 2 is a diagram showing an example of an image of profit manipulation. [Diagram 3] FIG. 3 is a diagram showing an example of an image of an income statement before and after profit manipulation. [Figure 4] FIG. 4 is a diagram showing an example of the initial analysis screen. [Diagram 5] FIG. 5 is a diagram showing an example of the analysis screen. [Figure 6] FIG. 6 is a diagram showing an example of the automatic detection execution schedule data and the acquisition range condition data. [Figure 7] FIG. 7 is a diagram showing an example of parameters used to acquire the automatic detection execution schedule data. [Figure 8] FIG. 8 is a diagram showing an example of parameters used for acquiring data from the inventory turnover calculation result data. [Figure 9] FIG. 9 is a diagram showing an example of the inventory data, the sales data, and the inventory turnover calculation result data. [Figure 10] FIG. 10 is a diagram showing an example of abnormal value detection using the interquartile range. [Figure 11] 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. [Figure 12] FIG. 12 is a diagram showing an example of parameters used to acquire the abnormality determination result message data. [Figure 13] FIG. 13 is a diagram showing an example of a summary message displayed on the initial analysis screen. [Figure 14] FIG. 14 is a diagram showing an example of a set of base dates on the initial analysis screen. [Figure 15] FIG. 15 is a diagram showing an example of a detailed message displayed on the analysis screen. [Figure 16] FIG. 16 is a diagram showing an example of analysis data acquisition range conditions. [Figure 17] FIG. 17 is a diagram showing an example of extraction condition initial values. [Figure 18] 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. [Figure 19] FIG. 19 is a diagram showing an example of parameters used to acquire data that is the basis for generating a graph. [Figure 20] FIG. 20 is a diagram showing an example of inventory data, monthly average inventory amount calculation result data, sales data, and inventory turnover calculation result data. [Figure 21] FIG. 21 is a diagram showing an example of a graph (Graph 1) showing a change in inventory turnover rate. [Figure 22] FIG. 22 is a diagram showing an example of a graph (graph 2) showing a transition of the end-of-month inventory amount. [Figure 23] FIG. 23 is a diagram showing an example of a graph (graph 3) comparing the cost of sales and the average inventory amount. [Figure 24] FIG. 24 is a diagram showing an example of the analysis screen on which graphs 1 to 3 are displayed. [Diagram 25] FIG. 25 is a diagram showing an example of coloring parameters that need to be set when highlighting a graph by coloring. [Figure 26] FIG. 26 is a diagram showing an example of a selected portion of graph 1 when switching the accounting year and month. [Figure 27] FIG. 27 is a diagram showing an example of parameters in which the accounting year and month have been switched. [Figure 28] FIG. 28 is a diagram showing an example of a graph 3 generated based on the post-switching accounting year and month (2021 / 02). [Figure 29] FIG. 29 is a diagram showing an example of Graph 3 before and after the accounting month and year change in a case where inventory fraud has occurred through inventory padding. [Diagram 30] 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. [Diagram 31] 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 occurs. [Diagram 32] FIG. 32 is a diagram showing an example of a selected portion of the graph 1 when switching products. [Diagram 33] FIG. 33 is a diagram showing an example of parameters when a product is switched. [Diagram 34] FIG. 34 is a diagram showing an example of graph 2 and graph 3 generated based on the post-switching product (product B). [Diagram 35] FIG. 35 is a diagram showing an example of department designation on the analysis screen. [Diagram 36] FIG. 36 is a diagram showing an example of parameters in which a department is newly designated. [Figure 37] 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. [Figure 38] FIG. 38 is a diagram showing an example of a graph 1 generated based on a specified department (department A). [Figure 39] FIG. 39 is a diagram showing an example of a graph 2 generated based on a specified department (department A). [Diagram 40] FIG. 40 is a diagram showing an example of a graph 3 generated based on a specified department (department A). [Diagram 41] 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. [Diagram 42] 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. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Hereinafter, an embodiment of an inventory fraud detection work support device, an inventory fraud detection work support method, and an inventory fraud detection work 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 embodiment.

