JP2016012300A - Demand prediction device and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a demand prediction device performing demand prediction at the time of promotional sales by calculating a regression coefficient for a promotion and using the regression coefficient for the promotion, and further to provide a program.SOLUTION: A demand prediction device comprises: storage means storing a regression coefficient of a variation factor obtained by substituting past result data at the time of promotional sales as variation factor information with respect to an objective variable for calculating a sales trend of merchandise on future reference date and terms of at least two variation factors or more of the objective variable and by making a calculation; input display means displaying an input section receiving a change of a sales mode from a basic item of merchandise to promotional sales on a display area; sales trend calculation means calculating the sales trend of the merchandise on the reference date as a value of the objective variable by using the regression coefficient of the variation factor stored in the storage means and the variation factor information on the reference date in accordance with the change of the sales mode received by the input section; and display means displaying the sales trend of the merchandise calculated on the display area.

Description

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

  Conventionally, POS (Point of Sales) systems have become widespread, and it has become possible to easily collect purchase performance (sales) data for each product. And the demand forecast according to goods, such as a retail store, based on the purchase performance data for every goods has begun to be used. For example, a demand forecast obtained by analyzing purchase record data can be used to suppress a loss of unsold products due to an excessive order quantity or an opportunity loss due to an out of order product due to an insufficient order quantity. Then, a technology is being proposed that combines demand forecasting with the digitization of business-to-business transactions using EDI (Electronic Data Interchange) to develop an automatic ordering system.

There is a multiple regression analysis as a demand forecasting technique that can be used in an automatic ordering system. And a demand prediction can be performed for every goods by using a multiple regression analysis.
In the demand forecast using the conventional multiple regression analysis, an attempt is made to use a sales price, a display position, the number of displays, an advertising method, a sales promotion method, etc. as a variable factor of the objective variable. In addition, in consideration of the mutual influence between a product and its related products, attempts have been made to use the number of purchases of each product as a variable factor.
By predicting demand by appropriately using these variable factors, it is possible to obtain the predicted number of purchases as the value of the objective variable.

By the way, in the demand forecast using the conventional multiple regression analysis, the purchase factor data at a relatively stable standard price is used as the value of the variable factor when calculating the coefficient of each variable factor (this coefficient is called the regression coefficient). Was used.
Since the bargain sale time shows data different from the standard time, even if the future demand forecast is performed using the regression coefficient given at the standard time, the predicted purchase number obtained by the calculation is not necessarily guaranteed.

  The problem to be solved by the present invention is to provide a demand forecasting apparatus and a program for calculating a regression coefficient for sale and forecasting demand at the time of sale using the regression coefficient for sale.

  The demand forecasting apparatus of the embodiment fluctuates past performance data at the time of sale for an objective variable for calculating a sales trend of a product on a future reference date and at least two variable factor terms of the objective variable. Storage means for storing the regression coefficient of the variation factor calculated by substituting as factor information, input display means for displaying an input part for accepting a change in sales mode from a standard product to a special sale in the display area, and the input part In accordance with the change in the sales mode received in step S1, the sales trend of the product on the reference date is determined using the regression coefficient of the variation factor stored in the storage unit and the variation factor information on the reference date. Sales trend calculation means for calculating the value of the product, and display means for displaying the sales trend of the product on the reference date calculated by the sales trend calculation means in the display area , Comprising a.

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 showing a system configuration of the headquarters including the demand prediction device. FIG. 4 is an example of a screen configuration diagram of the plan editing screen. FIG. 5 is a flowchart of the regression coefficient calculation process of the demand prediction apparatus. FIG. 6 is a flowchart of a demand prediction simulation process performed between the store ordering terminal and the demand prediction device. FIG. 7 is an example of a cannibalization effect confirmation screen.

  FIG. 1 is a block diagram illustrating a configuration of a demand prediction system 1 according to the embodiment. As shown in FIG. 1, the demand forecasting system 1 has a plurality of headquarters special sale plans for a headquarters system formed by a plurality of computers 3 electrically connected to each other via a wired or wireless communication line 2. A terminal 124 and a store ordering terminal 125 provided in each store are connected via a wired or wireless communication line (for example, the Internet or a LAN) 5.

  In the demand prediction system 1 of the present embodiment, one or more computers 3 function as an application server or a database server that provides a predetermined service (application). Further, the headquarters special sales planning terminal 124 and the store ordering terminal 125 function as clients that enjoy the service. In the demand prediction system 1, a service (application) can be provided in the form of SaaS (Software as a Service), for example. The demand prediction system 1 may be in the form of a server client.

  In addition, a store server 120 that connects a plurality of POS (Point of Sales) terminals 126 is connected to the head office system via a wired or wireless communication line (for example, the Internet or a LAN) 5.

