JP7023456B2 - Demand forecasting equipment, demand forecasting methods, and programs - Google Patents

Description

The present invention relates to a demand forecasting device, a demand forecasting method, and a program.

For products that use replaceable consumables (hereinafter, also referred to as the main body), there is a technique for predicting the demand for the consumables. For example, Patent Document 1 below describes a technique for forecasting the demand for consumables by incorporating the actual number of main units in operation into the forecast. It should be noted that Patent Document 1 below describes that the above-mentioned actual number of main units in operation is confirmed when a service engineer visits a customer for maintenance.

Japanese Patent Application Laid-Open No. 2003-263300

However, it was a heavy human burden for the service engineer to confirm the actual number of units in operation. In particular, if the main body of consumables for which demand is forecast is a product that exists in many homes, including home appliances such as vacuum cleaners and air conditioners, service engineers visit each home frequently to check the operating status of the main body. It was difficult to confirm.

Therefore, the present invention has been made in view of the above problems, and an object of the present invention is a new and improved invention capable of predicting a demand amount for consumables while reducing a human burden. The purpose is to provide a demand forecasting device, a demand forecasting method, and a program.

In order to solve the above problems, according to a certain viewpoint of the present invention, it is a demand forecasting device for forecasting the demand for replaceable consumables used in a product, and the cumulative number of shipments of the product at a predetermined time t. Expected value A (t), expected value B (t) of the number of operating units of the product at the predetermined time t, expected value C (t) of the number of shipments of the consumables at the predetermined time t, Using the parameter p representing the rate of suspension of use, the parameter q representing the usage rate of the consumables, and the parameter r representing the simultaneous purchase rate of the consumables at the time of purchasing the product, the demand curve of the consumables is mathematically expressed ( Demand forecast model storage that stores the demand forecast model based on the first model represented as F-1) and the second model represented by the time change rate of the number of operating units of the product as the mathematical formula (F-2) . Based on the number of sales of the product, the number of sales of the consumables, and the number of sales of the consumables, the parameters p, the parameter q, and the parameter r stored in the demand forecast model storage unit are estimated, and the parameters are estimated. A parameter calculation unit that calculates a parameter estimation value that is an estimated value of p, the parameter q, and the parameter r, a parameter storage unit that stores the parameter estimation value calculated by the parameter calculation unit, and a demand forecast model. Forecast value calculation to calculate the forecast value of the demand number of the consumables based on the demand forecast model stored in the storage unit, the parameter estimated value stored in the parameter storage unit, and the number of future product sales plans. A demand forecasting device including a unit and a unit is provided.

The forecast value calculation unit inputs the parameter estimated value, the number of sales results of the product, the number of sales records of the consumables, and the number of future product sales plans into the demand forecast model, and obtains the forecast value. It may be calculated.

The demand forecast model may be selected based on the user's operation.

Further, in order to solve the above problems, according to another viewpoint of the present invention, it is a demand forecasting method for forecasting the demand for replaceable consumables used in the product, and the product is manufactured at a predetermined time t. The expected value A (t) of the cumulative number of shipments, the expected value B (t) of the number of operating units of the product at the predetermined time t, the expected value C (t) of the number of shipments of the consumables at the predetermined time t, The demand curve of the consumables using the parameter p representing the rate of suspension of use of the product, the parameter q representing the usage rate of the consumables, and the parameter r representing the simultaneous purchase rate of the consumables at the time of purchasing the product. To store a demand forecast model based on the first model expressed as the formula (F-1) and the second model expressed as the time change rate of the number of operating units of the product as the formula (F-2) . Based on the actual number of sales of the product and the actual number of sales of the consumables, the parameter p, the parameter q, and the parameter r stored in the demand forecast model storage unit are estimated, and the parameter p is estimated. , The parameter estimated value which is an estimated value of the parameter q, and the parameter r is calculated, the parameter estimated value calculated by the parameter calculation unit is stored, and the demand forecast model storage unit stores the estimated value. Demand forecast including calculating the forecast value of the demand number of the consumables based on the demand forecast model, the parameter estimated value stored in the parameter storage unit, and the number of future product sales plans. The method is provided.

