JP2016184230A - Demand prediction device and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a demand prediction device capable of performing a demand prediction simulation by receiving regular values of a sale price at once and a program.SOLUTION: The demand prediction device includes: supply means that supplies to a display area a criteria input part for inputting criteria information corresponding to a plurality of regular values; reception means that receives at once a plurality of regular values corresponding to the criteria information as correction values on the basis of the criteria information input by a user at the criteria input part supplied to the display area; demand prediction means that substitutes each correction value received by the reception means in a parameter determining a demand prediction value of a commodity or a service on a reference date in future to calculate a demand prediction value on the reference date in future for each correction value; and result output means that outputs to the display area the demand prediction value calculated for each correction value by the demand prediction means.SELECTED DRAWING: Figure 3

Description

  Embodiments described herein relate generally to a demand prediction apparatus and a program.

Conventionally, with the popularization of POS (Point of Sales) systems, it has become possible to easily collect purchase record data for each product. The collected data is used for demand prediction of retail stores and the like.
When the demand prediction is performed, retail stores and the like can suppress product loss. For example, it is possible to suppress a product loss caused by discarding unsold products due to an excessive order quantity, or a product loss of a product that has lost a sales opportunity due to a shortage of orders due to an insufficient order quantity.

  In addition, demand forecasts have been proposed to be developed into an automatic ordering system by combining with electronic business transactions by EDI (Electronic Data Interchange).

In the automatic ordering system, the demand forecasting device uses a multiple regression analysis to forecast the demand for goods.
In the demand forecast, the number of purchases is an objective variable, and the selling price, the sales form implementation period, the display position, the number of displays, the advertising method, etc. are used as the variable factors of the objective variable. In addition to this, it has been tried to use the number of purchases of each product as a variable factor in consideration of the mutual influence between the product and its related products.

By the way, the conventional demand prediction device accepts one selling price and performs one simulation when performing demand prediction of a product.
However, when a user (for example, a buyer) decides the selling price of one product, the user often confirms a simulation result with respect to a continuous selling price at regular intervals. In this case, the buyer first inputs the first selling price into the demand prediction device and acquires the simulation result from the demand prediction device. Subsequently, the second selling price is input to the demand prediction apparatus, and the simulation result of the second selling price is acquired from the demand prediction apparatus. Similarly, after the third, one selling price is input to the demand forecasting apparatus, and the simulation result is obtained.

  As described above, when the user obtains the respective simulation results of the continuous selling price at fixed intervals, the user must instruct the demand prediction apparatus to execute the simulation for each selling price. If the increment range of the selling price is reduced or the selling price range is widened, the number of instructions increases, and the operation is burdensome for the user. In addition, since simulation results for each selling price cannot be received at a time, comparison of simulation results cannot be easily performed. For this reason, improvement has been desired.

  The problem to be solved by the present invention is to provide a demand prediction apparatus and program capable of receiving a regular value of selling price (for example, a value in a constant step) at once and performing a demand prediction simulation.

  The demand prediction apparatus according to the embodiment includes a supply means for supplying a condition input unit for inputting condition information corresponding to a plurality of regular values to a display area, and the condition input unit supplied to the display area. Based on the condition information input by the user in the above, accepting means for accepting a plurality of regular values corresponding to the condition information as correction values at a time, and each correction value accepted by the accepting means for the product or service Substituting in a parameter for determining the demand forecast value for the future reference date, a demand forecast means for calculating the demand forecast value for each of the correction values for the future reference date, and for each correction value calculated by the demand forecast means And a result output means for outputting the demand forecast value to the display area.

FIG. 1 is a block diagram illustrating a configuration of a demand prediction system according to an embodiment. FIG. 2 is a module configuration diagram of the computer. FIG. 3 is a block diagram illustrating the overall configuration of the headquarter system according to the demand prediction apparatus. FIG. 4 is a configuration diagram of a unit record. FIG. 5 is a configuration diagram of the plan edit screen (initial screen). FIG. 6 is a configuration diagram of the condition setting screen. FIG. 7 is a configuration diagram of the demand prediction result screen. FIG. 8 is a configuration diagram of a list display screen. FIG. 9 is a configuration diagram of a graph display screen. FIG. 10 is a flowchart of a demand prediction simulation process performed between the store ordering terminal and the demand prediction device. FIG. 11 shows extracted data. FIG. 12 is a flowchart of the demand prediction simulation process when the condition information is used. FIG. 13 is a processing flowchart of steps S402 and S403. FIG. 14 is a diagram illustrating the extraction data setting state (state 1). FIG. 15 is a diagram illustrating the extraction data setting state (state 2).

  FIG. 1 is a block diagram illustrating a configuration of a demand prediction system 1 according to the embodiment. The demand prediction system 1 includes a headquarter system 10, a store server 120, a headquarters special sales planning terminal 124, a store ordering terminal 125, and the like.

  The head office system 10 includes a plurality of computers 3. Each computer 3 is connected by a wired or wireless communication line (for example, a local area network (LAN) or a virtual private network (VPN)) 2. In the present embodiment, the headquarter system 10 is configured by a plurality of computers 3, but may be configured by a single computer.

  The store server 120, the headquarters special sale planning terminal 124, and the store ordering terminal 125 are configured to be connectable to the headquarters system 10 via a wired or wireless communication line (for example, the Internet, a LAN (Local Area Network), or the like) 5. ing.

  In the demand prediction system 1, the plurality of computers 3 function as application servers and database servers that provide services such as demand prediction described later. Further, the headquarters special sales planning terminal 124 and the store ordering terminal 125 function as clients that receive the service. In the present embodiment, a user screen of a client who enjoys the service is also referred to as a display area. In the demand prediction system 1, services can be provided from various servers to clients in the form of SaaS (Software as a Service), for example.

  The store server 120 is connected to a plurality of POS (Point of Sales) terminals 126 by the in-store network 6. The store server 120 is a device that collectively manages purchase record data uploaded from a plurality of POS terminals 126. The store server 120 in the demand prediction system 1 is mainly used as a collection destination of purchase record data by the head office system 10. This purchase record data is used later in the headquarter system 10 to calculate a demand forecast value.

  A general-purpose computer can be applied as the computer 3, the store server 120, the headquarters special sales planning terminal 124, and the store ordering terminal 125 described above.

  Next, the module configuration of the computer 3 to which a general-purpose computer is applied will be described, and the configuration of the headquarters special sales planning terminal 124 and the store ordering terminal 125 will be described using this module configuration as an example.

  FIG. 2 is a module configuration diagram of the computer 3. As shown in FIG. 2, the computer 3 includes a CPU (Central Processing Unit) 101 that performs information processing, a ROM (Read Only Memory) 102 that stores a BIOS (Basic Input / Output System), and various data. RAM (Random Access Memory) 103 is stored.

  The computer 3 includes an HDD 104, a medium reading device 105, a communication control device 106, a display unit 107, an input unit 108, and the like.

  The HDD 104 is an HDD (Hard Disk Drive) that stores various databases, an OS (Operating System), application programs, and the like.

  The medium reader 105 is a medium reader such as a CD-ROM drive or a DVD (Digital Versatile Disk) drive that reads a program or data from a portable storage medium 110.

  The storage medium 110 is a portable storage medium such as an optical information recording medium such as a CD-ROM or DVD that stores programs and data, or a magnetic medium such as a flexible disk (FD).

  The communication control device 106 is a communication board for transmitting information to an external device via the communication line 2 (in the case of other devices, the communication line 5).

  The display unit 107 is a display device such as an LCD (Liquid Crystal Display) that displays processing progress, results, and the like.

  The input unit 108 is a pointing device such as a keyboard and a mouse for a user to input commands and data to the CPU 101.

  In the computer 3, it is assumed that the bus controller 109 mediates transmission / reception of commands and data between the units 101 to 108.

  In the computer 3, when the user turns on the power, the BIOS calls the boot loader in the ROM 102, and the CPU 101 executes the boot loader. Subsequently, the CPU 101 loads an OS program from the HDD 104 to the RAM 103 and starts the OS. Further, the application program is started from the HDD 104 automatically or by user operation.

  As a representative OS, Windows (registered trademark) and the like are known. A program running on the OS is called an application program. The application program may cause the OS to execute some processing. The application program may be included in a part of a group of program files that constitute application software, an OS, and the like.

  An application program or a group of programs can be stored in the storage medium 110 and distributed to users. The distributed storage medium 110 is read by the medium reader 105 and installed in the HDD 104.

  In addition, the application program or the group of programs can be distributed to users via a communication network. In this case, the computer 3 downloads an application program or a group of programs from the outside via the communication control device 106 and installs these programs in the HDD 104.

  By changing the configuration of the application program or the like stored in the HDD 104 in the module configuration of the computer 3 shown in FIG. 2, the general-purpose computer can function as the store server 120, the headquarters special sales planning terminal 124, or the store ordering terminal 125. it can.

  For example, the headquarters special sales planning terminal 124 and the store ordering terminal 125 are constructed by installing a web browser on the HDD 104 and software provided using a plug-in format for the web browser. A typical plug-in is Microsoft Silverlight (registered trademark). Microsoft Silverlight is a framework that, after installation, displays animations and graphics on the browser and plays audio and video.

  Next, a demand prediction service performed by the headquarter system 10 will be described.

<< Configuration of demand forecasting device and its peripheral system >>
FIG. 3 is a block diagram illustrating an overall configuration of the headquarter system 10 according to the demand prediction apparatus of the present embodiment.
The configuration shown in the head office system 10 in FIG. 3 is realized integrally by all the computers 3 by each computer 3 (see FIG. 1) executing a predetermined program of its own machine and performing mutual communication as appropriate. It is. These configurations may be realized by a single computer.

  In this embodiment, a special sales planning support system 330 and an order quantity determination support system 331 are provided. These are subsystems for providing the service of the demand prediction device 31 to the user terminals (the headquarters special sales planning terminal 124 and the store ordering terminal 125). Hereinafter, for the sake of convenience, the special sales planning support system 330 and the order quantity determination support system 331 will be described as being constructed in a device different from the demand prediction device 31. However, the aspect of the demand prediction device 31 is not limited thereto. A part or all of the functions of the special sales planning support system 330 and the order quantity determination support system 331 may be installed in the demand prediction device 31.

