Inventory management device, inventory management method, and inventory management program

Abstract

To enable early detection and handling of unauthorized inventory operation.SOLUTION: A calculation unit calculates transition information according to a transition of an inventory of products in a calculation period based on inventory data including the number of the products in stock. A discrimination unit discriminates whether a calculated value of the transition information is a value indicating a transition of the inventory above a predetermined value. An alert output control unit performs output control of a predetermined alert in a case of obtaining a discrimination result in which the calculated value of the transition information indicates the value of the transition of the inventory transition equal to or greater than the predetermined value from the discrimination unit. This allows for early detection and handling of unauthorized inventory operation.SELECTED DRAWING: Figure 28

Description

[Technical Field]

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

[0002] In recent years, fraud has been committed by 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. As prior art related to inventory management, Patent Document 1 (JP Patent Publication No. 2002-279024) discloses a product quantity setting system.

[0003] In this product quantity setting system, the department server tally up the product sales records received from the information research company and the product resale records received from the sales information database server for each predetermined period, and calculates the store inventory amount indicating the product inventory in the store based on the sales records and resale records for each predetermined period.The department server then displays the sales records, resale records, and store inventory amount on a display device, associating them for each predetermined period.

[0004] This allows trends in the actual sales figures, resale figures, calculated store inventory, and number of weeks of store inventory for each product, which have been converted into electronic data, to be converted into fluctuation patterns, making it possible to estimate the shipping volume for a specified period and set the continuous production volume. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-279024 Summary of the Invention [Problem to be solved by the invention]

[0006] However, in recent years, fraudulent inventory manipulation has become more sophisticated, making it difficult to detect and deal with it early.

[0007] The present invention has been made in consideration of the above-mentioned problems, and aims to provide an inventory management device, an inventory management method, and an inventory management program that enable early detection and handling of fraudulent inventory manipulation. [Means for solving the problem]

[0008] 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 a transition of inventory of a product during a calculation period based on inventory data including an inventory quantity of the product, and a value of the calculated transition information is: A value greater than or equal to a given value a determination unit that determines whether the calculated transition information value is A value greater than or equal to a given value an alert output control unit that controls a predetermined alert output when a determination result indicating The calculation unit calculates, as 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 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 that is the third quartile to the calculated interquartile range multiplied by a predetermined number, and the alert output control unit controls the output of an alert when the calculated inventory value increase rate is equal to or greater than the upper limit. .

[0009] In order to solve the above-mentioned problems and achieve the object, the inventory management method according to the present invention includes a calculation step in which a calculation unit calculates transition information corresponding to a transition of inventory of a product during a calculation period based on inventory data including an inventory quantity of the product; and a determination step in which a value of the calculated transition information is: A value greater than or equal to a given value a determining step of determining whether the calculated value of the transition information is A value greater than or equal to a given value an alert output control step of outputting and controlling a predetermined alert output when a determination result indicating In the calculation step, 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, is calculated as the transition information, 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 that is the third quartile to the inventory value increase rate that is a predetermined multiple of the calculated interquartile range, and in the alert output control step, output control of an alert is performed when the calculated inventory value increase rate is equal to or greater than the upper limit.

[0010] In order to solve the above-mentioned problems and achieve the object, an inventory management program according to the present invention includes a computer, a calculation unit that calculates transition information corresponding to a transition of inventory of a product during a calculation period based on inventory data including an inventory quantity of the product, and a calculation unit that calculates a value of the calculated transition information as follows: A value greater than or equal to a given value a determination unit that determines whether the calculated transition information value is A value greater than or equal to a given value When a determination result indicating the above is obtained from the determination unit, an alert output control unit controls the output of a predetermined alert. The calculation unit calculates, as 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 inventory value increase rate during the calculation period and calculates an upper limit for the inventory value increase rate by adding the inventory value increase rate that is the third quartile multiplied by a predetermined number of times the calculated interquartile range, and the alert output control unit controls the output of an alert if the calculated inventory value increase rate is equal to or greater than the upper limit. [Effects of the Invention]