The computer 3, the headquarters special sales planning terminal 124, and the store ordering terminal 125 as described above are general personal computers. The headquarters special sales planning terminal 124 and the store ordering terminal 125 may be tablet terminals. Here, the computer 3 will be described as an example.
FIG. 2 is a module configuration diagram of the computer 3. As shown in FIG. 2, a computer 3 includes a CPU (Central Processing Unit) 101 that performs information processing, a ROM (Read Only Memory) 102 that is a read-only memory that stores BIOS, and a RAM that stores various data in a rewritable manner. (Random Access Memory) 103 is provided.

  In addition, the computer 3 functions as various databases and stores information using an HDD (Hard Disk Drive) 104 and a storage medium 110 which are storage units for storing various programs, and distributes information to the outside. Media reading device 105 such as a DVD drive for obtaining information, communication control device 106 for transmitting information to other external devices via each communication line, LCD for displaying processing progress and results to the operator A display unit 107 such as (Liquid Crystal Display) and an input unit 108 that is a pointing device such as a keyboard and a mouse for an operator to input commands and information to the CPU 101 are provided.

  In the computer 3, data transmitted / received between the above-described units is arbitrated by the bus controller 109.

  In such a computer 3, when an operator turns on the power, the CPU 101 activates a program called a loader in the ROM 102, reads a program for managing the hardware and software of the computer called OS (Operating System) from the HDD 104 into the RAM 103, and Start the OS. Such an OS activates a program, reads information, and stores information in response to an operator's operation. As a representative OS, Windows (registered trademark) and the like are known. These operation programs running on the OS are called application programs. The application program is not limited to one that runs on a predetermined OS, and may be one that causes the OS to execute some of the various processes described below, or constitutes predetermined application software, an OS, or the like. It may be included as part of a group of program files.

  That is, the personal computer functions as the headquarters special sales planning terminal 124, the store ordering terminal 125, and the computer 3 (application server or database server) that function as clients, depending on the difference of application programs stored in the HDD 104.

  For example, in the headquarters special sale planning terminal 124 and the store ordering terminal 125, a web browser and software provided to the web browser using a plug-in format are installed as application programs. 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.

  In general, an application program installed in the HDD 104 of the headquarters special sales planning terminal 124 or the store ordering terminal 125 has portability such as an optical information recording medium such as a CD-ROM or a DVD or a magnetic medium such as an FD. The operation program recorded in the storage medium 110 and recorded in the storage medium 110 is installed in the HDD 104. Furthermore, the application program may be imported from the outside via the communication control device 106 and installed in the HDD 104, for example.

  In the computer 3, when an application program operating on the OS is started, the CPU 101 executes various arithmetic processes according to the application program, and controls each unit intensively.

  In the following, a demand prediction process, which is a characteristic process of the present embodiment, will be described among various arithmetic processes executed by each CPU 101 of one or a plurality of computers 3 constituting the headquarters system using an application program.

<< Configuration of demand forecasting device and its peripheral system >>
FIG. 3 is a block diagram showing the configuration of the headquarter system including the demand prediction apparatus according to the present embodiment. As shown in FIG. 3, each CPU 101 of one or a plurality of computers 3 constituting the headquarters system executes an application program, whereby a demand prediction device 31, a data collection / conversion system 32, a scheduled data collection / conversion system. 33 (special sale planning support system 330, order quantity determination support system 331, etc.) and order system 34 are realized.
In the present embodiment, the special sales planning support system 330 and the order quantity determination support system 331 are shown separately from the demand prediction device 31. However, the special sales planning support system 330 and the order quantity determination support system 331 are subsystems for providing the service of the demand prediction device 31 to the user terminal. Therefore, both or any one of the special sales planning support system 330 and the order quantity determination support system 331 may be included in a part of the demand prediction process.

<Demand prediction device>
The demand prediction device 31 simulates the purchase status of the product on the customer side with respect to the sales status on the seller side such as the selling price, display, and promotion of the product in a store such as a retail business. The demand prediction device 31 uses the past information (past performance data) indicating the sales state and purchase status from the present time to the predetermined time in the past, for example, for several weeks, months, or one year, it is temporal. Construct a purchase model for buyers that is divided into a constant term indicating the number of purchases in the part that can be regarded as unchanged, and a term consisting of a variable factor and coefficient indicating the number of purchases in the part that changes more quickly with respect to the constant term. To do. In this purchasing model, the coefficient of the variation factor is obtained by substituting past information into the variation factor. Then, the demand prediction device 31 applies the future schedule information indicating the sales state and the purchase environment from the present time to a predetermined future time (future reference date) to the fluctuation factor of the constructed purchase model. Predict sales trends such as the number of purchases, sales, and profits for each product at a given point in time.
In the present embodiment, a case will be described in which there are N types of products that constitute a product group. N types of products are represented as product 1, product 2,..., Product n,.