<Demand prediction device>
The demand forecasting device 31 simulates the future purchase status of the product on the customer side against the past sales state on the seller side such as the selling price, display, and sales promotion of the product in each store such as retail business. The demand prediction device 31 constructs a purchase model of a customer by using past information (purchase result data) indicating a sales state on the seller side and a purchase situation on the customer side from the present time to a predetermined time in the past. . This purchasing model is composed of a constant term, a coefficient, and a variation factor. The constant term is a term indicating the number of purchases in a portion that can be regarded as not changing with time if it is, for example, about several weeks, several months, or one year. Coefficients and variation factors form a variation term in this combination. The fluctuation term is a term indicating the number of purchases of the portion that changes more quickly with respect to the constant term.

  In this purchasing model, the coefficient of the variation factor is determined by substituting past information for the variation factor (parameter). Then, the future schedule information indicating the sales state on the seller side and the purchase environment on the customer side at a predetermined future time point is applied to the variation factor of the purchase model for which the coefficient is obtained. As a result, the number of products purchased (predicted purchase number) at a predetermined time in the future is obtained as one of the demand forecast values. Further, the sales amount (predicted sales amount) and profit amount (predicted profit amount), which are other demand prediction values, are obtained from the predicted number of purchases obtained as one of the demand prediction values. The predicted sales amount is obtained by multiplying the predicted purchase quantity by the selling price. The predicted profit amount is obtained by multiplying the predicted purchase quantity by the difference between the selling price and the cost.

Next, a specific example of the purchase model will be described.
The following is a purchase model of a product group consisting of N (N is a positive integer) types of products. The product group is a product and a product that affects the number of purchases between the product (that is, related products). Hereinafter, N types of products are indicated as product 1, product 2,..., Product n,..., Product N (n = 1, 2,..., N).

  Further, the variation factors for the product n are denoted as Xn1, Xn2,..., Xnm,..., XnM (m = 1, 2,..., M). The variation factor Xnm is a factor that may cause the number of purchases to fluctuate, such as selling price, day of the week, holiday, temperature, and precipitation.

  Further, the number of purchases for the product n is indicated as Yn. Therefore, the number of purchases of the product 1, the product 2,..., The product n,..., The product N is indicated as the number of purchases Y1, Y2,.

  In this example, the number of purchases Y1, Y2,..., Yn,..., YN is also calculated as a variable factor considering that the number of purchases of each product interacts with the number of purchases of related products of the product. .

  As a result, a formula indicating the purchase model (that is, a calculation formula for obtaining the predicted number of purchases of the product) is expressed by the following formula (1).

  Yn = an1Y1 + an2Y2 + ... + an (n-1) Yn-1 + an (n + 1) Yn + 1 + ... + anNYN + bn0 + bn1Xn1 + bn2Xn2 + ... + bnMXnM (1)

  In the right side of the above equation (1), bn0 is a constant term. The rest is the variable term. Among the fluctuation terms, an1Y1 + an2Y2 + ... + an (n-1) Yn-1 + an (n + 1) Yn + 1 + ... + anNYNN corresponds to a term reflecting the influence of the number of related products purchased.

  In the above equation (1), an1,..., An (n-1), an (n + 1), ..., anN, bn0, ..., bnM are called regression coefficients. Each regression coefficient indicates the contribution rate that each variable factor contributes to the value of the objective variable. In this specification, the constant term bn0 is also referred to as a regression coefficient.

<Data collection / conversion system>
The data collection / conversion system 32 collects past information from the present time to a predetermined past time from the store server 120 or the like. Then, the data collection / conversion system 32 converts the collected past information into a predetermined data structure and transmits it to the past information database 310 of the demand prediction device 31.

  The data collection / conversion system 32 has various data collection systems such as a known POS system, an ordering system, a delivery product inspection system, a weather data reception system, a local event input system, and a data conversion system. doing. As a result, the data collection / conversion system 32, as past information, includes, for example, purchase quantity data, sales price / promotion (planning) data, ordering data, inventory data, weather data, day of the week data, and local events. Collect data.

<Planned data collection / conversion system>
The scheduled data collection / conversion system 33 is the planned sales price / promotion data (planned) data, planned arrival data, planned weather data, day of the week data, and planned event data for each store from the present time to a predetermined time in the future. Collect future schedule information such as. Then, the scheduled data collection / conversion system 33 converts the collected future schedule information into a predetermined data structure and transmits it to the future information database 311 of the demand prediction device 31.

  The scheduled data collection / conversion system 33 includes a special sale planning support system 330 and an order quantity determination support system 331.

  The special sales planning support system 330 accepts connections from a plurality of special sales planning terminals 124 at the headquarters, and supports the input of planning data by a special sales planner who is a user of each special sales planning terminal 124 at each headquarters.

  Specifically, the special sales planning support system 330 includes a demand prediction setting screen output by the input reception unit 317 described later in connection with the headquarters special sales planning terminal 124 and outputs it to the headquarters special sales planning terminal 124. The demand prediction setting screen output by the input receiving unit 317 is a screen for allowing the user to register or change the plan data in the future information database 311. Upon receiving the plan data and the change data, the special sale plan support system 330 transmits the received data to the order quantity determination support system 331 in order to reflect the received data in the future information database 331.

  The order quantity determination support system 331 accepts connection from the store order terminal 125 of each store, and supports the confirmation process of the order number of the orderer who is the user of each store order terminal 125.

  Specifically, the order quantity determination support system 331 includes a demand prediction setting screen and a plan edit screen (see FIG. 5) output from the input receiving unit 317 described later in the order processing screen by connection from the store order terminal 125. Then, the order processing screen is output to the store order terminal 125. Here, the plan editing screen is a transition screen of the demand prediction setting screen and is a screen for allowing the user to simulate the demand prediction of the product for each plan.

  For example, when the user edits a set value of a variable factor such as a selling price on the plan edit screen, the order quantity determination support system 331 passes the set value to the demand prediction device 31 (an input receiving unit 317 described later). Then, the plan edit screen including the demand prediction data of the set value returned from the demand prediction device 31 (input reception unit 317) is output to the store ordering terminal 125.

  Here, the demand prediction data is data including a simulation result of demand prediction performed by the demand prediction device 31. As will be described in detail later, the demand prediction data includes product information, schedule information, and prediction information as a simulation result.

  The order quantity determination support system 331 outputs data indicating the order quantity of the product to the order system 34 when the store order terminal 125 is instructed by the user to determine the order quantity of the product. The order quantity is determined on the order screen.

  The ordering system 34 requests the merchant that manufactures and sells the product to place an order for the product according to the order number of all the stores determined by the order number determination support system 331.

<< Configuration and processing of demand forecasting device >>
<Configuration of demand forecasting device>
The past information database 310 includes store / region data 310d, date / time data 311d, weather / event data 312d, classification / attribute data 313d, sales price / promotion data (314d), display data 315d, purchase quantity data 316d, inventory A plurality of data tables such as data 317d and order data 318d are stored.

  Data extraction, deletion, and update in the past information database 310 are performed in units of records having the following configuration, for example. In this example, the record is composed of store / region data, date data, weather / event data, classification / attribute data, selling price / promotion (planning) data, display data, purchase quantity data, inventory data, order data, etc. Record. Here, the number-of-purchase data in the record is the target of data that is substituted into the objective variable. Further, other arbitrary data (one or a plurality of data excluding purchase quantity data) in the same record can be a target to be substituted for the variation factor. Below, the data substituted into the variation factor is generically referred to as variation factor information.

  The regression coefficient calculation unit 312 calculates a regression coefficient using past information.

  Specifically, the past purchase quantity data of the product n is substituted for the objective variable Yn in the equation (1), and the past fluctuation factor information is obtained for the fluctuation factors Y1,..., YN, Xn1,. substitute. There are M + N regression coefficients in equation (1). Therefore, M + N or more simultaneous equations are established by using M + N or more data sets obtained by combining past purchase quantity data and past variation factor information corresponding thereto. Then, the regression coefficient of the objective variable Yn is obtained from the simultaneous equations. This operation is performed for each objective variable Y1,..., YN, and individual regression coefficients for each product 1,. In this case, in order to improve the prediction accuracy, a known correction conditional expression (for example, a least square method) may be introduced.

  Note that the products 1 to N are related products. The products are grouped as related products in units of “department”, “line” classified below, and “class” further classified below. The regression coefficient calculation unit 312 extracts only records corresponding to related products from the past information database 310 based on the group information set in the classification / attribute data. Thereby, the regression coefficient calculation part 312 can obtain | require the regression coefficient of each goods on the basis of the past information of related goods. Below, in order to make the explanation easy to understand, the unit of the related product is described as “department”.

  The processing for obtaining the regression coefficient is performed at night.

  The regression coefficient database (DB) 313 stores individual regression coefficients of the products 1,..., Products N,. For example, for product 2, A21, A23,..., A2N, B20, B21,. Here, A21, A23, ..., A2N, B20, B21, ..., B2M represent numerical values, respectively.

  The future information database (DB) 311 stores schedule information 311A at an arbitrary time in the future from the time of acquisition of data stored in the past information database 310. In this embodiment, as a data table for storing the schedule information 311A, the planned store / region data 320d, the planned date / time data 321d, the planned weather / event data 322d, the planned classification / attribute data 323d, the planned selling price / promotion (planning). A plurality of data tables such as data 324d and scheduled display data 325d are provided. The planned sales price / promotion (planning) data 324d includes “plan identification code”, “plan name”, “date”, “product code”, “planned standard price”, “planned sale price (planned selling price)”. ”,“ Scheduled discount ”,“ scheduled flyer placement ”and other fields.

  The future information database 311 stores prediction information 311B, which will be described later, indicating the result of demand prediction. The future information database 311 includes various data tables that store the prediction information 311B. Various data tables will be described later.