[0019] [1. Overview] In this section, the background, problems, overview and effects of the present invention will be explained in order.

[0020] [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. For this reason, 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.

[0021] Here, there are various types of fraud. The first type is fraud related to sales records. This type of fraud inflates records by falsifying sales figures. The second type is fraud related to purchase records. This type of fraud involves collusion with business partners through fictitious payments. The third type is fraud related to inventory. This type of fraud involves profit manipulation, such as by inflating end-of-period inventory values. This embodiment deals with the third of these, fraud related to inventory (hereinafter referred to as "inventory fraud").

[0022] There are two ways that inventory fraud can be committed. The first is to inflate ending inventory, as shown in Figure 2. The second is to reduce cost of sales. By committing inventory fraud, it is possible to manipulate gross profit on the income statement, as shown in Figure 3. Inventory fraud is particularly prevalent in subsidiaries, and parent companies are required to take measures to prevent fraud.

[0023] One of the indicators 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.

[0024] [1-2. Issues] However, with regard to the manual detection of inventory fraud by personnel, there were the following main problems (1) and (2), and also the secondary problem of (3).

[0025] (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, in order 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.

[0026] (2)The second major problem was that it took a lot of time to check whether the inventory value was reasonable. In other words, in order 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 compare the calculated result with the set amount as an index. 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.

[0027] (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, only observing the decline 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.

[0028] [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).

[0029] (1) It is now possible to automatically detect anomalies due to trend changes in inventory turnover and quickly notify the user of the presence or absence of anomalies. This makes it possible to reduce the time and cost required to calculate an indicator such as inventory turnover.

[0030] (2) It is now possible to visualize and compare the reasons for the detection of products for 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 allows, for example, when an abnormal inventory turnover is discovered, the trends in inventory value and cost of sales can be confirmed at the same time, making it possible to determine in one 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 stagnant inventory. The specific configuration and operation are explained below.

[0031] [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.

[0032] The inventory fraud detection work support device 100 is a commercially available desktop personal computer. The inventory fraud detection work 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, a PDA (Personal Digital Assistant), a smartphone, or a tablet personal computer.

[0033] The inventory fraud detection work support device 100 includes a control unit 102, a communication interface unit 104, a storage unit 106, and an input / output interface unit 108. The units included in the inventory fraud detection work support device 100 are connected to each other so as to be able to communicate with each other via any communication path.

[0034] The communication interface unit 104 communicatively connects the inventory fraud detection work 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 work 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.

[0035] 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 cooperates with a mouse to realize 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.

[0036] Various databases, tables, files, etc. are stored in the storage unit 106. Computer programs for cooperating with an OS (Operating System) to give instructions to a CPU (Central Processing Unit) to perform various processes are recorded in the storage unit 106. For example, memory devices such as RAM (Random Access Memory) and ROM (Read Only Memory), fixed disk devices such as hard disks, flexible disks, and optical disks can be used as the storage unit 106.

[0037] 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, abnormality determination result data 106f, abnormality determination result message data 106g, and abnormality determination result message detail data 106h.

[0038] Before describing the contents of each data in detail below, this paragraph will explain the outline and positioning of each data. Automatic detection execution schedule data 106a and acquisition range condition data 106b belong to "abnormality detection execution data" and are pre-set data for performing abnormality detection. Inventory data 106c and sales data 106d belong to "business data" and are assumed data accumulated in the course of business. Anomaly determination result data 106f, abnormality determination result message data 106g, and abnormality determination result message detail data 106h belong to "abnormality determination result data" and are data that store the determination results of anomaly detection execution.

[0039] As shown in FIG. 6, the automatic detection execution schedule data 106a includes, for example, a detection ID, a schedule ID, an execution condition, an execution time, and the like.

[0040] The acquisition range condition data 106b is data for setting a 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, a target column, a FROM condition, a TO condition, and the like.

[0041] 9 and 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 stock amount, average stock amount, etc. Although not shown, the inventory data 106c may further include, for example, end-of-month stock quantity, average stock quantity, etc.

[0042] 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 predetermined 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 predetermined period.

