Information processing apparatus

Abstract

To improve the efficiency of ordering work while optimizing stock.SOLUTION: Receiving, by an information processing apparatus, an input of receipt and payment data including inventory data related to an inventory of a material, sales achievement data related to a sales achievement of the material, and contract remaining quantity data related to a contract remaining quantity of the material, and acquiring setting data including lead time data related to a lead time of the material; A safety stock quantity is calculated based on sales result data and lead time data, a demand quantity of a material is predicted using a statistical prediction method based on the sales result data, whether or not to order the material is determined based on receipt and payment data, the lead time data, the safety stock quantity, and the demand quantity predicted by the demand quantity prediction part, an order quantity of the material is calculated when the order determination part determines to order the material, an order plan of the material is generated based on the order quantity, order data related to the order of the material is generated based on the order plan, and the order data is output.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to an information processing device. [Background technology]

[0002] Traditionally, users who perform ordering tasks have performed accurate and appropriate ordering tasks by taking into account inventory status based on inventory data and other factors, predicting future demand, and ensuring sufficient inventory levels to prevent stockouts and shortages. However, in the materials distribution industry, particularly the steel distribution industry, users manually import various data such as inventory data and shipping data, create order forms, and determine order quantities for each of the many items handled, resulting in a huge amount of work. In addition, because it is difficult to accurately predict demand for each of the many items handled, there is a risk of excess inventory or shortages for some items. Furthermore, only the ordering personnel are familiar with know-how regarding order quantities, and when the ordering personnel changes, this know-how must be passed on.

[0003] In recent years, technologies for calculating safety stock and forecasting demand have been proposed to support such ordering operations. However, these technologies do not support the ordering workflow for the materials distribution industry, so users must calculate safety stock and forecast demand using different services while performing ordering, making it difficult to perform ordering operations efficiently. Therefore, it is desirable to calculate safety stock and forecast demand, thereby optimizing inventory and improving the efficiency of ordering operations. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-242432 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide an information processing device that can optimize inventory while improving the efficiency of ordering operations. [Means for solving the problem]

[0006] According to one aspect of the present invention, an information processing device includes: a reception unit that receives input of receipt and payment data including inventory data on inventory of materials, sales performance data on sales performance of the materials, and contract remaining quantity data on the contract remaining quantity of the materials; an acquisition unit that acquires setting data including lead time data on the lead time of the materials; a reference number of months calculation unit that calculates a reference number of months indicating the inventory quantity that should be held for the number of months based on a safety stock quantity that indicates the minimum inventory quantity of the material, based on the sales performance data and lead time data; a demand quantity prediction unit that predicts a demand quantity of the material using a statistical prediction method, based on the sales performance data; an order determination unit that determines whether to order the material, based on the receipt and payment data, the lead time data, the reference number of months, and the demand quantity predicted by the demand quantity prediction unit; an order quantity calculation unit that calculates an order quantity of the material when the order determination unit determines to order the material; an order plan generation unit that generates an order plan for the material based on the order quantity; an order data generation unit that generates order data for the material based on the order plan; and an order data output unit that outputs the order data. [Effects of the Invention]

[0007] According to the present invention, it is possible to optimize inventory while improving the efficiency of ordering operations. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 2 is a diagram showing a business flow of ordering materials using the information processing system according to the first embodiment. [Figure 2] 1 is a diagram illustrating an example of a configuration of an information processing system according to a first embodiment. [Figure 3] 1 is a diagram illustrating an example of a configuration of an information processing device according to a first embodiment. [Figure 4] FIG. 10 is a diagram showing an example of an item data screen displayed on a display of a terminal device. [Figure 5] FIG. 10 is a diagram showing an example of an order category data screen displayed on a display of a terminal device. [Figure 6] FIG. 10 is a diagram showing an example of a receipt / payment data input screen displayed on the display of the terminal device. [Figure 7] FIG. 10 is a diagram showing an example of values ​​of safety coefficients set for each allowable stockout frequency. [Figure 8] FIG. 1 is a diagram illustrating a method for calculating a prediction error in the statistical prediction method of No. 1. [Figure 9] FIG. 9 is a diagram for explaining an outline of the learning method shown in FIG. 8. [Figure 10] FIG. 9 is a diagram for explaining an outline of the verification method shown in FIG. 8. [Figure 11] FIG. 10 is a diagram illustrating a method for selecting one statistical prediction method based on the prediction errors of each of a plurality of statistical prediction methods. [Figure 12] 10 is a flowchart of an order output process executed in the information processing device according to the first embodiment. [Figure 13] 10 is a flowchart of an order output process executed in the information processing device according to the first embodiment. [Figure 14] FIG. 10 is a diagram showing an example of a receipt and payment table screen displayed on the display of the terminal device. [Figure 15] FIG. 10 is a diagram showing an example of an order planning screen displayed on a display of a terminal device. [Figure 16] FIG. 10 is a diagram showing an example of an order data output screen displayed on a display of a terminal device. [Figure 17] FIG. 10 is a diagram illustrating an example of a configuration of an information processing device according to a second embodiment. [Figure 18] 10 is a flowchart of an order output process executed in an information processing device according to a second embodiment. [Figure 19]FIG. 10 is a diagram showing an example of a contract remaining amount adjustment screen displayed on a display of a terminal device. [Figure 20] FIG. 10 is a diagram illustrating an example of the configuration of an information processing device according to a third embodiment. [Figure 21] 10 is a flowchart of an order output process executed in an information processing device according to a third embodiment. [Figure 22] FIG. 10 is a diagram illustrating an example of the configuration of an information processing device according to a fourth embodiment. [Figure 23] FIG. 10 is a diagram illustrating an example of a reference number of months setting screen displayed on a display of a terminal device. [Figure 24] FIG. 10 is a diagram showing an example of a first demand forecast screen displayed on a display of a terminal device. [Figure 25] FIG. 10 is a diagram showing an example of a second demand forecast screen displayed on the display of the terminal device. [Figure 26] FIG. 10 is a diagram illustrating an example of a past performance screen displayed on a display of the terminal device. [Figure 27] FIG. 10 is a diagram showing an example of an inventory search screen displayed on a display of a terminal device. [Figure 28] FIG. 10 is a diagram illustrating an example of a Pareto analysis screen displayed on a display of a terminal device. [Figure 29] FIG. 10 is a diagram showing an example of a retained inventory screen displayed on a display of a terminal device. [Figure 30] FIG. 10 is a diagram showing an example of a third demand forecast screen displayed on a display of the terminal device. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings, but the present invention is not limited to the embodiment.