In the present embodiment, the variation factors for the product n (n = 1, 2,..., N) are indicated as Xn1, Xn2,..., Xnm,. The variation factor Xnm may change the number of purchases, such as sales price, day of the week, holidays, temperature, and precipitation.
The number of purchases for the product n is Yn. That is, the number of purchases of product 1, product 2,..., Product n,... Product N is indicated as purchase number Y1, Y2,.
In this embodiment, considering that the number of purchases of each product in the product group affects the number of purchases of other products (related products), Y1, Y2,..., Yn,. Including, the formula for calculating the predicted purchase quantity is shown by the following formula (1).

上記の式（１）において、ａn1Ｙ1＋ａn2Ｙ2＋…＋ａn(n-1)Ｙn-1＋ａn(n+1)Ｙn+1＋…＋ａnNＹNは関連商品の購買数による影響を反映した項である。
式（１）において、ａn1,…,ａn(n-1),ａn(n+1),…,ａnN,ｂn0（定数項）,…,ｂnM、を回帰係数とする。以下では、回帰係数を定数項ｂn0を含むものとして説明する。

In the above formula (1), an1Y1 + an2Y2 + ... + an (n-1) Yn-1 + an (n + 1) Yn + 1 + ... + anNYNN is a term reflecting the influence of the number of related products purchased.
In equation (1), an1,..., An (n-1), an (n + 1),..., AnN, bn0 (constant terms),. Hereinafter, the regression coefficient is described as including a constant term bn0.

<Data collection / conversion system>
The data collection / conversion system 32 includes purchase data, sales price data, sales promotion (planning) data, ordering data, inventory data, weather data, day-of-week data, and regions for a plurality of products in a store from the present time to a predetermined time in the past. The past information such as event data is collected from the store server 120 or the like. The data collection / conversion system 32 converts a plurality of various data (collected past information) into a predetermined data structure and transmits the data to the past information database 310 of the demand prediction device 31. The data collection / conversion system 32 includes 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. ing.

<Planned data collection / conversion system>
The scheduled data collection / conversion system 33 is configured to store planned sales price data, planned sales promotion (planning) data, planned arrival data, planned weather data, day of the week data, planned area of a plurality of products from the present time to a predetermined time in the future. Collect future schedule information such as event data. The planned data collection / conversion system 33 converts a plurality of various data (collected future planned information) into a predetermined data structure and transmits the data to the future information database 311 of the demand prediction device 31.
The scheduled data collection / conversion system 33 includes, in particular, a special sales planning support system 330 and an order quantity determination support system 331.

  The special sales planning support system 330 is a system constructed to support special sales planning, and is connected to a plurality of headquarters special sales planning terminals 124 for planning special sales. The headquarters special sale planning terminal 124 receives a special sale code (single item code), a special sale price, a special sale date, a flyer publication, etc. indicating a product to be sold in the special sale plan from a special sale planner who refers to past results and recommended information. Accepts promotional data input. The received data is transmitted to the order quantity determination support system 331.

  The order quantity determination support system 331 is connected to a plurality of store ordering terminals 125 that determine the order quantity of a special sale product and a standard product sold at a normal price, and connects a store orderer and the demand prediction device 31 of the headquarters. Functions as an interface. The store ordering terminal 125 is a user interface for the demand prediction device 31 and inputs data for controlling a purchase situation simulation. For example, various types of data received from the special sales planning support system 330 are transmitted to the demand prediction device 31 for the initial setting to the future schedule information. In addition, when the predicted number of purchases for the future schedule information set at the initial stage is not a target value for the orderer, the orderer changes a part of the future plan information such as the planned selling price from the store ordering terminal 125. Then, control data is input to the demand prediction device 31 so as to predict again.

  The ordering system 34 is a system that requests an order for a product from a manufacturer that manufactures or sells the product in accordance with the order number of all stores determined by the order number determination support system 331.

<< Configuration and processing of demand forecasting device >>
<Configuration of demand forecasting device>
In the demand forecasting device 31, the past information database 310 stores the number of purchases (sales data) in the first predetermined period for each product included in the product group for each past store, each day, each time, each product, and each sales condition. ) 316d, order data 318d, inventory data 317d, classification / attribute data 313d, sales price / promotion data 314d, display data 315d, store / region data 310d, date / time data 311d, weather / event data 312d, etc. are stored. Has been. In the present embodiment, the sales price / promotion data 314d includes information indicating the sales mode of the product (information indicating basic sales or special sales). Of the past information stored in the past information database 310, the purchase quantity data 316d of the product (target product) for which the purchase quantity is predicted is substituted into the objective variable. Further, other past information is substituted into the variation factor as past variation factor information.

  The regression coefficient calculation unit (sales trend calculation means) 312 calculates a regression coefficient (including a constant term) indicating the degree to which past variable factor information contributes to the fluctuation of the objective variable. Multiple fluctuation factors contribute to fluctuations in the number of purchases of a single product. In addition, the regression coefficient varies depending on the sales mode such as the standard sales and the special sales. Here, the regression coefficient is calculated for each product. At this time, the past information corresponding to the sales mode of the standard and the special sale is used. As a result, the standard regression coefficient and the special regression coefficient are calculated for each product.