  Data extraction, deletion, and updating in the future information database 311 are performed in units of records having the following configuration, for example. In this example, the record includes planned store / region data, planned date data, planned weather / event data, planned classification / attribute data, planned selling price / promotion (planning) data, planned display data, and purchase of forecast information 311B. The record is composed of number data, inventory data, order data, and the like. Here, the planned selling price / promotion (planning) data in the record includes “plan identification code”, “plan name”, “planned selling price”, “planned standard price”, etc. in the planned selling price / promotion (planning) data 324d. Data are included. Each item in the record corresponds to one of various data tables (including the product master table). At the time of extraction, corresponding data is extracted from the data table, and the data is set in each item of the record. At the time of update, the corresponding data in the data table is overwritten and updated by the data set in each item in the record.

FIG. 4 is a diagram showing the configuration of the record (unit record).
As shown in FIG. 4, the unit record of the future information database 311 is composed of schedule information 4A and prediction information 4B.

  The schedule information 4A includes “store code” 401, “date” 411, “day of the week” 412, “holiday” 413, “forecast maximum temperature” 421, “forecast minimum temperature” 422, “forecast precipitation” 423, “ Line code ”431,“ Class code ”432,“ Product code ”433,“ Plan identification code ”441,“ Plan name ”442,“ Scheduled standard price ”443,“ Scheduled selling price ”444,“ Scheduled discount ”445, It consists of “schedule flyer publication” 446 and “schedule display state” 451.

  The prediction information 4B includes “predicted purchase quantity” 461, “predicted sales amount” 462, “predicted profit amount” 463, and the like.

The “store code” 401 contains a store identification code.
In the “year / month / day” 411, “day of the week” 412, and “holiday” 413, a future date (year / month / day), information indicating the day of the week, and information indicating whether it is a holiday are respectively entered. In the “day of the week” 412 and the “holiday” 413, a flag “1” is set to the corresponding item.
“Predicted maximum temperature” 421, “Predicted minimum temperature” 422, and “Predicted precipitation” 423 contain weather data of the future date of “Date” 411.

  In the “line code” 431 and the “class code” 432, information (line code and class code) indicating the classification of the products scheduled for sale on the future date of the “year / month / day” 411 is entered. In this example, the product categories are classified into “department”, “line code”, and “class code” from the top to the bottom. In this example, since “department” is limited to one type, the “department” item is not included. When a plurality of departments are provided, an item “department” may be included.

In the “product code” 433, an identification code (also referred to as a product code or a single product code) of the product to be sold is entered.
The “plan identification code” 441 contains a plan identification code for uniquely identifying a special sale plan. For items not corresponding to the special sale plan, the “plan identification code” 441 is left blank.
In the “plan name” 442, a plan name corresponding to the plan identification code is entered.

In “scheduled standard price” 443, the selling price (scheduled standard price) at the time of standard sales of the product scheduled for sale on the future date of “date” 411 is entered.
In the “scheduled sale price” 444, the sale price (scheduled sale price) at the time of sale of the product scheduled for sale on the future date of “date” 411 is entered.
The “scheduled discount amount” 445 contains a discount amount from the scheduled standard price. The discount amount is indicated by an amount obtained by subtracting the planned sale price from the planned standard price.

The “schedule flyer publication” 446 contains information indicating the sales promotion method for the product scheduled for sale on the future date of “date” 411. In this example, a flag “1” is set when a flyer is placed.
In the “scheduled display state” 451, information indicating the sales state of the product scheduled for sale on the future date of the “date” 411 is entered.

The “predicted purchase quantity” 462 contains the estimated purchase quantity of the product scheduled for sale on the future date of “date” 411. The predicted number of purchases per day is one of demand forecast values obtained by a demand forecast simulation.
“Estimated sales amount” 462 includes the estimated daily sales amount of the product scheduled for sale on the future date of “date” 411. The predicted sales amount per day is one of demand forecast values obtained by a demand forecast simulation.
In the “predicted profit amount” 463, the predicted profit amount of one day of the product scheduled for sale on the future date of “date” 411 is entered. The predicted profit amount per day is one of the demand forecast values obtained by the demand forecast simulation.

  Various data corresponding to the schedule information 4A is substituted into the variation factor when calculating the demand forecast. Hereinafter, various data corresponding to the schedule information 4A are collectively referred to as “future variable factor information”.

  The predicted purchase number calculation unit 314 calculates the predicted purchase number of each product on a designated date in the future using the regression coefficient obtained at night or the like.

  Specifically, the predicted purchase number calculation unit 314 uses the regression coefficient of each product registered in the regression coefficient DB 313 and the schedule information 311A registered in the future information database, one week ahead or one month later. A demand prediction simulation up to a predetermined period ahead, such as the destination, is performed in advance. Then, the execution result of the demand prediction simulation (the predicted sales number, the predicted sales amount, and the predicted profit amount) is stored in the future information database 311 as the prediction information 311B.

  Further, the predicted purchase number calculation unit 314 receives a re-simulation execution instruction from an input reception unit 317 described later. Then, the predicted purchase number calculation unit 314 acquires correction data from the input reception unit 317 and performs re-simulation. For example, it is assumed that the predicted purchase number calculation unit 314 receives a re-simulation execution instruction from the input reception unit 317 at a special sale price at predetermined intervals. Then, the predicted purchase number calculation unit 314 performs re-simulation a plurality of times using each value of the predetermined interval. Then, the result of the re-simulation is returned to the input receiving unit 317.

The predicted purchase number calculation unit 314 performs a demand prediction simulation as follows.
First, the target record is acquired from the future information database 311 or the input receiving unit 317. The target record is a record of a product n for which demand prediction is performed, and other products 1,..., A product n−1, a product n + 1, a date information and a classification code (line code and class code) included in the record. ..., a record of product N. In the case of re-simulation, the record of the product n that has been rewritten with the correction value is used.

  The predicted purchase quantity calculation unit 314 applies future fluctuation factor information included in the record of the product n and the regression coefficient of the product n stored in the regression coefficient database 313 to the prediction formula (1). This procedure is performed for each product 1,..., Product N (product group), and a simultaneous expression for each product 1,. From these equations, the predicted purchase number Y1,..., YN of the product group on the specified date is calculated.

  Further, after calculating the predicted purchase number Y1,..., YN, the predicted purchase number calculation unit 314 obtains the predicted sales price of each product by multiplying the planned sale price (scheduled special sale price) of each product. Further, the predicted profit amount of each product is obtained from the cost of each product included in the product master information (not shown).

  After that, in the case of re-simulation, the predicted purchase number calculation unit 314 calculates the value set for each corresponding item in the target record, as the predicted purchase data, predicted sales amount data, predicted profit amount data of each product. Rewrite to And the record of each goods 1, ..., the goods N after the rewriting is returned to the input reception part 317.

  As described above, the future information database 311 stores prediction information 311B in addition to the schedule information 311A. In the present embodiment, the future information database 311 includes a plurality of data tables such as predicted purchase data 326d, predicted inventory data 327d, and recommended order data 328d as data tables for storing the predicted information 311B. Among these, the predicted purchase data 326d stores data of the predicted purchase number, the predicted sales amount, and the predicted profit amount calculated by the predicted purchase number calculation unit 314. The predicted inventory data 327d and the recommended order data 328d store data such as the predicted inventory number calculated by the order / inventory number calculation unit 316 described below and the recommended order number.

  The order / inventory quantity calculation unit 316 subtracts the predicted purchase quantity calculated by the predicted purchase quantity calculation unit 314 from the current inventory quantity to calculate the predicted inventory quantity at a set date and time in the future. Further, the order / inventory quantity calculation unit 316 increases the recommended order quantity based on the calculated predicted inventory quantity, for example, if the predicted inventory quantity is less than the minimum inventory quantity, and recommends if the predicted inventory quantity exceeds the minimum inventory quantity. The number of orders is reduced and finally processed into recommended order data according to a predetermined order unit and order lead time. The predicted inventory quantity is stored in the predicted inventory data 327d, and the recommended order quantity is stored in the recommended order data 328d.

  Next, the input receiving unit 317 will be described. The input reception unit 317 mainly includes a supply unit, a reception unit, and a result output unit. When the input reception unit 317 receives a predetermined request from the order quantity determination support system 331, the input reception unit 317 performs various processes according to the received request.

  For example, the input reception unit (reception unit) 317 receives an initial screen acquisition request from the order quantity determination support system 331. Then, the input reception unit (supply means) 317 generates initial screens (see FIG. 5) of the demand prediction setting screen and the plan editing screen, and outputs these screens to the order quantity determination support system 331. In the plan edit screen, a condition input unit for inputting conditions such as a step interval is provided.

  Further, the input receiving unit (supplying unit) 317 extracts data from the future information database 311 when generating a screen. Then, the extracted data is set in the corresponding part of the demand prediction setting screen or the plan editing screen (initial screen).

  For example, if it is a plan edit screen (initial screen), based on the plan identification data designated on the demand prediction setting screen, all records corresponding to the sales target products in the plan period are extracted. Further, product information such as the product name of the product to be sold is extracted from product master data (not shown). And a part or all of those extraction data is set to the target area of demand forecast data, and other setting items. Note that the extracted data at this time is temporarily saved in a storage unit such as a RAM for subsequent editing.

  The input receiving unit 317 (receiving unit) receives a request issued from the demand prediction setting screen or the plan editing screen (initial screen) from the order quantity determination support system 331. After the reception, the input reception unit 317 performs the following process.

  For example, it is assumed that the input reception unit (reception unit) 317 receives a registration request for new sale plan data from the demand prediction setting screen. Then, the input receiving unit (receiving unit) 317 registers the received plan data in the future information database 311.

  Further, it is assumed that the input receiving unit (receiving unit) 317 receives an editing request from the plan editing screen (initial screen). Then, the input receiving unit (receiving unit) 317 performs the following process according to the type of data received together with the editing request.

  First, a case where the input receiving unit (receiving unit) 317 receives correction data together with an editing request will be described.

  The input reception unit (reception unit) 317 rewrites the corresponding portion of the extracted data temporarily stored in the storage unit with the correction data received together with the editing request. Subsequently, the input reception unit (reception unit) 317 instructs the predicted purchase quantity calculation unit 314 to execute a demand simulation re-simulation.

  Then, the predicted purchase number calculation unit 314 executes re-simulation using the rewritten data in the storage unit. And the prediction information contained in the data after rewriting of a memory | storage part is updated with the result recalculated by re-simulation.