Further, in order to solve the above-mentioned problems, according to another viewpoint of the present invention, it is a program for realizing a demand forecasting function of replaceable consumables used in a product in a computer, and at a predetermined time t. The expected value A (t) of the cumulative number of shipments of the product, the expected value B (t) of the number of operating units of the product at the predetermined time t, and the expected value C of the number of consumables shipped at the predetermined time t. (T), the consumption using the parameter p representing the rate of suspension of use of the product, the parameter q representing the usage rate of the consumables, and the parameter r representing the simultaneous purchase rate of the consumables at the time of purchasing the product. A demand forecast model based on the first model in which the demand curve of a product is expressed as a mathematical formula (F-1) and the second model in which the time change rate of the number of operating units of the product is expressed as a mathematical formula (F-2) . The parameter p, the parameter q, and the parameter r stored in the demand forecast model storage unit are estimated based on the function of storing the product, the number of sales of the product, and the number of sales of the consumables. , The function of calculating the parameter estimated value which is the estimated value of the parameter p, the parameter q, and the parameter r, the function of storing the parameter estimated value calculated by the parameter calculation unit, and the demand forecast model storage. A function to calculate a forecast value of the demand number of the consumables based on the demand forecast model stored in the unit, the parameter estimated value stored in the parameter storage unit, and the number of future product sales plans. A program to make it happen is provided.

As described above, according to the present invention, it is possible to predict the amount of demand for consumables while reducing the human burden.

It is a block diagram which shows the functional structure example of the demand forecasting apparatus 1 which concerns on one Embodiment of this invention. It is a table which shows an example of the data of the main body sales record number stored in the main body sales record number storage unit 102. It is a table which shows an example of the data of the consumables sales record number stored in the consumables sales record number storage unit 104. It is a table which shows an example of the data of the main body sales plan volume stored in the main body sales plan unit storage unit 106. It is a flowchart which shows the operation outline of the demand forecasting apparatus 1 which concerns on this embodiment. It is a flowchart which shows the more detailed flow of a model construction step S10. It is a table which shows an example of the sales record of the main body which has been aggregated. It is a table showing an example of the actual sales volume of consumables that have been subjected to aggregation processing. It is a flowchart which shows the more detailed flow of the consumables demand forecast step S20. It is a table which shows an example of the data of the combined number of main body. It is a table which shows the example of the predicted value of the demand number of consumables output by an output part 122. It is explanatory drawing which shows the hardware composition of the demand forecasting apparatus 1 which concerns on embodiment of this invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are designated by the same reference numerals, so that duplicate description will be omitted.

Further, in the present specification and the drawings, a plurality of components having substantially the same functional configuration may be distinguished by adding different alphabets after the same reference numerals. However, if it is not necessary to particularly distinguish each of the plurality of components having substantially the same functional configuration, only the same reference numerals are given.

<< 1. Background >>
First, the background leading to the creation of the demand forecasting device according to the embodiment of the present invention will be described.

Conventionally, with respect to products (main bodies) that use replaceable consumables, the demand for the consumables is manually and empirically predicted from, for example, past sales results, market trends, and the number of sales plans for the main body. Was there. In addition to the heavy human burden, such manual predictions may be less accurate because the predicted values are greatly affected by the skill and experience of the individual.

Further, as in Patent Document 1, there is a technique for forecasting the demand for consumables, but the service engineer needs to confirm the actual number of main units in operation used for the demand forecast in the technique at the time of maintenance. The burden was heavy.

In order to reduce such a human burden, it is conceivable that the service engineer estimates the number of main units in operation by using a mathematical model instead of directly checking the actual number of units in operation. For example, it is possible to estimate the number of operating units of the main unit using a fixed model based on the mean time between failures and the Weibull distribution, and to forecast the demand for consumables using the estimated number of operating units of the main unit. ..

However, when estimating the number of operating units using such a model, the rate at which the use of the main unit is stopped for reasons other than failure is not taken into consideration. Therefore, when the number of operating units of the main unit is estimated using such a model, it is possible that the estimated number of operating units and the actual number of operating units will deviate from each other over time, and as a result, the demand forecast for consumables There is a risk that the accuracy will decrease.

Therefore, the inventor of the present invention has come to create an embodiment of the present invention with the above circumstances as the first point of view. The demand forecasting device according to the embodiment of the present invention directly uses a demand forecasting model based on the first model representing the demand curve of consumables and the second model representing the time change rate of the number of operating units of the main body. Clarify the relationship between the number of operating units of the main unit and the number of demands for consumables that cannot be observed. According to the present embodiment described below, the operating number of the main body at the time of making a prediction is estimated based on the past consumption record of consumables, so that the estimated operating number even if time elapses. It is possible to suppress the discrepancy between the actual number of units in operation and the actual number of units in operation. As described above, according to the present embodiment, it is possible to predict the number of operating units of the main body with higher accuracy, and as a result, it is possible to predict the demand for consumables with higher accuracy while reducing the human burden. can.

