JP7780394B2 - Inventory management device, inventory management method, and inventory management program - Google Patents

Description

The present invention relates to an inventory management device, an inventory management method, and an inventory management program.

In recent years, there have been cases of fraud involving inflating end-of-period inventory to inflate sales profits on income statements. Inventory management has become important in order to prevent such fraud from occurring. Prior art related to inventory management is disclosed in Patent Document 1 (JP 2002-279024 A), which discloses a product quantity setting system.

In this product quantity setting system, the department server aggregates the product sales records received from the information research company and the product resale records received from the sales information database server for each specified period, and calculates the store inventory amount, which indicates the product inventory at the store, based on the sales records and resale records for each specified period.The department server then associates the sales records, resale records, and store inventory amount for each specified period and displays them on a display device.

This allows trends in the electronically digitized sales figures, resale figures, calculated store inventory, and store inventory weeks for each product to be categorized into patterns of fluctuation, making it possible to estimate shipping volumes for a specified period and set continuous production volumes.

Japanese Patent Application Laid-Open No. 2002-279024

However, fraudulent inventory manipulation has become more sophisticated in recent years, making it difficult to detect and deal with it early.

The present invention was made in consideration of the above-mentioned problems, and aims to provide an inventory management device, inventory management method, and inventory management program that enable early detection and response to fraudulent inventory manipulation.

In order to solve the above-mentioned problems and achieve the object, an inventory management device according to the present invention includes a calculation unit that calculates transition information corresponding to changes in inventory of a product during a calculation period, based on inventory data including the number of products in stock; a discrimination unit that determines whether the value of the calculated transition information is equal to or greater than a predetermined value ; and an alert output control unit that controls output of a predetermined alert when the discrimination unit returns a discrimination result indicating that the value of the calculated transition information is equal to or greater than the predetermined value. The calculation unit calculates, as the transition information, an inventory value increase rate, which is the ratio of the difference between the inventory value at the beginning of the calculation period and the inventory value at the end of the calculation period to the inventory value corresponding to the number of products in stock at the beginning of the calculation period, and calculates an interquartile range based on each inventory value increase rate during the calculation period, and calculates an upper limit of the inventory value increase rate by adding an inventory value increase rate obtained by multiplying the calculated interquartile range by a predetermined factor to the inventory value increase rate that is the third quartile. The alert output control unit controls output of an alert when the calculated inventory value increase rate is equal to or greater than the upper limit .

Furthermore, in order to solve the above-mentioned problems and achieve the object, an inventory management method according to the present invention includes a calculation step in which a calculation unit calculates transition information corresponding to a transition in inventory of a product during a calculation period, based on inventory data including the number of products in stock; a determination step in which a determination unit determines whether the value of the calculated transition information is equal to or greater than a predetermined value ; and an alert output control step in which an alert output control unit controls output of a predetermined alert when a determination result is obtained from the determination unit indicating that the value of the calculated transition information is equal to or greater than the predetermined value . In the calculation step, an inventory value increase rate is calculated as the transition information, which is the ratio of the difference between the inventory value at the beginning of the calculation period and the inventory value at the end of the calculation period to the inventory value corresponding to the number of products in stock at the beginning of the calculation period, and an interquartile range is calculated based on each inventory value increase rate during the calculation period, and an upper limit of the inventory value increase rate is calculated by adding the inventory value increase rate obtained by multiplying the calculated interquartile range by a predetermined factor to the inventory value increase rate that is the third quartile. In the alert output control step, an alert output is controlled when the calculated inventory value increase rate is equal to or greater than the upper limit.

Furthermore, in order to solve the above-mentioned problems and achieve the object, an inventory management program according to the present invention causes a computer to function as: a calculation unit that calculates transition information corresponding to the transition of inventory of a product during a calculation period based on inventory data including the number of products in stock; a discrimination unit that determines whether the value of the calculated transition information is equal to or greater than a predetermined value ; and an alert output control unit that controls output of a predetermined alert when a discrimination result is obtained from the discrimination unit indicating that the value of the calculated transition information is equal to or greater than the predetermined value; the calculation unit calculates, as the transition information, an inventory value increase rate that is the ratio of the difference between the inventory value at the beginning of the calculation period and the inventory value at the end of the calculation period to the inventory value corresponding to the number of products in stock at the beginning of the calculation period; and calculates an interquartile range based on each inventory value increase rate during the calculation period and calculates an upper limit of the inventory value increase rate by adding an inventory value increase rate obtained by multiplying the calculated interquartile range by a predetermined factor to the inventory value increase rate that is the third quartile; and the alert output control unit controls output of an alert when the calculated inventory value increase rate is equal to or greater than the upper limit.

This invention enables early detection and response to fraudulent inventory manipulation.

FIG. 1 is a block diagram showing the hardware configuration of an inventory management device according to an embodiment. FIG. 2 is a diagram illustrating an example of automatic detection execution schedule data. FIG. 3 is a diagram showing an example of inventory data acquisition range condition data. FIG. 4 is a diagram illustrating an example of parameters including a detection ID and a schedule ID. FIG. 5 is a diagram for explaining the execution timing of inventory management. FIG. 6 is a diagram for explaining an extraction period for data extracted during inventory management. FIG. 7 is a diagram for explaining the operation of calculating the inventory amount amplification rate. FIG. 8 is a diagram for explaining a method for calculating the upper limit of the inventory amount amplification rate for determining an abnormality. FIG. 9 is a diagram illustrating an example of abnormality determination of the inventory amount amplification rate using the upper limit value. FIG. 10 is a diagram showing abnormality determination result data, abnormality determination result message data, and abnormality determination result message detail data generated based on the abnormality determination result of the inventory amount amplification rate. FIG. 11 is a diagram showing an example of an abnormality list screen displayed based on the abnormality determination result message data and the abnormality determination result message detail data. FIG. 12 is a diagram showing an input field for extraction conditions displayed on the abnormality list screen. FIG. 13 is a diagram for explaining the alert display area on the message details display screen. FIG. 14 is a diagram for explaining how to obtain extraction conditions. FIG. 15 is a diagram showing an example of extraction items that serve as extraction conditions. FIG. 16 is a diagram for explaining how to obtain an extraction period that is an extraction condition. FIG. 17 is a diagram showing data acquired when displaying the first graph. FIG. 18 is a diagram showing an example in which sales of warehouse A are set as the extraction condition. FIG. 19 is a diagram showing data acquired when the second graph is displayed. FIG. 20 is a diagram showing an example in which products in warehouse A are set as extraction conditions. FIG. 21 is a diagram showing data acquired when displaying the third graph. FIG. 22 is a diagram showing an example of initially set extraction conditions. FIG. 23 is a diagram showing data acquired when the fourth graph is displayed. FIG. 24 is a diagram showing a display example of the first graph. FIG. 25 is a diagram showing a display example of the second graph. FIG. 26 is a diagram showing a display example of the third graph. FIG. 27 is a diagram showing a display example of the fourth graph. FIG. 28 shows the message details display screen in which the first to fourth graphs are displayed. FIG. 29 is a diagram showing graph coloring parameters for displaying a predetermined portion of each graph in color. FIG. 30 is a diagram for explaining an example of coloring and displaying a graph based on graph coloring parameters. FIG. 31 is a diagram showing an example in which the extraction item "product type" is selected as an extraction condition in order to analyze the inventory status of warehouse A from which abnormality determination result data has been obtained. FIG. 32 is a diagram for explaining the operation of calculating the inventory value increase rate at the beginning and end of a period for product type A managed in warehouse A. FIG. 33 is a diagram showing an example of the third graph. FIG. 34 is a diagram showing an example of a message details display screen. FIG. 35 is a diagram showing an example of color display for the third graph. FIG. 36 is a diagram for explaining the analysis of inventory manipulation based on the third graph. FIG. 37 is a diagram showing an example in which the extraction item "order" is selected as an extraction condition in order to analyze the inventory status of warehouse A from which abnormality determination result data has been obtained. FIG. 38 is a diagram for explaining the operation of generating monthly inventory and order amount data. FIG. 39 is a diagram showing an example of the second graph. FIG. 40 is a diagram for explaining the analysis of inventory manipulation based on the second graph. FIG. 41 is a diagram showing an example in which the extraction item "Department A" is specified as an extraction condition in order to analyze the inventory status of Department A. FIG. 42 is a diagram for explaining the operation of generating monthly inventory amount data. FIG. 43 is a diagram for explaining the operation of generating monthly inventory and sales amount data. FIG. 44 is another diagram for explaining the operation of generating monthly inventory and sales amount data. FIG. 45 is a diagram for explaining the operation of generating data on the end-of-month inventory amount and the inventory amount increase rate between the beginning and end of a period. FIG. 46 is another diagram for explaining the operation of generating the calculation result data of the end-of-month inventory amount and the inventory amount increase rate between the beginning and end of the period. FIG. 47 is a diagram for explaining the operation of generating monthly inventory amount and shipping amount total data. FIG. 48 is a diagram for explaining the operation of generating monthly inventory amount and shipping amount total data. FIG. 49 is a diagram showing an example of the first graph. FIG. 50 is a diagram showing an example of the second graph. FIG. 51 is a diagram showing an example of the third graph. FIG. 52 is a diagram showing an example of the fourth graph. FIG. 53 is a diagram for explaining the analysis of inventory manipulation based on the first graph.