Specifically, the number of past purchases at the time of the standard sale of the product n is substituted for the objective variable Yn in the formula (1), and the standard sale is made for the variable factors Y1,..., YN, Xn1,. Substitute the past variable factor information of. Since there are M + N regression coefficients in equation (1), use M + N or more sets of data of past purchases and variable factor information, formulate M + N or more equations, and calculate the objective variable using the conventional regression coefficient calculation method. The regression coefficient of Yn is obtained. This operation is performed for the objective variables Y1,..., YN, and a standard regression coefficient is obtained for each product n. In this case, in order to improve the prediction accuracy, a known correction conditional expression (for example, a least square method) may be introduced.
In addition, the past purchase quantity at the time of sale of the product n is substituted for the objective variable Yn in the equation (1), and the past change factor at the time of sale is substituted for the fluctuation factors Y1,..., YN, Xn1,. Substitute information. Since there are M + N regression coefficients in equation (1), use M + N or more sets of data of past purchases and variable factor information, formulate M + N or more equations, and calculate the objective variable using the conventional regression coefficient calculation method. The regression coefficient of Yn is obtained. This operation is performed for the objective variables Y1,..., YN, and a regression coefficient for sale is obtained for each product n. In this case, in order to improve the prediction accuracy, a known correction conditional expression (for example, a least square method) may be introduced.

  The regression coefficient database (DB) 313 stores a standard regression coefficient and a sale regression coefficient for each product n calculated by the regression coefficient calculation unit 312. For example, in the case of product 2, a21, a23, ..., a2N, b20, b21, ..., b2M are stored as standard regression coefficients, and A21, A23, ..., A2N, B20 as regression coefficients for sale. , B21,..., B2M are stored. Here, “a, b” indicates the calculated regression coefficient value for standard use, and “A, B” indicates the calculated regression coefficient value for sale.

  The future information database (DB) 311 has schedule information 311A. The schedule information 311A includes store-by-store / day-by-store / date-of-day / Stores future information by time and product. For example, the scheduled classification / attribute data 323d, the planned selling price / promotion (planning) data 324d as sales conditions, the planned display data 325d, the planned store / region data 320d, the planned date / time data 321d, the planned weather / event data 322d, etc. is there.

In particular, the planned selling price / promotion data 324d includes information indicating a sales mode for each product (information indicating a special sale or a standard item).
Of the future information stored in the future information database 311, future information obtained by quantifying a variation factor that fluctuates an objective variable at a set time in the future (second predetermined period) is used as future variation factor information.

  The predicted purchase number calculation unit 314 calculates the predicted purchase number of each product. Specifically, various future fluctuation factor information stored in the schedule information 311A of the product n and the regression coefficient (regression coefficient of the product n) stored in the regression coefficient database 313 are predicted formulas (1 ). At this time, the sales coefficient of the future variable factor information of the product n is set to a standard, that is, the standard set in the planned selling price / promotion data 324d, the regression coefficient for the standard is used as a prediction formula ( Apply to 1). In addition, when the sales mode is set to sale, that is, set to sale in the planned sale price / promotion data 324d, the regression coefficient for sale is applied to the prediction formula (1). This calculation procedure is performed for products 1 to N, and simultaneous equations corresponding to products 1 to N are established. Then, the predicted number of purchases Y1,..., YN of all corresponding products (product group) is calculated from these equations.

  The future information database 311 has prediction information 311B. The prediction information 311B stores the predicted purchase number (predicted purchase data 326d) for each product of the product group (that is, the product and its related products) calculated by the predicted purchase number calculation unit 314.

  The order / inventory quantity calculation unit 315 subtracts the predicted purchase number calculated by the predicted purchase quantity calculation unit 314 from the current inventory quantity, and predicts the estimated inventory quantity at a set time in the future (second predetermined period). Is calculated. Further, the order / inventory quantity calculation unit 315 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 prediction information 311B stores the predicted inventory quantity (predicted inventory data 327d) calculated by the order / inventory quantity calculation unit 315 and the recommended order quantity (recommended order data 328d).

  The input reception unit (input unit) 316 generates a demand prediction setting screen and a plan editing screen 4G (see FIG. 4), transmits the demand prediction setting screen to the special sales planning support system 330, and sends it to the order quantity determination support system 331. The demand prediction setting screen and the plan editing screen 4G are transmitted. The headquarters special sales planning terminal 124, which is an external device, accesses the special sales planning support system 330, the store ordering terminal 125 accesses the order quantity determination support system 331, and displays various screens as an interface for simulation or the like (display). In the area.