[0011] The present invention allows for early detection and response to fraudulent inventory manipulation. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing a hardware configuration of an inventory management device according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the automatic detection execution schedule data. [Figure 3] FIG. 3 is a diagram illustrating an example of inventory data acquisition range condition data. [Figure 4] FIG. 4 is a diagram illustrating an example of parameters including a detection ID and a schedule ID. [Figure 5] FIG. 5 is a diagram for explaining the execution timing of inventory management. [Figure 6] FIG. 6 is a diagram for explaining an extraction period for data extracted during inventory management. [Figure 7] FIG. 7 is a diagram for explaining the operation of calculating the inventory amount amplification rate. [Figure 8] FIG. 8 is a diagram for explaining a method for calculating the upper limit of the inventory amount amplification rate for determining an abnormality. [Figure 9] FIG. 9 is a diagram illustrating an example of abnormality determination of the inventory amount amplification rate using the upper limit value. [Figure 10] 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. [Figure 11] 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. [Figure 12] FIG. 12 is a diagram showing an input field for extraction conditions displayed on the abnormality list screen. [Figure 13] FIG. 13 is a diagram for explaining the alert display area on the message details display screen. [Figure 14]FIG. 14 is a diagram for explaining how to obtain extraction conditions. [Figure 15] FIG. 15 is a diagram showing an example of extraction items that serve as extraction conditions. [Figure 16] FIG. 16 is a diagram for explaining how to acquire an extraction period that is an extraction condition. [Figure 17] FIG. 17 is a diagram showing data acquired when displaying the first graph. [Figure 18] FIG. 18 is a diagram showing an example in which sales of warehouse A are set as the extraction condition. [Figure 19] FIG. 19 is a diagram showing data acquired when the second graph is displayed. [Figure 20] FIG. 20 is a diagram showing an example in which products in warehouse A are set as extraction conditions. [Figure 21] FIG. 21 is a diagram showing data acquired when displaying the third graph. [Figure 22] FIG. 22 is a diagram showing an example of initially set extraction conditions. [Figure 23] FIG. 23 is a diagram showing data acquired when the fourth graph is displayed. [Figure 24] FIG. 24 is a diagram showing a display example of the first graph. [Figure 25] FIG. 25 is a diagram showing an example of the display of the second graph. [Figure 26] FIG. 26 is a diagram showing a display example of the third graph. [Figure 27] FIG. 27 is a diagram showing a display example of the fourth graph. [Figure 28] FIG. 28 is a diagram showing the message details display screen in which the first to fourth graphs are displayed. [Figure 29] FIG. 29 is a diagram showing graph coloring parameters for displaying a predetermined portion of each graph in color. [Figure 30] FIG. 30 is a diagram for explaining an example of coloring and displaying a graph based on graph coloring parameters. [Figure 31]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. [Figure 32] FIG. 32 is a diagram for explaining the operation of calculating the stock value increase rate at the beginning and end of a period for product type A managed in warehouse A. [Figure 33] FIG. 33 is a diagram showing an example of the third graph. [Figure 34] FIG. 34 is a diagram showing an example of a message details display screen. [Figure 35] FIG. 35 is a diagram showing an example of color display for the third graph. [Figure 36] FIG. 36 is a diagram for explaining the analysis of inventory manipulation based on the third graph. [Figure 37] 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. [Figure 38] FIG. 38 is a diagram for explaining the operation of generating monthly inventory and order amount data. [Figure 39] FIG. 39 is a diagram showing an example of the second graph. [Figure 40] FIG. 40 is a diagram for explaining the analysis of inventory manipulation based on the second graph. [Figure 41] 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. [Figure 42] FIG. 42 is a diagram for explaining the operation of generating monthly inventory amount data. [Figure 43] FIG. 43 is a diagram for explaining the operation of generating monthly inventory and sales amount data. [Figure 44] FIG. 44 is another diagram for explaining the operation of generating monthly inventory and sales amount data. [Figure 45] FIG. 45 is a diagram for explaining the operation of generating calculation result data of the end-of-month stock amount and the stock amount increase rate between the beginning and end of the period. [Figure 46]FIG. 46 is another diagram for explaining the operation of generating the calculation result data of the end-of-month stock amount and the stock amount increase rate between the beginning and end of the period. [Figure 47] FIG. 47 is a diagram for explaining the operation of generating monthly inventory amount and shipping amount total data. [Figure 48] FIG. 48 is a diagram for explaining the operation of generating monthly inventory amount and shipping amount total data. [Figure 49] FIG. 49 is a diagram showing an example of the first graph. [Figure 50] FIG. 50 is a diagram showing an example of the second graph. [Figure 51] FIG. 51 is a diagram showing an example of the third graph. [Figure 52] FIG. 52 is a diagram showing an example of the fourth graph. [Figure 53] FIG. 53 is a diagram for explaining the analysis of inventory manipulation based on the first graph. DETAILED DESCRIPTION OF THE INVENTION

[0013] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An inventory management device according to an embodiment of the present invention will now be described in detail with reference to the accompanying drawings.

[0014] (overview) In recent years, there has been an increase in corporate fraud, embezzlement by employees and executives, and other fraudulent activities. 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.

[0015] There are many scenario patterns, such as those shown below, and it is necessary to build a system that corresponds to each one.

[0016] ·Fraud regarding sales results: Falsifying sales figures → Inflating results · Fraud regarding purchasing records: fictitious payments made in collusion with business partners → collusion Inventory fraud: Inflating end-of-period inventory amounts → Profit manipulation

[0017] The inventory management device 1 of the 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 income statements. The characteristics of fraud include the following:

[0018] 1. Inventory increases unnaturally between the beginning and end of the period 2. Inventory increases without accompanying sales. 3. Manipulating inventory values ​​by using products that are not meticulously managed and are difficult to detect fraudulently

[0019] If 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, in order to hide the fraudulent increase in inventory, operations may be carried out to gradually increase inventory throughout the year.

[0020] 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. Then, the inventory management device 1 checks the warehouse that manages the inventory, the inventory status of the products, and the timing of inventory changes to determine whether the data is abnormal.

[0021] Inventory management requires the aggregation and comparison of a combination of multiple warehouses and multiple types of products, which takes time for the aggregation process.

[0022] Check target → Inventory data and in / out 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 increase trend every month. "Warehouse x Product" → In order to detect misused products, it is necessary to check the increasing trend of inventory in the warehouse and products. "Sales amount - Inventory amount" → It is necessary to confirm that this is inventory that is not related to sales. How to check → Calculate the monthly inventory amount and sales amount for each "required calculation unit"

[0023] Since multiple calculations are required from a large amount of data, the work takes time. In addition, since comparisons must be made monthly within the same calculation unit, oversights and mistakes are likely to occur.