Below, an inventory management device embodying the present invention will be described in detail with reference to the drawings.

(overview)
In recent years, there has been an increase in fraud, including corporate fraud and embezzlement by employees and executives. Such fraud is difficult to detect manually, and there is a demand for the development of a device that can detect it mechanically and early. The inventory management device of the embodiment meets this demand, and is capable of creating scenarios of fraudulent patterns and automatically detecting them on a regular basis.

There are many scenario patterns, such as those shown below, and it is necessary to build a system that corresponds to each one.

・Fraud regarding sales results: Falsifying sales amounts → Inflating actual results ・Fraud regarding purchasing results: Conspiring with business partners to make fictitious payments → Collusion ・Fraud regarding inventory: Inflating end-of-year inventory amounts → Profit manipulation

The inventory management device 1 of this embodiment focuses on fraudulent scenarios related to inventory. In many cases, the purpose of fraud is to inflate end-of-period inventory to inflate sales profits on the income statement. Characteristics of fraud include the following:

1. Unnatural increase in inventory between the beginning and end of the period. 2. Increase in inventory unrelated to sales. 3. Manipulation of inventory value using products that are not meticulously managed and are difficult to detect fraudulently.

When inventory is in a normal state, the number of products in stock will generally remain constant throughout the year. However, if fraudulent adjustments are made to inventory in order to manipulate sales profits, the inventory at the end of the period will be abnormally high. Furthermore, a sudden increase in inventory may lead to fraud being discovered during inventory counts during the period. For this reason, operations may be carried out to gradually increase inventory throughout the year in order to make fraudulent increases in inventory less noticeable.

For this reason, the inventory management device 1 of the embodiment focuses on the "abnormal inventory growth rate from the beginning to the end of the period." Next, the inventory management device 1 of the embodiment allows the user to check the detected data using the analysis screen. The user then checks the warehouse that manages the inventory, the inventory status of the product, and the timing of inventory changes to determine whether the data is abnormal.

Here, when managing inventory, it is necessary to tally and compare multiple warehouses and multiple types of products, which takes time to complete the tallying process.

Items to check → Inventory data and inbound/outbound data for all warehouses and all products Required aggregation unit → All data must be aggregated monthly Warehouse → It is necessary to check the inventory status and increasing trend for each month "Warehouse x Product" → To detect products that are being misused, it is necessary to check the increasing trend of inventory for warehouses and products "Sales amount/inventory amount" → It is necessary to confirm that this is inventory that is not accompanied by sales How to check → Aggregate inventory amount and sales amount by month for each "required aggregation unit"

The process takes time because multiple calculations are required from a huge amount of data. Furthermore, comparisons must be made monthly within the same calculation unit, which can lead to oversights and mistakes.

In addition, a comparison must be made for each combination of "warehouse x product," which takes time and is prone to confirmation errors and omissions.

Check target → Comparison of inventory amounts from the beginning to the end of the period within the combination of "warehouse x product" Check method → Aggregate the inventory amounts at the beginning and end of the period for each combination of "warehouse x product", or compare the inventory amounts at the beginning and end of the period for each combination of "warehouse x product" to calculate the growth rate

In this case, the number of warehouses or products would be enormous. Furthermore, the number of "warehouse x product" combinations would be even greater, and it would be necessary to compare the data at the beginning and end of each period. This would require a lot of time to compile, and would lead to human errors such as typographical errors and mistakes in selecting the items to compare.

For this reason, the inventory management device 1 of this embodiment resolves the above-mentioned problem by performing the following operations.

1. The inventory value growth rate from the beginning to the end of the period is calculated for each warehouse and product, and warehouse and product combinations with abnormally high inventory levels are automatically detected. By automatically aggregating inventory values between the beginning and end of the period and calculating the inventory value growth rate, aggregation costs can be reduced and aggregation and calculation errors can be prevented. Data for the same warehouse and product is also periodically checked to detect warehouse and product combinations with abnormally high inventory levels. This prevents oversights and errors. Users can also easily identify warehouse and product combinations with abnormal inventory levels, enabling them to take countermeasures and investigate.

2. Aggregate the monthly inventory amounts for warehouses where abnormalities were detected and visualize the inventory increase trend from the beginning to the end of the period. Possible examples of inventory manipulation include the following:

Large increase in inventory at the end of the period → Monthly inventory increase: Large (end of period only)
Gradual increase in inventory → Monthly inventory increase: Small (possibility of manipulation to avoid detection each month (concealment manipulation))

For this reason, the inventory management device 1 of this embodiment aggregates inventory values by month and visualizes the changes in inventory values over time. This allows users to check how inventory values change over time, making it possible to identify signs of fraudulent operations. It also makes it possible to compare inventory movements between warehouses and determine how abnormal a warehouse where an abnormality has been detected is. It also eliminates the cost of each aggregation and prevents omissions and mistakes in comparison confirmations.

3. By aggregating the "inventory value of warehouses where anomalies were detected" and the "sales value of sales data shipped from warehouses," and visualizing the sales and inventory values, it becomes possible to check changes in inventory due to sales. Normally, inventory is replenished for products that are expected to sell. An increase in fraudulent inventory is an increase that is not accompanied by sales. For this reason, by aggregating and visualizing the sales and inventory values for each warehouse by month, it is possible to check how sales and inventory values change over time. It is also possible to check for signs of fraud, such as an increase in inventory value while sales remain unchanged. It also eliminates the cost of aggregating both sales and inventory, making it easier to compare sales and inventory.

4. Aggregate the "inventory value for each product managed in the warehouse where an anomaly was detected" and the "inventory value increase rate from the beginning to the end of the period" to visualize products with significant increases in inventory. The characteristics of the products for which inventory is being increased are important when it comes to inventory increases. In other words, products with the following characteristics are more likely to be used fraudulently.

・Products with miscellaneous inventory management ・Products that are managed as "multiple products as one product" because the trading volume is not expected to increase much

For this reason, the inventory management device 1 of this embodiment visualizes products with a large increase in inventory from the beginning to the end of the period, and compares the inventory amount with the inventory amount increase rate. If the product amount is small and the inventory amount increase rate is large, the inventory amount at the end of the period will not change much. If the product amount is large and the inventory amount increase rate is large, the inventory amount at the end of the period will increase significantly. A large increase in inventory amount at the end of the period can make it appear as if the cost of sales on the income statement has decreased significantly. For this reason, fraud can be detected by detecting products with large inventory amounts and large inventory amount increase rates.

5. The "inventory value of warehouses where anomalies were detected" and the "amount of inventory reduced during shipping processing" are aggregated and visualized. If inventory is increased fraudulently at the end of the period, it is likely that inventory will be reduced after the financial statements are settled in order to conceal the increase. For this reason, the monthly inventory value and the reduced inventory value are visualized. This makes it possible to detect the "amount at the time of fraudulent processing" and the "amount concealed."