[0043] 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 month-end inventory amount, the average inventory amount, the cost of sales, etc.

[0044] 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 inventory turnover (hereinafter referred to as "anomaly detection" in this paragraph), an abnormality detection JOBID (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 detected abnormal inventory turnover, and a lower limit value calculated using a specified statistical method.

[0045] 11, 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 (degree of abnormality) for identifying the presence or absence of an abnormality, a definition name specified by the abnormality detection JOBID, an overview, the detected contents (detection target), etc. The detected contents are the detected abnormal inventory turnover rate and the corresponding combination of the period (fiscal year and month) and the product identification data (product).

[0046] As shown in FIG. 11, 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.

[0047] The control unit 102 is a CPU or the like that performs 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 based on these stored programs.

[0048] 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 turnovers calculated by the inventory turnover calculation means in accordance with a predetermined statistical method. (3) an abnormality display unit 102c serving as an abnormality display means for displaying the stock 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 stock turnover ratio; and (4) a stock turnover ratio trend graph, which has the stock turnover ratio calculated by the stock turnover ratio calculation means on one axis and the period on the other axis and is a graph for checking the trend of the stock turnover ratio per period, displayed on a commodity display unit. (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 having one axis representing 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 representing the period, and which is a graph for checking the trend of the month-end inventory amount per period, for products in which there is an abnormality in the 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 in which there is an abnormality in the 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.

[0049] When performing a calculation focusing on "amount", the inventory turnover calculation unit 102a calculates the inventory turnover for each combination of the accounting 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 for the accounting month and the product. When performing a calculation focusing on "quantity", the inventory turnover calculation unit 102a calculates the inventory turnover for each combination of the accounting 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 for the accounting month and the product.

[0050] The abnormal value detection unit 102b detects, according to a predetermined statistical method, any of the inventory turnover rates calculated by the inventory turnover calculation unit 102a that is below a lower limit as an abnormal value. The predetermined statistical method is not particularly limited, but may be, for example, a method using an 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.

[0051] The abnormality display unit 102c displays the inventory turnover rate detected as an abnormal value by the abnormal value detection unit 102b and 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 predetermined statistical method and the lower limit value.

[0052] The inventory turnover rate transition graph display unit 102d displays an inventory turnover rate transition graph (Graph 1), which is a graph with the inventory turnover rate calculated by the inventory turnover rate calculation unit 102a on one axis and the accounting year / month on the other axis, for checking the transition of the inventory turnover rate in the accounting year / month unit, for the number of products (see Fig. 21, Fig. 24, etc.). Since the inventory turnover rate transition graph is displayed for the number of products, from the viewpoint of graph readability, it is preferable that the graph is a line graph with the inventory turnover rate on the vertical axis and the period on the horizontal axis (see Fig. 21, Fig. 24, etc.).

[0053] The month-end inventory value trend graph display unit 102e displays a month-end inventory value trend graph (Graph 2), which is a graph (e.g., a bar graph) with the month-end inventory value in the inventory data 106c linked to the product corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection unit 102b on one axis (e.g., vertical axis) and the fiscal year and month on the other axis (e.g., horizontal axis) for checking the trend in the month-end inventory value by fiscal year and month, for the product in which an abnormality in the inventory turnover rate occurred (see Figures 22 and 24, etc.).

[0054] The cost of sales-average inventory value comparison graph display section 102f displays a graph (e.g., a bar graph) corresponding to the cost of sales in the sales data 106d linked to the combination of the fiscal year / month and the product corresponding to the inventory turnover rate detected as an abnormal value by the abnormal value detection section 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 fiscal year / month and the product in which the inventory turnover rate was abnormal (see Figures 23 and 24, etc.).

[0055] [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 about an abnormality is highlighted by changing color and font size so that it can be recognized as an abnormality at a glance. The data to be detected is inventory turnover by month and product. Data in which inventory turnover is abnormally low compared to past performance, based on the timing of the anomaly detection process, is detected as abnormal data.

[0056] [3-2. Screen composition] In this embodiment, the displayed screen changes in the following order: (1)→(2).