Hereinafter, in the present embodiment, the demand forecasting device for realizing the above effect will be described in more detail.

<< 2. Configuration >>
FIG. 1 is a block diagram showing a functional configuration example of the demand forecasting device 1 according to the embodiment of the present invention. The demand forecasting device 1 according to the present embodiment is a device for forecasting demand for a product using replaceable consumables, and more specifically, an information processing device for forecasting demand for the consumables. The demand forecasting device 1 calculates a predicted value of the demand number of consumables, and the predicted value may be used, for example, as a sales planned number of consumables (future sales number).

As shown in FIG. 1, the demand forecasting device 1 according to the present embodiment has a main unit sales record unit storage unit 102, a consumables sales record unit storage unit 104, a main unit sales plan unit unit storage unit 106, an acquisition unit 108, and a data processing unit. It is an information processing apparatus including 110, an input unit 112, a demand forecast model storage unit 114, a parameter calculation unit 116, a parameter storage unit 118, a forecast value calculation unit 120, and an output unit 122.

In the main unit sales record storage unit 102, data on the number of main units sold so far (in the past) (hereinafter, also referred to as the main unit sales record), which is a product that uses consumables for which demand is forecast, is stored. Accumulate. FIG. 2 is a table showing an example of the data of the main body sales actual number accumulated in the main body sales actual number accumulating unit 102.

The format of the data stored in the main unit sales record unit storage unit 102 is not limited to the example shown in FIG. For example, the date and time when each unit was sold may be recorded, or the total number of units sold for each time unit (for example, month or day) for which prediction is desired is recorded. May be good.

The consumables sales record unit storage unit 104 stores data on the number of consumables sold (hereinafter, also referred to as consumable sales record) so far (in the past) for which the demand forecast is to be made. FIG. 3 is a table showing an example of data on the number of consumables sold in the consumables sales record storage unit 104.

The format of the data stored in the consumables sales record unit storage unit 104 is not limited to the example shown in FIG. For example, as in the case of the main body described above, the date and time when the consumables were sold may be recorded, or the consumables sold for each time unit (for example, month or day) for which prediction is desired. The value obtained by totaling the number of units may be recorded.

The main unit sales plan unit storage unit 106 stores data on the main unit sales planned number (hereinafter, also referred to as the main unit sales plan unit) in a future period for which the demand forecasting device 1 is targeted for demand forecasting. FIG. 4 is a table showing an example of data of the main body sales plan unit stored in the main body sales plan unit storage unit 106. However, the format of the data stored in the main unit sales plan unit storage unit 106 is not limited to the example shown in FIG.

The acquisition unit 108 is the main unit sales actual unit, the consumables sales actual unit, and the main unit sales from each of the main unit sales actual unit accumulation unit 102, the consumables sales actual unit accumulation unit 104, and the main unit sales plan unit accumulation unit 106. The data regarding the planned number of units is acquired and provided to the data processing unit 110.

The data processing unit 110 performs predetermined processing on the data acquired by the acquisition unit 108 and provides the data to the parameter calculation unit 116 and the predicted value calculation unit 120. For example, the data processing unit 110 wants to make a forecast for each data of the main unit sales actual number, the consumables sales actual number, and the main unit sales planned number acquired by the acquisition unit 108 (for example, month or day). ) Perform aggregation processing to total the number of units. In addition, the data processing unit 110 performs a combination process of combining the data of the main unit sales actual unit to which the above-mentioned aggregation processing has been performed and the data of the main unit sales planned number of units to which the above-mentioned aggregation processing has been performed. The data processing unit 110 provides the parameter calculation unit 116 with the data of the number of units sold in the main body subjected to the above-mentioned aggregation processing and the data of the number of actual sales of consumables subjected to the above-mentioned aggregation processing. Further, the data processing unit 110 provides the predicted value calculation unit 120 with data on the number of combined main units obtained by the combination processing and data on the actual number of consumables sold after the aggregation processing.