(Hardware configuration)
FIG. 1 is a block diagram showing the hardware configuration of an inventory management device 1 according to an embodiment. The inventory management device 1 shown in FIG. 1 includes a storage unit 2, a control unit 3, a communication interface unit 4, and an input/output interface unit 5. An input device 6 and an output device 7 are connected to the input/output interface unit 5. The output device 7 may be a display unit such as a monitor device (including a home television). The input device 6 may be a keyboard device, a mouse device, a microphone device, or a monitor device that functions as a pointing device in cooperation with a mouse device. The communication interface unit 4 is connected to a network, such as a wide area network (WLAN) or a private network such as a local area network (LAN).

The storage unit 2 can be, for example, a storage device such as ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), or SSD (Solid State Drive). The storage unit 2 stores an inventory management program that enables the above-mentioned inventory management. The storage unit 2 also stores data used for inventory management, including inventory data, order entry data, sales entry data, product shipping data, product master data, automatic detection execution schedule data, inventory data acquisition range condition data, and anomaly determination result data.

In addition, the memory unit 2 stores abnormality determination result message data, detailed abnormality determination result message data, inventory value increase rate calculation result data, monthly inventory value data, monthly inventory and sales amount data, end-of-month inventory value and inventory value increase rate between the beginning and end of the period calculation result data, and monthly inventory value and shipment value summary data. Details will be provided below.

(Functional configuration of inventory management device)
Next, the control unit 3 executes the inventory management program stored in the memory unit 2, thereby functioning as a calculation unit 21, a discrimination unit 22, an alert output control unit 23, a display control unit 24, a data generation unit 25, and a memory control unit 26.

The calculation unit 21 calculates transition information corresponding to the change in product inventory over a calculation period, such as the beginning to end of a company's fiscal year, based on inventory data including the number of products in stock. The determination unit 22 determines whether the value of the calculated transition information is a value indicating an inventory transition greater than or equal to a predetermined value. The alert output control unit 23 controls the output of a predetermined alert when the determination unit 22 determines that the value of the calculated transition information is a value indicating an inventory transition greater than or equal to a predetermined value. The alert output may, for example, display an alert message on the display unit, or output an alert audio message or electronic sound via a speaker unit (not shown).

The calculation unit 21 also calculates the inventory value increase rate, which is the ratio of the difference between the inventory value at the beginning of the calculation period and the inventory value at the end of the calculation period to the inventory value corresponding to the inventory quantity of the product at the beginning of the calculation period, as the above-mentioned transition information. The alert output control unit 23 controls the output of an alert when the calculated inventory value increase rate is equal to or greater than a predetermined upper limit.

The calculation unit 21 also calculates the interquartile range based on each inventory value increase rate during the calculation period, and calculates the upper limit of the inventory value increase rate by adding a predetermined inventory value increase rate to the inventory value increase rate that is the third quartile. The alert output control unit 23 controls the output of an alert when the calculated inventory value increase rate is equal to or greater than the upper limit.

The display control unit 24 displays a graph showing the change in inventory value over the calculation period via the output device 7, which is an example of a display unit. The display control unit 24 also displays a graph showing the change in inventory value over the calculation period on the output device 7, along with a graph showing the normal change in inventory value. The display control unit also displays a graph showing the change in inventory value over the calculation period and a graph showing the change in sales value of inventory items over the calculation period on the output device 7.

The display control unit 24 also displays on the output device 7 a correlation table of inventory value and inventory value increase rate, in which the inventory value for the calculation period and the inventory value increase rate for the calculation period are plotted on a two-dimensional coordinate system with inventory value on one axis and inventory value increase rate on the other. The display control unit 24 also displays on the output device 7 a bar graph of the inventory value for the calculation period and a bar graph of the shipping amount of inventory items for the calculation period. The "shipping amount" is the amount of inventory that has been reduced. In other words, possible cases in which inventory is reduced include, for example, "when goods are shipped for sales," "when a purchase is returned," and "when inventory is discarded." In the inventory management device 1 of this embodiment, the amount of inventory reduced due to such actions is referred to as the "shipping amount."

The data generation unit 25 generates data used for inventory management. The memory control unit 26 stores the various data generated by the data generation unit 26 and the data calculated by the calculation unit 21 in the memory unit 2.

(Inventory management operation)
The inventory management device 1 checks the "inventory data" in the business data and automatically detects warehouses and products where the "inventory value increase rate between the beginning and end of the period" is abnormally large. The inventory management device 1 also displays the "detected abnormal data" and "data related to the abnormal data referenced to detect it" on the analysis screen.

The inventory management device 1 also displays a graph on the analysis screen that allows users to check inventory reduction operations carried out to conceal fraudulently increased inventory. Note that the "amount of reduced inventory" is considered to be the "shipping amount." Possible processes for reducing inventory include, for example, shipments for sales, purchases and returns, and inventory disposal. Hereinafter, all of these will be referred to as the "shipping amount."

(A process that detects warehouses and products with abnormally high inventory value growth rates from the [inventory data] in business data)
The inventory management device 1 checks the "inventory data" in the business data and automatically detects warehouses and products where the "inventory value increase rate between the beginning and end of the period" is abnormally large. This process uses "business data,""anomaly detection execution data," and "anomaly determination result data," etc., which are accumulated as business operations are carried out.

"Business data" includes inventory data, order entry data, sales entry data, product shipment data, and product master data. "Anomaly detection execution data" is pre-configured data for performing anomaly detection, and includes automatic detection execution schedule data and inventory data acquisition range condition data.

Figure 2 shows an example of automatic detection execution schedule data. As shown in Figure 2, the automatic detection execution schedule data includes a detection identification number (detection ID), a schedule identification number (schedule ID), execution conditions, and execution time. The example in Figure 2 shows that inventory value increase rate detection is performed at 23:00 on the fifth business day in April every year.

Figure 3 shows an example of inventory data acquisition range condition data. As shown in Figure 3, the inventory data acquisition range condition data includes a detection ID, schedule ID, target column, FROM condition, and TO condition. The example in Figure 3 shows that inventory data is acquired from the month corresponding to one year prior to the month in which the inventory value increase rate is detected (anomaly detection) to one month prior to the month in which the anomaly detection is performed.

"Abnormality determination result data" is data indicating the determination results of an abnormality detection execution, and includes abnormality determination result data, abnormality determination result message data, and abnormality determination result message detail data.

This type of data is stored in advance in a specified table in the memory unit 2. This allows the anomaly detection operation to begin.

Next, the calculation unit 21 references the automatic execution schedule data shown in Figure 2 based on the pre-set parameters, detection ID and schedule ID, shown in Figure 4, and recognizes the timing for abnormality detection (23:00 on the fifth business day in April every year).

Next, as shown in FIG. 5, the calculation unit 21 determines whether the startup timing of the inventory management device 1, for example, on April 7, 2021, corresponds to the fifth business day in April of each year, thereby determining whether the startup timing is the timing to perform anomaly detection. The example in FIG. 5 is an example in which the startup timing is determined to be the timing to perform anomaly detection (to be executed). Note that if it is determined that the startup timing is not the timing to perform anomaly detection, the anomaly detection process is terminated.

The calculation unit 21 determines whether a day is a holiday or a business day by referencing a calendar master (not shown) stored in the memory unit 2. It is preferable that the calendar master be kept up to date based on the sales business calendar.

Next, if it is determined that the activation timing is the timing to perform anomaly detection, the calculation unit 21 references the inventory data acquisition range condition data shown in FIG. 3 based on the detection ID and schedule ID shown in FIG. 4, and acquires the extraction condition column (target column), FROM condition, and TO condition as illustrated in FIG. 6. The example in FIG. 6 is an example in which "April 2020 to March 2021" is acquired as the "fiscal year and month."

Next, as shown in Figure 7(b), the calculation unit 21 uses the inventory amounts tallied at the beginning and end of the period (inventory data) to calculate the inventory amount amplification rate for each warehouse and each product based on the following formula:

Inventory value increase rate = ((Ending inventory value - Beginning inventory value) / Beginning inventory value x 100)

The calculated inventory value increase rate is stored by the storage control unit 26 as part of the inventory value increase rate calculation result data, as shown in Figure 7(a).