[0057] (1) Display of anomaly detection results First, as shown by A in the example screen in Figure 4, the results of detecting an anomaly in stock turnover are displayed at summary message level. The area shown by A in Figure 4 is an area that displays 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 multiple pieces of information are detected as an anomaly, the multiple pieces of information are displayed vertically as shown by A in Figure 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.

[0058] (2) Display of business data related to detected abnormal information It then displays detailed messages and graphs necessary for analyzing the detected abnormal information.

[0059] The area shown in the lower half of A in Fig. 5 is an area that displays a detailed message regarding the anomaly detection result. This area displays the "detection method used to detect the anomaly" and "key information of the data in which the anomaly was detected", etc. 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. Furthermore, after the detailed message is displayed, the following graphs B to D are displayed.

[0060] 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 by month and product." This graph makes it possible to compare the inventory turnover rate of the month in which an abnormality was detected with the inventory turnover rates of previous months.

[0061] 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, focusing only on the inventory amount.

[0062] 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 confirming "the cost of sales and average inventory value of one product within a month." By comparing the cost of sales and the average inventory value with this graph, it is possible to identify whether the abnormality in inventory turnover has occurred due to an abnormal cost, an abnormal inventory, or a change in inventory due to sales.

[0063] Area E in FIG. 5 is an area for setting extraction conditions for data extraction. In other words, this area is used when it is desired to display the results of data extraction under different conditions for the graphs displayed in areas B to D in FIG. 5. For example, when it is desired to switch and analyze output data at the organizational level, the business establishment and department, etc. are specified in area E in FIG. 5. Also, when it is desired to switch and analyze output data at the product level, the product classification, product type, product number, product, etc. are specified in area E in FIG. 5.

[0064] [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) for automatically detecting products with abnormally low inventory turnover by checking the contents of inventory data 106c in the business data will be described. In [4-2], the processing (analysis screen display processing) for 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.

[0065] [4-1: Anomaly detection processing] (1) Pre-settings As a 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.

[0066] (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.

[0067] (2-2) Determine whether it is time to detect an abnormality Next, it is determined whether the timing automatically acquired in (1) is the timing to detect an abnormality. 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.

[0068] Here, 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 determination result is "execute," and the processing from (2-3) onward is executed. On the other hand, if there is no match, the execution determination result is "do not execute," and the processing ends.

[0069] (2-3) Acquire the range conditions of data to detect 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).

[0070] (2-4) Detection of products with abnormally low inventory turnover Finally, the inventory data 106c and sales data 106d in the business data are referenced to detect products with abnormally low inventory turnover. In this example, the inventory data 106c and sales data 106d are set and stored as shown in FIG.

[0071] (2-4-1) Calculating 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 of Figure 9 by the average inventory amount in the inventory data 106c of Figure 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 Figure 9.

[0072] Here, it is assumed that the parameters are set as shown in Fig. 8 as "extraction condition column: accounting year / month, FROM condition: 2020 / 04, TO condition: 2021 / 03". In this case, the inventory turnover calculation unit 102a acquires the inventory turnover linked to the accounting year / month 2020 / 04 to 2021 / 03 from the inventory turnover calculation result data 106e in Fig. 9.

[0073] (2-4-2) Detection of abnormal values Next, the upper and lower limits are obtained using the "interquartile range," which is one of the statistical methods, and an abnormal value is 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 of 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.

[0074] 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 or abnormality. Standard deviation is an analysis method 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, consolidating 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 a range for taking an average value and calculating it for each time series. Although moving average + outlier requires a threshold value for judging abnormality from the average value to be set in advance, 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 for the average value for judging abnormality. For this reason, in this embodiment, it is preferable to employ an interquartile range that can automatically calculate a threshold value and determine an abnormality.

[0075] (2-4-3) Saving 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.

[0076] [4-2: Analysis screen display processing] (1) Displaying a summary message Next, information that is automatically detected as an anomaly from 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).

[0077] (1-1) Acquiring a message when an abnormality is detected First, it is assumed that "JOBID: inventory rotation alert" is set as a parameter, as shown in Fig. 12. Note that, in order to display detected information in a list, it is assumed that the detection ID is in 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".