  The input receiving unit (result output unit) 317 sets the update data generated in this manner in an area for displaying the demand forecast data, and generates an update screen for the plan edit screen (initial screen). Then, the update screen is output to the order quantity determination support system 331.

  After that, the input receiving unit (receiving unit) 317 receives the future signal database 311 based on the update data temporarily stored in the storage unit, for example, when receiving a pressing signal of the confirmation button on the plan edit screen (update screen). Update the setting value of the corresponding data.

  Next, a case where the input receiving unit 317 receives condition information such as a step value together with an editing request will be described.

  Assume that the input reception unit (reception unit) 317 receives, as a predetermined request, for example, a start value, a step value, and an end value of a sale price that is one of future fluctuation factors (parameters). Then, the input reception unit (reception unit) 317 repeatedly generates a correction value at a step interval indicated by the step value from the start value, and stops when the end value is exceeded. Subsequently, the input receiving unit 317 rewrites the value of the sale price of the extracted data temporarily stored in the storage unit with a value of a predetermined step interval from the start value to the end value (correction value of a plurality of patterns). .

  Specifically, the input receiving unit (receiving unit) 317 generates the same number of extracted data as the number of correction values by copying the extracted data temporarily stored in the storage unit. Further, the input reception unit (reception unit) 317 assigns one of the correction values of each pattern to each extracted data (copy), and rewrites each sale price value with the correction value.

  Note that the rewritten data is temporarily stored in the storage unit.

  Subsequently, the input reception unit (reception unit) 317 instructs the predicted purchase number calculation unit (demand prediction unit) 314 to execute the re-simulation process of the demand prediction based on the rewritten data.

  Then, the predicted purchase number calculation unit (demand prediction unit) 314 executes re-simulation using the data after the first pattern rewrite in the storage unit, and obtains the prediction information of the data after the first pattern rewrite in the storage unit. Update with the result recalculated by re-simulation.

  Subsequently, the predicted purchase number calculation unit (demand prediction unit) 314 performs the same process as the first pattern on the data after rewriting the second pattern in the storage unit.

  Further, after that, the same processing is executed on the data after rewriting the remaining patterns.

  When processing such as re-simulation on the data after rewriting of all patterns is completed, the input receiving unit (result output unit) 317 compares the update data of all patterns with each other, and the demand prediction result screen 51G (see FIG. 7). ), A list display screen 52G (see FIG. 8), and a graph display screen 53G (see FIG. 9). These generated screens are output to the order quantity determination support system 331.

  Specifically, the input receiving unit (result output unit) 317 generates the demand prediction result screen 51G as follows. First, the value of the predicted number of purchases in the data string in which the value of the sale price is corrected is compared with the update data of all patterns. Then, the maximum value of the predicted number of purchases and the value of the sale price that takes the maximum value are set in a predetermined area of the demand prediction result screen 51G. Further, the input receiving unit (result output means) 317 compares the value of the predicted profit amount in the data string with the value of the sale price being corrected, with the update data of all patterns as a target. Then, the maximum value of the predicted profit amount, the value of the sale price at which the predicted profit amount has the maximum value, the value of the predicted purchase number, etc. are set in a predetermined area of the demand prediction result screen 51G.

  The input receiving unit (result output unit) 317 generates the list display screen 52G as follows. First, each value of the predicted number of purchases of related products included in the original one extracted data, and each of the predicted purchase number of related products included in the update data of one pattern in all patterns Compare the value of. Furthermore, the increase / decrease width of the predicted purchase number for the related product of the pattern is obtained one by one. Then, the correction value of the special sale price of the pattern, the predicted purchase number, the predicted profit amount, and the obtained increase / decrease range of each related product are set in association with the predetermined area of the list display screen 52G. This operation is performed on the update data of all patterns. That is, change information (cannification) of the predicted number of purchases of other related products by correcting the sale price of one product is obtained for each pattern of update data.

  The input receiving unit (result output unit) 317 generates the graph display screen 53G as follows. First, from the update data of each pattern, a set of values of the special sale price and the predicted purchase number is extracted from the data string in which the special sale price value is corrected. Then, a graph is generated by plotting a set of values extracted from the update data of each pattern on a coordinate plane with the sale price and the predicted purchase number as axes. Further, a set of values of the special sales price and the predicted profit amount is extracted from the update data of each pattern in the data string in which the special sales price value is corrected. Then, a graph is generated by plotting a set of values extracted from the update data of each pattern on a coordinate plane with the sale price and the predicted profit amount as axes. These graphs are set as image information in a predetermined area of the graph display screen 53G.

  The input receiving unit (receiving unit) 317 may delete the update data of a plurality of patterns temporarily stored in the storage unit after outputting the generation screen to the order quantity determination support system 331.

Next, various screen configurations will be described.
FIG. 5 is an example of a configuration diagram of the plan edit screen (initial screen).
The plan editing screen 5G shown in FIG. 5 is generated as a transition screen of the demand prediction setting screen by the input receiving unit (supply means) 317 based on the selection of one plan by the user on the demand prediction setting screen (not shown). It is a screen output toward the store ordering terminal 125.

  The plan editing screen 5G shown in FIG. 5 includes a plan display area 51, a product group selection area 52, a data display area 53, a data operation area 54, and a navigation button area 56 in which various buttons are arranged. It is out.

  The plan display area 51 displays “plan No. (plan identification code)” 510, “plan name” 511, “start date” 512, and “end date” 513 of the plan selected on the demand prediction setting screen of the previous screen. It is provided as an area. Here, “start date” 512 and “end date” 513 represent planning periods.

  The product group selection area 52 is provided as a selection area for the user to specify the range of products to be displayed in the data display area 53, such as part or all of the product group to be planned. The merchandise group selection area 52 includes a range designation part divided into “department” 520-1, “line” 520-2, and “class” 520-3. In “Department” 520-1, the range of the highest product classification category can be designated. In “Line” 520-2, the range of the product classification category immediately below “Department” 520-1 can be designated. In the “class” 520-3, the range of the lowest product classification category can be designated. Each range designating section has a check box B1 for designating all of the product category categories displayed therein as a range.

  The data display area 53 is provided as a display area for displaying demand prediction data for some or all products in the product group to be planned. As display items of demand forecast data, “single item code (product code)” 530, “manufacturer” 531, “product name” 532, “standard” 533, “sales date (future scheduled date)” 534, “sale price” 535 , “Forecast sales number (predicted purchase number)” 536, “forecast sales amount” 537, “forecast profit amount” 538, “actual sales number (actual purchase number)” 539, “actual sales amount” 540, “actual profit amount” ”541. In the data display area 53, by default, demand forecast data is displayed for all products in the product group. Only the products within the restriction are displayed by the user's operation for restricting the displayed products in the product group selection area 52.

  Note that the demand forecast data shown as an example in the data display area 53 of FIG. 5 is calculated based on schedule information preset in the future information database 311.

  The data operation area 54 is provided as an area for editing data displayed in the data display area 53. In the data operation area 54, an input box B2 for editing data, a condition input button B4, and a correction button B3 are provided.

  The input box B2 is provided so that data editing for each item (“single item code” 530, “manufacturer” 531...) In the data display area 53 can be accepted.

  When the data string of one product is selected by the user in the data display area 53, the individual data of each item of the selected data string is displayed so as to be editable in each input box B2.

  The condition input button (instruction part) B4 is a condition setting screen (condition input part) 50G (FIG. 6) that accepts data editing of a predetermined item in the data display area 53 (in this example, an item of “special sale price” 535). Button).

  When the user presses the condition input button B4, a condition setting screen 50G is displayed, and data can be edited by inputting a condition for a predetermined item (“sale price” 535). For example, the user inputs condition information including a start value, a step value, and an end value on the condition setting screen 50G. As a result, all values corresponding to the start value and the step values from the start value to the end value are designated as correction values for the sale price.

FIG. 6 is an example of a configuration diagram of the condition setting screen.
The condition setting screen 50G shown in FIG. 6 includes input boxes B50, B51, and B52 for inputting step values, start values, and end values as condition information. In addition, a condition confirmation button B5 is provided for confirming values input in the input boxes B50, B51, and B52 as condition information.

The input box B50 is an input box for inputting the value of the step interval.
The input box B51 is an input box for inputting the start value of the increment.
The input box B52 is an input box for inputting the end value of the increment.

Returning to the description of FIG.
The correction button B3 is an operation for making a declaration for correcting a part of the data string of the product designated in the data display area 53 to a value determined by the rewritten data in the input box B2 or the input condition on the condition setting screen 50G. Button.

  For example, the user designates a data string of one product in the data display area 53 and displays the data string in each input box B2. Subsequently, the value of the special sale price in the input box B2 is rewritten, and the correction button B3 is pressed. As a result, the value after rewriting the special sale price is transmitted to the demand prediction device 31 via the order quantity determination support system 331 as correction data.

  The user can also press the condition input button B4 to open the condition setting screen 50G and specify the correction value for the sale price on the screen in a batch by the condition input. In this case, by pressing the correction button B3, the condition information input to the condition setting screen 50G, for example, the step value, the start value, the end value, and the like is transmitted to the demand prediction device 31 via the order quantity determination support system 331. . As described above, the condition setting screen 50G functions as a condition input unit for instructing to change the value of the special sale price to a plurality of values such as a predetermined interval at a time.

  In the input box B2, when an item that is not related to a variation factor, such as an item such as product information, is corrected, the master information and schedule information of the product are corrected to correction data. The corrected update data is set in the data display area 53.

  When the item corresponding to the variation factor is corrected in the input box B2, the demand prediction re-simulation is executed in the demand prediction device 31. Then, the demand prediction data obtained by the re-simulation is set in the data display area 53. The update to the future information database 311 is performed after the user presses the confirmation button B9.

  In addition, when the condition information is input on the condition setting screen 50 </ b> G and correction is instructed, the demand prediction device 31 executes the demand prediction re-simulation. In this case, a demand prediction result screen 51G (see FIG. 7) showing aggregate information such as the maximum number of sales and the maximum profit amount among the demand prediction data obtained by the re-simulation is output to the store ordering terminal 125. It will be.