[0024] Furthermore, a comparison is required for each combination of "warehouse x product," which takes time and is prone to confirmation errors and omissions.

[0025] Check target → Comparison of inventory value from the beginning to the end of the period within the combination of "warehouse x product" How to check: Tally 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.

[0026] 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.

[0027] For this reason, the inventory management device 1 according to the embodiment performs the following operations to solve the above-mentioned problems.

[0028] 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 are automatically detected. By automatically aggregating inventory values ​​from the beginning to the end of the period and calculating the inventory value growth rate, aggregation costs can be reduced and aggregation and calculation errors can be prevented. In addition, data for the same warehouse and product is checked periodically to detect warehouse and product combinations with abnormally high inventory values. This prevents oversights and mistakes. Users can also easily identify warehouse and product combinations with abnormal inventory values ​​and take measures and investigate.

[0029] 2. Aggregate the monthly inventory value of the warehouse where the abnormality was detected and visualize the inventory increase trend from the beginning to the end of the period. The following are possible examples of fraudulent inventory manipulation.

[0030] 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))

[0031] For this reason, the inventory management device 1 of the embodiment aggregates the inventory value by month and visualizes the change in the inventory value over time. This allows the user to check how the inventory value changes over time, making it possible to identify the characteristics of fraudulent operations. It also allows the user 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.

[0032] 3. By aggregating the "inventory amount of the warehouse where an anomaly was detected" and the "sales amount of the sales data shipped from the warehouse," and visualizing the sales amount and inventory amount, 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 amount and inventory amount for each warehouse by month, it becomes possible to check how the sales amount and inventory amount change with each passing month. It is also possible to check for characteristics that indicate fraud, where sales remain unchanged but only inventory amount increases. It also eliminates the cost of aggregating both sales and inventory, making it easier to compare sales and inventory.

[0033] 4. 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" are tabulated to visualize products with a large increase in inventory. The characteristics of the products for which inventory is being increased are important in determining the increase in inventory. In other words, products with the following characteristics are more likely to be used fraudulently.

[0034] -Products with miscellaneous inventory management - Products that are managed as "multiple products as one product" because the trading volume is not expected to increase significantly

[0035] For this reason, the inventory management device 1 of the embodiment visualizes products whose inventory has increased significantly 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.

[0036] 5. The "inventory value of the warehouse where an anomaly was detected" and the "amount of inventory reduced by 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 settlement 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."

[0037] (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 like the Internet or a private network like a local area network (LAN).

[0038] The storage unit 2 may be, for example, a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), or a solid state drive (SSD). An inventory management program that enables the above-mentioned inventory management is stored in the storage unit 2. The storage unit 2 also stores data used for inventory management, such as 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 abnormality determination result data.

[0039] The storage unit 2 also 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 a period calculation result data, and monthly inventory value and shipping amount aggregate data. Details will be described later.

[0040] (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.

[0041] The calculation unit 21 calculates transition information corresponding to the transition of product inventory over a calculation period, such as from the beginning to the 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 a transition of inventory equal to or greater than a predetermined value. The alert output control unit 23 controls the output of a predetermined alert when the determination unit 22 gives a determination result indicating that the value of the calculated transition information is a transition of inventory equal to or greater than a predetermined value. The alert output may, for example, be the display of an alert message on the display unit, or the output of an alert voice message or electronic sound via a speaker unit (not shown).

[0042] The calculation unit 21 also 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. 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.

[0043] Furthermore, calculation unit 21 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. 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.

[0044] The display control unit 24 displays a graph showing the change in inventory price during 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 price during the calculation period on the output device 7, along with a graph showing the normal change in inventory price. The display control unit also displays a graph showing the change in inventory price during the calculation period and a graph showing the change in sales price of inventory items during the calculation period on the output device 7.

[0045] 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 the inventory value on one axis and the inventory value increase rate on the other axis. 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 the 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 products 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 by such actions is referred to as the "shipping amount."

[0046] The data generating unit 25 generates data used for inventory management. The storage control unit 26 stores the various data generated by the data generating unit 26 and the data calculated by the calculation unit 21 in the storage unit 2.

[0047] (Inventory management operations) Such an 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 for detection" on the analysis screen.

[0048] Furthermore, the inventory management device 1 displays a graph on the analysis screen that allows users to check the operations to reduce inventory that are carried out to conceal fraudulently increased inventory. Note that the "amount of reduced inventory" is assumed to be the "shipping amount." Possible processes for reducing inventory include, for example, shipment for sales, purchase and return, and inventory disposal. Hereinafter, all of these will be referred to as the "shipping amount."

[0049] (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," which are accumulated as business operations are performed.

[0050] "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 anomaly detection, and includes automatic detection execution schedule data and inventory data acquisition range condition data.

[0051] Fig. 2 is a diagram showing an example of automatic detection execution schedule data. As shown in Fig. 2, the automatic detection execution schedule data includes a detection identification number (detection ID), a schedule identification number (schedule ID), execution conditions, execution time, etc. The example in Fig. 2 shows that detection of inventory value increase rate is executed at 23:00 on the fifth business day in April every year.