[0078] (1-2) Displaying a message on the screen when an abnormality is detected 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.

[0079] This allows the operator to know that there is a product with an abnormally low stock turnover rate, and what that product is (product E in this example). At this time, the operator can also know the accounting year and month in which the abnormality occurred (2021 / 03 in this example) and the stock turnover rate value detected as an abnormal value (0.85 in this example).

[0080] (1-3) Setting the base 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.

[0081] (2) Displaying detailed messages and graphs Next, a 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).

[0082] (2-1) Displaying detailed messages and securing the area for graph output 1. First, when the summary message displayed in (1-2) is selected, an analysis screen is launched. The abnormality display unit 102c displays the detection method "interquartile range", the threshold "1.5 times normal range", and the lower limit "0.91" in the abnormality 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 are collectively referred to as a detailed message.

[0083] 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 standard value (inventory turnover) for determining that an anomaly was detected, etc. This enables the operator to perform 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 was detected can be analyzed using the three graphs explained below.

[0084] 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.

[0085] (2-2) Setting extraction conditions Next, in order to output the 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 using the method described in this section (2-2).

[0086] 1. First, the range of data to be analyzed is obtained based on the accounting year and month (2021 / 03) held by the anomaly determination result data 106f in FIG. 11 that is linked to the summary message displayed in (1-2).

[0087] (i) Specifically, the accounting year and month within a range of one year before and after the accounting year and month (2021 / 03) in which the anomaly was detected 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.

[0088] (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. 16: · Extraction criteria column: Fiscal year / month FROM condition: 2020 / 04 (adopted from the month after 2020 / 03, which is the fiscal year month one year prior to the above) TO Condition: 2021 / 03 (hired 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.

[0089] 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 on 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.

[0090] (2-3) Acquiring the data to generate the graph 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. In the following, 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".

[0091] As shown in the parameter table in FIG. 19, the start of the period (2020 / 04) and the end of the period (2021 / 03) are parameters used to acquire the data that is the basis for generating graphs 1 and 2, the designated product (product E) is a parameter used to acquire the data that is the basis for generating graphs 2 and 3, and the designated accounting year and month (2021 / 03) is a parameter used to acquire the data that is the basis for generating graph 3. As will be explained in detail in (2-4) below, when generating graphs 2 and 3, only information related to data that detects an abnormality in inventory turnover is acquired. Below, a method for acquiring the data that is the basis for generating graphs 1 to 3 is explained.

[0092] The inventory turnover rate transition graph display unit 102d acquires products and inventory turnover rates associated with the period start 2020 / 04 to period end 2021 / 03 in the parameters of Fig. 19 from the inventory turnover rate calculation result data 106e of Fig. 20. In other words, the data that is the basis for generating Graph 1 is acquired.

[0093] The month-end inventory amount trend graph display unit 102e acquires, from the inventory data 106c in Fig. 20, the month-end inventory amount linked to the period start 2020 / 04 to period end 2021 / 03 in the parameters in Fig. 19 and also linked to the designated product E (the product for which an abnormality in inventory turnover rate was detected) in the parameters in Fig. 19. In other words, the data that is the basis for generating Graph 2 is acquired.

[0094] The cost of sales-average inventory amount comparison graph display unit 102f obtains the average inventory amount linked to the specified accounting year and month 2021 / 03 (accounting year and month when an abnormality in inventory turnover was detected) in the parameters of FIG. 19 from the monthly average inventory amount calculation result data of FIG. 20 and to the specified product E (product where an abnormality in inventory turnover was detected) in the parameters of FIG. 19. In addition, the cost of sales-average inventory amount comparison graph display unit 102f obtains the cost of sales linked to the specified accounting year and month 2021 / 03 (accounting year and month when an abnormality in inventory turnover was detected) in the parameters of FIG. 19 from the sales data 106d of FIG. 20 and to the specified product E (product where an abnormality in inventory turnover was detected) in the parameters of FIG. 19. In other words, the data that is the basis for generating graph 3 is obtained.