  The navigation button area 56 is provided as an area for displaying screen operation buttons common to other screens. On the plan edit screen 5G, an end button B6, a print button B7, a return button B8, and a confirm button B9 are displayed so as to be operable.

The end button B6 is a button for declaring that the demand prediction setting is ended.
The print button B7 is a button for outputting a print command for the plan editing screen 5G to a printer (not shown).
The return button B8 is a button for returning to the previous demand prediction setting screen.

  The confirm button B9 is a button for confirming the setting information displayed in the data display area 53 by re-simulation and instructing to update the future information database 311 with the setting information.

  FIG. 7 is an example of a configuration diagram of the demand prediction result screen.

  The demand prediction result screen 51G shown in FIG. 7 includes an information area J1 indicating the maximum profit amount and an information area J2 indicating the maximum number of sales.

  In the information area J1 indicating the maximum profit amount, the special sale price data L2 and the predicted sales number data L3 at that time are set together with the maximum profit amount data L1.

  In the information area J2 indicating the maximum sales number, the special sales price data L6 at that time is set together with the maximum sales number data L5.

  Here, as an example, the demand forecast result when the step value “5” is input to the input box B50 of the condition setting screen 50G, the start value “300” is input to the input box B51, and the end value “350” is input to the input box B52. The contents are set. In the information area J1, “450 yen” is set as the maximum profit amount data L1, “325 yen” is set as the special sale price data L2, and “30” is set as the predicted sales quantity data L3. In the information area J2, “78” is set as the maximum sales number data L5, and “300 yen” is set as the special sales price data L6.

  The demand prediction result screen 51G is provided with two call buttons for calling a detailed information screen of the demand prediction result for each correction value. One is a list display button B11 for calling up a list display screen 52G (see FIG. 8) in which a demand forecast value for each correction value, cannibalization information, and the like are collected in a list. The other is a graph display button B12 for calling up a graph display screen 53G (see FIG. 9) that displays the transition of the demand forecast value based on the change of the correction value as a graph.

  Furthermore, a close button B13 is provided on the demand prediction result screen 51G. The demand prediction result screen 51G is closed when the user presses the close button B13.

  FIG. 8 is an example of a configuration diagram of the list display screen.

  The list display screen 52G shown in FIG. 8 includes “scheduled selling price” 1K, “predicted purchase quantity” 2K, “predicted profit amount” 3K, and “increase / decrease value” 4K-1, 4K-2,.・ Establish items such as. Further, an area J5 for arranging data is provided below each item. Each value shown in “scheduled selling price” 1K is a plurality of correction values generated from the condition information.

  Here, as an example, for each correction value when the step value “5” is input to the input box B50 of the condition setting screen 50G, the start value “300” is input to the input box B51, and the end value “350” is input to the input box B52. This shows a list of demand forecast values and cannibalization information. The order of the correction values is the descending order of the planned selling price as shown in each value of “scheduled selling price” 1K. The order of correction values may be ascending. The order may be rearranged using other items as keys.

  Signed values shown in “Increase / decrease values” 4K-1, 4K-2, etc. of each product indicate the increase / decrease (minus decrease) and the range (number) of the predicted purchase number of related products after re-simulation. Represents. The reference for increase / decrease is the value of the predicted purchase number set in the future information database 311, that is, the value of the predicted purchase number of related products included in the original extracted data.

  The data column shown in the first row of the area J5 shows that when the “scheduled selling price” of the product is changed to “300 (yen)”, the “predicted purchase quantity” is “78 (pieces)” and the “predicted profit amount” is It becomes “−780 (yen)”, the “predicted purchase number” of the product B decreases by “−3 (pieces)”, the “predicted purchase number” of the product C increases by “1 (pieces)”, and so on. Show.

  In addition, the minus sign of “predicted profit amount” in the data row in the first row indicates that the planned selling price (300 yen) of the product is lower than the cost.

  The list display screen 52G includes a close button B14. When the user presses the close button B14, the list display screen 52G is closed.

FIG. 9 is an example of a configuration diagram of the graph display screen.
A graph image display area J9 is provided on the graph display screen 53G shown in FIG.

  The graph image shown as an example in the display area J9 in FIG. 9 is generated as follows.

  First, the value of the planned selling price from the start value to the end value input on the condition setting screen 50G is set on the horizontal axis. As the value of each memory, a value given at an interval from the start value to the increment value is set.

  Two axes are set on the vertical axis. The predicted purchase quantity is set on the first axis (left axis in FIG. 9). The first axis has a range from the vicinity of the minimum value to the vicinity of the maximum value of the predicted purchase number after re-simulation, and a value of 10 units is set in each memory. The predicted sales profit is set on the second axis (the right axis in FIG. 9). On the second axis, the range from the vicinity of the minimum value to the vicinity of the maximum value of the predicted sales profit after re-simulation is set, and a value of 100 yen unit is set in each memory.

  Then, the graph information is generated by plotting the coordinates of the set of the planned selling price and the predicted purchase number obtained by re-simulation on these coordinate planes and connecting the plotted places with lines.

  Also, the graph information is generated by plotting the coordinates of the set of the planned selling price and the predicted profit amount and connecting the plotted places with a line. These are converted into images and set in the display area J9.

  As an example, the graph display screen 53G in FIG. 9 shows a graph image generated based on the data string in the list display screen 52G in FIG.

  A solid line graph GR1 is a graph showing a change in the predicted purchase number. A broken line graph GR2 is a graph showing a change in the predicted sales profit. From the solid line graph GR1, it can be seen that the maximum number of predicted purchases is 78 at the start planned selling price (300 yen), and thereafter the predicted number of purchases decreases every time the planned selling price increases. On the other hand, from the broken line graph GR2, the predicted profit amount increases each time the planned selling price increases, and the predicted profit amount becomes maximum when the planned selling price (325 yen) is reached, and after that, the planned selling price increases. It can be seen that the predicted profit amount decreases every time.

  The graph display screen 53G includes a close button B15. When the user presses the close button B15, the graph display screen 53G is closed.

<Processing of demand forecasting device>
FIG. 10 is a flowchart of a demand prediction simulation process performed between the store ordering terminal 125 and the demand prediction device 31. It is assumed that an order quantity determination support system 331 is interposed between the store order terminal 125 and the demand prediction device 31.

  Broken line arrows shown in the figure indicate the flow of signals between the store ordering terminal 125 and the demand prediction device 31.

  First, the store ordering terminal 125 displays a demand prediction setting screen on the display screen (S10). For example, the person in charge of ordering (user) issues an instruction to start the demand prediction setting program at the store ordering terminal 125. As a result, the store ordering terminal 125 first displays the planning screen, and then receives a press signal of the screen call button by the user and displays the demand prediction setting screen.

  Next, the store ordering terminal 125 displays a plan list on the demand prediction setting screen and receives a selection input for one plan from the user (S11). When the store ordering terminal 125 receives a selection input of one plan from the user, the store ordering terminal 125 transmits an instruction signal for acquiring demand prediction data of one plan to the demand prediction device 31. The store ordering terminal 125 includes plan identification data in the instruction signal so that the demand prediction apparatus 31 can identify the plan to be designated.

  One demand prediction apparatus 31 (input reception part 317) determines whether the said instruction | indication signal was received (S20). When receiving the instruction signal from the store ordering terminal 125, the demand prediction device 31 (input reception unit 317) determines Yes in the determination process of step S20.

  When a Yes determination is made in step S20, the demand prediction device 31 (input reception unit 317) generates a plan edit screen 5G (see FIG. 5) and transmits the plan edit screen 5G to the requesting store ordering terminal 125. (S21). The plan edit screen 5G is generated as described later based on the following assumptions.

  As a premise, it is assumed that the demand prediction device 31 (regression coefficient calculation unit 312) calculates the regression coefficient of each product at night using past purchase records, and registers the calculated regression coefficient in the regression coefficient DB 313.

  Further, the demand prediction device 31 (the predicted purchase number calculation unit 314) uses the regression coefficient of each product registered in the regression coefficient DB 313 and the schedule information 311A registered in the future information database 311 for one week. It is assumed that a demand prediction simulation up to a predetermined period ahead, such as ahead or one month ahead, is performed in advance.

  Further, it is assumed that the execution result of the demand prediction simulation (predicted sales number, predicted sales amount, and predicted profit amount) is stored in the future information database 311 as the prediction information 311B.

  Further, the plan information 311A includes plan data. The plan data is future plan data input by the planner from the plan screen displayed on the headquarters special sales plan terminal 124.

  Based on the above, the demand prediction device 31 (input reception unit 317) first extracts the demand prediction data corresponding to the plan identification data received from the store ordering terminal 125 from the future information database 311. Specifically, the demand prediction device 31 (input reception unit 317) extracts all corresponding records (demand prediction data) from the future information database 311 using the received plan identification data as a key. The product information is acquired from product master data (not shown).

FIG. 11 shows extracted data.
As an example, extracted data of a special sale plan (plan identification code: 001, special sale period: January 3, 2015 to January 9, 2015) is shown.

  As illustrated in FIG. 8, the extracted data includes a record group corresponding to the value “001” of the “plan identification code” 441.

  In the planned selling price / promotion data 324d, a “planning period” of a plan is set for each plan indicated by the “plan identification code”.

  Furthermore, according to the day of the planning period, the “product code” of the target product, the “scheduled selling price (sale price)” of the product, the “scheduled standard price” of the product, the “scheduled discount”, and the “scheduled flyer” "Post" is set.

  Therefore, by searching the future information database 311 using the set value of the “plan identification code” as a key, the “plan identification code” and the “year / month” of the plan period are searched from the planned selling price / promotion data 324d. All records including combinations of “day”, “product code”, “scheduled selling price (special selling price)”, “scheduled standard price”, and the like are extracted. Each value included in the record is set in the corresponding item of the unit record shown in FIG.

  Further, the corresponding record is extracted from the other data table using “date” and “product code” included in the record extracted from the planned sale price / promotion data 324d as a key. For example, “day of the week”, “temperature”, “precipitation”, and the like are extracted from “date”. Further, “line code” and “class code” are extracted from “product code”. Further, prediction information such as the predicted purchase number is extracted from the “date” and the “product code”.