The input unit 112 inputs a demand forecast model (for example, a mathematical formula) for the demand forecast device 1 to forecast the demand for consumables. The demand forecast model input by the input unit 112 is input to the demand forecast model storage unit 114 and stored in the demand forecast model storage unit 114. The details of the demand forecast model will be described later, but the demand forecast model according to this embodiment includes a first model that represents a demand curve for consumables, a second model that represents the time change rate of the number of operating units of the main body, and a second model. It may be a mathematical model based on.

For example, the function of the input unit 112 may be realized by an interface that accepts operations by the user, such as a mouse, keyboard, touch panel, and buttons, and the demand forecast model may be input based on the user's operations. In such a case, for example, one demand forecast model may be selected and input from a plurality of demand forecast models prepared in advance by the user's operation, or the demand forecast model is created and input by the user's operation. May be good.

Further, the input unit 112 may receive data related to the demand forecast model prepared in advance from an external device or read it from a configuration (not shown) of the demand forecast device 1 and input it to the demand forecast model storage unit 114. ..

The demand forecast model storage unit 114 stores the demand forecast model input by the input unit 112. The function of the demand forecast model storage unit 114 may be realized by, for example, a volatile memory that temporarily stores data, or may be realized by a non-volatile memory.

The parameter calculation unit 116 estimates unknown parameters included in the demand forecast model stored in the demand forecast model storage unit 114, and calculates the parameter estimated value. The details of the demand forecast model will be described later, but the demand forecast model stored in the demand forecast model storage unit 114 includes at least one unknown parameter as a coefficient or a term, and the parameter calculation unit. 116 estimates such unknown parameters.

The parameter calculation unit 116 may calculate an estimated value of the parameter included in the demand forecast model based on the actual sales volume of the main body and the actual sales volume of consumables. As described above, the data of the actual sales volume of the main body and the actual sales volume of consumables are aggregated by the data processing unit 110 for each time unit, and then the parameter calculation unit 116 is performed. May be provided to.

Further, the parameter calculation unit 116 sets a parameter that makes the difference between the actual sales volume of consumables calculated by the demand forecast model based on the actual sales volume of the main body smaller and the actual sales volume of consumables smaller. It may be calculated as an estimated value. With this configuration, the demand forecast model to which the parameter estimates are applied is consistent with the actual sales volume of the main unit and the actual sales volume of consumables.

The parameter storage unit 118 stores the parameter estimated value calculated by the parameter calculation unit 116. The function of the parameter storage unit 118 may be realized by, for example, a volatile memory that temporarily stores data, or may be realized by a non-volatile memory.

The forecast value calculation unit 120 is used for the demand forecast model stored in the demand forecast model storage unit 114, the parameter estimated value stored in the parameter storage unit 118, the main unit sales actual number, the consumables sales actual unit, and the main unit sales plan unit. Based on this, the forecast value of the demand for consumables is calculated. As described above, the data on the actual sales volume of consumables is provided to the forecast value calculation unit 120 after being subjected to aggregation processing for totaling the units for each time unit for which the data processing unit 110 makes a forecast. May be good. Further, as described above, the data of the main unit sales actual unit and the main unit sales planned unit are subjected to the aggregation processing of totaling the units for each time unit to be predicted by the data processing unit 110, and further predicted as combined data. It may be provided to the value calculation unit 120.

For example, the forecast value calculation unit 120 inputs the above-mentioned parameter estimated value, main unit sales actual number, consumables sales actual unit, and main unit sales planned number into the demand forecast model stored in the demand forecast model storage unit 114. , Calculate the forecast value of the demand for consumables. The predicted value calculation unit 120 may calculate the predicted value of the demand number of consumables for each time unit (for example, month, day, etc.) for which a preset prediction is desired. Further, the period to be predicted by the forecast value calculation unit 120 (prediction target period) may be the same as the period in which the data of the main unit sales plan unit is accumulated in the main unit sales plan unit accumulation unit 106.

The output unit 122 outputs a predicted value of the demand number of consumables calculated by the predicted value calculation unit 120. For example, the output unit 122 may be configured to include a monitor, and in such a case, the output unit 122 displays and outputs a predicted value of the demand number of consumables. Alternatively, the output unit 122 may output the predicted value of the demand number of consumables to the storage in the demand forecasting device 1 or an external device as a file in a file format designated in advance.

<< 3. Operation >>
<3-1. Operation overview >>
The functional configuration example of the demand forecasting apparatus 1 according to the present embodiment has been described above. Subsequently, an operation example of the demand forecasting device 1 according to the present embodiment will be described. FIG. 5 is a flowchart showing an operation outline of the demand forecasting device 1 according to the present embodiment.