  In addition, the input receiving unit 316 receives data (for example, sales price, sales mode, day of the week, holiday, temperature, precipitation, regional event, presence / absence of flyer publication, planning, etc.) input from the order quantity determination support system 331 or the like. Registered as future variation factor information in the schedule information 311A. Further, according to the edited data input from the order quantity determination support system 331, various future variable factor information stored in the schedule information 311A is corrected. In the present embodiment, as a modification of future variable factor information, in particular, the sales mode of the product is changed. Specifically, the change from the standard to the special sale or the change from the special sale to the standard sale is performed. As a result of this change, the sales mode of the planned selling price / promotion data 324d is changed from the standard sale to the special sale, or from the special sale to the standard sale. Further, according to this change, the standard regression coefficient or the regression coefficient for sale in the regression coefficient DB 313 is used, and the predicted purchase number calculation unit 314 recalculates the predicted purchase number. The result of recalculation of the predicted purchase number is stored in the prediction information 311B as predicted purchase data 326d.

  Further, in the input reception unit 316, the calculated predicted purchase quantity, predicted inventory quantity, and recommended order quantity are set to the predicted purchase quantity target value, predicted inventory quantity target value, and recommended order quantity that are input from the store ordering terminal 125, respectively. When the target value is not satisfied, the forecast purchase quantity is recalculated by correcting various future fluctuation factor information stored in the schedule information 311A as the forecast condition, and the calculated forecast value (predicted purchase quantity) , Forecast inventory quantity, recommended order quantity)

FIG. 4 is a configuration diagram of the plan edit screen.
The plan edit screen is displayed by displaying a list of plans on the demand prediction setting screen (not shown) and selecting one plan.

  The plan editing screen 4G shown in FIG. 4 includes a plan display area 41, a product group selection area 42, a data display area 43, a data operation area 44, a cannibalization confirmation button 45, and a navigation in which various buttons are arranged. And a gate button area 46.

  The plan display area 41 is provided as an area for displaying the “plan No.” 410, “plan name” 411, “start date” 412, and “end date” 413 of the plan selected on the demand prediction setting screen of the previous screen. Yes.

  The product group selection area 42 is provided as a selection area for displaying part or all of the product group to be planned in the data display area 43 by user selection. The product group selection area 42 includes a range designating section divided into “department” 420-1, “line” 420-2, and “class” 420-3. In the “department” 420-1, the range of the highest product classification category can be designated. In the “line” 420-2, the range of the product classification category immediately below the “department” 420-1 can be designated. In the “class” 420-3, the range of the lowest product classification category can be designated. Each range designating section is provided with a check box B1 for designating all of the displayed product category categories as a range.

  The data display area 43 is provided as an 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” 430, “manufacturer” 431, “product name” 432, “standard” 433, “sales date” 434, “special sale price” 435, “predicted sales number” 436, “prediction” “Sales amount” 437, “Predicted profit amount” 438, “Actual sales number” 439, “Actual sales amount” 440, and “Actual profit amount” 441 are set. In the data display area 43, by default, demand prediction data is displayed for all of the product group. Moreover, the range of the product group displayed by the designation in the product group selection area 42 is limited.

  The data operation area (input unit) 44 is provided as an area for performing data editing such as addition or deletion of products in the data display area 43. In the data operation area 44, an input box B2 for data editing, a registration button B3, and a delete button B4 are provided. The input box B <b> 2 is provided for each item as including each item of the demand prediction data in the data display area 43. When one product is selected in the data display area 43, the data of this product is displayed in each input box B2 so that it can be edited.

  The registration button B3 is an operation for adding a new product to the product group to be planned. For example, the data displayed in each input box B2 is diverted, and a part of the data is rewritten to input new product data into each input box B2. Then, by pressing the registration button B3, a new product in the input box B2 is added to the product group to be planned. This addition here means that the product has been changed from the standard sales to the special sales. For this reason, the demand prediction is executed again for the product group of the plan by the additional product, and the demand prediction data after the execution is displayed in the data display area 43.

  The delete button B4 performs an operation for deleting a product from the product group. For example, the product to be deleted is designated in the data display area 43 and the delete button B4 is pressed. As a result, the product is deleted from the product group. This deletion means that the product has been changed from the special sale to the basic sale. For this reason, demand prediction is executed again for the product group by the deleted product, and the demand prediction data after execution is displayed in the data display area 43.

  The cannibalization confirmation button 45 is a button for comparing and displaying sales trends such as the number of purchases of other products (related products) that change in accordance with the sales of one product. When one product is designated in the data display area 43 and the cannibalization confirmation button 45 is pressed, a list of demand forecast information of the related product is displayed on the screen. Both standard and special sale products are displayed here. In the present embodiment, a product that belongs to the same product classification category as one product is set as a related product. The merchandise classification categories include those having “department” as a unit, those having “line” as a unit, and those having “class” as a unit. Here, the “department” is the unit.