  In this way, the values extracted from the various data tables are set in the remaining corresponding items of the unit record shown in FIG.

  In the extracted data shown in FIG. 11, the vertical order of each record follows the future date order of “year / month / day” 411. Further, within the same date, the product code order of “product code” 433 is followed. That is, first, records of products scheduled for sale on “January 3, 2015” are arranged in the order of product codes. Subsequently, records of products scheduled for sale on the following day “January 4, 2015” are arranged in the order of product codes. Although omitted in FIG. 4, records of products to be sold are arranged in the same order as product codes from January 5, 2015 to January 9, 2015.

Here, the description returns to the generation of the plan edit screen 5G in step S21 of FIG.
Subsequently, the demand prediction device 31 (input reception unit 317) arranges the extracted records (see FIG. 11) in a predetermined format. Here, as an example, it is assumed that the extracted records are sorted for each single item code, and further sorted in order of sales date within each single item code, thereby arranging them in a predetermined format.

  And the demand prediction apparatus 31 (input reception part 317) sets data to each data item of a plan edit screen. Specifically, the demand prediction device 31 (input reception unit 317) sets the demand prediction data arranged in a predetermined format in the data display area 53 of the plan editing screen 5G (see FIG. 5). Further, the demand prediction device 31 (input reception unit 317) sets the plan information (plan identification code, plan name, and plan holding period) of the plan in the plan display area 51, and related products in the product group selection area 52. Set the classification of. For example, when line 1 is a related product, all the classes under line 1 and its subordinates are set. In this embodiment, since the range of related products is a department, the department and all the lines and classes below it are set.

  The demand prediction device 31 (input reception unit 317) generates the plan edit screen 5G as described above in step S21 and transmits the screen to the store ordering terminal 125.

  Note that demand forecast data (extracted data) including product information extracted by the demand forecasting device 31 (input reception unit 317) is temporarily stored in a storage unit such as a RAM for later editing. To do.

  When the demand prediction device 31 (input reception unit 317) transmits the plan edit screen 5G in step S21, the other store ordering terminal 125 acquires the plan edit screen 5G and displays it on the display screen (S12).

  The store ordering terminal 125 accepts various user operations while the plan edit screen 5G is displayed. As one of various user operations, correction of demand forecast data is accepted.

Hereinafter, as an example of correction of demand forecast data, first, the operation of each device when the sale price of one product is corrected in the input box B2 (see FIG. 5) will be described.
Next, the operation of each device when the sale price of one product is corrected on the condition setting screen 50G (FIG. 6) will be described.

  The store ordering terminal 125 receives from the input box B2 a correction value for the sale price of one product input by the user. Then, the signal which instruct | indicates re-simulation is transmitted toward the demand prediction apparatus 31 (S13).

  Specifically, the store ordering terminal 125 accepts selection of a data string of one product from the list of the data display area 53 on the plan edit screen 5G by moving selection using the up / down key or mouse click. When the store ordering terminal 125 accepts selection of a data string of one product by the user, the shop ordering terminal 125 displays the value of the special sales price of the data string in the input box B2 corresponding to the special sales price. Then, the correction of the value of the sale price displayed in the input box B2 is accepted.

  When the value is rewritten in the input box B2 by the user, the store ordering terminal 125 subsequently accepts a press signal for the correction button B3. The user rewrites the value in the input box B2 and presses the correction button B3. Then, the store ordering terminal 125 transmits a re-simulation request signal to the demand prediction device 31.

  The re-simulation request signal includes the input value (corrected value) of the special selling price in the input box B2 and key information. The key information is for the demand prediction device 31 to know the correction location in the extracted data of the storage unit. The key information is information for specifying the data string selected by the store ordering terminal 125 (single item code and sales date), information indicating the item of the special sale price in the data string, and the like.

  One demand prediction device 31 stands by until an operation signal is received from the store ordering terminal 125. When the operation signal is received, processing based on the operation signal is performed.

  Specifically, the demand prediction device 31 (input reception unit 317) first determines whether or not an operation signal for the return button B8 has been received (S22). If this determination is No, it is then determined whether or not an operation signal for the correction button B3 has been received (S23). If this determination is also No, it is subsequently determined whether an operation signal for the condition confirmation button B5 has been received (S40). If this determination is also No, it is determined whether an operation signal for the end button B6 has been received (S32). If this determination is also No, the determination process from step S22 is repeated.

  When the re-simulation request signal is received from the store ordering terminal 125, the demand prediction device 31 (input reception unit 317) determines Yes in the determination process of step S23. Following this determination, the demand prediction device 31 (input reception unit 317) executes a re-simulation of demand prediction based on the correction value of the sale price included in the request signal, and obtains a demand prediction result (S24).

  Specifically, the demand prediction device 31 (input reception unit 317) first specifies a correction location in the extracted data temporarily stored in the storage unit based on key information or the like included in the request signal. . That is, the corresponding data string in the extracted data is specified from the key information indicating the predetermined date and the product, and the position of the sale price in the data string is specified from the key information indicating the item of the sale price.

  Subsequently, the demand prediction device 31 (input reception unit 317) overwrites the correction value of the sale price included in the request signal with the value at the specific position of the extracted data. Here, the value of the scheduled discount amount calculated as the difference between the scheduled standard selling price and the scheduled selling price is also overwritten.

  Subsequently, the demand prediction device 31 (input reception unit 317) instructs the demand prediction device 31 (predicted purchase number calculation unit 314) to execute re-simulation, and acquires a demand prediction result.

  That is, first, the demand prediction device 31 (the predicted purchase number calculation unit 314) is instructed to execute re-simulation. Then, the demand prediction device 31 (the predicted purchase number calculation unit 314) extracts, from the regression coefficient DB 313, regression coefficients of all products (related products) included in the extracted data rewritten with the correction value in the storage unit. Subsequently, the demand prediction device 31 (the predicted purchase number calculation unit 314) applies the extracted regression coefficient to the equation (1), and applies the schedule information for each record of the extracted data. Thereby, re-simulation is performed, and the predicted number of purchases of each product as one of demand forecast values is obtained. Further, the demand prediction device 31 (the predicted purchase number calculation unit 314) obtains the predicted sales amount, the predicted profit amount, and the like from the obtained predicted purchase number, and the extracted data of the pattern 1 stored in the storage unit with the obtained value. Rewrite the value of prediction information.

  When the demand prediction device 31 (predicted purchase number calculation unit 314) finishes updating the extracted data in the storage unit, the demand prediction device 31 (input reception unit 317) notifies the demand prediction device 31 (input reception unit 317).

  Thereby, the demand prediction apparatus 31 (input reception part 317) obtains the demand prediction data of the re-simulation result based on the correction value of the sale price included in the request signal.

  Note that demand forecast data (extracted data) including product information and the like updated by the demand forecasting device 31 (predicted purchase number calculation unit 314) is temporarily stored in a storage unit such as a RAM for later registration. Shall.

  Subsequently, the demand prediction device 31 (input reception unit 317) generates an update screen by setting the demand prediction data obtained by re-simulation in the data display area 53 of the plan edit screen, and requests the update screen. It transmits to the original store order terminal 125 (S25). On the update screen, the input box B2 and the condition input button B4 are grayed out so that further data correction cannot be accepted.

  Then, the other store ordering terminal 125 receives the update screen. Then, the store ordering terminal 125 updates the display screen to the update screen (S14).

  Subsequently, the store ordering terminal 125 accepts various operations on the update screen (S15).

  Here, it is assumed that the user presses the confirmation button B9 at the store ordering terminal 125. Then, the store ordering terminal 125 transmits an operation signal for the confirmation button B <b> 9 toward the demand prediction device 31.

  One demand prediction device 31 (input reception unit 317) performs various processes according to the type of operation signal transmitted from the store ordering terminal 215. First, the demand prediction device 31 (input reception unit 317) determines whether or not an operation signal for the confirmation button B9 has been received (S26). If it is not an operation signal for the confirm button B9 (No determination), it is subsequently determined whether an operation signal for the return button B8 has been received (S27). When the operation signal of the return button B8 is not received (No determination), the process returns to step S26 and the determination process is repeated in the same manner.

  When the demand prediction device 31 (input reception unit 317) receives the operation signal of the confirmation button B9 from the store ordering terminal 125, Step S26 becomes Yes. In this case, the demand prediction device 31 (input reception unit 317) performs a confirmation process (S28).

  In this confirmation process, the demand prediction device 31 (input reception unit 317) updates the corresponding data in the future information database 311 with the data after rewriting the extracted data that is temporarily stored in the storage unit.

  In the future information database 311, the date of the sale price correction and the value of the sale price corresponding to the product are updated. In addition, the value of the prediction information (predicted sales number, predicted sales amount, predicted profit amount,...) Of the product is updated. Furthermore, the value of the prediction information (the predicted number of sales, the predicted sales amount, the predicted profit amount,...) Of each related product of the product is updated.

  After the above determination process, the demand prediction device 31 (input reception unit 317) transmits an update screen from which the grayout setting is canceled toward the store ordering terminal 125 (S29).

  And it returns to the process of step S22 and accepts further operations, such as correction data.

  One store ordering terminal 125 receives the update screen transmitted in step S29 and displays the screen from which the gray-out setting has been canceled (S16). This update screen has an image in which the data display area 53 of the plan edit screen 5G (see FIG. 5) is updated to the latest demand forecast data. After step S16, the process proceeds to step S13.

  Note that, when the return button B8 is pressed on the store ordering terminal 125 and the determination in step S27 is YES, the process is as follows. The store ordering terminal 125 displays the plan edit screen 5G temporarily stored in the storage unit of its own device. The demand prediction device 31 (input reception unit 317) leaves only the original extracted data in the storage unit, and erases the extracted data after rewriting (S30). And it returns to step S22 and repeats the same process.

  In addition, the demand prediction apparatus 31 determines whether the end button B6 was pushed down in the case of No determination in the determination process of step S20 (S31). In the case of Yes determination that the end button B6 is pressed in the determination process of step S31, this process ends. In the case of No determination in the determination process in step S31, the process returns to the determination process in step S20.