Next, the calculation unit 21 uses the interquartile range to calculate the upper limit (= threshold) of the inventory value increase rate included in the inventory value increase rate calculation result data shown in Figure 7(a). Specifically, the calculation unit 21 calculates an upper limit value such as "15%" using the normal range calculation method of "third quartile + (1.5 x interquartile range)" as shown in Figure 8. Figure 9 is a box-and-whisker plot that schematically illustrates this upper limit value. As shown in Figure 9, if the inventory value increase rate is 64%, it exceeds the upper limit and is therefore detected as an abnormal value.

Here, methods for detecting abnormal values include a detection method using the "interquartile range," a detection method using the "standard deviation" that detects the distribution of values at a certain point and sets a confidence interval to determine whether something is normal or abnormal, and a detection method using the "moving average and outliers" that determines a range for detecting the average value and calculates it for each time series.

However, because the detection method using "standard deviation" does not involve the concept of a time axis, it is difficult to use for cross-sectional data analysis of "past trends." Furthermore, the detection method using "moving averages and outliers" can detect abnormal increases or decreases in time series trends, but requires the user to set a threshold for determining an abnormality in advance.

In contrast, with detection methods using the "interquartile range," you can define the data analysis range and aggregate it on the vertical axis, and specify the range magnification at which data can be determined to be normal by defining the interquartile range. Furthermore, because time series data and point data are also treated as vertical axis elements, it can also handle cross-sectional data analysis. Furthermore, with detection methods using the "interquartile range," you can automatically calculate the upper limit (threshold) and detect abnormal values.

Next, the discrimination unit 22 determines that data with an inventory value increase rate above the upper limit of the interquartile range is abnormal, as shown in FIG. 9. Based on this discrimination result, the data generation unit 25 generates abnormality determination result data shown in FIG. 10(a), abnormality determination result message data shown in FIG. 10(b), and detailed abnormality determination result message data shown in FIG. 10(c).

As shown in Figure 10(a), the abnormality determination result data is generated and includes the detection ID, job ID (JOBID), message ID, start of period (FROM), end of period (TO), warehouse name, product name, inventory value increase rate, and upper limit value. In this case, the job ID is "Inventory Increase Alert." Furthermore, as shown in Figure 10(b), the abnormality determination result message data is generated and includes the detection ID, job ID (JOBID), message ID, abnormality level, definition name, summary, and detection target. The "summary" is a message that indicates the warehouse and product name where an abnormal value was detected, such as "Warehouse A, Product A detected."

Furthermore, as shown in FIG. 10(c), the abnormality determination result message detail data is generated, including the detection ID, job ID, message ID, detection method, threshold, determination method, and upper limit value. The detection method is the aforementioned "interquartile range," and the threshold value is 1.5 times the normal range. The determination method is "determining values greater than the upper limit value as abnormal," and the upper limit value is "15%." The memory control unit 26 stores such abnormality determination result data, abnormality determination result message data, and abnormality determination result message detail data in the memory unit 2.

(Process of displaying the detected abnormal data and data related to the abnormal data referenced for the detection on the analysis screen)
Next, the display control unit 24 displays the detected abnormal data and data related to the abnormal data that was referenced when the abnormal data was detected on an analysis screen via the output device 7, which is an example of a display unit. Specifically, the data generation unit 25 references the abnormality determination result message data shown in FIG. 11( b) among the abnormality determination result data exemplified in FIG. 11( a), the abnormality determination result message data shown in FIG. 11( b), and the detailed abnormality determination result message data shown in FIG. 11( c). Then, the data generation unit 25 extracts the abnormality level, definition name, summary, and detection target from the abnormality determination result message data shown in FIG. 11( b) to generate abnormality display data as shown in FIG. 11( d).

The example in Figure 11(d) shows an example in which data for displaying an abnormality is generated using an "X" mark indicating an abnormal value, the definition name "Inventory Increase Alert" indicating the possibility of inventory increase, the warehouse name and product name where the abnormality was detected, such as "Warehouse A, Product A detected," the year and month showing the abnormal value, such as "March 2021," and the inventory value increase rate, such as "64%."

The data generation unit 24 display control unit 24 generates such abnormality display data for each warehouse and product in which an abnormal value for the inventory value increase rate has been detected. The display control unit 24 displays the generated abnormality display data in a list on the abnormality list screen shown in FIG. 11(e).

The anomaly list screen includes a list display area where a list of anomaly display data is displayed, as well as an extraction condition input area with input fields for the base date, period start date, and period end date, and a display specification button for the analysis screen, as shown in Figures 12(a) and 12(b). When the anomaly display data is displayed in the list display area, the display control unit 24 obtains the date at the time the screen is launched and automatically enters it into the base date input field in the extraction condition input area.

Next, when the user selects the anomaly display data to be analyzed from the displayed list of anomaly display data (for example, by double-clicking), the display control unit 24 switches the message to display detailed information and secures an output area for the graph.

Specifically, the display control unit 24 acquires the detection method used, such as "interquartile range," the threshold value, such as "1.5 times the normal range," and the upper limit value, such as "15%," from the detailed data of the abnormality determination result message shown in FIG. 13(a). The display control unit 24 then displays the detection method, threshold value, and detailed message, such as "Detected because the inventory value increase rate exceeded the upper limit value of 15%," shown in FIG. 13(b), together with the abnormality display data described above, in the alert display area of the message details display screen, as shown in FIG. 13(c). The display control unit 24 also generates display areas for, for example, four types of graphs on this message details display screen, as shown in FIG. 13(c).

Next, the display control unit 24 acquires the accounting year and month of the beginning and end of the period included in the anomaly determination result data related to the message shown in FIG. 14(a) as the accounting year and month of the beginning and end of the period in which the anomaly was detected, as shown in FIG. 14(b) and FIG. 14(c). Note that it is also possible to widen the scope of analysis by changing the start and end of the period in the extraction conditions.

In addition, as shown in FIG. 14(d), the display control unit 24 sets the beginning year and month of "April 2020" as the FROM condition (start of period) of the analysis data acquisition range condition for the extraction condition in which "April 10, 2021" is set as the reference date for the timing of launching the analysis screen on the message details display screen. Furthermore, the display control unit 24 sets the ending year and month of "March 2021" as the TO condition (end of period) of the analysis data acquisition range condition.

Furthermore, the display control unit 24 displays extraction items for extracting data for graph output in the extraction conditions on the message details display screen, as shown in Figures 15(a) and 15(b). In the examples of Figures 15(a) and 15(b), of the "aggregate" extraction items, namely, product type, product number, product, order, and sales, the "product" and "sales" selected in the initial state are actively displayed. Note that it is also possible to display only the selectable extraction items.

Next, as shown in Figures 16(a) and 16(b), the display control unit 24 acquires the inventory amount for the period from the beginning of the period (start of the period) in April 2020 to the end of the period (end of the period) in March 2021, which was initially set as the extraction condition, as the display data for the "first graph," using the monthly inventory amount data shown in Figure 17(a) collected from the inventory data shown in Figure 17(b).

Next, the display control unit 24 acquires monthly inventory and sales amount data based on the initially set extraction conditions, as shown in Figures 18(a) to 18(c). That is, the examples in Figures 18(a) to 18(c) are abnormality determination result data indicating an inventory abnormality at "Warehouse A." Therefore, the display control unit 24 acquires the monthly inventory and sales amount data for Warehouse A shown in Figure 19(a), which was generated by aggregating the inventory amount for Warehouse A in the inventory data shown in Figure 19(b) and the sales amount for Warehouse A in the sales posting data shown in Figure 19(c), by month, as display data for the "second graph."

Next, the display control unit 24 acquires the end-of-month inventory amount and the calculation result data for the inventory amount increase rate between the beginning and end of the period based on the initially set extraction conditions as shown in Figures 20(a) to 20(c). That is, in the examples of Figures 20(a) to 20(c), the abnormality determination result data indicates an abnormality in "Warehouse A." Therefore, the display control unit 24 acquires the end-of-month inventory amount for Warehouse A in the inventory data shown in Figure 21(b) and the calculation result data for the end-of-month inventory amount and the inventory amount increase rate between the beginning and end of the period shown in Figure 21(a) as display data for the "third graph." This data includes the end-of-month inventory amount for Warehouse A in the inventory data shown in Figure 21(b) and the inventory amount increase rate calculated by the calculation unit 21 using the formula "((end-of-month inventory amount - beginning-of-month inventory amount) / beginning-of-month inventory amount x 100)."