As shown in FIG. 5, first, the demand forecasting device 1 according to the present embodiment estimates unknown parameters included in the demand forecasting model, and constructs the demand forecasting model (S10). In the following, step S10 is also referred to as a model construction step, and the details of the model construction step will be described later with reference to FIGS. 6 to 8.

Subsequently, the demand forecasting device 1 according to the present embodiment predicts the demand for consumables by using the demand forecasting model constructed in step S10 (S20). In the following, step S20 is also referred to as a consumables demand forecasting step, and details of the consumables demand forecasting step will be described later with reference to FIGS. 9 to 11.

The process shown in FIG. 5 may be repeated as appropriate. For example, after the demand forecast of consumables is performed by the process shown in FIG. 5, the forecast target period is before the end of the forecast target period in the demand forecast. The process shown in FIG. 5 may be performed again in order to forecast the demand for consumables later. Further, FIG. 5 shows an example in which the consumables demand forecasting step S20 is performed once after the model building step S10, but the consumables demand forecasting step S20 is once the model building step S10 is completed. , Can be done as many times as you like. That is, the period in which the model construction step S10 is repeated may be longer than the period in which the consumables demand forecast step S20 is repeated.

The operation outline of the demand forecasting apparatus 1 according to this embodiment has been described above. Subsequently, the model building step S10 and the consumables demand forecasting step S20 shown in FIG. 5 will be described in more detail in sequence.

<3-2. Model building steps >>
FIG. 6 is a flowchart showing a more detailed flow of the model construction step S10. First, the demand forecast model is input by the input unit 112 (S102). For example, the demand forecast model may be input by the user operating the keyboard interface and selecting one demand forecast model from a plurality of demand forecast models prepared in advance. Alternatively, data regarding a demand forecast model prepared in advance based on a predetermined format may be received from an external device or read from a configuration (not shown) of the demand forecast device 1 and input.

The demand forecast model input in step S102 is stored in the demand forecast model storage unit 114 (S104).

Subsequently, the acquisition unit 108 acquires data (actual data) regarding the main unit sales actual number and the consumables sales actual number from each of the main body sales actual unit accumulation unit 102 and the consumables sales actual unit accumulation unit 104 (actual data). S106).

Subsequently, the data processing unit 110 performs aggregation processing on each of the actual data acquired in step S106 to total the number of units for each time unit (for example, month or day) for which prediction is to be performed (S108). FIG. 7 is a table showing an example of the actual sales volume of the main body that has been subjected to the aggregation process. Further, FIG. 8 is a table showing an example of the actual number of consumables sold that has been subjected to the aggregation process. The examples shown in FIGS. 7 and 8 are obtained by subjecting the actual sales volume of the main body and the actual sales volume of consumables shown in FIGS. 2 and 3, respectively, to the hourly unit of one day. It is a thing. It should be noted that the data of the number of main units sold and the number of consumables sold stored in the main unit sales record storage unit 102 and the consumables sales record storage unit 104 are totaled for each time unit for which a prediction is desired in advance. If so, the process of step S108 may be skipped.

As shown in FIG. 6, in the following step S110, the parameter calculation unit 116 includes unknown parameters included in the demand forecast model based on the actual sales volume of the main body and the actual sales volume of consumables subjected to the aggregation process in step S108. Calculate the estimated value of.

Here, the details of the demand forecast model according to the present embodiment will be described. As described above, the demand forecast model according to the present embodiment is a mathematical model based on the first model representing the demand curve of consumables and the second model representing the time change rate of the number of operating units of the main body. For example, when the first model and the second model are expressed as equations, the demand forecast model is a simultaneous equation of the first model and the second model, or a model obtained by transforming the simultaneous equations. May be good.

For example, the first model and the second model can be expressed as differential equations, respectively, and in such a case, the demand prediction model is a simultaneous differential equation in which the first model and the second model are combined, or the simultaneous differential equation. It may be a model obtained by transforming the equation. The following equations (1-1) and (1-2) are examples of the first model and the second model expressed as differential equations, respectively, and the demand forecast model is the equation (1) as follows. It can be expressed as a formula (1) which is a simultaneous differential equation obtained by combining -1) and the formula (1-2).