[0052] Fig. 3 is a diagram showing an example of inventory data acquisition range condition data. As shown in Fig. 3, the inventory data acquisition range condition data includes a detection ID, a schedule ID, a target column, a FROM condition, and a TO condition. The example in Fig. 3 shows that inventory data is acquired from the month corresponding to one year before the month in which the inventory value increase rate is detected (anomaly detection) to one month before the month in which the anomaly detection is performed.

[0053] The "abnormality determination result data" is data indicating the determination result of the abnormality detection execution, and includes abnormality determination result message data and abnormality determination result message detail data in addition to the abnormality determination result data.

[0054] Such various data are stored in advance in a predetermined table in the storage unit 2. This allows the anomaly detection operation to be started.

[0055] Next, the calculation unit 21 refers to the automatic execution schedule data shown in Figure 2 based on the detection ID and schedule ID, which are pre-set parameters shown in Figure 4, and recognizes the timing for abnormality detection (23:00 on the fifth business day in April every year).

[0056] Next, as shown in Figure 5, the calculation unit 21 determines whether the startup timing of the inventory management device 1, for example, 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 Figure 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.

[0057] Furthermore, the determination of whether a day is a holiday or a business day is made by the calculation unit 21 by referring to a calendar master (not shown) stored in the storage unit 2. It is preferable that the calendar master is always updated based on the sales business calendar.

[0058] Next, if it is determined that the activation timing is the timing to perform anomaly detection, the calculation unit 21 refers to 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 exemplified in Fig. 6. The example in Fig. 6 is an example in which "April 2020 to March 2021" is acquired as the "accounting year and month."

[0059] Next, as shown in Figure 7(b), the calculation unit 21 uses the inventory amounts aggregated 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 equation.

[0060] Inventory value increase rate = ((ending inventory value - beginning inventory value) / beginning inventory value x 100)

[0061] The calculated stock amount increase rate is stored by the storage control unit 26 as part of the stock amount increase rate calculation result data, as shown in FIG. 7(a).

[0062] Next, 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 FIG. 7(a). Specifically, calculation unit 21 calculates an upper limit value such as "15%" using the normal range calculation method of "third quartile + (1.5 × interquartile range)" as shown in FIG. 8. FIG. 9 is a box plot that schematically shows this upper limit value. As shown in FIG. 9, when the inventory value increase rate is 64%, it exceeds the upper limit value and is therefore detected as an abnormal value.

[0063] 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.

[0064] However, the detection method using "standard deviation" is difficult to use for cross-sectional data analysis of "past trends" because it does not involve the concept of a time axis. Also, the detection method using "moving average and outliers" can detect abnormal increases or decreases in time series trends, but the user must set a threshold for determining an abnormality in advance.

[0065] In contrast, with detection methods using the "interquartile range," you can define the data analysis range and aggregate it on the vertical axis, and you can specify the range magnification at which data can be judged as 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.

[0066] Next, the discrimination unit 22 determines that data having an inventory value increase rate exceeding the upper limit of the interquartile range is abnormal, as shown in Fig. 9. Based on such a 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).

[0067] 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." Also, 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 the abnormal value was detected, such as "Warehouse A, Product A was detected."

[0068] Furthermore, as shown in FIG. 10(c), the detailed abnormality determination result message data is generated, including a detection ID, a job ID, a message ID, a detection method, a threshold, a determination method, and an upper limit value. The detection method is the above-mentioned "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 detailed abnormality determination result message data in the memory unit 2.

[0069] (The 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 degree of abnormality, 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).

[0070] The example in Figure 11(d) is an example in which data for displaying an abnormality is generated using an "X" mark indicating an abnormal value, a definition name indicating the possibility of inventory increase, "Inventory Increase Alert," 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%."

[0071] The display control unit 24 generates such abnormality display data for each warehouse and each product for which an abnormal value of the inventory value increase rate has been detected. The display control unit 24 displays the generated abnormality display data in a list on an abnormality list screen, an example of which is shown in FIG. 11(e).

[0072] The anomaly list screen has 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 was launched and automatically inputs it into the input field for the base date in the extraction condition input area.

[0073] Next, when the user performs an operation (e.g., double-clicking) to select the abnormality display data to be analyzed from the list of abnormality display data displayed, the display control unit 24 switches the message to display detailed information and secures an output area for the graph.

[0074] 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).

[0075] 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 range of analysis by changing the period start to end of the extraction conditions.

[0076] 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 detail display screen. Also, 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.

[0077] 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). The examples in Figures 15(a) and 15(b) show that of the extraction items for "aggregation," namely, product type classification, product type, product number, product, order, and sales, "product" and "sales," which are selected in the initial state, are actively displayed. Note that it is also possible to display only the selectable extraction items.

[0078] 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) of "April 2020" to the end of the period (end of the period) of "March 2021", which was initially set as the extraction condition, as the monthly inventory amount data shown in Figure 17(a), which is collected from the inventory data shown in Figure 17(b), as display data for the "first graph".

[0079] 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 of Figures 18(a) to 18(c) are abnormality determination result data indicating an inventory abnormality in "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 is generated by monthly 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 as shown in Figure 19(c), as display data for the "second graph."

[0080] Next, the display control unit 24 acquires 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 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 of Warehouse A in the inventory data shown in Figure 21(b) and 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 shown in Figure 21(a) as display data for the "third graph." The calculation result data includes the end-of-month inventory amount of 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 arithmetic expression "((end-of-month inventory amount - beginning-of-month inventory amount) / beginning-of-month inventory amount x 100)."