[0010] [First embodiment] First, with reference to FIG. 1, an overview of an information processing system 1 according to a first embodiment will be described. FIG. 1 is a diagram showing a workflow for ordering materials using the information processing system 1 according to the first embodiment. This information processing system 1 is a system for supporting a series of ordering tasks, including user ordering tasks, in materials distribution industries such as the steel distribution industry. For example, this information processing system 1 supports tasks when a user orders materials such as steel. In the following description, the present invention will be described using an example in which the material is steel, but the material in the present invention is not limited to steel and can also be applied to other materials. As shown in FIG. 1, the information processing system 1 includes a user company system 10 and an information processing device 30. The user company system 10 and the information processing device 30 are connected to each other so as to be able to communicate with each other via a network.

[0011] When a user of the user company system 10 launches an application, the information processing device 30 extracts data from the core system of the user company system 10 and acquires core system data (step S1). Next, the information processing device 30 processes the core system data into sales performance data and inventory data using a processing tool, and inputs this sales performance data and inventory data as receipt and payment data (step S2). Next, when the receipt and payment data is input, the information processing device 30 calculates a safety stock amount indicating the minimum inventory amount using safety stock theory based on the sales performance data and setting data including lead time data, and calculates a reference month number indicating the inventory amount for the number of months that should be held based on the calculated safety stock amount (step S3). Next, the information processing device 30 predicts demand using a statistical forecasting method based on the sales performance data (step S4). Next, the information processing device 30 determines whether or not to order steel based on the receipt and payment data, lead time data, reference number of months, and the demand volume predicted in step S4, calculates the order volume of steel that needs to be ordered, and generates a receipt and payment table that lists inventory volume, reference number of months, order volume, etc. (step S5). Next, the information processing device 30 generates an order plan based on the order volume (step S6). Next, the information processing device 30 generates order data based on the order plan and outputs the generated order data (step S7). Next, the information processing device 30 generates order form data using a purchase order generation tool based on the order output data that has been output as order data (step S8).

[0012] Next, a detailed configuration of the information processing system 1 according to this embodiment will be described with reference to FIG. 2. FIG. 2 is a diagram showing an example of the configuration of the information processing system 1 according to the first embodiment. As shown in FIG. 2, the information processing system 1 is configured to include a user company system 10A of user company A, a user company system 10B of user company B, a user company system 10C of user company C, and an information processing device 30. The user company system 10A of user company A, the user company system 10B of user company B, the user company system 10C of user company C, and the information processing device 30 are communicably connected to each other via a network NW. In the following, when there is no need to distinguish between the user company system 10A of user company A, the user company system 10B of user company B, and the user company system 10C of user company C, they will simply be referred to as "user company systems 10."

[0013] The user company system 10 is a system owned by a user company. Here, the user company is, for example, a company that purchases steel materials from steel manufacturers or steel trading companies, stores them in a warehouse, and sells them to end users or wholesalers. This type of business is also called retail sales. As shown in FIG. 2, the user company system 10 is configured to include a terminal device 11 and a core system 12.

[0014] The terminal device 11 is a terminal device owned by a user who uses the information processing device 30, and is, for example, a mobile terminal, a tablet terminal, a smartphone, a wearable terminal, a personal computer, or the like.

[0015] The core system 12 stores various information of the user company. This core system 12 is configured, for example, by a server device. As shown in Fig. 2, the core system 12 stores core system data. The core system 12 then transmits the core system data to the information processing device 30 via the network NW.

[0016] 2 in this embodiment, three systems, user company system 10A of user company A, user company system 10B of user company B, and user company system 10C of user company C, are connected to the network NW, but the number of user company systems connected to the network NW is not limited to this. For example, one or two user company systems 10 may be connected to the network NW, or four or more user company systems 10 may be connected to the network NW.

[0017] The information processing device 30 is a device that supports a user's ordering operations. This information processing device 30 is, for example, in a cloud format, and as a cloud-based information processing device 30, for example, the functions and processes described below are provided in the form of SaaS (Software as a Service). Note that the information processing device 30 may be in a cloud format and provide the above functions and processes in the form of cloud computing. Furthermore, the information processing device 30 may be in an on-premise format.

[0018] The network NW refers to any information and communication network that utilizes telecommunications technology, including, for example, a hospital backbone LAN (Local Area Network) or other wireless / wired LAN, the Internet, as well as a telecommunications circuit network, an optical fiber communication network, a cable communication network, and a satellite communication network.

[0019] 3 is a diagram showing an example of the configuration of the information processing device 30 according to the first embodiment. As shown in FIG. 3, the information processing device 30 is configured to include a communication unit 31, a storage unit 32, and a control unit 33.

[0020] The communication unit 31 implements various information communication protocols according to the configuration of the network NW. The communication unit 31 realizes communication with other devices via the network NW in accordance with these various protocols. In particular, in this embodiment, the information processing device 30 is connected to the network NW via the communication unit 31, and communication with the user company system 10 is realized.

[0021] The storage unit 32 stores various data for each user company. For example, the storage unit 32 stores setting data for each user company. This setting data includes lead time data related to the lead time of steel products. This lead time data includes, for example, a procurement period, which is the period from procurement to arrival, and an order interval, which is the interval from the previous order to the next order. The setting data also includes, for example, item data, which includes data such as the item name, weight, unit price, minimum order quantity, and supplier for each item, and order category data, which includes data assigning a steel manufacturer's rolling line (hereinafter referred to as a mill) to each item.

[0022] Fig. 4 is a diagram showing an example of an item data screen displayed on the display of terminal device 11. As shown in Fig. 4, on item data screen SC1, item names, which are the names of items, are associated with unit weights, which indicate the weight per unit, purchase prices, standard suppliers, minimum order lots, which indicate minimum order quantity data, and the like, as item data. A user can edit the item data via the input device of terminal device 11. The display of terminal device 11 corresponds to the display unit in this embodiment.

[0023] Fig. 5 is a diagram showing an example of an order category data screen displayed on the display of the terminal device 11. As shown in Fig. 5, on the order category data screen SC2, mills are assigned to each item as order category data. The user can edit the order category data via the input device of the terminal device 11.

[0024] The control unit 33 performs various processes. For example, the control unit 33 receives input of receipt and payment data, acquires setting data, calculates the number of reference months, predicts the demand amount, determines whether or not an order is necessary, calculates the order amount, generates an order plan, generates order data, and outputs the order data.

[0025] For this reason, as shown in FIG. 3, the control unit 33 has a reception unit 331, an acquisition unit 332, a reference month number calculation unit 333, a demand quantity prediction unit 334, an order determination unit 335, an order quantity calculation unit 336, a first display control unit 337, an order plan generation unit 338, a second display control unit 339, an order data generation unit 3310, and an order data output unit 3311.

[0026] The reception unit 331 receives input (import) of receipt and payment data including inventory data relating to the inventory of materials, sales performance data relating to the sales performance of materials, and contract remaining amount data relating to the contract remaining amount of materials.