[0095] (2-4) Graph output (display) Next, the data obtained in (2-3) is bound to the graph.

[0096] 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 the inventory turnover on the vertical axis and the 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 is information on the basis for determining that there is an abnormality. In this example, by comparing the inventory turnover in March 2021 with the inventory turnover before February 2021, the operator can understand that the inventory turnover in March 2021 has indeed abnormally decreased.

[0097] 2. 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.

[0098] 3. 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 a bar graph showing the average inventory value for product E in which an abnormality in inventory turnover was detected and for the accounting year / 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 used to calculate the inventory turnover and the average inventory value for the month. Note that at the initial display stage, the amount confirmation (actual amount confirmation) is only for the accounting year / month and month in which the abnormality occurred, but as explained in (3) below, the relationship between the cost of sales and the average inventory value for the month can be analyzed by switching the accounting year / month.

[0099] 4. The generated graphs 1 to 3 are displayed in a list on the analysis screen, as shown in FIG.

[0100] 5. When displaying Graph 1, the graph of the product (product E) for which an abnormality in the stock turnover rate was detected may be highlighted (for example, by changing the color). As shown in Fig. 25, if the coloring parameters are set so that the color of the anomaly flag in the legend is True for the product in the legend that is the same as the product for which an abnormality was detected, and the color of the anomaly flag in the legend is False for the legends other than the product for which an abnormality was detected, the graph can be highlighted by coloring.

[0101] (3) Switching graph display Finally, switching the display of analysis graphs will be explained in the following (3-1) to (3-2).

[0102] (3-1) Changing 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 to be output in Graph 3 (leaving the product as Product E) and analyze the relationship between the cost of sales and the monthly inventory amount, 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 portion 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.

[0103] 2. Next, based on the selected changed accounting year / month 2021 / 02, data that will be the basis for generating graphs 1 to 3 is acquired. The acquisition method itself is the same as (2-3), but the content of the data acquired only for graph 3 is different from (2-3), so it will be explained again. The sales cost-average inventory amount comparison graph display unit 102f acquires data that will be the basis for generating graph 3. That is, the sales cost-average inventory amount comparison graph display unit 102f acquires 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 of FIG. 20. In addition, the sales cost-average inventory amount comparison graph display unit 102f acquires the sales cost linked to the changed accounting year / month 2021 / 02 and linked to product E from the sales data 106d of FIG. 20.

[0104] 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), however, the content of the graph generated for graph 3 only differs from "3." in (2-4), so 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 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 E and the changed accounting year and month 2021 / 02, based on the data acquired in "2.", as shown in Figure 28.

[0105] 4. The generated graphs 1 to 3 are displayed in a list on the analysis screen (not shown).

[0106] 5. When displaying Graph 1, the graph for Product E may be highlighted (for example, by changing the color) in the same manner as in “5.” of (2-4).

[0107] 6. As for 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 observations can be made. 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 been reversed, but the amount of cost of sales has hardly changed. In other words, since it can be seen that an increase in inventory unrelated to sales occurred in March 2021, it is possible to infer the possibility of inventory fraud due to inventory padding.

[0108] In this example, we have explained a pattern of inventory fraud in which only the average inventory value increases. However, by switching the graph, it is also possible to discover patterns of inventory fraud in which only the cost of sales decreases, and patterns in which backlogged inventory occurs, as described below.

[0109] 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.

[0110] 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.

[0111] In this example, the accounting year and month of graph 3 is switched from 2021 / 03 to 2021 / 02, but the product can also be switched. That is, when it is desired to switch and analyze the product to be output in graphs 2 and 3, the product to be output in graphs 2 and 3 (product B in this example) is selected from graph 1. The 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.

[0112] 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 detailed explanations are omitted. However, graphs 2 and 3 generated after switching to product B are shown in Figure 34.

[0113] (3-2) Switching graph display by organization and product designation 1. By specifying an organization or product in the extraction conditions area on the analysis screen, it is possible to perform analysis by the specified organization or product. For example, as shown in Figure 35, when the department code "Department A" is specified as the department in the extraction conditions area on the analysis screen, "Department A" is set in the department that was left blank in the parameters of Figure 19, as shown in Figure 36.