[0081] Next, the display control unit 24 acquires monthly inventory value and shipment value 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 monthly inventory value and shipment value aggregate data shown in Figure 23(a), which includes the inventory value of Warehouse A in the inventory data shown in Figure 23(b) and the shipment value of Warehouse A in the product shipment data shown in Figure 23(c), as display data for the "fourth graph."

[0082] In this example, data up to +1 month (data one month later) is referenced relative to the parameter's end of period value. This is based on the premise that the parameter's end of period = 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 (data one month later) relative to the parameter's end of period value, it is possible to confirm data in which such fraudulent processing to reduce inventory has been performed.

[0083] 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. Specifically, based on the data binding information shown in Fig. 24(a), the display control unit 24 displays a first graph shown in Fig. 24(b) on the message details display screen, which shows the transition of the inventory value of each warehouse in a two-dimensional coordinate system with the X axis representing the accounting year and month and the Y axis representing the inventory value. The example of Fig. 24(b) is an example that shows a list of graphs showing the transition of the inventory values ​​of warehouses A to E.

[0084] Next, based on the data binding information shown in Fig. 25(a), the display control unit 24 displays on the message detail display screen a second graph shown in Fig. 25(b), which shows the trends in the sales amount and inventory amount of warehouse A, with the X axis of the two-dimensional coordinate system representing the fiscal year and month and the Y axis representing the inventory amount and sales amount. The example of Fig. 25(b) is an example that shows a list of graphs showing the trends in the monthly sales amount and inventory amount of warehouse A, where an abnormality was detected.

[0085] Next, based on the data binding information shown in Figure 26(a), the display control unit 24 displays on the message detail display screen a third graph shown in Figure 26(b), which shows the correlation between the inventory value increase rate and inventory value in 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) show the inventory value increase rate (%) in warehouse A where an abnormality was detected and the inventory value at that time.

[0086] Next, based on the data bind information shown in Fig. 27(a), the display control unit 24 displays a fourth graph on the message detail display screen, in which the X-axis of the two-dimensional coordinate system represents the fiscal year and month, and the Y-axis represents the inventory amount or shipping amount of warehouse A, and a dual-axis bar graph in which a bar graph of inventory amount and a bar graph of shipping amount are superimposed along the fiscal year and month, as shown in Fig. 27(b). In the dual-axis bar graph shown in Fig. 27(b), the lower bar graph represents the inventory amount, and the bar graph layered on top of this inventory amount bar graph represents the shipping amount.

[0087] Fig. 28 is a diagram showing the message details display screen in a state in which the first to fourth graphs are displayed. As shown in Fig. 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 together with the above-mentioned abnormality display data in an alert display area provided along the top side of the screen, with the left side abutting the display area for the extraction conditions.

[0088] Furthermore, on the message details display screen, the first graph described above is displayed in a display area about half the size of the alert display area, with its left side abutting the extraction condition display area and its upper side abutting the lower side of the alert display area.

[0089] Furthermore, 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 side abutting the display area for the first graph and its upper side abutting the lower side of the alert display area. In other words, the first graph and the second graph are displayed adjacent to each other on the lower side of the alert display area.

[0090] In addition, on the message details display screen, the above-mentioned third graph is displayed in a display area that is arranged so that its left side is abutting the display area for the extraction conditions and its upper side is abutting the lower sides of the display area for the first graph and the display area for the second graph.

[0091] In addition, on the message details display screen, the above-mentioned fourth graph is displayed in a display area that is arranged so that its left side abuts the display area for the extraction conditions and its upper side abuts the lower side of the display area for the third graph.

[0092] Next, the display control unit 24 performs a display process to change the colors of the legends and lines (graphs) of the first to fourth graphs to highlight them. FIG. 29 is a diagram showing an example of a coloring pattern for such graphs. The "legend abnormality flag" shown in FIG. 29 is a flag that is set when the inventory is abnormal. 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.

[0093] That is, when displaying the first graph and the third graph, the display control unit 24 applies the color of the "True" abnormality flag in the legend to the warehouse and product in the legend that are the same as the warehouse and product 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 value data shown in FIG. 30(b). This makes it easier for the user to recognize abnormal values. Furthermore, the display control unit 24 highlights the legend of product A in the third graph and the points corresponding to the abnormal inventory value increase rate and abnormal inventory value in red based on the end-of-month inventory value and the inventory value increase rate calculation result data between the beginning and end of the period shown in FIG. 30(c).

[0094] In response to this, the display control unit 24 applies the color of the legend's abnormality flag "False" to legends other than the warehouses and products for which an abnormality has been detected. For the second graph, the legend and line (graph) are displayed in blue, corresponding to the reference information flag "True," which is displayed as reference information for determining whether the trend in inventory value is normal or not for the sales amount. 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, since there is a possibility that data corresponding to a concealment operation to reduce fraudulently increased inventory is included, the display control unit 24 displays it in red, similar to the abnormality detection result data, highlighting it.

[0095] The message displayed in the alert display area of ​​the message details display screen allows the user to check detailed information, including the target (stock) for which an anomaly was detected, the detection method, and the standard value used to determine the anomaly, and to identify warehouses and products that may be abnormal. This allows the user to begin analyzing fraudulent inventory manipulation after first identifying warehouses and products that may be abnormal.