[0027] FIG. 6 is a diagram showing an example of a receipt and payment data input screen displayed on the display of a terminal device. As shown in FIG. 6, on the receipt and payment data input screen SC3, the receipt and payment data includes the previous month's inventory actual and the current month's inventory actual as inventory data for each steel product item; the previous month's shipment actual as sales performance data for each steel product item; and the contract remaining quantity data for each steel product item. Also, as shown in FIG. 6, the current month's inventory actual in the steel product inventory data includes, for each item, the steel product inventory quantity at the home base where the user company system 10 is located, the steel product inventory quantity at other bases different from the home base, and the steel product inventory quantity at relay points between the home base and other bases. The sales performance data in the receipt and payment data displayed on the receipt and payment data input screen SC3 may include the current month's shipment actual, which is the current month's shipment quantity, or shipment actual from the previous month or from the previous month, in addition to or instead of the previous month's shipment actual. Hereinafter, the previous month's shipment actual and shipment actual from the previous month or from the previous month or earlier are also referred to as past shipment actual. The user can check the receipt and payment data before input by checking the receipt and payment data input screen SC3 displayed on the display of the terminal device 11.

[0028] The acquisition unit 332 acquires various data. Specifically, the acquisition unit 332 acquires core system data. The acquisition unit 332 also acquires setting data. The setting data acquired by the acquisition unit 332 includes lead time data related to the lead time of steel materials.

[0029] The base month number calculation unit 333 calculates the base month number. The base month number indicates the inventory amount for the number of months that should be held. In order to calculate the base month number, the base month number calculation unit 333 calculates the safety stock amount that indicates the minimum inventory amount of steel products. In other words, the base month number calculation unit 333 calculates the base month number based on the safety stock amount. The formula for calculating this safety stock amount is expressed as formula (1).

number

[0030] The safety coefficient k in this formula (1) is a value set for each allowable stockout frequency. This safety coefficient k is set for each item as setting data and stored in the storage unit 32. FIG. 7 is a diagram showing an example of the value of the safety coefficient k set for each allowable stockout frequency. In the example shown in FIG. 7, for example, if the allowable stockout frequency is three months / quarter, that is, if stockouts are allowed once every three months, the safety coefficient k is 0.43. As shown in FIG. 7 and formula (1), the safety stock amount increases as the allowable stockout frequency increases. In addition, the standard deviation σ of the demand amount is calculated based on past shipment records in the sales performance data.

[0031] The demand quantity forecasting unit 334 forecasts the demand quantity of steel materials using a statistical forecasting method based on the sales performance data. By using a statistical forecasting method to forecast the demand quantity of steel materials in this demand quantity forecasting unit 334, it is possible to forecast the demand quantity even in cases where data accumulation takes time due to infrequent transactions, such as in the steel distribution industry where transactions are conducted only once a month, or where the types of accumulated data are limited to shipment quantities, inventory quantities, etc., or where a large number of items are handled and demand fluctuations vary for each item.

[0032] The demand forecasting unit 334 according to this embodiment forecasts demand by selecting one forecasting method from among multiple statistical forecasting methods for each item. Examples of the multiple statistical forecasting methods include linear regression, logarithmic regression, exponential regression, power regression, moving average, double moving average, simple exponential smoothing, double exponential smoothing, triple exponential smoothing, Holt linear trend, seasonal smoothing (Winter additive), seasonal smoothing (Winter multiplicative), seasonally adjusted regression analysis (additive decomposition), seasonally adjusted regression analysis (multiplicative decomposition), seasonally adjusted power function (multiplicative decomposition), seasonally adjusted exponential function (multiplicative decomposition), seasonally adjusted logarithmic function (multiplicative decomposition), Croston's method, cumulative method, marketing end-of-year (EOL), null method, naive method, year-over-year comparison, previous year, and linear approximation. These multiple statistical forecasting methods are stored in the storage unit 32. The demand forecasting unit 334 calculates the forecast error for each of a plurality of statistical forecasting methods based on sales performance data, and forecasts the demand for steel using the forecasting method that produces the smallest forecast error among the plurality of statistical forecasting methods.

[0033] A method for selecting a statistical prediction method will be described in detail using Figures 8 to 11. Figure 8 is a diagram showing a method for calculating a prediction error in one statistical prediction method. Figure 9 is a diagram explaining an outline of the learning method shown in Figure 8. Figure 10 is a diagram explaining an outline of the verification method shown in Figure 8. Figure 11 is a diagram explaining a method for selecting one statistical prediction method based on the prediction errors in each of a plurality of statistical prediction methods.

[0034] As shown in FIG. 8 , the demand quantity prediction unit 334 calculates the mean absolute percentage error (MAPE) for each of the plurality of statistical prediction methods as the prediction error for each of the plurality of statistical prediction methods while sliding the learning period and the validation period using the cross-validation method for each of the plurality of statistical prediction methods based on the sales performance data. For example, during the learning period of the first learning and validation shown in FIG. 8 , the demand quantity prediction unit 334 trains each model corresponding to each statistical prediction method using the learning data from the learning start month to month (N+1) and each statistical prediction method, as shown in FIG. 9 . Furthermore, during the validation period of the first learning and validation shown in FIG. 8 , the demand quantity prediction unit 334 calculates the MAPE for the first learning and validation using the sales performance data for the validation period and the predicted values ​​output from the trained model, as shown in FIG. 10 . Specifically, the demand quantity prediction unit 334 calculates the MAPE using the absolute value of MAPE = (total value of values ​​for the validation period - total value of predicted values) / total value for the validation period. Then, the demand prediction unit 334 repeats the learning and verification X times.

[0035] 11, the demand forecasting unit 334 calculates the average value of the MAPE for the first to Xth rounds of training and validation for each model corresponding to each statistical forecasting method, and selects the statistical forecasting method that minimizes the MAPE.The demand forecasting unit 334 then forecasts the demand for steel using the statistical forecasting method that minimizes the MAPE.

[0036] The order determination unit 335 determines whether to order the steel material. Specifically, the order determination unit 335 determines whether to order the steel material based on receipt and payment data, lead time data, the number of reference months, and the demand amount predicted by the demand amount prediction unit 334.

[0037] The order quantity calculation unit 336 calculates the order quantity of steel when the order determination unit 335 determines to order steel. Specifically, in the steel distribution industry, orders are placed on a monthly basis as a business practice, so the order quantity calculation unit 336 calculates the order quantity based on a periodic ordering method. Here, the periodic ordering method is a method of placing orders at predetermined times, and determines the order quantity for each order so as to balance "(procurement period T + order interval O) × planned amount of use (monthly requirement) + safety stock amount A = current inventory amount (current month inventory amount) + contract remaining amount + order quantity." In this embodiment, the order quantity calculation unit 336 calculates the order quantity based on the periodic ordering method by using the base number of months and the holding month number, which will be described in detail later, etc.

[0038] The first display control unit 337 controls the display of the terminal device 11 to display an order quantity management screen for managing the order quantity. For example, the first display control unit 337 generates a receipt and payment table and displays the receipt and payment table screen including the receipt and payment table on the display of the terminal device 11 as the order quantity management screen.

[0039] The ordering plan generation unit 338 generates an ordering plan for steel products based on the order quantity. Here, the ordering plan is a plan for allocating the order quantity of each item. The second display control unit 339 controls the display of the terminal device 11 to display an ordering plan screen for managing the ordering plan generated by the ordering plan generation unit 338. The ordering plan screen corresponds to the ordering plan management screen in this embodiment.

[0040] The order data generating unit 3310 generates order data for ordering steel materials based on the order plan. The order data output unit 3311 outputs the order data.

[0041] 12 and 13 are flowcharts of an order output process executed by the information processing device 30 according to the first embodiment. In this order output process, the information processing device 30 accepts input of receipt and payment data, acquires setting data, calculates the number of reference months, predicts demand, determines whether an order is necessary, calculates the order quantity, generates a receipt and payment table, generates an order plan, generates order data, displays the receipt and payment table, displays an order plan screen, displays an order data output screen, and outputs order data. For example, this order output process is executed when a user executes a dedicated application on the terminal device 11.

[0042] 12, the reception unit 331 in the control unit 33 of the information processing device 30 receives input of receipt and payment data (step S11). Specifically, the reception unit 331 receives input (import) of receipt and payment data including inventory data, sales performance data, and contract remaining amount data generated by processing core system data acquired from the core system 12 of the user company system 10 via the communication unit 31. More specifically, the reception unit 331 receives input of receipt and payment data including inventory data, sales performance data, and contract remaining amount data based on an input operation from a user via the input device of the terminal device 11.

[0043] 12, the acquisition unit 332 in the control unit 33 of the information processing device 30 acquires setting data (step S13). Specifically, the acquisition unit 332 acquires setting data including lead time data from the storage unit 32. In this embodiment, the setting data includes minimum order quantity data regarding the minimum order quantity of steel materials.

[0044] 12, the reference number of months calculation unit 333 in the control unit 33 of the information processing device 30 calculates the reference number of months (step S15). Specifically, the reference number of months calculation unit 333 calculates the reference number of months based on the sales performance data and the lead time data. More specifically, the reference number of months calculation unit 333 calculates the reference number of months based on the sales performance data and the lead time data using the formula: Reference number of months = safety stock amount / average monthly shipping amount + supply period T + order interval O.

[0045] A method for calculating the reference number of months will be described below. First, in step S15, the reference number of months calculation unit 333 calculates the safety stock amount using the above-mentioned formula (1). Specifically, the reference number of months calculation unit 333 calculates the standard deviation σ of the demand amount, which indicates the variation in the shipping amount, based on the sales performance data, and calculates the safety stock amount A using the above-mentioned formula (1) based on the safety coefficient k for each item included in the setting data, the supply period T and order interval O in the lead time data included in the setting data, and the calculated standard deviation σ of the demand amount, which indicates the variation in the shipping amount.

[0046] Next, in step S15, the reference number of months calculation unit 333 calculates the monthly average shipment volume based on the sales performance data. Specifically, the reference number of months calculation unit 333 calculates the monthly average shipment volume by calculating the average of past shipment volumes in the sales performance data. The period of past shipment volumes in the sales performance data used to calculate this average of past shipment volumes is the same as the period of past shipment volumes in the sales performance data used to calculate the standard deviation σ of demand volume in the above-mentioned formula (1). For example, if the past shipment volume data in the sales performance data used to calculate the standard deviation σ of demand volume is shipment volume data for the past 60 months, the past shipment volume data in the sales performance data used to calculate the average of past shipment volume data is also shipment volume data for the past 60 months.

[0047] Then, in step S15, the reference month number calculation unit 333 calculates the reference month number based on the calculated safety stock amount, the calculated average monthly shipping amount, and lead time data including the supply period and order interval, using the formula: Reference month number = safety stock amount ÷ average monthly shipping amount + supply period T + order interval O.

[0048] Next, as shown in FIG. 12, the demand quantity prediction unit 334 in the control unit 33 of the information processing device 30 predicts the demand quantity (step S17). Specifically, the demand quantity prediction unit 334 predicts the demand quantity using a statistical prediction method based on the sales performance data. More specifically, the demand quantity prediction unit 334 selects one statistical prediction method from a plurality of statistical prediction methods based on the sales performance data, and predicts the demand quantity using the selected statistical prediction method based on the sales performance data. The demand quantity prediction unit 334 derives a predicted value of the shipping quantity up to the lead time ahead by predicting the demand quantity, for example.

[0049] 12, the order determination unit 335 determines whether or not an order is necessary (step S19). Specifically, the order determination unit 335 determines whether or not to order steel materials based on receipt and payment data, lead time data, the reference number of months, and the demand amount predicted by the demand amount prediction unit 334. More specifically, the order determination unit 335 calculates the shortage amount using Shortage Amount = (Reference Number of Months - Number of Holding Months) x Monthly Requirement Amount, and determines whether or not to order steel materials by determining whether the shortage amount is greater than 0.

[0050] The method for determining whether to order steel materials will be described in detail below. First, in step S19, the order determination unit 335 calculates the monthly required quantity. Specifically, the order determination unit 335 calculates the monthly required quantity based on the lead time data and the predicted value of the shipping quantity up to the lead time ahead, using the formula: monthly required quantity = predicted value of shipping quantity up to the lead time ahead / (procurement period T + order interval O).

[0051] Next, in step S19, the order determination unit 335 calculates the current month's inventory quantity. Specifically, the order determination unit 335 calculates the current month's inventory quantity using the following equation: Current month's inventory quantity = Previous month's inventory quantity - Shipment quantity (Current month's shipment quantity) + Arrival quantity (Current month's arrival quantity) based on the inventory data, sales performance data, contract remaining quantity data, and lead time data in the receipt and payment data.

[0052] Next, in step S19, the order determination unit 335 calculates the number of holding months. Here, the number of holding months is the inventory amount for the number of months currently held. Specifically, the order determination unit 335 calculates the number of holding months using the formula Number of holding months = (inventory amount for the current month + contract remaining amount) ÷ single-month requirement, based on the calculated single-month requirement amount, the calculated current month's inventory amount, and the contract remaining amount data in the receipt and payment data.

[0053] Next, in step S19, the order determination unit 335 calculates the shortage amount. Specifically, the order determination unit 335 calculates the shortage amount based on the reference number of months calculated in step S15, the holding number of months calculated in step S19, and the monthly required amount, using Shortage Amount = (Reference Number of Months - Holding Number of Months) × Monthly Required Amount.

[0054] Then, in step S19, the order determination unit 335 determines whether or not to order steel by determining whether the calculated shortage amount is greater than 0, that is, whether or not the shortage amount > 0 is satisfied.

[0055] Then, in step S19, if it is determined that an order is necessary, that is, that steel is to be ordered (step S19: Yes), the order quantity calculation unit 336 in the control unit 33 of the information processing device 30 calculates the order quantity (step S21). Specifically, the order quantity calculation unit 336 calculates the order quantity so that it exceeds the minimum order quantity of steel included in the setting data. More specifically, for example, the order quantity calculation unit 336 compares the minimum order quantity of steel included in the setting data with the shortage quantity, and calculates the order quantity so that the shortage quantity exceeds the minimum order quantity. Note that in step S19, if the shortage quantity exceeds the minimum order quantity, the order quantity calculation unit 336 may calculate the shortage quantity calculated in step S19 as the order quantity.

[0056] On the other hand, in step S19, if it is determined that an order is not necessary, i.e., that steel will not be ordered (step S19: No), or after processing of step S27, the first display control unit 337 in the control unit 33 of the information processing device 30 generates a receipt and payment table (step S23).

[0057] 13, the ordering plan generating unit 338 in the control unit 33 of the information processing device 30 generates an ordering plan (step S25). Specifically, the ordering plan generating unit 338 generates the ordering plan based on the order quantity calculated in step S21.

[0058] 13, the order data generation unit 3310 in the control unit 33 of the information processing device 30 generates order data (step S27). Specifically, the order data generation unit 3310 generates the order data based on the order plan generated in step S25.

[0059] 13, the first display control unit 337 in the control unit 33 of the information processing device 30 determines whether or not to display the receipt and payment table (step S29). Specifically, the first display control unit 337 determines whether or not to display the receipt and payment table by determining whether or not an input operation for displaying the receipt and payment table has been accepted from the user via the input device of the terminal device 11. Here, the input operation for displaying the receipt and payment table screen including the receipt and payment table is, for example, an input operation for selecting this receipt and payment table screen from a pull-down menu including the names of each screen.

[0060] Then, if it is determined in step S29 that the receipt and payment table is to be displayed (step S29: Yes), the first display control unit 337 displays the receipt and payment table (step S31).

[0061] FIG. 14 is a diagram showing an example of a receipt and payment table screen displayed on the display of the terminal device 11. As shown in FIG. 14, the receipt and payment table in the receipt and payment table screen SC4 includes, for each item, item data, the inventory quantity of steel at the home base, the inventory quantity of steel at other bases, the inventory quantity of steel at relay locations, the recommended quantity indicating the order quantity calculated by the order quantity calculation unit 336, the monthly required quantity, the number of holding months, the base number of months, etc. The receipt and payment table also includes a field for adjusting the number of staff members so that the order quantity can be adjusted. The first display control unit 337 accepts an input operation for adjusting the order quantity from the user via the input device of the terminal device 11, i.e., an adjustment amount for the order quantity in the field for adjusting the number of staff members, thereby adjusting the order quantity. When the order quantity is adjusted via the receipt and payment table screen SC4, the ordering plan generation unit 338 regenerates an ordering plan based on the order quantity adjusted via the receipt and payment table screen SC4. Furthermore, when an ordering plan is regenerated, the ordering data generating unit regenerates ordering data based on the regenerated ordering plan.

[0062] On the other hand, if it is determined in step S29 that the receipt and payment table should not be displayed (step S29: No), or after the processing of step S31, the second display control unit 339 in the control unit 33 of the information processing device 30 determines whether or not to display the order planning screen (step S33). Specifically, the second display control unit 339 determines whether or not to display the order planning screen by determining whether or not an input operation for displaying the order planning screen has been accepted from the user via the input device of the terminal device 11. Here, the input operation for displaying the order planning screen is, for example, an input operation for selecting the order planning screen from a pull-down menu including the names of each screen.

[0063] If it is determined in step S33 that the order planning screen should be displayed (step S33: Yes), the second display control unit 339 in the control unit 33 of the information processing device 30 displays the order planning screen (step S35). FIG. 15 is a diagram showing an example of the order planning screen displayed on the display of the terminal device 11. The second display control unit 339 displays the standard supplier, order quantity, and number of orderers for each item in the order planning on the order planning screen SC5 shown in FIG. 15. The order planning screen SC5 also includes information IF1 regarding the order quantity for each mill. Specifically, the information IF1 regarding the order quantity for each mill includes the possible order quantity, the already ordered quantity, the current order quantity, and the excess quantity for each mill. The second display control unit 339 controls the display of the order planning screen SC5 so that the color of the excess quantity column changes as an alert when the sum of the already ordered quantity and the current order quantity exceeds the possible order quantity. Furthermore, on the order planning screen SC5, the second display control unit 339 controls the display of the terminal device 11 so that only items assigned to mills with order timings can be sorted based on the user's input operation for sorting, such as checking the checkbox of the mill with order timing. For example, at a certain manufacturer A, items with large outer diameters are handled by mill 1, and the order timing for items assigned to mill 1 is the beginning of each month, while items with small outer diameters are handled by mill 2, and the order timing for items assigned to mill 2 is the end of each month. As a result of the user's order timing differing for each mill, the period during which the work of calculating the order quantity for each item is carried out is divided into, for example, the beginning, middle, and end of the month. This is to enable sorting only for items assigned to mills with order timings in the beginning of the month, items assigned to mills with order timings in the middle of the month, and items assigned to mills with order timings in the end of the month. In addition, the order plan screen SC5 has an item for the number of orderers so that the order plan can be adjusted, and the order plan is adjusted by accepting input operations for adjusting the order plan from the user via the input device of the terminal device 11 for this item for the number of orderers.When the ordering plan is adjusted via this ordering plan screen SC5, the ordering data generating unit 3310 generates the ordering data again based on the ordering plan adjusted via the ordering plan screen SC5.

[0064] On the other hand, if it is determined in step S33 that the order planning screen SC5 should not be displayed (step S33: No), or after the processing of step S35, the second display control unit 339 in the control unit 33 of the information processing device 30 determines whether or not to display the order data output screen (step S37). Specifically, the second display control unit 339 determines whether or not to display the order data output screen by determining whether or not an input operation for displaying the order data output screen has been accepted from the user via the input device of the terminal device 11. Here, the input operation for displaying the order data output screen is, for example, an input operation for selecting the order data output screen from a pull-down menu including the names of each screen.

[0065] Then, in step S37, if it is determined that the order data output screen should be displayed (step S37: Yes), the second display control unit 339 in the control unit 33 of the information processing device 30 displays the order data output screen (step S39). Fig. 16 is a diagram showing an example of the order data output screen displayed on the display of the terminal device 11. The second display control unit 339 displays the supplier, purchase order form, quantity, weight, unit price, etc. for each item in the order data in the order data output screen SC6 shown in Fig. 16. In addition, an order details output button is displayed on the order data output screen SC6.

[0066] 13, the second display control unit 339 in the control unit 33 of the information processing device 30 determines whether or not to output the order data (step S41). Specifically, the second display control unit 339 determines whether or not to output the order data by determining whether or not the order details output button B1 displayed on the order data output screen SC6 has been pressed. If it is determined in step S41 that the order data will not be output (step S41: No), or if it is determined in step S37 that the order data output screen will not be displayed (step S37: No), the process returns to step S29 described above, and the process from step S29 is repeated.

[0067] On the other hand, if it is determined in step S41 that the order data is to be output (step S41: Yes), the order data output unit 3311 in the control unit 33 of the information processing device 30 outputs the order data (step S43). By executing step S43, the order output process ends. After outputting this order data, the order data is converted into order form data by the order form generation tool as order output data.

[0068] As described above, the information processing device 30 according to this embodiment accepts input of receipt and payment data, acquires setting data, calculates the number of base months, predicts demand, determines whether to order steel, calculates the order quantity if it is determined to order, generates an ordering plan based on the order quantity, generates ordering data based on the ordering plan, and outputs the generated ordering data, thereby optimizing inventory while realizing efficient ordering operations.

[0069] Second Embodiment In the information processing device 30 in the information processing system 1 according to the first embodiment described above, it is also possible to adjust the contract remaining amount in the contract remaining amount data included in the receipt and payment data. In the second embodiment, an information processing device 30 capable of adjusting the contract remaining amount will be described. Note that the configuration of the information processing system according to the second embodiment is the same as that of the information processing system 1 according to the first embodiment described above, and therefore a description thereof will be omitted.

[0070] Fig. 17 is a diagram showing an example of the configuration of the information processing device 30 according to the second embodiment, and corresponds to Fig. 3 described above. As shown in Fig. 17, the control unit 33 is configured by adding a contract remaining amount adjustment unit 3312 to the control unit 33 according to the first embodiment. The configuration other than the contract remaining amount adjustment unit 3312 is the same as that in Fig. 3, and therefore description thereof will be omitted.

[0071] The contract remaining amount adjustment unit 3312 receives an input operation from the user regarding adjustment of the contract remaining amount in the contract remaining amount data included in the receipt and payment data, and adjusts the contract remaining amount in accordance with the received input operation.

[0072] FIG. 18 is a flowchart of an order output process executed by the information processing device 30 according to the second embodiment, and corresponds to FIG. 12. In this order output process, the information processing device 30 receives input of receipt and payment data, generates a contract remaining amount adjustment screen, acquires setting data, calculates the number of reference months, predicts demand, determines whether an order is necessary, and calculates the order amount. For example, this order output process is executed when a user executes a dedicated application on the terminal device 11. Note that the process of step S11 shown in FIG. 18 is the same as that in FIG. 12, and therefore will not be described here.

[0073] 18, the contract remaining amount adjusting unit 3312 in the control unit 33 of the information processing device 30 determines whether to adjust the contract remaining amount (step S51). Specifically, the contract remaining amount adjusting unit 3312 determines whether to adjust the contract remaining amount by determining whether an input operation related to the generation of a contract remaining amount adjustment screen has been received from the user via the input device of the terminal device 11. Here, the input operation related to the generation of the contract remaining amount adjustment screen is, for example, an input operation to select this contract remaining amount adjustment screen from a pull-down menu including the names of each screen.

[0074] Then, in step S51, if it is determined that the contract remaining amount is to be adjusted (step S53: Yes), the contract remaining amount adjusting unit 3312 in the control unit 33 of the information processing device 30 generates a contract remaining amount adjustment screen (step S53). Specifically, the contract remaining amount adjusting unit 3312 generates the contract remaining amount adjustment screen based on the contract remaining amount data.

[0075] 18, the contract remaining amount adjusting unit 3312 in the control unit 33 of the information processing device 30 displays a contract remaining amount adjustment screen (step S55). Specifically, the contract remaining amount adjusting unit 3312 controls the display of the terminal device 11 to display the contract remaining amount adjustment screen generated in step S53.

[0076] FIG. 19 is a diagram showing an example of a contract remaining quantity adjustment screen displayed on the display of the terminal device 11. As shown in FIG. 19, the display of the terminal device 11 displays a contract remaining quantity adjustment screen SC7. This contract remaining quantity adjustment screen SC7 displays the order number, contract remaining quantity, planned purchase date, and adjusted contract remaining quantity for each item. While this contract remaining quantity adjustment screen SC7 is displayed, the contract remaining quantity adjustment unit 3312 accepts an input operation from the user via the input device of the terminal device 11 regarding the adjustment of the contract remaining quantity in the contract remaining quantity data, and adjusts the contract remaining quantity in accordance with the accepted input operation. In the example shown in FIG. 19, the contract remaining quantity adjustment unit 3312 accepts an input operation from the user regarding the adjustment of the contract remaining quantity, in which a check mark is entered in the completion flag set for each steel item, and sets the contract remaining quantity of the material for which a check mark is entered in the completion flag set for each steel item to 0, i.e., the contract is completed.

[0077] On the other hand, if the contract remaining amount is not adjusted in step S51 (step S51: No), or after the processing of step S55, the acquisition unit 332 in the control unit 33 of the information processing device 30 acquires the setting data (step S13). The processing from step S13 onwards is the same as in Figures 12 and 13, so the description will be omitted. Then, by executing step S43, the order output processing according to this embodiment ends.

[0078] As described above, in the information processing device 30 in the information processing system 1 of the second embodiment, when adjusting the contract remaining quantity, a contract remaining quantity adjustment screen is displayed, and the contract remaining quantity adjustment unit 3312 accepts input operations from the user regarding the adjustment of the contract remaining quantity in the contract remaining quantity data, and adjusts the contract remaining quantity according to the accepted input operations.Therefore, in cases where, due to business practices in material distribution industries such as the steel distribution industry, the inventory quantity does not necessarily match the order quantity, particularly when the inventory quantity is small compared to the order quantity, it is possible to adjust the contract remaining quantity, thereby preventing the contract remaining quantity from becoming noise in calculating the order quantity and generating the order plan, and thereby achieving the efficiency of ordering operations while optimizing inventory.

[0079] Third Embodiment In the information processing device 30 according to the first and second embodiments described above, it is possible to predict demand when demand for steel is intermittent. Below, a case where this modified example is applied to the first embodiment will be referred to as the third embodiment, and differences from the first embodiment described above will be described. Note that although a case where this modified example is applied to the first embodiment will be described, this modified example can also be applied to the second embodiment described above. Note that the configuration of the information processing system according to the third embodiment is the same as that of the information processing system 1 according to the first embodiment described above, and therefore description thereof will be omitted.

[0080] Fig. 20 is a diagram showing an example of the configuration of an information processing device 30 according to the third embodiment, and corresponds to Fig. 3 described above. As shown in Fig. 20, the control unit 33 is configured by adding an intermittent determination unit 3313 to the control unit 33 according to the first embodiment. In addition, in the information processing device 30 in the information processing system 1 according to this embodiment, the demand amount prediction unit of the control unit 33 is different from that in the first embodiment described above, and is therefore represented as a demand amount prediction unit 334a. The configuration other than the demand amount prediction unit 334a and the intermittent determination unit 3313 is the same as that in Fig. 3, and therefore description thereof will be omitted.

[0081] The intermittent determination unit 3313 determines whether the demand for steel is intermittent based on the sales performance data. Here, demand is intermittent when the shipping performance, which is the shipping amount in the sales performance data, is 0 kg for N consecutive months. This N can be set arbitrarily by the user.

[0082] When it is determined that the demand for steel is intermittent, the demand quantity forecasting unit 334a according to this embodiment forecasts the demand for steel using the moving average method as a statistical forecasting method. When it is determined that the demand for steel is not intermittent, the demand quantity forecasting unit 334a forecasts the demand for steel using the statistical forecasting method that produces the smallest prediction error among the prediction errors of a plurality of statistical forecasting methods.

[0083] FIG. 21 is a flowchart of an order output process executed by the information processing device 30 according to the third embodiment, and corresponds to FIG. 12. In this order output process, the information processing device 30 accepts input of receipt and payment data, acquires setting data, calculates the number of reference months, determines whether demand is intermittent, predicts the demand amount, determines whether an order is necessary, and calculates the order amount. For example, this order output process is executed when a user executes a dedicated application on the terminal device 11. Note that the processes from step S11 to step S15 shown in FIG. 21 are the same as those in FIG. 12, and therefore will not be described here.

[0084] 21, the intermittent demand determination unit 3313 in the control unit 33 of the information processing device 30 determines whether the demand is intermittent or not (step S61). Specifically, the intermittent demand determination unit 3313 determines whether the demand for steel materials is intermittent or not based on sales performance data.

[0085] Then, if it is determined in step S61 that the demand is intermittent (step S61: Yes), the demand quantity prediction unit 334a in the control unit 33 of the information processing device 30 predicts the demand quantity using the moving average method (step S63). Specifically, the demand quantity prediction unit 334a predicts the demand quantity using the moving average method based on the sales performance data. Here, the moving average method is a method in which the average value of the shipment quantity (demand quantity) over the most recent N months is used as the predicted value.

[0086] On the other hand, if it is determined in step S61 that the demand is not intermittent (step S61: No), the demand quantity prediction unit 334 in the control unit 33 of the information processing device 30 predicts the demand quantity using the statistical prediction method 1 (step S17). The processing from step S17 onwards is the same as in Figures 12 and 13, so its description will be omitted. Then, by executing step S45, the order output processing according to this embodiment ends.

[0087] As described above, in the information processing device 30 according to this embodiment, it is determined whether or not demand is intermittent, and if it is determined that demand is intermittent, the moving average method is used to predict the demand quantity. Therefore, even when demand is intermittent, it is possible to predict the demand quantity, and therefore, even when demand is intermittent, it is possible to optimize inventory and achieve efficient ordering operations.

[0088] [Fourth embodiment] In the information processing device 30 according to the first to third embodiments described above, the reference number of months used for determining whether to place an order or calculating the order quantity is a value calculated by the reference number of months calculation unit 333, but this is not limited to this. In the fourth embodiment, the information processing device 30 may accept a selection from the user between the reference number of months input by the user and the reference number of months calculated by the reference number of months calculation unit 333, and set the selected reference number of months as the reference number of months used for calculating the order quantity. A case in which this modification is applied to the first embodiment described above will be referred to as the fourth embodiment, and differences from the first embodiment described above will be described. The configuration of the information processing system according to the fourth embodiment is the same as that of the information processing system 1 according to the first embodiment described above, and therefore description thereof will be omitted.

[0089] Fig. 22 is a diagram showing an example of the configuration of an information processing device 30 according to the fourth embodiment, and corresponds to Fig. 3 described above. As shown in Fig. 22, the control unit 33 is configured by adding a reference number of months setting unit 3314 to the control unit 33 according to the first embodiment. The configuration other than this reference number of months setting unit 3314 is the same as that in Fig. 3, and therefore description thereof will be omitted.

[0090] The reference number of months setting unit 3314 accepts from the user a selection between the reference number of months input by the user and the reference number of months calculated by the reference number of months calculation unit 333, and sets the selected reference number of months as the reference number of months to be used for calculating the order quantity. When the reference number of months to be used for calculating the order quantity has been set by this reference number of months setting unit 3314, the order determination unit 335 uses the set reference number of months to again determine whether or not to place an order, and the order quantity calculation unit 336 recalculates the order quantity using the set reference number of months.

[0091] Fig. 23 is a diagram showing an example of a base number of months setting screen SC8 displayed on the display of the terminal device 11. As shown in Fig. 23, the base number of months can be input for each item, and a selection between the input base number of months and the calculated base number of months can be accepted from the user for each material, and the selected base number of months can be set as the base number of months to be used in calculating the order quantity.

[0092] As described above, the information processing device 30 in the information processing system 1 according to the fourth embodiment accepts a selection from the user between the number of base months input by the user and the number of base months calculated by the number of base months calculation unit 333, and sets the selected number of base months as the number of base months to be used in calculating the order quantity. As a result, the value of the number of base months can be adjusted, thereby optimizing inventory while improving the efficiency of ordering operations.

[0093] [Other Modifications] In the information processing device 30 according to the first to fourth embodiments, the demand quantity forecasting unit 334, 334a may receive a user's selection between the moving average method and a statistical forecasting method that minimizes the forecast error among the forecast errors of a plurality of statistical forecasting methods, and forecast the demand quantity using the selected statistical forecasting method. FIG. 24 is a diagram illustrating an example of a first demand forecasting screen displayed on the display of the terminal device 11. As shown in FIG. 24, the first demand forecasting screen SC9 displays, for each item, a check box for selecting the moving average method and a check box for selecting the statistical forecasting method that minimizes the forecast error as recommended by the system. This allows the user to select, for each item, either the moving average method or the statistical forecasting method that minimizes the forecast error via the input device of the terminal device 11.

[0094] Furthermore, the information processing device 30 according to the first to fourth embodiments described above can also allow the user to check the monthly required quantity for steel product items for which the moving average method is used as the statistical forecasting method. FIG. 25 is a diagram showing an example of a second demand forecast screen displayed on the display of the terminal device 11. As shown in FIG. 25, the second demand forecast screen SC10 displays the monthly required quantity for each steel product item for which the moving average method is used as the statistical forecasting method. This allows the user to easily check the monthly required quantity for steel product items for which the demand is predicted using the moving average method, which is likely to be used as the statistical forecasting method.

[0095] Furthermore, in the information processing device 30 according to the first to fourth embodiments described above, it is also possible to check the past actual shipping and inventory amounts for each item. Fig. 26 is a diagram showing an example of a past actual screen displayed on the display of the terminal device 11. As shown in Fig. 26, the past actual screen SC11 displays the past actual shipping and inventory amounts for the selected steel item based on the inventory data and sales actual data in the receipt and payment data accepted by the accepting unit 331. By displaying this past actual screen, the user can easily refer to the past actual shipping and inventory amounts for each item.