  The navigation button area 46 is provided as an area for displaying screen operation buttons common to other screens. On the plan edit screen 4G, 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 edit screen 4G to the printer. The return button B8 is a button for returning to the previous demand prediction setting screen. The confirmation button B9 is a button for confirming the setting contents of the data display area 43 and instructing an order.

<Processing of demand forecasting device>
FIG. 5 is a flowchart of a regression coefficient calculation process performed at night in the demand prediction apparatus.
This process is started once a day at a fixed time (nighttime) and performs the process.
First, data collection processing is performed (S1). In the data collection process of step S1, past performance data such as each store server 120, schedule data, and the like are collected.

  Next, the past performance data is applied to the prediction formula (1) to calculate the regression coefficient. First, a standard regression coefficient is calculated (S2). In order to obtain the standard regression coefficient, the past performance data when the product is sold regularly is applied. And a regression coefficient is calculated | required from the prediction formula (1) which fitted the past performance data.

Next, a regression coefficient for sale is calculated (S3). In order to obtain a regression coefficient for special sale, past performance data when the product is sold specially is applied. Then, a regression coefficient is obtained from the prediction formula (1).
Then, the storage means (regression coefficient DB 313) such as the HDD 104 or the RAM 103 stores the regression coefficients for standard and special sales (S4).

FIG. 6 is a flowchart of a demand prediction simulation process performed between the store ordering terminal 125 operated by the person in charge of ordering and the demand prediction device 31. It is assumed that an order quantity determination support system 331 is interposed as an interface 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, in the store ordering terminal 125, the person in charge of ordering activates the demand prediction setting program and displays the demand prediction setting screen on the display screen (S10).

Next, a plan list is displayed, and one plan is selected from them (S11). By this selection, an instruction signal for acquiring demand prediction data of one plan is transmitted from the store ordering terminal 125 to the demand prediction device 31.
The demand prediction device 31 determines whether or not an instruction signal has been received (S20). When the instruction signal is received from the store ordering terminal 125, the determination process in Step S20 is Yes, and the plan edit screen 4G is transmitted to the store ordering terminal 125 (S21). This plan edit screen 4G includes demand forecast data of one plan selected at the store ordering terminal 125. This demand prediction data is calculated in advance based on the sales mode set in the planned selling price / promotion data 324d, and is stored as the predicted purchase data 326d in the prediction information 311B of the future information database 311. Note that the demand prediction data may not be calculated in advance, but may be calculated when a plan is selected from the store ordering terminal 125.

The store ordering terminal 125 displays the plan edit screen 4G (S12).
When the process of adding or deleting data is performed on the plan editing screen 4G, the store ordering terminal 125 transmits a signal indicating the process to the demand prediction device 31 (S13).

  Upon receiving a signal from the store ordering terminal 125, the demand prediction device 31 determines whether or not there has been an operation of the return button B8 based on the signal (S22), whether or not a product has been added (S23), Then, it is determined whether or not the product has been deleted (S24).

In the case of Yes determination in step S22, since the return button B8 is operated on the plan edit screen 4G, the process returns to the determination process in step S20, and the processes from the determination in step S20 are repeated. In the case of No determination in step S22, the process proceeds to the determination process in step S23.
In the case of Yes determination in step S23, the regression coefficient of the added product is changed to a regression coefficient for sale, and the number of purchases of each product is recalculated (S25). At this time, the sales mode set in the planned sale price / promotion data 324d of the product is changed from the standard to the special sale. Further, the forecast purchase data 326d stored in the forecast information 311B of the future information database 311 is updated with the result after recalculation. In the case of No determination in step S23, the process proceeds to the determination process in step S24.

  In the case of Yes determination in step S24, the regression coefficient of the deleted product is changed to a standard regression coefficient, and the number of purchases of each product is recalculated (S26). At this time, the sales mode set in the planned sale price / promotion data 324d of the product is changed from the special sale to the standard. Further, the forecast purchase data 326d stored in the forecast information 311B of the future information database 311 is updated with the result after recalculation.

And after each process of step S25 and S26, the demand forecast data after recalculation are transmitted to the shop ordering terminal 125 (S27).
If the determination process in step S20 is No, it is determined whether or not the end button B6 has been pressed (S30). In the case of Yes determination that the end button B6 is pressed in the determination process of step S30, this process ends. If the determination process in step S30 is No, the process returns to the determination process in step S20.
If the determination process in step S24 is No, it is determined whether or not the end button B6 has been pressed (S31). In the case of Yes determination that the end button B6 is pressed in the determination process of step S31, this process ends. If the determination process in step S31 is No, the process returns to the determination process in step S22.
Moreover, after the process of step S27, the process from the determination of step S22 is repeated.

The store ordering terminal 125 updates the information in the data display area 43 using the recalculated demand prediction data transmitted from the demand prediction device 31 (S14).
In the above processing, the plan edit screen 4G of the store ordering terminal 125 is updated with data as follows.