Next, the display control unit 24 acquires monthly inventory amount and shipping amount aggregate data based on the initially set extraction conditions as shown in Figures 22(a) to 22(c). That is, in the examples of Figures 22(a) to 22(c), the abnormality determination result data indicates an abnormality in "Warehouse A." Therefore, the display control unit 24 acquires the monthly inventory amount and shipping amount aggregate data shown in Figure 23(a), which includes the inventory amount for Warehouse A in the inventory data shown in Figure 23(b) and the shipping amount for Warehouse A in the product shipping data shown in Figure 23(c), as display data for the "fourth graph."

In this example, data up to +1 month after the parameter's end of period value (data one month later) is referenced. This assumes that the parameter's end of period value = the fiscal year and month at the end of the period. That is, in the case of the inventory management device 1 of this embodiment, attention is focused on the fraud of "fraudulently increasing inventory by the end of the period." Since increased inventory is evidence of fraud, there is a possibility that a process to reduce inventory at the beginning of the next period will be carried out as a cover-up operation. For this reason, by obtaining data up to +1 month after the parameter's end of period value (data one month later), it is possible to confirm data in which such fraudulent processing to reduce inventory has been performed.

Next, the display control unit 24 displays a total of four types of graphs, the first to fourth graphs described above, on the message details display screen. Specifically, based on the data binding information shown in FIG. 24(a), the display control unit 24 displays the first graph shown in FIG. 24(b) on the message details display screen. This graph shows the changes in inventory value at each warehouse, with the X axis representing the fiscal year and month and the Y axis representing the inventory value in a two-dimensional coordinate system. The example in FIG. 24(b) is an example that shows a list of graphs showing the changes in inventory value at warehouses A to E.

Next, based on the data binding information shown in Figure 25(a), the display control unit 24 displays on the message details display screen a second graph shown in Figure 25(b), which shows the trends in sales and inventory amounts for Warehouse A, with the X axis representing the fiscal year and month and the Y axis representing the inventory amount and sales amount in a two-dimensional coordinate system. The example in Figure 25(b) is an example that shows a list of graphs showing the trends in sales and inventory amounts by month for Warehouse A, where an abnormality was detected.

Next, based on the data binding information shown in Figure 26(a), the display control unit 24 displays on the message details display screen the third graph shown in Figure 26(b), which shows the correlation between the inventory value increase rate and inventory value for Warehouse A from April 2020 (beginning of the period: start of the period) to March 2021 (end of the period: end of the period) with the inventory value increase rate (%) on the X axis and the ending inventory value on the Y axis of the two-dimensional coordinates. The plotted points on the third graph in Figure 26(b) indicate the inventory value increase rate (%) for Warehouse A where an abnormality was detected, and the inventory value at that time.

Next, based on the data binding information shown in Figure 27(a), the display control unit 24 displays a fourth graph on the message details display screen, with the X-axis of the two-dimensional coordinate system representing the fiscal year and month, and the Y-axis representing the inventory amount or shipping amount of warehouse A, as shown in Figure 27(b).The graph shows a dual-axis bar graph, with a bar graph of inventory amount and a bar graph of shipping amount superimposed, along with the fiscal year and month.In the dual-axis bar graph shown in Figure 27(b), the lower bar graph shows the inventory amount, and the bar graph layered on top of this inventory amount bar graph shows the shipping amount.

Figure 28 shows the message details display screen with the first to fourth graphs displayed. As shown in Figure 28, the message details display screen displays the above-mentioned extraction conditions along the left side of the screen. Furthermore, the message details display screen displays a detailed message along with the above-mentioned abnormality display data in an alert display area along the top of the screen, with the left side abutting the extraction conditions display area.

In addition, the message details display screen displays the first graph described above in a display area about half the size of the alert display area, with its left edge abutting the extraction condition display area and its top edge abutting the bottom edge of the alert display area.

In addition, on the message details display screen, the second graph described above is displayed in an area about half the size of the alert display area, with its left edge abutting the display area for the first graph and its top edge abutting the bottom edge of the alert display area. In other words, the first graph and second graph are displayed adjacent to each other on the lower side of the alert display area.

In addition, the message details display screen displays the third graph described above in a display area with its left edge abutting the extraction condition display area and its upper edge abutting the lower edges of the first graph display area and the second graph display area.

In addition, the message details display screen displays the fourth graph described above in a display area with its left edge abutting the extraction condition display area and its upper edge abutting the lower edge of the display area for the third graph.

Next, the display control unit 24 performs display processing to highlight the legends and lines (graphs) of the first to fourth graphs by changing their colors. Figure 29 is a diagram showing an example of a coloring pattern for such graphs. The "legend abnormality flag" shown in Figure 29 is a flag that is set when there is an abnormality in the inventory. Furthermore, the "reference information" is information for confirming how abnormal the inventory is. When the legend abnormality flag is "True" and the reference information flag is "False," the display control unit 24 highlights both the legends and lines (graphs) in red.

In other words, when displaying the first graph and the third graph, the display control unit 24 applies the color "True" to the abnormality flag in the legend for warehouses and products that are the same as those for which an abnormality was detected. If an abnormality is detected in the inventory of warehouse A and product A based on the abnormality determination result data shown in FIG. 30(a), the display control unit 24 highlights the legend and line (graph) of the first graph in red as shown in FIG. 30(d) based on the monthly inventory amount data shown in FIG. 30(b). This makes it easier for the user to recognize abnormal values. Furthermore, the display control unit 24 highlights in red the legend for product A in the third graph and the points corresponding to the abnormal inventory amount increase rate and abnormal inventory amount based on the end-of-month inventory amount and the inventory amount increase rate calculation result data shown in FIG. 30(c).

In response to this, the display control unit 24 applies the color of the legend's abnormality flag "False" to legends other than those for warehouses and products for which an abnormality has been detected. For the second graph, the legend and line (graph) for the sales amount are displayed in blue, corresponding to the reference information flag "True," which is displayed as reference information for determining whether the trend in inventory amount is normal. Furthermore, for the fourth graph, the display control unit 24 applies the color of the abnormality flag "True" to the shipping amount. In other words, in this case, because there is a possibility that data corresponding to a concealment operation to reduce inventory that has been fraudulently increased is included, the display control unit 24 displays it in highlighted red, similar to the abnormality detection result data.

By viewing detailed information such as the detected abnormality (inventory), the detection method, and the criteria used to determine the abnormality, messages displayed in the alert display area of the message details screen allow users to identify warehouses and products that may be abnormal. This allows users to begin analyzing inventory manipulations after first identifying warehouses and products that may be abnormal.

In addition, based on the first graph, it is possible to check the inventory value development status of the warehouse where an abnormality was detected. It is also possible to check the abnormally high inventory quantity in the detected warehouse compared to other warehouses. Furthermore, it is possible to check the inventory increase trend based on the change in inventory value over time. In the example of the first graph in Figure 30 (d), it can be seen that the inventory value is gradually increasing towards the end of the period only in the detected warehouse.

In addition, based on the second graph, it is possible to check the trends in sales and inventory amounts related to the warehouse where an abnormality was detected. It is also possible to check whether changes in inventory are related to sales. It is assumed that the basic inventory management is to replenish inventory for sales. In the example of the second graph in Figure 30 (d), it can be seen that while sales have remained constant, only inventory has increased.

Furthermore, based on the third graph, it is possible to check the inventory value and inventory value increase rate for each product managed in the warehouse where an abnormality was detected, and to see which products have abnormal data. Furthermore, the correlation between inventory value and inventory value increase rate makes it possible to check the monetary impact. In other words, if the amount is large and the increase rate is also high, it can be assumed that there has been a large increase in inventory at the end of the period. In the example of the third graph in Figure 30 (d), it can be seen that only Product A, which has a high inventory value, has a high inventory value increase rate. If only specific products are involved, it is possible that fraud is taking advantage of the characteristics of the products. For example, miscellaneous products and other items managed miscellaneous could be used as indicators to consider reconsidering product management.