[0096] Furthermore, based on the first graph, it is possible to check the inventory value occurrence status of the warehouse where an abnormality was detected. It is also possible to check the abnormally high inventory quantity of the warehouse where the abnormality was detected 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 warehouse where the abnormality was detected.

[0097] Furthermore, 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 the change in inventory is 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 only inventory is increasing, even though sales are staying constant.

[0098] Furthermore, based on the third graph, it is possible to check the inventory value and inventory value growth rate for each product managed in the warehouse where an abnormality was detected, and to identify which products have abnormal data. Furthermore, the correlation between inventory value and inventory value growth rate allows the degree of monetary impact to be confirmed. In other words, if the value is high and the growth rate is also high, it can be assumed that the inventory at the end of the period has increased significantly. 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 growth rate. If only specific products are involved, fraud utilizing the characteristics of the product is likely. For example, miscellaneous products and other items managed as miscellaneous items could be used as indicators to consider reconsidering product management.

[0099] Additionally, based on the fourth graph, it is possible 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 also the 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), it can be seen 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.

[0100] (Switching display of analysis graphs) Next, the inventory management device 1 according to the embodiment is capable of switching the display of analytical graphs by selecting a desired extracted item from among the extracted items of "aggregation" explained using FIG. 22(a).

[0101] (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.

[0102] 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%". As shown in Figure 32(a), 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, which includes this inventory value increase rate. The memory control unit 26 stores this end-of-month inventory value and the calculation result data for the inventory value increase rate between the beginning and end of the period in the memory unit 2.

[0103] 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 increase rate and inventory value corresponding to each product type in Warehouse A from April 2020 to March 2021, in the display area of ​​the third graph on the message details display screen as shown in Figure 34.

[0104] As explained using Figures 16 and 17, Figures 18 and 19, Figures 22 and 23, the first, second and fourth graphs other than this third graph are displayed in the corresponding display areas on the message details display screen as shown in Figure 34 by the display control unit 24 based on various data acquired by referring to parameters.

[0105] Next, the display control unit 24 performs a display process of highlighting the legends and lines (graphs) of the first to fourth graphs by changing their colors. 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 with reference to FIG. 29, "True" 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 and displays them 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.

[0106] 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 increase 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 between the beginning and end of the period shown in Figure 35(e).

[0107] In response to this, the display control unit 24 applies a color indicating that the abnormality flag in the legend is "False" to legends other than the warehouse and product type for which an abnormality was detected. For the second graph, the legend and line (graph) are displayed in blue, corresponding to the reference information flag "True," which is displayed as reference information for determining whether the trend in inventory value is normal or not for the sales amount. Furthermore, for the fourth graph, the display control unit 24 applies a color indicating that the abnormality flag is "True" to the shipping amount. In other words, in this case, since there is a possibility that data corresponding to a concealment operation to reduce fraudulently increased inventory is included, the display control unit 24 displays the data in red, similar to the abnormality detection result data, in an emphasized manner.

[0108] The following can be seen mainly from the third graph on this message details display screen. As shown in Fig. 36, the fact that the inventory value increase rate is high for a specific product type in the third graph means that there is a possibility that fraud is being committed on a product-by-product 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).

[0109] Looking at the third graph in Figure 36, the inventory value increase rate and inventory value are similar for types B, C, D, and E, other than type A. For this reason, it is unlikely that fraudulent inventory manipulation is taking place, and these can be excluded from the investigation.

[0110] In contrast, in the case of product type A, both the inventory value increase rate and inventory value show high figures (64%, 100,000 yen), and it can be seen that product type A has a high impact on the overall inventory value at the end of the period. For this reason, it is possible to investigate in detail whether or not there has been any fraud in product type A.

[0111] (Display behavior when selecting order extraction items) 37(a) to 37(d) show an example in which the extraction item "order" is selected as an extraction condition to analyze the inventory status of warehouse A from which abnormality determination result data has been obtained.

[0112] In this case, the data generation unit 25 extracts the inventory amount for each month and warehouse from the inventory data shown in Figure 38(b), and also extracts the order amount 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 amount and order amount.The memory control unit 26 stores this monthly inventory and order amount data in the memory unit 2.

[0113] 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 display area for the second graph on the message details display screen as shown in FIG. 34.

[0114] As explained using Figures 16 and 17, 20 and 21, 22 and 23, the first, third and fourth graphs other than the second graph are displayed in the corresponding display areas on the message details display screen as shown in Figure 34 by the display control unit 24 based on various data acquired by referring to parameters.

[0115] Next, the display control unit 24 performs a display process of highlighting by changing the colors of the legends and lines (graphs) of the first to fourth graphs, as described 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.

[0116] Furthermore, the display control unit 24 applies a color to the fourth graph when the abnormality flag is "True" for the shipping amount. That is, in this case, since there is a possibility that data corresponding to a concealment operation to reduce the fraudulently increased inventory is included, the display control unit 24 highlights and displays it in red, similar to the abnormality detection result data.

[0117] The second graph on this message detail screen, in particular, shows that the lack of confirmation of an increase in orders, which 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 is not linked to orders or sales requires immediate investigation into the purpose of the increase in inventory.

[0118] First, 40(a) is an example of the second graph when "sales" at warehouse A is used as the extraction condition (second graph when sales are totaled). In this FIG. 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 FIG. 40(a), it can be seen that the inventory amount is gradually increasing, but the sales amount is roughly constant. Therefore, since 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 totaled) in order to confirm the purpose of the increase in inventory.

[0119] Figures 40(b) and 40(c) are diagrams showing examples of the second graph when summarizing orders. In Figures 40(b) and 40(c), the upper graph shows the trend in the order amount, which is displayed in blue as described above, and the lower graph shows the trend in the inventory amount. First, the example in Figure 40(b) is 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.

[0120] In contrast, the example in Figure 40(c) shows that the inventory value is gradually increasing, but the order value is roughly constant. In this case, it is clear that there are no sales forecasts. Furthermore, since this indicates an increase in inventory for an unknown reason, an immediate investigation is required.

[0121] (Display behavior when the 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.

[0122] In this case, the data generation unit 25 extracts the inventory value of department A for each month and warehouse from the inventory data shown in Figure 42(b) and generates the monthly inventory value data shown in Figure 42(a). The storage control unit 26 stores this monthly inventory value data in the storage unit 2.

[0123] 43(a) to 43(c) and 44(a) and 44(b), the data generation unit 25 extracts the inventory amount of department A for each month and warehouse from the inventory data, and extracts the sales amount of department A for each month and warehouse from the sales posting data (see FIG. 44(b)), and generates monthly inventory and sales amount data (FIG. 44(a)). The memory control unit 26 stores this monthly inventory and sales amount data in the memory unit 2.

[0124] Next, as shown in Figures 45(a) and 45(b), the calculation unit 21 calculates the inventory value increase rate for each product in warehouse A where the abnormal value was detected and in department A where extraction was specified, as shown in Figures 46(a) and 46(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 calculation result data for the end-of-month inventory value and the inventory value increase rate between the beginning and end of the period in the memory unit 2.

[0125] Next, as shown in Figures 47(a) and 47(b), the data generation unit 25 extracts the inventory value and shipping value of 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 from the product shipping data shown in Figure 48(c).The data generation unit 25 then generates monthly inventory value and shipping value aggregate 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 from the parameter end period value.The memory control unit 26 stores this monthly inventory value and shipping value aggregate data in the memory unit 2.

[0126] 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, the display control unit 24 displays a first graph shown in FIG. 49(b) on the message details display screen based on the data binding information shown in FIG. 49(a). The first graph shows the transition of the inventory value of each warehouse, with the X axis representing the accounting year and month and the Y axis representing the inventory value in a two-dimensional coordinate system. The example of FIG. 49(b) is an example that shows a list of graphs showing the transition of the inventory values ​​of 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.

[0127] Next, based on the data binding information shown in Fig. 50(a), the display control unit 24 displays on the message detail display screen a second graph shown in Fig. 50(b), which shows the trends in the sales amount and inventory amount of warehouse A, with the X axis of the two-dimensional coordinate system representing the fiscal year and month and the Y axis representing the inventory amount and sales amount. The example of Fig. 50(b) is an example that shows a list of graphs showing the trends in the monthly sales amount and inventory amount of warehouse A, where an abnormality was detected. In Fig. 50(b), the upper line graph is a graph of sales amount, and the lower straight line graph is a graph of inventory amount.

[0128] Next, based on the data binding information shown in FIG. 51(a), the display control unit 24 displays a third graph shown in FIG. 51(b) on the message detail display screen. The graph shows the correlation between the inventory value increase rate and the inventory value in 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 X-axis representing the inventory value increase rate (%) and the Y-axis representing the ending inventory value in two-dimensional coordinates. The points plotted on the third graph in FIG. 51(b) indicate the inventory value increase rate (%) in 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 increase rate and inventory value than the inventory value increase rates and inventory values ​​of Products B to K, can be identified as a target for fraud investigation.

[0129] Next, based on the data bind information shown in Fig. 52(a), the display control unit 24 displays a fourth graph on the message detail display screen, in which the X-axis of the two-dimensional coordinate system represents the fiscal year and month, and the Y-axis represents the inventory amount or shipping amount of warehouse A, and a dual-axis bar graph in which a bar graph of inventory amount and a bar graph of shipping amount are superimposed along the fiscal year and month, as shown in Fig. 52(b). In the dual-axis bar graph shown in Fig. 52(b), the lower bar graph represents the inventory amount, and the bar graph layered on top of this inventory amount bar graph represents the shipping amount.

[0130] 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 thereof, as described with reference to Fig. 29. The message details display screen on which the first to fourth graphs are displayed is as shown in Fig. 34.

[0131] Next, among 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 trend of inventory in warehouses managed by a specific department. Profit manipulation is often carried out on an organizational level. In this embodiment, it is possible to specify suspicious organizations and check the data.

[0132] Specifically, the first graph example shown in Figure 53(a) shows that the inventory value of Warehouse A, which is the upper linear graph, is gradually increasing, while the inventory value of Warehouse B, which is the lower broken line graph, is 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.).

[0133] In addition, the first graph example shown in Figure 53(b) is an example in which the inventory value of Warehouse C, shown as the upper straight line graph, is gradually increasing, and the inventory value of Warehouse D, shown as the lower broken line graph, is also gradually increasing while repeatedly 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.