[0114] 2. Next, data that will be the basis for generating 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 items: Product A, Product D, and Product F. Inventory data 106c, sales data 106d, and inventory turnover calculation result data 106e used in the explanation of this example are shown in FIG. 37. Note that inventory turnover calculation result data 106e only includes information about product A, product D, or product F, which are items handled by "Department A". The acquisition process will be explained in detail below.

[0115] The inventory turnover trend graph display unit 102d acquires, from the inventory turnover calculation result data 106e in Fig. 37, the inventory turnover linked to the period start 2020 / 04 to period end 2021 / 03 in the parameters in Fig. 36 and linked to product A, product D, or product F handled by "department A" in the parameters in Fig. 36. In other words, the data that is the basis for generating graph 1 is acquired.

[0116] The month-end inventory amount trend graph display unit 102e acquires, from the inventory data 106c in Fig. 37, the month-end inventory amount linked to the period start 2020 / 04 to period end 2021 / 03 in the parameters in Fig. 36 and linked to product A, product D, or product F handled by "department A" in the parameters in Fig. 36. In other words, the data that is the basis for generating graph 2 is acquired.

[0117] The cost of sales-average inventory amount comparison graph display unit 102f obtains the average inventory amount linked to the specified accounting year and month 2021 / 03 (accounting year and month when an abnormality in inventory turnover was detected) in the parameters of FIG. 36 and to the product A, product D, or product F handled by the "department A" in the parameters of FIG. 36 from the monthly average inventory amount calculation result data of 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 year and month 2021 / 03 (accounting year and month when an abnormality in inventory turnover was detected) in the parameters of FIG. 36 and to the product A, product D, or product F handled by the "department A" in the parameters of FIG. 36 from the sales data 106d of FIG. 37. In other words, the data that is the basis for generating graph 3 is obtained.

[0118] 3. Next, the data obtained in step 2 is bound to the graph.

[0119] The data binding information used when binding data to graph 1 and the generated graph 1 are shown in Fig. 38. 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 Fig. 38, for each of products A, D, and F handled by "department A".

[0120] Data binding information used when binding data to graph 2 and the generated graph 2 are shown in Fig. 39. Based on the data acquired in "2.", month-end inventory value trend graph display unit 102e generates a bar graph for product A with the month-end 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 designated 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, month-end inventory value trend graph display unit 102e displays only the month-end inventory value for product A, which is the product at the top of the inventory turnover calculation result data 106e in Fig. 37, out of the month-end inventory values ​​of product A, product D, and product F acquired in "2.", as a graph as shown in Fig. 39.

[0121] FIG. 40 shows the data binding information used to bind data to graph 3 and the generated graph 3. 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 the inventory turnover rate was detected, based on the data acquired in “2.”. Note that information on product E, which is the designated product in the parameters of FIG. 36, is not included in the inventory turnover rate calculation result data 106e in 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 rate calculation result data 106e in FIG. 37, among the average inventory values ​​and cost of sales of products A, D, and F acquired in “2.”, as a graph as shown in FIG. 40.

[0122] 4. The generated graphs 1 to 3 are displayed in a list on the analysis screen (not shown).

[0123] 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 that seems 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 acquired as another anomaly detection result, or that the product may be detected as abnormal 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).

[0124] In this example (3-2), an example of specifying a specific department as an extraction condition has been described, but a specific product group (product classification, product type, and product number, etc.) can also be specified as an extraction condition. For example, for graphs 1 to 3, the following considerations can be made by comparing the graph before the switch (graphs of products A, D, and E belonging to product type 1) with the graph after the switch (graphs 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) whose stock turnover rate is abnormally low, whereas in the graph after the switch that reflects product type 2, the trend of the 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.

[0125] <If 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.

[0126] [5. Summary of this embodiment] In this manner, the inventory fraud detection support device 100 according to this embodiment, as mainly described in [4-1], can automatically calculate the inventory turnover rate and detect abnormally low values ​​in the calculated inventory turnover rate, thereby supporting the inventory fraud detection process.