The fourth graph also allows you to check the inventory value of the warehouse where an anomaly was detected and the amount of shipments that occurred within the month. Normally, there is a fixed amount of inventory shipped and replenished. Therefore, a continuous increase in inventory value and a sudden increase in shipment value at the beginning of the period may indicate that fraudulent manipulation or fraudulent concealment has occurred. While there is a possibility of continued unsold goods and disposal, if there are unsold goods, it is unlikely that additional stock will be ordered. In the example of the fourth graph in Figure 30(d), we can see that the inventory value continues to increase towards the end of the period, and a large amount of shipments occurs at the beginning of the following period.

(Switching display of analysis graphs)
Next, the inventory management device 1 of the embodiment is capable of switching the display of the analysis graph by selecting the desired extracted item from the "aggregation" extracted items described using Figure 22 (a).

(Display behavior when selecting the product type extraction item)
31(a) to 31(c) show an example in which the extraction item "product type" is selected as an extraction condition to analyze the inventory status of warehouse A from which abnormality determination result data was obtained. In this case, the calculation unit 21 refers to the inventory amounts at the beginning and end of the period in the inventory data shown in FIG. 32(b) and calculates the inventory amount increase rates at the beginning and end of the period for product type A managed in warehouse A, as shown in FIG. 32(a), based on the above-mentioned calculation formula.

In the example of Figure 32(b), the inventory value of product type A managed in warehouse A at the beginning of the period is "5,280 yen" and the inventory value at the end of the period is "8,448 yen", so the inventory value increase rate from the beginning to the end of the period is calculated as "60%". The data generation unit 25 generates end-of-month inventory value and inventory value increase rate calculation result data between the beginning and end of the period, including this inventory value increase rate, as shown in Figure 32(a). The memory control unit 26 stores this end-of-month inventory value and inventory value increase rate calculation result data between the beginning and end of the period in the memory unit 2.

Next, based on the data binding information shown in Figure 33(a), the display control unit 24 displays the third graph shown in Figure 33(b), which shows the correlation between the inventory value growth rate and inventory value for each product type in Warehouse A from April 2020 to March 2021, in the display area for the third graph on the message details display screen as shown in Figure 34.

In addition, as explained using Figures 16 and 17, 18 and 19, 22 and 23, the display control unit 24 displays the first, second and fourth graphs in the corresponding display areas on the message details display screen as shown in Figure 34 based on various data acquired by referencing parameters.

Next, the display control unit 24 performs a display process that changes the colors of the legends and lines (graphs) of the first to fourth graphs to highlight them. That is, when displaying the first and third graphs, the display control unit 24 applies the color of the abnormality flag in the legend, explained using FIG. 29, to the warehouse and product type in the legend that are the same as the warehouse and product type in which an abnormality was detected. If an abnormality is detected in the inventory of warehouse A and product A based on the abnormality determination result data shown in FIG. 35(a), the display control unit 24 highlights the legend and lines (graphs) of the first graph in red, as shown in FIG. 35(e), based on the monthly inventory amount data shown in FIG. 30(b). This makes it easier for the user to recognize abnormal values.

In addition, the display control unit 24 displays the legend for product type A in the third graph, and points corresponding to abnormal inventory value growth rates and abnormal inventory values, highlighted in red, based on the product master shown in Figure 35(c) and the end-of-month inventory value and the inventory value increase rate calculation result data shown in Figure 35(e).

In response to this, the display control unit 24 applies the color of the legend's abnormality flag "False" to legends other than those for warehouses and product types for which an abnormality was detected. For the second graph, the legend and line (graph) for the sales amount are displayed in blue, corresponding to the reference information flag "True," which is displayed as reference information for determining whether the trend in inventory amount is normal. Furthermore, for the fourth graph, the display control unit 24 applies the color of the abnormality flag "True" to the shipping amount. In other words, in this case, because there is a possibility that data corresponding to a concealment operation to reduce inventory that has been fraudulently increased is included, the display control unit 24 displays it in highlighted red, similar to the abnormality detection result data.

The following can be seen primarily from the third graph on this message details display screen. As shown in Figure 36, the fact that the inventory value increase rate for a specific product type is high in the third graph means that there is a possibility that fraud is being committed on a product type basis. In other words, there is a possibility that fraud is being committed by exploiting the characteristics of a specific product type (such as a product type that is not managed in detail).

Looking at the third graph in Figure 36, varieties B, C, D, and E, other than variety A, have similar inventory value growth rates and inventory values. For this reason, it is unlikely that fraudulent inventory manipulation is taking place, and these can be excluded from investigation.

In contrast, in the case of product type A, both the inventory value increase rate and inventory value are high (64%, 100,000 yen), indicating that product type A has a large impact on the overall inventory value at the end of the period. Therefore, it is possible to investigate product type A in detail to determine whether or not there has been any fraud.

(Display behavior when selecting an order extraction item)
37(a) to 37(d) show an example in which the extraction item "orders" is selected as an extraction condition to analyze the inventory status of warehouse A from which abnormality determination result data has been obtained.

In this case, the data generation unit 25 extracts inventory amounts for each month and warehouse from the inventory data shown in Figure 38(b), and extracts order amounts for each month and warehouse from the order entry data shown in Figure 38(b).The data generation unit 25 then generates the monthly inventory and order amount data shown in Figure 38(a) based on the extracted inventory amounts and order amounts.The memory control unit 26 stores this monthly inventory and order amount data in the memory unit 2.

Next, based on the data binding information shown in FIG. 39(a), the display control unit 24 displays the second graph shown in FIG. 39(b), which shows the trends in order amount and inventory amount by month for Warehouse A, in the second graph display area of the message details display screen as shown in FIG. 34.

In addition to this second graph, the first, third, and fourth graphs are displayed in the corresponding display areas on the message details display screen as shown in FIG. 34 based on various data acquired by the display control unit 24 with reference to parameters, as explained using FIGS. 16 and 17, 20 and 21, 22 and 23.

Next, the display control unit 24 performs a display process to highlight the legends and lines (graphs) of the first to fourth graphs by changing the colors, as explained using FIG. 29. In this example, when displaying the second graph, the display control unit 24 displays the graph of the change in order amount using "blue," which is the color when the reference information flag shown in FIG. 29 is "True," as reference information for determining whether the change in inventory amount is normal.

In addition, for the fourth graph, the display control unit 24 applies the color of the abnormality flag "True" to the shipping amount. In other words, in this case, since there is a possibility that the data may correspond to a concealment operation to reduce fraudulently increased inventory, the display control unit 24 highlights it in red, just like the abnormality detection result data.

The second graph on this message detail screen, in particular, shows that the lack of confirmation of an increase in orders that would be the cause of an increase in inventory means that the increase in inventory is not due to sales being planned. This simple increase in inventory that does not lead to orders or sales requires immediate investigation into the purpose of the increase in inventory.

First, 40(a) is an example of a second graph when "Sales" at Warehouse A is used as the extraction condition (second graph when sales are being tallied). In this Figure 40(a), the upper graph shows the trend in sales amount, and the lower graph shows the trend in inventory amount. When the second graph is displayed using "Sales" as the extraction condition, as shown in Figure 40(a), it can be seen that the inventory amount is gradually increasing, but the sales amount remains roughly constant. Because this is a simple increase in inventory that is not linked to orders or sales, the user switches the extraction condition to "Orders" and displays the second graph (second graph when orders are being tallied) to confirm the purpose of the increase in inventory.

Figures 40(b) and 40(c) are examples of the second graph when summarizing orders. In Figures 40(b) and 40(c), the upper graph shows the trend in order amount, displayed in blue as described above, and the lower graph shows the trend in inventory amount. First, the example in Figure 40(b) shows an example in which the order amount is increasing along with the inventory amount. In such an example, it is thought that inventory was replenished due to expected sales. However, there is a possibility that sales will increase later, or that the order will not lead to sales and will become a backlog.

In contrast, the example in Figure 40(c) shows that the inventory amount is gradually increasing, but the order amount is roughly constant. In this case, it is clear that there are no sales planned. Furthermore, since this indicates an increase in inventory for an unknown purpose, an immediate investigation is required.

(Display behavior when a desired department is specified in the extraction item)
41(a) to 41(c) show an example in which the extraction item "Department A" is specified as an extraction condition in order to analyze the inventory status of Department A.