  The plan edit screen 4G shown in FIG. 4 includes a plan name “Department 1 Week Special Sale”, a plan period “Monday 17 March 2014 to 23 March 2014 (Sunday)”, and a product category “Department 1”. The demand forecast simulation result of all the goods which belong to is shown.

  In the data display area 43, a part of all the products belonging to “Department 1” is shown. By operating the scroll bar Bar1, other undisplayed products are displayed in the data display area 43, and demand forecast data can be confirmed over all products.

In FIG. 4, the record Rec01 in the first line of the product classification category “class 1” is indicated by a thick frame. This is illustrated to show that the record Rec01 is in a designated state. The mode indicating the record designation state may take various modes such as changing the color.
In the input box B2 of the data operation area 44, the demand forecast data of the commodity of the record Rec01 in the first line designated in the data display area 43 is displayed.

  By rewriting the single product code B20, manufacturer B21, product name B22, etc. of the data displayed in each input box B2, and pressing the registration button B3, the product written in the single product code B20 is changed from the standard to the special sale, The demand forecast data after recalculation due to this change is displayed in the data display area 43. When the delete button B4 is pressed, the product of the record Rec01 in the first line indicated by a thick frame is changed from a special sale to a standard item, deleted from the data display area 43, and other products are demand forecasts after recalculation due to the change. Data will be displayed.

Returning to the description of FIG.
After the process of step S14, the store ordering terminal 125 returns to the process of step S13 and repeats the addition or deletion process.

  The store ordering terminal 125 transmits a signal instructing the return to the demand prediction device 31 at an arbitrary timing when the return button B8 of the plan editing screen 4G is pressed by the ordering person, and the processing in step S10 is performed. Return to the demand forecast setting screen. When the end button B6 is pressed, a signal instructing termination is transmitted to the demand prediction device 31 at an arbitrary timing, and the process is terminated. When the demand prediction device 31 receives a signal for instructing termination from the store ordering terminal 125 in the determination of termination in step S30 and step S31, the processing is terminated.

  In the present embodiment, standard and special regression coefficients are calculated in advance and stored in storage means (regression coefficient DB 313) such as the RAM 103 and the HDD 104. For this reason, when performing a demand prediction simulation, if the product is changed from a standard to a special sale or from a special sale to a standard, the process of recalculating the changed standard or special regression coefficient using the past performance data may be omitted. it can.

Next, the cannibalization display will be described.
Cannibalization is a means for comparing the predicted number of purchases of other products (related products) that change depending on the selling price of one product.
FIG. 7 is an example of a cannibalization effect confirmation screen.
FIG. 7 is displayed by pressing the cannibalization confirmation button 45 on the plan editing screen 4G shown in FIG.

  In this example, in the data display area 43 of FIG. 4, other products (that is, related products in the case where the cannibalization confirmation button 45 is pressed in the state where the record Rec01 in the first row is selected, are the same “department” in this example. The number of predicted purchases for products classified as

  In the cannibalization effect confirmation screen 7G shown in the figure, the selected product display area 70 for displaying the setting information of the product selected in the data display area 43 of the plan edit screen 4G, the predicted number of purchases of the related product, and the like are displayed. Related product list display area 71 is provided.

  In the selected product display area 70, “single product code” 700, “manufacturer” 701, “product name” 702, “standard” 703, “sales date” 704, and “special sale price” 705 are provided.

  The related product list display area 71 includes “single product code” 711, “manufacturer” 712, “product name” 713, “standard” 714, “selling price” 715, “type” 716, “number of sales” 717, “ Sales change "718 is provided.

In the related product list display area 71, all related products are displayed.
In the example shown in the figure, a part thereof is displayed, and all the products can be displayed in the related product list display area 71 by operating the scroll bar Bar02.
Thus, since all the related products are displayed, it becomes possible to confirm the respective predicted sales.

Furthermore, in this embodiment, an item of “type” 716 is provided as a sales mode, and information on a standard or special number indicating the sales mode of each product is displayed.
After confirming the cannibalization influence screen, the confirmation button B10 can be pressed to exit the screen.
In this embodiment, because the sales forecast uses the regression coefficient for special sales and the standard product uses the regression coefficient for standard sales to forecast demand, the forecast sales numbers for the special product and the standard product are displayed in the cannibalization impact confirmation screen. It can be displayed simultaneously.
In the present embodiment, the input reception unit 316 generates a demand prediction setting screen and a plan editing screen 4G (see FIG. 4), transmits the demand prediction setting screen to the special sales planning support system 330, and orders quantity determination support system 331. However, the present invention is not limited to this. The input reception unit 316 transmits / receives data indicating a demand prediction result, input data, an instruction signal, and the like with the special sales planning support system 330 and the order quantity determination support system 331, and generates a demand prediction setting screen and a plan editing screen 4G. May be performed by the special sales planning support system 330 or the order quantity determination support system 331.