[0127] In addition, according to the inventory fraud detection work support device 100 of this embodiment, as mainly described 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.

[0128] Here, inventory fraud is often discovered for the first time during end-of-term inventory counts, since inventory investigations themselves incur operational costs. Previously, in order to discover inventory fraud, it was necessary to gather sales and inventory data from business data, calculate and confirm. This resulted in the problem of it taking a long time to discover fraud. In addition, 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 also resulted in the problem of a large scope and amount of data to be confirmed before the cause can be identified.

[0129] Therefore, in this embodiment, for example, fraudulently registered data in transaction data in sales operations can be automatically detected and notified. In this case, abnormalities can be detected using the inventory turnover performance, which is one of the indicators used in accounting audit investigations. In other words, inventory data and sales data in 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 performing anomaly detection. Also, in this embodiment, for example, a screen can be output that allows analysis of the cost of sales and inventory amount, which are the base data for inventory turnover. In other words, analysis information on products with abnormal values ​​can be displayed.

[0130] 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 has become possible to detect fraud from an auditing perspective. Furthermore, since anomalies are detected periodically, inventory investigations, which were previously costly, can now be performed periodically and accurately. Furthermore, since there is a screen for analyzing and confirming anomalies and their reasons, it has become possible to detect anomalies with high reliability.

[0131] [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 contributing to goals 8 and 9 of the SDGs.

[0132] 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.

[0133] Furthermore, this embodiment can contribute to strengthening control and governance, making it possible to contribute to Goal 16 of the SDGs.

[0134] 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.

[0135] 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.

[0136] In addition, the processing procedures, control procedures, specific names, registered data for each process, information including parameters such as search conditions, screen examples, and database configurations shown in this specification and drawings may be changed as desired unless otherwise specified.

[0137] Moreover, with regard to the inventory fraud detection work support device 100, the components shown in the figures are merely functional concepts, and the device does not necessarily have to be physically configured as shown in the figures.

[0138] 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 includes 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 for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into a RAM, and cooperates with the CPU to form the control unit.

[0139] 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.

[0140] In addition, the program for executing the process described in this embodiment may be stored in a non-transient computer-readable recording medium, or may be configured as a program product. Here, the "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.

[0141] Moreover, a "program" is a data processing method written in any language or description method, and may be in any form, 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 as multiple modules or libraries, and a program that works 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, may use well-known configurations and procedures.

[0142] The various databases etc. stored in the memory unit 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 etc. used for various processes and providing websites.

[0143] In addition, the inventory fraud detection work 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 connected to any peripheral device. In addition, the inventory fraud detection work support device 100 may be realized by installing software (including programs, data, etc.) that causes the device to realize the processing described in this embodiment.

[0144] 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 functionally or physically distributed and integrated in any unit according to 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. [Industrial Applicability]

[0145] The present invention is useful, for example, in any industry or business where inventory management occurs. [Explanation of symbols]

[0146] 100 Inventory fraud detection support device 102 Control section 102a Inventory turnover calculation section 102b Abnormal value detection unit 102c Abnormality display section 102d Inventory turnover rate trend graph display section 102e End-of-month inventory amount trend graph display section 102f Cost of sales - average inventory amount comparison graph display section 104 Communication interface section 106 Storage section 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 judgment result data 106g Abnormality judgment result message data 106h Error judgment result message details data 108 Input / Output Interface Section 112 Input Devices 114 Output Device 200 Servers 300 Network

Claims

1. 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.

2. An inventory fraud detection business support device having a control unit and a memory 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.

3. 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,

4. 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:

5. The control unit is An inventory turnover rate transition graph display means for displaying an inventory turnover rate transition graph, which is a graph with 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. Further comprising:

3. The inventory fraud detection support device according to claim 1 or 2,

6. 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,

7. 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 in units of period; Further comprising:

2. The inventory fraud detection support device according to claim 1,

8. 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 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 comparison of both graphs for a period and a product in which the inventory turnover rate was abnormal; Further comprising:

2. The inventory fraud detection support device according to claim 1,

9. 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.

10. 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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