In this case, the data generation unit 25 extracts the inventory amount for department A for each month and warehouse from the inventory data shown in Figure 42 (b) and generates the monthly inventory amount data shown in Figure 42 (a). The memory control unit 26 stores this monthly inventory amount data in the memory unit 2.

In addition, as shown in Figures 43(a) to 43(c) and Figures 44(a) and 44(b), the data generation unit 25 extracts the inventory amount for department A for each month and warehouse from the inventory data, and extracts the sales amount for department A for each month and warehouse from the sales posting data (see Figure 44(b)), and generates monthly inventory and sales amount data (Figure 44(a)). The memory control unit 26 stores this monthly inventory and sales amount data in the memory unit 2.

Next, the calculation unit 21 calculates the inventory value increase rate as shown in Figures 46(a) and 46(b) for each product in Warehouse A where the abnormal value was detected and Department A for which extraction was specified, as shown in Figures 45(a) and 45(b). The data generation unit 25 generates calculation result data for the end-of-month inventory value and the inventory value increase rate between the beginning and end of the period, including the calculated inventory value increase rate, as shown in Figures 45(c) and 46(a). The memory control unit 26 stores this end-of-month inventory value and the inventory value increase rate between the beginning and end of the period calculation result data in the memory unit 2.

Next, as shown in Figures 47(a) and 47(b), the data generation unit 25 extracts the inventory amount and shipping amount for Warehouse A, where the abnormal value was detected, and Department A, where extraction was specified, from the inventory data shown in Figure 48(b), and also from the product shipping data shown in Figure 48(c). The data generation unit 25 then generates monthly inventory amount and shipping amount summary data for Warehouse A and Department A, as shown in Figures 47(c) and 48(a). As mentioned above, the data generation unit 25 references data for one month extra beyond the parameter end period value. The memory control unit 26 stores this monthly inventory amount and shipping amount summary data in the memory unit 2.

Next, the display control unit 24 displays a total of four types of graphs, the first to fourth graphs, on the message details display screen based on the various data described above. Specifically, based on the data binding information shown in FIG. 49(a), the display control unit 24 displays the first graph shown in FIG. 49(b) on the message details display screen. The first graph shows the changes in inventory value for each warehouse, with the X axis representing the fiscal year and month and the Y axis representing the inventory value in a two-dimensional coordinate system. The example in FIG. 49(b) is an example that lists graphs showing the changes in inventory value for warehouses A and B. In FIG. 49(b), the upper linear graph is the graph for warehouse A, and the lower broken line graph is the graph for warehouse B.

Next, based on the data binding information shown in Figure 50(a), the display control unit 24 displays on the message details display screen a second graph shown in Figure 50(b), which shows the trends in sales and inventory values for Warehouse A, with the X axis representing the fiscal year and month and the Y axis representing inventory values and sales values in a two-dimensional coordinate system. The example in Figure 50(b) is an example of a list of graphs showing the trends in sales and inventory values by month for Warehouse A, where an abnormality was detected. In Figure 50(b), the upper line graph is a graph of sales values, and the lower straight line graph is a graph of inventory values.

Next, based on the data binding information shown in FIG. 51(a), the display control unit 24 displays on the message details display screen a third graph shown in FIG. 51(b), which shows the correlation between the inventory value growth rate and inventory value at Warehouse A from April 2020 (beginning of the period: start of the period) to March 2021 (end of the period: end of the period) in two-dimensional coordinates, with the inventory value growth rate (%) on the X axis and the ending inventory value on the Y axis. The points plotted on the third graph in FIG. 51(b) indicate the inventory value growth rate (%) at Warehouse A where an abnormality was detected and the inventory value at that time. In FIG. 51(b), Product A, which has a higher inventory value growth rate and inventory value than the inventory value growth rates and inventory values of Products B to K, can be identified as a target for fraud investigation.

Next, based on the data binding information shown in Figure 52(a), the display control unit 24 displays a fourth graph on the message detail display screen, with the X-axis of the two-dimensional coordinate system representing the fiscal year and month, and the Y-axis representing the inventory amount or shipping amount of warehouse A, as shown in Figure 52(b).The graph shows a dual-axis bar graph, with a bar graph of inventory amount and a bar graph of shipping amount superimposed, along with the fiscal year and month.In the dual-axis bar graph shown in Figure 52(b), the lower bar graph shows the inventory amount, and the bar graph layered on top of this inventory amount bar graph shows the shipping amount.

As explained using Figure 29, the display control unit 24 performs display processing to highlight the legends and lines (graphs) of the first to fourth graphs by changing their colors. An example of the message details display screen on which the first to fourth graphs are displayed is shown in Figure 34.

Next, of the graphs displayed in this way, mainly the first graph can be used to check whether profit manipulation is occurring on an organizational level, based on the increasing inventory trends in warehouses managed by a specific department. Profit manipulation is often carried out on an organizational level. In this embodiment, you can specify suspicious organizations and check the data.

Specifically, the first graph example shown in Figure 53(a) shows a gradual increase in the inventory value of Warehouse A, represented by the upper linear graph, while the inventory value of Warehouse B, represented by the lower broken line graph, remains roughly constant. In this example, an unnatural increase in inventory can be confirmed in one warehouse (Warehouse A). This indicates that fraud may be occurring at the individual in charge or at an organizational unit (subsidiary, office, etc.).

The first graph example shown in Figure 53(b) shows a gradual increase in the inventory value of Warehouse C, represented by the upper linear graph, and a gradual increase in the inventory value of Warehouse D, represented by the lower broken line graph, with the inventory value also rising and leveling off. In this example, an unnatural increase in inventory can be confirmed in all warehouses managed by a specific department, raising concerns about profit manipulation or the occurrence of slow-moving inventory in the department.

Furthermore, when comparing the order and sales data shown in the second graph with the increase in inventory value not associated with orders and sales, if this increase in inventory value not associated with orders and sales can be confirmed in the first graph, it is unlikely that inventory that is not expected to sell would be continuously ordered, and therefore inventory manipulation can be suspected.

Furthermore, if the first graph shows an increase in inventory value due to orders compared to the order and sales data shown in the second graph, it is possible that the product is not selling well and the inventory is only increasing, resulting in stagnant inventory. For this reason, it can be determined that an investigation is necessary.

(Effects of the embodiment)
As is clear from the above description, the inventory management device 1 according to the embodiment can mainly achieve the following effects.

1. The inventory value growth rate from the beginning to the end of the period is calculated for each warehouse and product, allowing you to automatically detect warehouse and product combinations with abnormally high inventory. By automatically aggregating inventory values from the beginning to the end of the period and calculating the inventory value growth rate, you can reduce aggregation costs and prevent aggregation and calculation errors.

In addition, by regularly checking data for the same warehouse and product and detecting warehouse and product combinations with abnormally high inventory values, you can prevent oversights and mistakes. You can also quickly identify warehouse and product combinations with abnormal inventory values, allowing you to take measures and investigate.

2. By aggregating the monthly inventory amounts of warehouses where abnormalities have been detected, it is possible to visualize the trend of inventory increases from the beginning to the end of the period. Possible examples of fraudulent inventory manipulation include the following:

・Inventory increases significantly at the end of the period → Monthly inventory increase: Large (end of period only)
・Inventory gradually increases → Inventory increase per month: Small → Possibility of manipulation to make it difficult to detect each month (concealment manipulation)

However, with the inventory management device 1 of this embodiment, monthly inventory amounts can be tallied and the changes in inventory amounts over time can be visualized. This makes it possible to check how inventory amounts change over time and identify any signs of fraudulent activity. It also makes it possible to compare inventory movements between warehouses and check the severity of abnormalities in warehouses where an abnormality has been detected. This also eliminates the cost of each aggregation and prevents omissions and mistakes in comparison checks.

3. By aggregating the "inventory value of the warehouse where an abnormality was detected" and the "sales value of the sales data shipped from the warehouse," you can compare the sales value with the inventory value and visualize whether the inventory change associated with sales is normal.