  The various programs used in the demand prediction apparatus of the present embodiment 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), etc. The program may be provided by being recorded on a readable recording medium, and read into a flash ROM or the like of the demand prediction device.

  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, the demand prediction apparatus according to the present embodiment uses the objective variable for calculating the sales trend of the commodity on the future reference date and at least two variable factors of the objective variable in the past at the time of the special sale. Storage means for storing the regression coefficient of the above-mentioned variable factor calculated by substituting the actual data of the product as the variable factor information, and an input display means for displaying an input unit for accepting a change in the sales mode from the standard product to the special sale in the display area In accordance with the change of the sales mode received at the input unit, the sales of the commodity on the reference date using the regression coefficient of the variable factor stored in the storage means and the variable factor information on the reference date Sales trend calculation means for calculating the trend as the value of the objective variable, and the sales trend of the product on the base date calculated by the sales trend calculation means are displayed in the display area. And display means for, and intended to comprise a.

  Since the regression coefficient for sale is calculated in advance and the regression coefficient is stored in the storage means, demand prediction at the time of sale can be performed using the regression coefficient for sale. Since the regression coefficient must be calculated based on past performance data obtained from each store or the like, the processing is enormous. However, by calculating the regression coefficient for sale in advance, the processing is omitted, and the sales trend on the reference day can be calculated in a short time using only the schedule data on the reference day. Therefore, even when the sales mode is changed from the special sale to the standard or from the basic sale to the special sale, in order to obtain the respective regression coefficients in advance and store them in the storage means, the process for obtaining the regression coefficients is omitted, and the simulation result is immediately obtained. Can be obtained.

  In the above embodiment, although the structure about the demand prediction apparatus was demonstrated, this embodiment is shown as an example and is not intending limiting the range of invention. The novel embodiment can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. This embodiment is included in the scope and gist of the invention, and is also included in the invention described in the claims and the equivalent scope 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 312 Regression coefficient calculation unit 313 Regression coefficient database 314 Predictive purchase number calculation unit 315 Order / Inventory number calculation unit 316 Input unit 330 Sale plan support system 331 Order number determination support system

JP 2012-150671 A

Claims (6)

Calculated by substituting past performance data at the time of special sale as variable factor information for the objective variable for calculating product sales trends on the future reference date and at least two variable factor terms for the objective variable. Storage means for storing a regression coefficient of the variation factor;
An input display means for displaying an input unit for accepting a change in a sales form from a standard item to a special sale in a display area;
In accordance with the change of the sales mode received at the input unit, the sales trend of the commodity on the reference date is determined using the regression coefficient of the variation factor stored in the storage unit and the variation factor information on the reference date. Sales trend calculating means for calculating the value of the objective variable;
Display means for displaying the sales trend of the product on the reference date calculated by the sales trend calculation means in the display area;
A demand prediction apparatus comprising: The storage means includes a regression coefficient for sale calculated by substituting past performance data at the time of sale as variable factor information for the objective variable and each variable factor term of the objective variable, the objective variable, and the objective variable For each variable factor term of the objective variable, store the classic regression coefficient calculated by substituting the past past performance data as the variable factor information,
The input display means displays in the display area an input unit that accepts a change in sales mode from a standard item to a special item and from a special item to a special item,
The sales trend calculation means replaces the regression coefficient for special sale stored in the storage means with the regression coefficient for standard use or changes the regression coefficient for standard use according to the change of the sales mode received by the input unit. Instead of using the regression coefficient for the special sale, using the variable factor information on the base date, to calculate the sales trend of the product on the base date as the value of the objective variable,
The demand prediction apparatus according to claim 1, wherein: Sales trend comparison display means for displaying the sales trend of other products that change due to the change of the sales mode of one product in the display area,
The demand prediction apparatus according to claim 2, further comprising: The sales trend comparison display means further displays in the display area information indicating a standard and special sale as a sales mode of the other products.
The demand forecasting device according to claim 3 characterized by things. The sales trend calculation means omits the simulation for recalculation of the regression coefficient of the variation factor when the sales mode is changed by the input unit, and determines the sales trend of the commodity on the reference date as the purpose. As a variable value,
The demand prediction apparatus according to any one of claims 2 to 4, wherein the demand prediction apparatus is characterized in that: A computer that controls the demand forecasting device,
Calculated by substituting past performance data at the time of special sale as variable factor information for the objective variable for calculating product sales trends on the future reference date and at least two variable factor terms for the objective variable. Storage means for storing a regression coefficient of the variation factor;
An input display means for displaying an input unit for accepting a change in a sales form from a standard item to a special sale in a display area;
In accordance with the change of the sales mode received at the input unit, the sales trend of the commodity on the reference date is determined using the regression coefficient of the variation factor stored in the storage unit and the variation factor information on the reference date. Sales trend calculating means for calculating the value of the objective variable;
Display means for displaying the sales trend of the product on the reference date calculated by the sales trend calculation means in the display area;
Program to function as.

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