[0096] Furthermore, the information processing device 30 according to the first to fourth embodiments described above may be configured to allow the user to search for inventory quantities. FIG. 27 is a diagram showing an example of an inventory search screen displayed on the display of the terminal device 11. As shown in FIG. 27, the inventory search screen SC12 displays an input box for inputting specifications, dimensions, etc., a search button, and the current inventory of items that match the content entered in the input box. By allowing the user to search for inventory quantities in this way, the user can save the effort of searching for the inventory quantity of a specific steel item and can easily check the inventory quantity of a specific steel item.

[0097] Furthermore, the information processing device 30 according to the first to fourth embodiments described above may be configured to display a Pareto analysis. FIG. 28 is a diagram showing an example of a Pareto analysis screen displayed on the display of the terminal device 11. As shown in FIG. 28, the Pareto analysis screen SC13 displays a Pareto chart, which is the result of the Pareto analysis. In the Pareto chart, customers with large shipping weights and their shipping weights are arranged from left to right, with the cumulative shipping weight ratios superimposed. In this way, the user can refer to the results of this Pareto analysis.

[0098] Furthermore, the information processing device 30 according to the first to fourth embodiments described above may allow the user to check slow inventory. FIG. 29 is a diagram showing an example of a slow inventory screen displayed on the display of the terminal device 11. As shown in FIG. 29, the slow inventory screen SC14 displays the inventory amount and the stocking date for each item. For example, if there is an item for which the difference between the date when this screen is displayed and the reference number of months is later than the stocking date, the first display control unit 337 may change the color of the stocking date for the corresponding item on the slow inventory screen SC14. By checking this slow inventory screen SC14, the user can easily manage slow inventory.

[0099] Furthermore, in the information processing device 30 according to the first to fourth embodiments described above, the user may be able to check the actual monthly shipping volume and predicted demand volume for each item. FIG. 30 is a diagram showing an example of a third demand forecast screen displayed on the display of the terminal device 11. As shown in FIG. 30, the third demand forecast screen SC15 displays the actual monthly shipping volume and predicted demand volume for each item. In this way, the user can refer to the actual monthly shipping volume and predicted demand volume for each item.

[0100] Furthermore, in the information processing devices according to the first to fourth embodiments described above, as an input operation for displaying each screen, a button for transitioning to another screen may be displayed on each screen, and each screen may be displayed by pressing the button to transition to another screen.

[0101] Finally, while various embodiments of the present invention have been described, these are presented by way of example only and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. The embodiments and their modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the appended claims. [Explanation of symbols]

[0102] 1...information processing system, 10...user company system, 11...terminal device, 12...core system, 30...information processing device, 31...communication unit, 32...storage unit, 33...control unit, 331...reception unit, 332...acquisition unit, 333...reference month number calculation unit, 334, 334a...demand quantity forecasting unit, 334a...demand quantity forecasting unit, 335...order determination unit, 336...order quantity calculation unit, 337...first display control unit, 338...order plan generation unit, 339...second display control unit, 3310...order data generation unit, 3311...order data output unit, 3312...contract remaining amount adjustment unit, 3313...intermittent determination unit, 3314...reference month number setting unit

Claims

1. a receiving unit that receives input of receipt and payment data including inventory data relating to inventory of materials, sales performance data relating to sales performance of the materials, and contract remaining amount data relating to the contract remaining amount of the materials; an acquisition unit that acquires setting data including lead time data related to the lead time of the material; a reference months calculation unit that calculates a reference months' worth of inventory that indicates the number of months that should be held based on a safety stock amount that indicates the minimum inventory amount of the material, based on the sales performance data and the lead time data; a demand quantity prediction unit that predicts the demand quantity of the material using a statistical prediction method based on the sales performance data; an order determination unit that determines whether to order the material based on the receipt and payment data, the lead time data, the reference number of months, and the demand amount predicted by the demand amount prediction unit; an order quantity calculation unit that calculates an order quantity of the material when the order determination unit determines to order the material; an order plan generation unit that generates an order plan for the material based on the order quantity; an order data generation unit that generates order data related to ordering the materials based on the order plan; an order data output unit that outputs the order data; An information processing device comprising:

2. The information processing device according to claim 1 , wherein the material is steel.

3. The information processing device according to claim 1 , wherein the order quantity calculation unit calculates the order quantity based on a periodic ordering method.

4. Further, a storage unit that stores a plurality of the statistical prediction methods, The demand prediction unit calculating a prediction error for each of the plurality of statistical prediction methods based on the sales performance data; predicting the demand quantity of the material using a statistical forecasting method that produces the smallest prediction error among the prediction errors of the plurality of statistical forecasting methods; The information processing device according to claim 1 .

5. 5. The information processing device according to claim 4, wherein the demand quantity forecasting unit calculates a mean absolute percentage error as a prediction error for each of the plurality of statistical forecasting methods by using a cross-validation method for each of the plurality of statistical forecasting methods based on the sales performance data, and forecasts the demand quantity of the material by using the statistical forecasting method that minimizes the mean absolute percentage error.

6. an intermittent determination unit that determines whether demand for the material is intermittent based on the sales performance data; The information processing device according to claim 1 , wherein the demand prediction unit predicts the demand for the material using a moving average method as the statistical prediction method when it is determined that the demand for the material is intermittent.

7. The information processing device according to claim 1 , further comprising a contract remaining amount adjusting unit that receives an input operation from a user regarding adjustment of a contract remaining amount in the contract remaining amount data, and adjusts the contract remaining amount in accordance with the received input operation.

8. The information processing device according to claim 1 , further comprising a first display control unit that controls the display unit to display an order quantity management screen for managing the order quantity.

9. 9. The information processing device according to claim 8, wherein, when the order quantity is adjusted via the order quantity management screen, the order plan generation unit regenerates the order plan based on the order quantity adjusted via the order quantity management screen.

10. the inventory data includes an inventory amount of the material at the home base, an inventory amount of the material at another base different from the home base, and an inventory amount of the material at a relay point between the home base and the another base, The information processing device according to claim 8 , wherein the order quantity management screen includes an inventory amount of the material at the home base, an inventory amount of the material at the other base, and an inventory amount of the material at the relay point.

11. The information processing device according to claim 1 , further comprising a second display control unit that controls the display unit to display an ordering plan management screen for managing the ordering plan generated by the ordering plan generating unit.

12. 12. The information processing device according to claim 11, wherein, when the ordering plan is adjusted via the ordering plan management screen, the ordering data generation unit regenerates the ordering data based on the ordering plan adjusted via the ordering plan management screen.

13. the setting data includes minimum order quantity data regarding a minimum order quantity of the material; The information processing device according to claim 1 , wherein the order quantity calculation unit calculates the order quantity so that the order quantity exceeds a minimum order quantity of the material.

14. 2. The information processing device according to claim 1, further comprising a reference number of months setting unit that accepts a selection from a user between a reference number of months input by the user and a reference number of months calculated by the reference number of months calculation unit, and sets the selected reference number of months as the reference number of months to be used in calculating the order quantity.

Images