Inventory is typically replenished for products that are expected to sell. Therefore, an increase in inventory without sales indicates that inventory has been fraudulently increased. For this reason, the inventory management device 1 of this embodiment aggregates and visualizes sales and inventory amounts by month for each warehouse. This allows you to see how sales and inventory amounts are changing with each passing month.

In other words, it is possible to confirm that when fraud occurs, sales remain unchanged and only inventory value increases. This eliminates the cost of aggregating sales and inventory on two axes, making it easier to compare sales and inventory.

4. By aggregating the "inventory value for each product managed in the warehouse where an abnormality was detected" and the "inventory value increase rate from the beginning to the end of the period," it is possible to visualize products with a large increase in inventory. The characteristics of the products for which inventory is being increased are important when it comes to inventory increases. The following are considered to be characteristics of products that are prone to fraudulent inventory manipulation.

・Products with miscellaneous inventory management ・Products that are managed as "multiple products as one product" because the trading volume is not expected to increase much

As a result, the inventory management device 1 of this embodiment can visualize products whose inventory has increased significantly from the beginning to the end of a period, and compare the inventory value with the inventory value increase rate.

If the product value is small and the inventory value growth rate is large, the inventory value at the end of the period will not change much. If the product value is large and the inventory value growth rate is large, the inventory value at the end of the period will increase significantly.

If the inventory value at the end of the period increases significantly, it can appear as if the cost of sales on the income statement has decreased significantly. Therefore, if there is a large inventory value and a large inventory value increase rate, this can be detected as fraud.

5. The "inventory value of warehouses where abnormalities were detected" and "the amount of inventory reduced during shipping processing" can be aggregated and visualized.

If inventory is fraudulently increased at the end of a fiscal year, it is likely that inventory will be reduced after the financial statements are settled in order to conceal the increase. For this reason, the inventory management device 1 of this embodiment visualizes the monthly inventory amount and the reduced inventory amount. This makes it easier to detect the "amount at the time of fraudulent processing" and the "amount that has been concealed."

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

Furthermore, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to SDGs Goals 12, 13, and 15.

Furthermore, this embodiment can contribute to strengthening control and governance, thereby contributing to Goal 16 of the SDGs.

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

For example, of 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.

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.

Furthermore, with regard to the inventory management device 1, the components shown are functional concepts and do not necessarily have to be physically configured as shown.

For example, all or any part of the processing functions of the inventory management device 1, particularly the control unit 3 and the various processing functions performed by the control unit 3, may be implemented by a CPU (Central Processing Unit) and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory, computer-readable recording medium containing programmed instructions for causing the information processing device to execute the processes described in this embodiment, and is mechanically read by the inventory management device 1 as needed. That is, a computer program is recorded in a storage unit such as a ROM or HDD for working with the OS to issue commands to the CPU and perform various processes. This computer program is executed by being loaded into RAM, and works with the CPU to form the control unit 3.

In addition, the inventory management program of this inventory management device 1 may be stored on another server device connected to the inventory management device 1 via any network, and all or part of it may be downloaded as needed.

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

A "program" is a data processing method written in any language or description method, and may be in any format, such as source code or binary code. It should be noted that a "program" is not necessarily limited to a single structure, but also includes a distributed structure consisting of multiple modules or libraries, or a structure that achieves its function by working in conjunction with a separate program, such as an OS. The specific structure and reading procedure for reading a recording medium in the inventory management device 1 shown in the embodiment, as well as the installation procedure after reading, can use well-known structures and procedures.

The memory unit 2 is a storage means such as a memory device such as RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, or an optical disk, and stores various programs, tables, databases, and web page files used for various processes and website provision.

The inventory management device 1 may also be configured as an information processing device such as a known personal computer or workstation, or may be configured as an information processing device to which any peripheral device is connected. The information processing device may also be implemented by installing software (including programs, data, etc.) that realizes the processing described in this embodiment.

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

This invention is suitable for use in inventory management in all types of industries.

REFERENCE SIGNS LIST 1 inventory management device 2 memory unit 3 control unit 4 communication interface unit 5 input/output interface unit 6 input device 7 output device 21 calculation unit 22 determination unit 23 alert output control unit 24 display control unit 25 data generation unit 26 storage control unit

Claims (8)

a calculation unit that calculates transition information corresponding to a transition of product inventory during a calculation period based on inventory data including the number of products in stock;
a determination unit that determines whether the calculated value of the transition information is equal to or greater than a predetermined value ;
an alert output control unit that controls output of a predetermined alert when a determination result indicating that the calculated value of the transition information is a predetermined value or more is obtained from the determination unit ,
the calculation unit calculates, as the transition information, an inventory value increase rate, which is the ratio of the difference between the inventory value at the beginning of the calculation period and the inventory value at the end of the calculation period to the inventory value corresponding to the inventory quantity of the product at the beginning of the calculation period, and calculates an interquartile range based on each of the inventory value increase rates during the calculation period, and calculates an upper limit of the inventory value increase rate by adding an inventory value increase rate that is a third quartile to the inventory value increase rate that is a predetermined multiplication of the calculated interquartile range;
the alert output control unit controls the output of the alert when the calculated inventory amount increase rate is equal to or greater than the upper limit value;
An inventory management device comprising : Further comprising a display control unit that displays a graph showing the change in the inventory amount during the calculation period on a display unit;
2. The inventory management device according to claim 1, wherein : the display control unit displays, on the display unit, a graph showing a normal change in the inventory price, as well as a graph showing a change in the inventory price during the calculation period;
3. The inventory management device according to claim 2, wherein : the display control unit displays, on the display unit, a graph showing a change in inventory value during the calculation period and a graph showing a change in sales value of the inventory items during the calculation period;
4. The inventory management device according to claim 3, wherein : the display control unit displays on the display unit a correlation table of the inventory amount and the inventory amount increase rate, in which the inventory amount during the calculation period and the inventory amount increase rate during the calculation period are plotted on a two-dimensional coordinate system with the inventory amount on one axis and the inventory amount increase rate on the other axis;
5. The inventory management device according to claim 4, wherein : the display control unit displays a bar graph of the inventory amount for the calculation period and a bar graph of the shipping amount of the inventory items for the calculation period on the display unit;
6. The inventory management device according to claim 5 , a calculation step in which a calculation unit calculates transition information corresponding to a transition of inventory of the product during a calculation period based on inventory data including an inventory quantity of the product;
a determining step in which a determining unit determines whether or not the calculated value of the transition information is equal to or greater than a predetermined value ;
an alert output control step in which an alert output control unit controls output of a predetermined alert when a determination result indicating that the calculated value of the transition information is a predetermined value or more is obtained from the determination unit ,
In the calculation step, an inventory value increase rate is calculated as the transition information, which is the ratio of the difference between the inventory value at the beginning of the calculation period and the inventory value at the end of the calculation period to the inventory value corresponding to the inventory quantity of the product at the beginning of the calculation period, and an interquartile range is calculated based on each of the inventory value increase rates in the calculation period, and an upper limit of the inventory value increase rate is calculated by adding an inventory value increase rate obtained by multiplying the calculated interquartile range by a predetermined factor to the inventory value increase rate that is the third quartile,
In the alert output control step, when the calculated inventory value increase rate is equal to or greater than the upper limit value, output control of the alert output is performed.
An inventory management method characterized by : Computer,
a calculation unit that calculates transition information corresponding to a transition of product inventory during a calculation period based on inventory data including the number of products in stock;
a determination unit that determines whether the calculated value of the transition information is equal to or greater than a predetermined value ;
When the determination unit obtains a determination result indicating that the calculated value of the transition information is equal to or greater than a predetermined value , the device functions as an alert output control unit that controls output of a predetermined alert;
the calculation unit calculates, as the transition information, an inventory value increase rate, which is the ratio of the difference between the inventory value at the beginning of the calculation period and the inventory value at the end of the calculation period to the inventory value corresponding to the inventory quantity of the product at the beginning of the calculation period, and calculates an interquartile range based on each of the inventory value increase rates during the calculation period, and calculates an upper limit of the inventory value increase rate by adding an inventory value increase rate that is a third quartile to the inventory value increase rate that is a predetermined multiplication of the calculated interquartile range;
the alert output control unit controls the output of the alert when the calculated inventory amount increase rate is equal to or greater than the upper limit value;
An inventory management program that features: