Assistance device, assistance program, and assistance method

Abstract

To provide a support device, a support program and a support method for optimizing the number of orders.SOLUTION: In a support system in which a server device and a user device are connected by a network, the server device for supporting inventory management of an object predicts a future consumption amount based on a past consumption result of the object, and includes an inventory transition schedule information generation part for generating a plurality of inventory transition schedules based on a current inventory amount and the consumption amount prediction, an inventory situation prediction part for predicting a future inventory situation based on a plurality of order plans of the object, and a selection part for selecting the order plan in which a score of the inventory situation becomes a predetermined state.SELECTED DRAWING: Figure 3

Description

[Technical Field]

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

[0002] A technology has been disclosed that predicts product demand and optimizes order quantities based on the prediction. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-129090 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 describes a technology that performs inventory volume transition simulations for multiple ordering methods at once and displays the results in a list format on a display screen based on demand information, making it possible to easily compare multiple ordering methods without having to select an ordering method again and set parameters. However, with this technology, the user must confirm and decide based on the simulation results.

[0005] The present invention has been made in view of the above background, and aims to optimize the number of orders. [Means for solving the problem]

[0006] In order to solve the above problems, the support device for supporting inventory management of an object in the present disclosure comprises an inventory change schedule information generation unit that predicts future consumption quantities based on past consumption records of the object and generates multiple inventory change schedules based on the current inventory quantity and the consumption quantity forecast, an inventory status prediction unit that generates multiple order plans for the object and predicts the future inventory status based on the inventory change schedule information and the arrival date when an order is placed according to the order plan, a calculation unit that calculates a score for the inventory status, and a selection unit that selects the order plan that will result in a predetermined score for each of the multiple inventory change schedules.

[0007] Other problems and solutions disclosed in this application will be made clear in the section on preferred embodiments of the invention and the drawings. [Effects of the Invention]

[0008] According to the present invention, the number of orders can be optimized. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating an example of the overall configuration of a support system according to an embodiment of the present invention. [Figure 2] 2 is a diagram illustrating an example of a hardware configuration of a server device 1 according to the embodiment. FIG. [Figure 3] 2 is a diagram illustrating an example of a functional configuration of a server device 1 according to the embodiment. FIG. [Figure 4] 10 is a diagram showing an example of actual consumption information stored in an actual consumption information storage unit according to the embodiment. FIG. [Figure 5] 10 is a diagram showing an example of inventory transition schedule information stored in an inventory transition schedule information storage unit according to the embodiment. FIG. [Figure 6] 10 is a diagram showing an example of actual receipt information stored in an actual receipt information storage unit according to the embodiment; FIG. [Figure 7] FIG. 10 is a diagram illustrating an example of processing performed by the server device 1 according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] <Summary of the Invention> The present invention will be described by listing the contents of the embodiments. For example, the present invention has the following configuration. [Item 1] A support device for supporting inventory management of an object, an inventory transition schedule information generation unit that predicts future consumption quantities based on past consumption records of the target items and generates multiple inventory transition schedules based on the current inventory amount and the consumption quantity predictions; an inventory status prediction unit that generates a plurality of ordering plans for the target items and predicts future inventory status based on the inventory transition schedule information and arrival times when orders are placed according to the ordering plans; A calculation unit that calculates a score of the inventory status; a selection unit that selects the order plan in which the score becomes a predetermined state in each of the plurality of inventory transition plans; An assistance device comprising: [Item 2] The predetermined state is that the score is maximized for each of the plurality of inventory transition plans. Item 1. The assistive device according to item 1. [Item 3] The calculation unit calculates the score to be low when the inventory status meets a predetermined condition. 3. The assistive device according to item 1 or 2. [Item 4] an ordering plan generation unit that generates the ordering plan; Furthermore, the order plan generation unit, when there is no order plan with the maximum score for each of the plurality of inventory transition schedules among the plurality of scores calculated by the calculation unit, generates the order plan again based on the order plan with the maximum sum of the plurality of scores; 3. The assistive device according to item 1 or 2. [Item 5] A support program for supporting inventory management of an object, The processor an inventory transition schedule information generation step of predicting future consumption quantities based on past consumption records of the target items, and generating multiple inventory transition schedules based on the current inventory quantity and the consumption quantity predictions; an inventory status forecasting step of generating a plurality of ordering plans for the target items and forecasting future inventory status based on the inventory transition schedule information and arrival times when orders are placed according to the ordering plans; a calculation step of calculating a score of the inventory status; a selection step of selecting the order plan in which the score becomes a predetermined state in each of the plurality of inventory transition plans; A support program to help you achieve this. [Item 6] A method for supporting inventory management of an object, comprising: The processor: an inventory transition schedule information generation step of predicting future consumption quantities based on past consumption records of the target items, and generating multiple inventory transition schedules based on the current inventory quantity and the consumption quantity predictions; an inventory status forecasting step of generating a plurality of ordering plans for the target items and forecasting future inventory status based on the inventory transition schedule information and arrival times when orders are placed according to the ordering plans; a calculation step of calculating a score of the inventory status; a selection step of selecting the order plan in which the score becomes a predetermined state in each of the plurality of inventory transition plans; How to support this.

[0011] ==Server device 1== The server device 1 may be, for example, a general-purpose computer such as a workstation or a personal computer, or may be logically realized by cloud computing. In this embodiment, for convenience of explanation, one server device is illustrated as an example, but the present invention is not limited to this and multiple servers may be used.

[0012] ==User terminal 3== The user terminal 3 is a computer operated by a user who is in charge of placing an order. For example, the user terminal 3 is a smartphone, a tablet computer, a personal computer, etc. The user can access the server device 1 using, for example, an application or a web browser executed on the user terminal 3.

[0013] FIG. 2 is a diagram illustrating an example of the hardware configuration of the server device 1. Note that the illustrated configuration is an example, and other configurations may also be used. The server device 1 includes a CPU 101, a memory 102, a storage device 103, a communication interface 104, an input device 105, and an output device 106. The storage device 103 stores various data and programs, and is, for example, a hard disk drive, a solid state drive, or a flash memory. The communication interface 104 is an interface for connecting to the communication network 2, and is, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, a wireless communication device for wireless communication, a USB (Universal Serial Bus) connector or an RS232C connector for serial communication, etc. The input device 105 is, for example, a keyboard, a mouse, a touch panel, a button, a microphone, etc. for inputting data. The output device 106 is, for example, a display, a printer, a speaker, etc. for outputting data. Each functional unit of the server device 1, which will be described later, is realized by the CPU 101 reading a program stored in the storage device 103 into the memory 102 and executing it, and each storage unit of the server device 10 is realized as part of the storage area provided by the memory 102 and the storage device 103.

[0014] Fig. 3 also shows the functional configuration of the server device 1. As shown in Fig. 3, the server device 1 includes storage units, namely, an actual consumption information storage unit 131, a planned inventory transition information storage unit 132, and an actual arrival information storage unit 133, and processing units, namely, an actual consumption information acquisition unit 111, a consumption quantity prediction unit 112, a planned inventory transition information generation unit 113, an actual arrival information acquisition unit 114, an arrival time prediction unit 115, an order plan generation unit 116, an inventory status prediction unit 117, a calculation unit 118, a selection unit 119, and a presentation unit 120.

[0015] The actual consumption information storage unit 131, the expected inventory transition information storage unit 132, and the actual arrival information storage unit 133 will be described below.

[0016] The actual consumption information storage unit 131 stores the actual consumption information acquired by the actual consumption information acquisition unit 111, an example of which is shown in Fig. 4. As shown in Fig. 4, the actual consumption information includes, for example, information such as the name of the object, the object code, the specifications, the lot number, the serial number, the purchase price, the supplier, the delivery date, the number of delivered items, the delivery date, the number of items shipped, the number in stock, the stock location, the expiration date, the condition (new, used, defective, etc.).

[0017] The inventory transition schedule information storage unit 132 stores the inventory transition schedule information generated by the inventory transition schedule information generation unit, an example of which is shown in Fig. 5. As shown in Fig. 5, the inventory transition information includes, for example, information such as the name of the object, the object code, the specification, the start date, the inventory quantity on the start date, the end date, the predicted consumption quantity from the start date to the end date, and the inventory quantity on the end date. Includes information such as:

[0018] The receipt record information storage unit 133 stores the receipt record information, an example of which is shown in Fig. 6, acquired by the receipt record information acquisition unit 114. As shown in Fig. 6, the receipt record information includes, for example, information such as the name of the object, the object code, the specifications, the lot number, the serial number, the purchase price, the supplier, the order date, the number of orders, the delivery date, and the number of deliveries. Includes information such as

[0019] Below is an explanation of each processing unit, namely, the actual consumption information acquisition unit 111, the consumption quantity prediction unit 112, the inventory trend information generation unit 113, the actual arrival information acquisition unit 114, the arrival time prediction unit 115, the order plan generation unit 116, the inventory status prediction unit 117, the calculation unit 118, the selection unit 119, and the presentation unit 120.

[0020] The actual consumption information acquisition unit 111 acquires, for example, actual consumption information relating to the current inventory quantity of inventory management objects and past consumption records. The actual consumption information acquisition unit 111, for example, presents an input form for actual consumption information to the user terminal 3 via the network 2, accepts user input operations, and acquires actual consumption information. The communication for sending and receiving may be wired or wireless, and any communication protocol may be used as long as mutual communication is possible. The actual consumption information acquisition unit 111 may acquire part or all of the actual consumption information via the network 2 from another inventory management system or the like used by the user of the server device 1. The actual consumption information acquisition unit 111 stores the acquired actual consumption information in the actual consumption information storage unit 131.

[0021] In this embodiment, the objects of inventory management may include, but are not limited to, merchandise for sale such as food, clothing, and miscellaneous goods, raw materials such as parts and materials, consumables such as office supplies, cleaning tools, and packaging materials, work equipment such as tools and measuring instruments, store fixtures such as display shelves and showcases, office equipment such as computers and printers, and payment methods for fees and other charges such as postage stamps and revenue stamps.

[0022] As an example, the consumption quantity prediction unit 112 predicts the consumption quantity of an item under inventory management from a predetermined point in time to a predetermined point in the future. The consumption quantity prediction unit 112 predicts the consumption quantity based on, for example, actual consumption information. In this case, the consumption quantity prediction unit 112 may predict the consumption quantity based on, for example, the consumption quantity for the same period in the past, or the average or median of the consumption quantities for the same period over several years. The consumption quantity prediction unit 112 may also predict the consumption quantity based on, for example, past consumption history and predetermined external factors (which may include, for example, economic factors such as business indexes, price indexes, and interest rates; political and social factors such as political situations and demographics; environmental factors such as temperature, weather, and natural disasters; competitor factors such as sales performance of competitors and sales status of similar products). In this case, the consumption quantity prediction unit 112 may predict the consumption quantity based on numerical values, such as quantities to be added or subtracted from actual performance information and coefficients to be multiplied by past actual performance information, which are set for each external factor.

[0023] Furthermore, the consumption quantity prediction unit 112 may predict consumption quantities in a plurality of scenarios by combining the above-mentioned external factors, for example, and generate a distribution of consumption quantities.

[0024] As an example, the consumption quantity prediction unit 112 may predict the consumption quantity using a model for predicting the consumption quantity. For example, the consumption quantity prediction unit 112 may generate a prediction model using past consumption record information as training data, an object for which the consumption quantity is to be predicted and a target period as input information, and the consumption quantity as output information, and predict the consumption quantity using the prediction model.

[0025] The specified point in time that serves as the starting point for the consumption quantity prediction unit 112 to predict the consumption quantity may be any point in time, and the future point in time that serves as the end point may be any point in time, such as one day, three days, or one week after the specified starting point, or may be a future point in time determined for each item under inventory management (for example, the average period in which a specified quantity will be consumed based on past consumption records, a use-by date, best-before date, expiration date, use-by date, warranty period, etc.), but is not limited to these.

[0026] As an example, the inventory transition schedule information generation unit 113 generates inventory transition schedule information based on the current inventory quantity and the consumption quantity predicted by the consumption quantity prediction unit 112. The inventory transition schedule information generation unit 113 may generate inventory transition schedule information, for example, by subtracting the consumption quantity predicted by the consumption quantity prediction unit 112 from the current inventory quantity. The inventory transition schedule information generation unit 113 stores the generated inventory transition schedule information in the inventory transition schedule information storage unit 132.

[0027] For example, when there are multiple consumption quantities predicted by the consumption quantity prediction unit 112, the inventory transition schedule information generation unit 113 generates multiple pieces of inventory transition schedule information. As a result, the inventory transition schedule information generation unit 113 generates inventory transition schedule information for patterns such as when the consumption quantity is large, when it is small, and when it is normal.

[0028] As an example, the receipt history information acquisition unit 114 presents an input form for receipt history information to the user terminal 3 via the network 2, accepts input operations from the user, and acquires the receipt history information. The communication for sending and receiving may be either wired or wireless, and any communication protocol may be used as long as mutual communication is possible. The receipt history information acquisition unit may acquire part or all of the receipt history information from another receipt management system used by the user of the server device 1. The receipt history information acquisition unit 114 stores the acquired receipt history information in the receipt history information storage unit 133.

[0029] As an example, the arrival time prediction unit 115 predicts the arrival time when a predetermined quantity of an object is ordered. The arrival time prediction unit 115 predicts the arrival time when a predetermined quantity of an object is ordered, for example, based on the arrival record information. The arrival time prediction unit 115 calculates the average or median, etc. of the difference (the time required from order to arrival) between the order date and the arrival date for the predetermined quantity included in the arrival record information, and predicts the arrival time obtained by adding the average, median, etc. of the difference on the specified order date. Furthermore, based on the arrival record information, if there is an external factor at the time when the arrival time is predicted or a predetermined period in the future from that time, the arrival time prediction unit 115 may predict the arrival time using information on the arrival time (if there is multiple information, the average, median, etc. of the information) when a predetermined quantity was ordered at a time when a similar external factor occurred in the past.

[0030] As an example, the arrival time prediction unit 115 may predict the arrival time using a model for predicting the arrival time. For example, the arrival time prediction unit 115 may use past arrival record information as training data, input information including the object whose arrival time is to be predicted, the order quantity, and the order timing (date, time, etc.), and generate a prediction model that outputs the arrival time, and then predict the arrival time using the prediction model. Note that, depending on the object, the period from ordering to arrival may be fixed, so the arrival time prediction unit 115 may add information about the fixed period set for the object to information about the order date to predict the arrival time.

[0031] For example, the ordering plan generation unit 116 generates multiple ordering plans for the target items. The ordering plan includes, but is not limited to, the target items to be ordered, order quantity, order timing (date, time, etc.), and order point (the remaining inventory quantity, which should be ordered when the inventory falls below that value). The ordering plan generation unit 116 may generate an ordering plan in which order element information is set randomly, for example. Alternatively, the ordering plan generation unit 116 may generate an ordering plan using, for example, Monte Carlo simulation. In this case, the ordering plan generation unit 116 may generate multiple ordering plans by repeatedly executing the following steps: defining a probability distribution of consumption quantities from the distribution of consumption quantities generated by the consumption quantity prediction unit 112, randomly sampling consumption quantities, simulating inventory quantities based on the sampled consumption quantities, and setting order element information based on the simulated inventory quantities.

[0032] Furthermore, as an example, the ordering plan generating unit 116 may predict an ordering plan using a model for predicting an ordering plan. The ordering plan generating unit 116 may, for example, acquire the ordering element information from past ordering records, use the ordering element information as training data, generate a prediction model in which the target product, ordering timing, and ordering point are used as input information, and order quantity is used as output information, and predict the ordering plan using the prediction model.

[0033] As an example, the inventory status prediction unit 117 predicts the inventory status at a future point in time based on the inventory transition schedule information and the predicted arrival status when orders are placed according to the ordering plan. The inventory status prediction unit 117 can predict the inventory status at a future point in time by combining the inventory transition schedule information with information on the arrival time prediction. The inventory status prediction unit 117 predicts multiple inventory statuses.

[0034] The calculation unit 118, for example, calculates a score for the predicted inventory status. The calculation unit 118 calculates the score for the predicted inventory status using, for example, a calculation formula for calculating the score for the inventory status. The calculation formula used by the calculation unit 118, for example, calculates a score that is low when the inventory status meets a predetermined condition. The predetermined condition may be, for example, when the inventory status becomes out of stock, when the arrival occurs on a predetermined day of the week, when the inventory quantity (such as an order point) set for each object is exceeded, when the inventory quantity (such as an order point) set for each object is exceeded at a predetermined time (such as the end of the month), or when the inventory quantity set for a predetermined object (such as a seasonal item) is exceeded at a predetermined time (such as the season or the end of a predetermined month). Furthermore, the points deducted due to the predetermined condition may be weighted, or the weighting may vary depending on the object, the time of year, etc. Furthermore, information on the weighting of the points deducted due to the predetermined condition may be acquired by accepting an operation of the user terminal 3.

[0035] When costs vary depending on the production volume of printed materials, etc. Furthermore, the calculation unit 118 may calculate the cost of each ordering method for the inventory status predicted by the inventory status prediction unit 117 based on ordering plans that order the same number of the same object but with different order quantities (for example, ordering 100 items at once and ordering 10 items 10 times), and may deduct points from the score if the cost is high.

[0036] As an example, the selection unit 119 selects an order plan that maximizes the inventory status score calculated by the calculation unit 118 for each of a plurality of inventory transition plans. The selection unit 119 selects an order plan that maximizes the inventory status score predicted by applying the order plan generated by the order plan generation unit 116 for inventory transition plan information under a plurality of predetermined conditions, such as when the inventory transition plan information is maximum, minimum, average, or median.

[0037] In addition, in the inventory trend forecast information for multiple specified conditions, if the inventory status score predicted by applying the ordering plan generated by the ordering plan generation unit 116 does not meet a specified value, the ordering plan generation unit 116 may generate an ordering plan again, and the inventory status prediction unit 117 may predict the inventory status using the ordering plan, and the calculation unit 118 may calculate the inventory status score, repeating this cycle.

[0038] Furthermore, when the score of the inventory status predicted by applying the ordering plan generated by ordering plan generation unit 116 in inventory transition forecast information for a plurality of predetermined conditions does not satisfy a predetermined value, ordering plan generation unit 116 may generate a new ordering plan based on ordering plan A that results in a relatively high score (for example, the largest average or total value of a plurality of scores). In this case, ordering plan generation unit 116 may generate an ordering plan by Monte Carlo simulation using the method described above, for example, starting from ordering plan A.

[0039] As an example, the presenting unit 120 presents the ordering plan selected by the selecting unit 119 to the user terminal 3. For example, the presenting unit 120 may present the ordering plan to a supplier set for each target item and automatically place an order.

[0040] A typical processing flow of the server device 1 of this embodiment will be described with reference to FIG. 7. The actual consumption information acquisition unit 111 acquires actual consumption information (1001). The consumption quantity prediction unit 112 predicts the consumption quantity (1002). The inventory transition schedule information generation unit 113 generates inventory transition schedule information (1003). The actual arrival information acquisition unit 114 acquires actual arrival information (1004). The arrival time prediction unit 115 predicts the arrival time (1005). The order plan generation unit 116 generates an order plan (1006). The inventory status prediction unit 117 predicts the inventory status (1007). The calculation unit 118 calculates the inventory status score (1008). If there is no order plan that will result in a predetermined score, the order plan is regenerated (1009). If there is an order plan that will result in a predetermined score, the selection unit 119 selects an order plan that will result in a predetermined score (1010). The presentation unit 120 presents the ordering plan to the user terminal 3 (1011).

[0041] Other examples are shown below.

[0042] The server device 1 may include a demand actual information acquisition unit 211 instead of the consumption actual information acquisition unit 111. In this case, the demand quantity prediction unit 212 predicts the demand quantity at a predetermined time point in the future using a method similar to that used by the consumption quantity prediction unit 112.

[0043] As an example, the inventory transition schedule information generation unit 113 generates inventory transition schedule information based on the current inventory quantity and the demand quantity predicted by the demand quantity prediction unit 212. The inventory transition schedule information generation unit 113 may generate inventory transition schedule information by, for example, subtracting the demand quantity predicted by the demand quantity prediction unit 212 from the current inventory quantity.

[0044] The server device 1 includes a manufacturing plan generation unit 213 instead of the order plan generation unit 116. The manufacturing plan generation unit 213 generates, for example, multiple manufacturing plans for the target object. The manufacturing plan includes, for example, but is not limited to, the target object to be manufactured, the manufacturing quantity, the timing to start manufacturing (date, time, etc.), and manufacturing point (the remaining inventory quantity, which should be manufactured when the inventory falls below that figure). The manufacturing plan generation unit 213 generates, for example, a manufacturing plan in which the manufacturing quantity is set randomly. Alternatively, the manufacturing plan generation unit 213 may generate a manufacturing plan using, for example, Monte Carlo simulation. In this case, the manufacturing plan generation unit 213 may generate multiple manufacturing plans by repeatedly executing the following steps: defining a probability distribution of the demand quantity from the distribution of the demand quantity generated by the demand quantity forecasting unit 212; randomly sampling the demand quantity; simulating the inventory quantity based on the sampled demand quantity; and setting manufacturing element information based on the simulated inventory quantity.

[0045] Furthermore, as an example, the manufacturing plan generation unit 213 may predict a manufacturing plan using a model for predicting a manufacturing plan. For example, the manufacturing plan generation unit 213 may acquire the manufacturing element information from past manufacturing results, use the manufacturing element information as training data, generate a prediction model in which the target object, manufacturing timing, and manufacturing point are used as input information, and manufacturing quantity is used as output information, and predict the manufacturing plan using the prediction model.

[0046] As an example, the stock status prediction unit 117 predicts the future stock status when production is carried out according to the stock transition schedule information and the production plan.

[0047] The calculation unit 118, for example, calculates a score for the predicted inventory status. The calculation unit 118 calculates the score for the predicted inventory status using, for example, a calculation formula for calculating the score for the inventory status. In the calculation formula used by the calculation unit 118, for example, the score decreases when the inventory status meets a predetermined condition. The predetermined condition may be, for example, a stockout, arrival on a predetermined day of the week, falling below the inventory quantity (reorder point, etc.) set for each object, falling below the inventory quantity (reorder point, etc.) set for each object at a predetermined time (e.g., the end of the month), or exceeding the inventory quantity set for a predetermined object (e.g., a seasonal item, etc.) at a predetermined time (e.g., a season, a predetermined end of the month, etc.), but is not limited to these. Furthermore, the points deducted due to the predetermined condition may be weighted, or the weighting may vary depending on the object, the time, etc.

[0048] As an example, the selection unit 119 selects a production plan in which the inventory status score calculated by the calculation unit 118 is a predetermined state in multiple inventory transition plans. For example, when the inventory transition plan is maximum, minimum, average, or median, the selection unit 119 selects, as an excellent production plan, a production plan in which the inventory status score predicted by applying the production plan generated by the order plan generation unit 116 is maximum.

[0049] For example, the presenting unit 120 presents the manufacturing plan selected by the selecting unit 119 to the user terminal 3.

[0050] Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. It is clear that a person skilled in the art of the present disclosure can conceive of various modified or altered examples within the scope of the technical idea described in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0051] The devices described in this specification may be realized as a single device, or may be realized by multiple devices (e.g., cloud servers) some or all of which are connected via a network. For example, the CPU and storage device of management server 20 may be realized by different servers connected to each other via a network.

[0052] The series of processes performed by the device described herein may be implemented using software, hardware, or a combination of software and hardware. A computer program for implementing each function of the management server 20 according to this embodiment may be created and installed on a PC or the like. A computer-readable recording medium on which such a computer program is stored may also be provided. Examples of the recording medium include a magnetic disk, an optical disk, a magneto-optical disk, and a flash memory. The computer program may also be distributed, for example, via a network, without using a recording medium.

[0053] Additionally, the processes described herein do not necessarily have to be performed in the order described, some process steps may be performed in parallel, additional process steps may be employed, and some process steps may be omitted.

[0054] Furthermore, the effects described herein are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that will be apparent to those skilled in the art from the description of this specification, in addition to or in place of the above-described effects. [Explanation of symbols]

[0055] 1. Server device 2 Network 3. User terminal 101 CPU 102 memory 103 Storage device 104 Communication Interface 105 Input Device 106 Output Device 111 Consumption performance information acquisition unit 112 Consumption Quantity Forecasting Department 113 Inventory transition forecast information generation unit 114 Arrival Record Information Acquisition Department 115 Arrival Forecast Department 116 Ordering Plan Generation Unit 117 Inventory Status Forecasting Department 118 Calculation Department 119 Selection Department 120 Presentation section 131 Consumption performance information storage unit 132 Stock transition schedule information storage unit 133 Arrival record information storage unit

Claims

1. A support device for supporting inventory management of an object, an inventory transition schedule information generation unit that predicts future consumption quantities based on past consumption records of the target items and generates multiple inventory transition schedules based on the current inventory amount and the consumption quantity predictions; an inventory status prediction unit that generates a plurality of ordering plans for the target items and predicts future inventory status based on the inventory transition schedule information and arrival times when orders are placed according to the ordering plans; A calculation unit that calculates a score of the inventory status; a selection unit that selects the order plan in which the score becomes a predetermined state in each of the plurality of inventory transition plans; An assistance device comprising:

2. The predetermined state is that the score is maximized for each of the plurality of inventory transition plans. The support device according to claim 1 .

3. The calculation unit calculates the score to be low when the inventory status meets a predetermined condition. The support device according to claim 1 or 2.

4. an ordering plan generation unit that generates the ordering plan; Furthermore, the order plan generation unit, when there is no order plan with the maximum score for each of the plurality of inventory transition schedules among the plurality of scores calculated by the calculation unit, generates the order plan again based on the order plan with the maximum sum of the plurality of scores; The support device according to claim 1 or 2.

5. A support program for supporting inventory management of an object, The processor an inventory transition schedule information generation step of predicting future consumption quantities based on past consumption records of the target items, and generating multiple inventory transition schedules based on the current inventory quantity and the consumption quantity predictions; an inventory status forecasting step of generating a plurality of ordering plans for the target items and forecasting future inventory status based on the inventory transition schedule information and arrival times when orders are placed according to the ordering plans; a calculation step of calculating a score of the inventory status; a selection step of selecting the order plan in which the score is in a predetermined state for each of the plurality of inventory transition plans; A support program to help you achieve this.

6. A method for supporting inventory management of an object, comprising: The processor: an inventory transition schedule information generation step of predicting future consumption quantities based on past consumption records of the target items, and generating multiple inventory transition schedules based on the current inventory quantity and the consumption quantity predictions; an inventory status forecasting step of generating a plurality of ordering plans for the target items and forecasting future inventory status based on the inventory transition schedule information and arrival times when orders are placed according to the ordering plans; a calculation step of calculating a score of the inventory status; a selection step of selecting the order plan in which the score is in a predetermined state for each of the plurality of inventory transition plans; How to support this.

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