[0134] 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 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.

[0135] 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 slow-moving inventory. Therefore, it can be determined that an investigation is necessary.

[0136] (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.

[0137] 1. The inventory value increase 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 can be automatically detected. By automatically aggregating inventory values ​​from the beginning to the end of the period and calculating the inventory value increase rate, aggregation costs can be reduced and aggregation and calculation errors can be prevented.

[0138] Furthermore, by periodically checking the data of the same warehouse and product and detecting warehouse and product combinations with abnormally high inventory values, it is possible to prevent oversights and mistakes. It is also possible to quickly identify warehouse and product combinations with abnormal inventory values, allowing for countermeasures and investigations to be implemented.

[0139] 2. The monthly inventory value of the warehouse where an abnormality was detected can be aggregated, and the trend of increasing inventory from the beginning to the end of the period can be visualized. Examples of fraudulent inventory manipulation include the following:

[0140] 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 make it difficult to detect each month (concealment manipulation)

[0141] However, in the case of the inventory management device 1 of the embodiment, the inventory amount can be tallied by month, and the change in the inventory amount over time can be visualized. This makes it possible to check how the inventory amount changes over time, and identify the characteristics of any fraudulent operations. It is also possible to compare inventory movements between warehouses, and to check the degree of abnormality in a warehouse where an abnormality has been detected. It also eliminates the cost of tallying each item, and prevents oversights and mistakes in comparison confirmations.

[0142] 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," it is possible to compare the sales value with the inventory value and visualize whether or not the inventory change associated with sales is normal.

[0143] Typically, inventory is 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 the embodiment aggregates and visualizes the sales amount and inventory amount for each warehouse by month. This allows you to check how the sales amount and inventory amount are changing with each passing month.

[0144] 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.

[0145] 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.

[0146] -Products with miscellaneous inventory management - Products that are managed as "multiple products as one product" because the trading volume is not expected to increase significantly

[0147] Therefore, the inventory management device 1 according to the 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.

[0148] The product value is small and the inventory value increase rate is large → The inventory value at the end of the period does not change much The product value is large and the inventory value increase rate is large → The inventory value at the end of the period will increase significantly

[0149] 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.

[0150] 5. The "inventory value of the warehouse where an abnormality was detected" and the "amount of inventory reduced during shipping processing" can be aggregated and visualized.

[0151] If inventory is fraudulently increased at the end of a fiscal year, it is likely that inventory will be reduced after the settlement to conceal the increase. For this reason, the inventory management device 1 of the embodiment visualizes the monthly inventory amount and the reduced inventory amount. This makes it easy to detect the "amount at the time of fraudulent processing" and the "amount that has been concealed."

[0152] [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 Goals 8 and 9 of the SDGs.

[0153] Furthermore, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to the achievement of SDGs Goals 12, 13, and 15.

[0154] Furthermore, this embodiment can contribute to strengthening control and governance, which can contribute to the achievement of Goal 16 of the SDGs.

[0155] [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.

[0156] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0157] Furthermore, the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions for each process, screen examples, and database configurations shown in this specification and drawings can be changed as desired unless otherwise specified.

[0158] Furthermore, with regard to the inventory management device 1, the components shown in the figures are functional concepts, and do not necessarily have to be physically configured as shown in the figures.

[0159] For example, all or any part of the processing functions of the inventory management device 1, particularly the control unit 3 and the 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 storage unit such as a ROM or HDD stores a computer program that works in conjunction with the OS to issue instructions to the CPU and perform various processes. The computer program is executed by being loaded into RAM, and works in conjunction with the CPU to constitute the control unit 3.

[0160] In addition, the inventory management program of this inventory management device 1 may be stored in another server device connected to the inventory management device 1 via any network, and all or part of it may be downloaded as needed.

[0161] 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, the term "recording medium" includes any portable physical medium, such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM), a Compact Disk Read Only Memory (CD-ROM), a Magneto-Optical Disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray Disc (Blu-ray).

[0162] Furthermore, a "program" is a data processing method written in any language or description method, regardless of the format, such as source code or binary code. Note 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 cooperating with a separate program, such as an OS. Note that the specific configuration and reading procedure for reading a recording medium in the inventory management device 1 described in the embodiment, as well as the installation procedure after reading, can use well-known configurations and procedures.

[0163] 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 providing websites.

[0164] The inventory management device 1 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as an information processing device connected to any peripheral device. The information processing device may be realized by installing software (including programs, data, etc.) that realizes the processing described in this embodiment.

[0165] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be configured by functionally or physically distributing and integrating them in any unit according to various additions or functional additions. In other words, the above-described embodiments can be implemented in any combination, or embodiments can be implemented selectively. [Industrial Applicability]

[0166] The present invention is suitable for application to inventory management in all types of business. [Explanation of symbols]

[0167] 1. Inventory management device 2 Storage section 3. Control Unit 4. Communication interface section 5 Input / output interface section 6 Input Devices 7 Output Devices 21 Calculation section 22 Discrimination part 23 Alert output control section 24 Display control unit 25 Data Generation Unit 26 Memory control unit

Claims

1. 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:

2. 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:

3. 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:

4. 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:

5. 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:

6. the display control unit displays, on the display unit, 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; 6. The inventory management device according to claim 5,

7. 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:

8. 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:

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