  If the determination in step S23 is No, it is determined whether or not the end button B6 has been pressed (S32). In the case of Yes determination that the end button B6 is pressed in the determination process of step S32, this process ends. In the case of No determination in the determination process in step S32, the process returns to the determination process in step S22.

  Further, when the end button B6 is pressed, the store ordering terminal 125 transmits a signal for instructing the demand prediction device 31 to end at an arbitrary timing, and ends this processing. Further, the demand prediction device 31 detects that the end button B6 has been operated at the store ordering terminal 125 in the determinations of step S30 and step S31. When the operation of the end button B6 is detected, the transaction processing with the store ordering terminal 125 is ended.

  Next, the operation of each device when the sale price of one product is corrected on the condition setting screen 50G (FIG. 6) will be described.

  FIG. 12 is a flowchart of a demand prediction simulation process performed between the store ordering terminal 125 and the demand prediction device 31 when a Yes determination is made in step S40 of FIG. Note that the processing after step S13 (S130 to S136) of the store ordering terminal 125 shown in FIG. 12 is a subroutine processing when the condition input button B4 is operated on the store ordering terminal 125. Accordingly, the processing flow is different from the processing in step S13 and subsequent steps in FIG.

  In step S13, the store ordering terminal 125 accepts selection of a data string of one product in the data display area 53 of the plan edit screen 5G (see FIG. 5) by moving selection using the up and down keys, mouse clicking, or the like. When the store ordering terminal 125 accepts selection of a data string of one product by the user, the store order terminal 125 displays the value of the data string in the input box B2. The store ordering terminal 125 further receives an input of the condition input button B4.

  Here, the user presses the condition input button B4. Then, the store ordering terminal 125 transmits the operation signal toward the demand prediction device 31 (S13).

  When the demand prediction device (input reception unit 317) 31 receives the operation signal of the condition input button B4 from the store ordering terminal 125, it determines Yes in the determination process of step S40.

  Then, the demand prediction device (input reception unit 317) 31 transmits a condition setting screen 50G (see FIG. 6) to the store ordering terminal 125 (S400).

  On the other hand, the store ordering terminal 125 displays the condition setting screen 50G transmitted from the demand prediction device 31 in an overlapping manner on the plan editing screen 5G by pop-up or the like (S130).

  Here, the user inputs a step value, a start value, and an end value in each of the input boxes B50, B51, and B52 on the condition setting screen 50G, and presses the condition confirmation button B5.

  Then, the store ordering terminal 125 closes the condition setting screen 50G and transmits a re-simulation request signal to the demand prediction device 31 (S131).

  The re-simulation request signal includes condition information including the step value, start value, and end value input on the condition setting screen 50G, and key information. The key information is for the demand prediction device (input reception unit 317) 31 to know the correction location in the extracted data in the storage unit. The key information is information for specifying the data string selected by the user (single item code, sale date), information for specifying the selection item, and the like.

  One demand prediction device (input reception unit 317) 31 waits until an operation signal of the condition confirmation button B5 is received from the store ordering terminal 125 (S401).

  When receiving the operation signal, the demand prediction device (input reception unit 317) 31 instructs re-simulation (S402), and generates a demand prediction result screen 51G (see FIG. 7) in which the re-simulation results are totaled (see FIG. 7). S403).

  FIG. 13 is a processing flowchart of steps S402 and S403.

  First, the demand prediction device (input reception unit 317) 31 generates a correction value at a predetermined step interval of the sale price from the condition information included in the re-simulation request signal. Specifically, the demand prediction device 31 (input reception unit 317) determines all the interval values indicated by the increment values from the start value to the end value based on the increment value, the start value, and the end value that are the condition information. These values are generated and stored in the storage unit as correction values (S402-1). For example, the increment value, the start value, and the end value are “5”, “300”, and “350”, respectively. In this case, eleven correction values (“300”, “305”, “310”,..., “345”, “350”) are generated.

  Next, the demand prediction device 31 (input reception unit 317) specifies a correction location in the extracted data temporarily stored in the storage unit based on the key information included in the request signal (S402-). 2). Specifically, the demand prediction device 31 (input reception unit 317) specifies a corresponding data string in the extracted data from the key information indicating a predetermined date and product, and further specifies the position of the sale price in the data string. To do.

  Subsequently, the demand prediction device 31 (input reception unit 317) generates the same number of extracted data as the number of correction values generated, and overwrites the correction values one by one on the value at the specific position of the extracted data (S402-3). ). For example, for the step value “5”, the start value “300”, and the end value “350”, 11 sets of extracted data copies are created. And the value of the specific position of each extraction data is overwritten in order by one of 11 correction values. In this example, the value of the scheduled discount amount calculated as the difference between the scheduled standard selling price and the scheduled selling price is also overwritten.

  FIG. 14 is a diagram illustrating a setting state of the extraction data in the storage unit after the process of step S402-3.

  Of the 11 sets of extracted data, only the extracted data of the correction value “300” (extracted data of pattern 1) and the extracted data of the correction value “305” of the next step value (extracted data of pattern 2) are shown. The other extracted data of correction value “310” to correction value “350” (extraction data of pattern 3 to pattern 11) is not shown.

  In the example shown here, the user at the store ordering terminal 125 is the data in the first column of the data display area 53 of the plan edit screen 5G (see FIG. 5), the sales date “January 3, 2015” and the single item code “0001”. This is when a column is selected and correction of the value of the special sale price is instructed using the condition information. That is, the value “320” of the “scheduled selling price” 444 of the record in the first line of the extracted data shown in FIG.

  As shown in FIG. 14, in the extracted data D1 of pattern 1, the value of the “scheduled selling price” 444 to be corrected is rewritten from “320” to “300”.

  Also, in the extracted data D2 of pattern 2, the value of the “scheduled selling price” 444 to be corrected is rewritten from “320” to “305”.

  Although not shown, for the extracted data of the patterns 3 to 11, the value of the “scheduled selling price” 444 to be corrected is rewritten to a corrected value obtained by sequentially adding the step value “5”.

  In this example, the value of “scheduled discount amount” 745 calculated as the difference between the scheduled regular selling price and the scheduled selling price is also calculated and rewritten at this stage.

  Subsequently, the demand prediction device 31 (input reception unit 317) instructs the demand prediction device 31 (predicted purchase quantity calculation unit 314) to execute re-simulation (S402-4).

  Then, the demand prediction device 31 (the predicted purchase number calculation unit 314) extracts the regression coefficients of all products (related products) included in the extracted data of the pattern 1 in the storage unit from the regression coefficient DB 313.

  Subsequently, the demand prediction device 31 (the predicted purchase number calculation unit 314) applies the extracted regression coefficient to the equation (1), and further applies the schedule information for each record of the extracted data. Thereby, re-simulation is performed, and the predicted number of purchases of each product as one of demand forecast values is obtained.

  Further, the demand prediction device 31 (the predicted purchase number calculation unit 314) obtains the predicted sales amount, the predicted profit amount, and the like from the obtained predicted purchase number, and the extracted data of the pattern 1 stored in the storage unit with the obtained value. Rewrite the value of prediction information.

  The above-described processing performed by the demand prediction device 31 (the predicted purchase number calculation unit 314) is performed similarly for the patterns 2 to 11. Therefore, the prediction information of the extracted data of patterns 1 to 11 stored in the storage unit is all updated by re-simulation.

  FIG. 15 is a diagram illustrating a setting state of the extracted data stored in the storage unit after the re-simulation.

  FIG. 15 shows only the extracted data of pattern 1 and the extracted data of pattern 2 out of the extracted data of 11 patterns as in FIG. The extracted data of the other patterns 3 to 11 is not shown.

  In the example shown in FIG. 15, the extracted data D1a of pattern 1 is rewritten with the result of re-simulation based on the rewrite information (corrected value of the planned selling price) in FIG.

  Specifically, in FIG. 14, the planned selling price value of the product code “0001” in the first row of the extracted data D1 of pattern 1 is corrected to “300”. As a result, in the extracted data D1a of the pattern 1 in FIG. 15, the “predicted purchase quantity” 461, the “predicted sales amount” 462, the “predicted profit amount” 463, and “ The value of “predicted order quantity” 481 is rewritten to “78”, “23,400”, “−780”, and “67”, respectively.

  Further, the product code “0002” in the second column, which is one of the related products of the product code “0001”, is also influenced by the predicted purchase number of the product code “0001”, and the “predicted purchase number” 461 of the prediction information. The value of changes to “3”. As a result, the values of “predicted purchase quantity” 461, “predicted sales amount” 462, “predicted profit amount” 463, and “predicted order quantity” 481 indicating the prediction information of the product code “0002” are “3”. , “444”, “24”, and “9”.

  Although not shown, for the product codes “0003”, “0004”,... Of other related products, the predicted purchase number changes due to the mutual influence, and the value of the prediction information is rewritten.

  Further, in FIG. 14, the planned selling price value of the product code “0001” in the first row of the extracted data D2 of pattern 2 is corrected to “305”. As a result, in the extracted data D2a of the pattern 2 in FIG. 15, the “predicted purchase quantity” 461, the “predicted sales amount” 462, the “predicted profit amount” 463, and “ The value of the “predicted order quantity” 481 is rewritten to “70”, “21, 350”, “−350”, and “59”, respectively.

  Further, the product code “0002” in the second column, which is one of the related products of the product code “0001”, is also influenced by the predicted purchase number of the product code “0001”, and the “predicted purchase number” 461 of the prediction information. The value of changes to “3”. As a result, the values of “predicted purchase quantity” 461, “predicted sales amount” 462, “predicted profit amount” 463, and “predicted order quantity” 481 indicating the prediction information of the product code “0002” are “3”. , “444”, “24”, and “9”.

  Although not shown, for the product codes “0003”, “0004”,... Of other related products, the predicted purchase number changes due to the mutual influence, and the value of the prediction information is rewritten.

  Similarly, for patterns 3 to 11, the re-simulation based on the value of the prediction information (the predicted sales number, the predicted sales amount, the predicted profit amount,...) Of each extracted data is based on the correction value of each pattern. It is rewritten with the demand forecast value obtained by.

  When the demand prediction device 31 (the predicted purchase number calculation unit 314) finishes updating the extracted data in the storage unit in this manner, the demand prediction device 31 (the input reception unit 317) notifies the completion.

  Thereby, the demand prediction apparatus 31 (input reception unit 317) obtains the demand prediction data of each of the generated eleven correction values.

  Subsequently, the demand prediction device 31 (input reception unit 317) analyzes the 11 patterns of demand prediction data in the storage unit and generates a demand prediction result screen.

  In this process, the demand prediction device 31 (input reception unit 317) first compares the predicted purchase quantity values in the data string with the sale price corrected for 11 patterns of demand prediction data in the storage unit. Then, from these comparisons, the maximum value of the predicted purchase number is obtained, and the maximum value of the predicted purchase number and the value of the sale price of the data string in which the predicted purchase number has the maximum value are extracted. The demand prediction device 31 (input reception unit 317) sets the extracted information in the information area J2 of the demand prediction result screen 51G (see FIG. 7) (S403-1).

  Next, the demand prediction device 31 (input reception unit 317) compares the value of the predicted profit amount in the data string in which the selling price is corrected with the 11 patterns of demand prediction data in the storage unit as a target. Then, the maximum value of the predicted profit amount is obtained from the comparison, and the maximum value of the predicted profit amount, the value of the sale price and the predicted number of purchases in the data string in which the predicted profit amount has the maximum value are extracted. The demand prediction device 31 (input reception unit 317) sets the extracted information in the information area J1 of the demand prediction result screen 51G (S403-2).

Returning to FIG. 12, the continuation of the flow will be described.
The demand prediction device 31 (input reception unit 317) transmits the demand prediction result screen 51G generated in this way to the store ordering terminal 125 (S404).

  On the other hand, the store ordering terminal 125 displays the demand prediction result screen 51G transmitted from the demand prediction device 31 so as to be superimposed on the plan editing screen 5G by pop-up or the like (S132).

  Here, the store ordering terminal 125 receives the operation by the user of the various operation buttons on the demand prediction result screen 51G, and when the operation of the various operation buttons is performed by the user, the operation signal is transmitted to the demand prediction device 51 (S133). ).

  When receiving the operation signal, the demand prediction device 31 (input reception unit 317) determines the type of the operation signal.

  First, it is determined whether or not the operation signal is for the list display button B11 (S405). If this determination is No, whether or not the operation signal is for the graph display button B12 is subsequently determined (S406). If this determination is also No, it is determined whether or not it is an operation signal for the close button B13 (S407). When this determination is No, the determination is repeated from the determination process in step S405.

  In step S133, it is assumed that the user presses the list display button B11. Then, the demand prediction apparatus 31 (input reception part 317) makes the determination of step S405 Yes, and performs the production | generation process of the list display screen 52G (S408).

  Subsequently, the demand prediction device 31 (input reception unit 317) transmits the generated list display screen 52G to the store ordering terminal 125 (S409).

  Then, the demand prediction apparatus 31 (input reception part 317) repeats the process from step S405.

  In step S133, the user presses down the graph display button B12. Then, the demand prediction apparatus 31 (input reception part 317) makes the determination of step S406 Yes, and performs the production | generation process of the graph display screen 53G (S410).

  Subsequently, the demand prediction device 31 (input reception unit 317) transmits the generated graph display screen 53G toward the store ordering terminal 125 (S411).

  Then, the demand prediction apparatus 31 (input reception part 317) repeats the process from step S405.

  In step S133, the user presses the close button B13. Then, the demand prediction apparatus 31 (input reception part 317) makes the determination of step S407 Yes. And after deleting the extraction data of the patterns 1-11 except the initial extraction data of a memory | storage part, it transfers to the process of step S22 (refer FIG. 10), and repeats the process from step S22.

  On the other hand, the store ordering terminal 125 operates as follows after the process of step S133.

  First, the store ordering terminal 125 determines whether or not the close button B13 is pressed (S134). If the user presses the close button B13 on the demand prediction result screen 51G, this determination is a Yes determination. Thereby, the store ordering terminal 125 closes the demand prediction result screen 51G and shifts the processing to step S13. After that, the store ordering terminal 125 performs the process from step S13 shown in FIG. 10 or the process from step S13 shown in FIG.

  When the user presses the list display button B11 (or the graph display button B12) on the demand prediction result screen 51G, the determination in step S134 is No. In this case, the store ordering terminal 125 displays the list display screen 52G (or the graph display screen 53G) generated by the demand prediction device 31 so as to overlap the demand prediction result screen 51G by pop-up or the like (S135).

  Thereafter, the store ordering terminal 125 determines whether or not the close button B14 (or the close button B15) is pressed (S136). When the user presses the close button B14 on the list display screen 52G (or the close button B15 on the graph display screen 53G), this determination is Yes.

  Thereby, the store ordering terminal 125 closes the list display screen 52G (or the graph display screen 53G), and proceeds to the process of step S133.

  In the present embodiment, the step value, the start value, and the end value are exemplified as the condition information. All values of the step interval indicated by the step value from the start value to the end value are shown as regular values corresponding to the condition information.

  However, the condition information is not limited to this. The condition information may be in other forms as long as it can show a plurality of regular values. For example, a geometrical formula may be applied.

  In the present embodiment, a condition influential button B4 is provided on the plan editing screen 5G generated by the demand prediction device 31, and the condition setting screen 50G is called. However, it is not limited to this aspect. Input boxes B50, B51, and B52 for inputting condition information on the condition setting screen 50G may be provided on the plan editing screen 5G.

  In the present embodiment, the configuration for inputting the value of the sale price at once to the demand prediction device as the value of the plurality of patterns is shown, but it is not limited to the value of the sale price. As other values, it may be transformed into future variable factor information, for example, standard selling price, temperature, precipitation value and the like.

  In this embodiment, the demand forecast of goods was shown as an example. However, the present invention is not limited to this, and modifications may be made to a form in which a demand forecast for services such as services is predicted. In this case, the number of purchases is the number of times the service is used. The numbers indicated by the inventory data and the order data are the number of staff members who provide services, the number of machines that provide services, and the like.

  In this example, an instruction button is shown as an aspect of the instruction unit, but the instruction unit is not limited to this. As long as it becomes a trigger for sending an instruction signal, other modes may be adopted.

  Various programs used in the demand prediction apparatus according to the present embodiment and the modification are files in an installable or executable format, such as a CD-ROM, a flexible disk (FD), a CD-R, a DVD (Digital Versatile Disk), and the like. The program may be provided by being recorded on a computer-readable recording medium and read into a flash ROM of a demand prediction device or the like.

  Further, the program may be provided by being stored on a computer connected to a network such as the Internet and downloaded via the network.

  As described above, in the demand prediction apparatus according to the present embodiment and the modified example, it is possible to perform a demand prediction simulation by receiving regular values such as selling prices (for example, values in constant increments) at a time. Even if the selling price increment range is reduced or the selling price range is widened, regular values can be designated by the condition information, which makes the operation easy for the user. Moreover, since the simulation result of each selling price can be received at once, data analysis becomes easy.

  In the above embodiment and modification, although the structure about the demand prediction apparatus was demonstrated, embodiment and the modification are shown as an example and are not intending limiting the range of invention. The novel embodiments and modifications can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 1 Demand prediction system 2 Communication line 3 Computer 5 Communication line 31 Demand prediction apparatus 32 Data collection / conversion system 33 Scheduled data collection / conversion system 34 Ordering system 120 Store server 124 Headquarters special sales planning terminal 125 Store ordering terminal 126 POS (Point of Sales ) Terminal 310 Past information database 311 Future information database 311A Schedule information 311B Prediction information 312 Regression coefficient calculation unit 313 Regression coefficient database 314 Predicted purchase number calculation unit 316 Order / inventory number calculation unit 317 Input reception unit 320d Scheduled store / regional data 321d Schedule Date / time data 322d Scheduled weather / event data 323d Scheduled classification / attribute data 324d Scheduled sales price / promotion data 325d Scheduled display data 326d Forecasted purchase data 327d Forecasted inventory data 28d recommended ordering data 330 SALE planning support system 331 ordering number of decision support system

JP 2012-150671 A

Claims (6)

Supply means for supplying a condition input unit for inputting condition information corresponding to a plurality of regular values to the display area;
Based on the condition information input by the user in the condition input unit supplied to the display area, accepting means for receiving a plurality of regular values corresponding to the condition information as correction values at a time;
Demand prediction for calculating the demand prediction value for each correction value of the future reference date by substituting each correction value received by the reception means into a parameter for determining the demand prediction value of the future reference date for the product or service Means,
A result output means for outputting a demand prediction value for each correction value calculated by the demand prediction means to the display area;
A demand prediction apparatus comprising: The supply means further supplies an instruction unit for causing the user to request the display area to display the condition input unit in the display area.
The demand prediction apparatus according to claim 1, wherein: The result output means outputs a maximum value to the display area from the demand prediction value for each correction value calculated by the demand prediction means.
The demand forecasting device according to claim 1 or 2 characterized by things. The demand forecasting means calculates the predicted purchase number of the related products of the future reference date using the value before correction and each correction value,
The result output means includes, for each correction value, the predicted purchase number calculated based on the value before correction by the demand prediction means, and the predicted purchase number calculated based on the correction value by the demand prediction means. Output the change information of the predicted purchase number to the display area.
The demand prediction apparatus according to claim 1, wherein the demand prediction apparatus is one of the following. The result output means outputs the demand prediction value for each correction value calculated by the demand prediction means to the display area as graph information.
The demand prediction apparatus according to claim 1, wherein the demand prediction apparatus is characterized in that: A computer that controls the demand forecasting device,
Supply means for supplying a condition input unit for inputting condition information corresponding to a plurality of regular values to the display area;
Based on the condition information input by the user in the condition input unit supplied to the display area, accepting means for receiving a plurality of regular values corresponding to the condition information as correction values at a time;
Demand prediction for calculating the demand prediction value for each correction value of the future reference date by substituting each correction value received by the reception means into a parameter for determining the demand prediction value of the future reference date for the product or service Means,
A result output means for outputting a demand prediction value for each correction value calculated by the demand prediction means to the display area;
Program to function as.

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