In the above formula (1), A (t) is an expected value of the cumulative number of units shipped at a predetermined time t, B (t) is an expected value of the number of operating units of the main body at a predetermined time t, and C. (T) is an expected value of the number of consumables shipped at time t. In the above formula (1), in addition to the idea that the time change rate of the expected value C (t) of the number of consumables shipped increases linearly according to the number of operating units of the main unit, the amount of change in the number of operating units of the main unit is the main unit. It is a demand forecast model based on the idea that it is represented by the sum of the increase due to shipment and the steady decay.

Further, in the above formula (1), p, q, and r are unknown parameters. p represents the rate of suspension of use of the main body, and 0 <p <1. Further, q represents the usage ratio of consumables, and q> 0. Further, r represents the simultaneous purchase ratio of consumables at the time of purchasing the main body, and r> 0.

As shown in the formula (1-1), the first model representing the demand curve of consumables is a model based on the idea that the demand number of consumables is based on the operating number of the main body at the present time. Further, as in the formula (1-1), in the first model representing the demand curve of consumables, consumables purchased at the same time as the newly shipped main body may be considered. In the formula (1-1), the time change rate dC (t) / dt of the expected value C (t) of the number of consumables shipped is the expected value B (t) of the usage ratio q of the consumables and the number of operating units of the main body. Expressed by the sum of the first term, which is the product of, the simultaneous purchase ratio r of consumables at the time of purchasing the main body, and the second term, which is the product of the number of newly shipped main bodies dA (t) / dt. Has been done.

Further, the formula (1-2) which is the second model is a formula for estimating the expected value B (t) of the number of operating units of the main body which has not been observed. In the formula (1-2), the time change rate dB (t) / dt of the expected value B (t) of the number of operating units of the main unit is the number of operating units of the main unit from the newly shipped number of main units dA (t) / dt. It is estimated by subtracting a predetermined percentage of the total. In the formula (1-2), a predetermined ratio of the operating number of the main body is expressed by the product of the ratio p of the suspension of use of the main body and the expected value B (t) of the operating number of the main body.

Further, the demand forecast model replaces the above equation (1) with the first model, the equation (2-1) which is another example of the second model, and the equation as shown in the following equation (2). (2-2) may be expressed as a simultaneous differential equation.

In the above formula (2), D (t) is the number of main bodies that have left at time t. Further, therefore, A (t) -D (t) in the above-mentioned formula (2) means an expected value of the number of operating units of the main body, and corresponds to B (t) in the above-mentioned formula (1). Therefore, in the above formula (2), as in the above formula (1), the time change rate of the expected value C (t) of the number of consumables shipped increases linearly according to the number of operating units of the main body. It is an expression that is expressed, and is another expression of the same mechanism as the expression (1).

In step S110 shown in FIG. 6, the parameter calculation unit 116 calculates, for example, formula (1) or unknown parameters p, q, and r in formula (2). An example of the calculation method of the unknown parameters p, q, and r by the parameter calculation unit 116 will be described below. In the following, the case where the demand forecast model is expressed as the formula (1) will be described as an example, but the same calculation can be performed even when the demand forecast model is expressed as the formula (2). be.

First, when the mathematical formula (1) is solved for the expected value B (t) of the number of operating units of the main body, the following mathematical formula (3) is obtained.

Further, by solving the mathematical formula (1) with respect to the expected value C (t) of the number of consumables shipped, the following mathematical formula (4) is obtained.

Here, it is assumed that a non-stationary Poisson process is assumed for events such as shipment of consumables and shipment of the main body, and that the expected value A (t) of the cumulative number of shipments of the main body is a distribution according to the Bass model. Since the formula (1) is assumed for the intensity function, the likelihood function is as shown in the following formula (5).

Here, ΔA (t) = A (t + 1) −A (t), θ is a parameter of A (t), and η is a parameter of B (t) and C (t). Here, A (t) does not depend on the parameters of B (t) and C (t). Therefore, first, the parameter of A (t) is obtained from the number of shipments x _t of the main body in the first stage, and then the parameter of A (t) is given in the second stage, and B is obtained from the number of shipments of consumables y _t . A two-step method of obtaining the parameters of (t) and C (t) can be adopted.

The estimation equation to be maximized in the first stage is expressed by the following equation (6).

Further, the estimation equation to be maximized in the second stage is expressed by the following equation (7).

The parameter calculation unit 116 uses an optimization method such as Newton's method to stepwise calculate parameter estimates that maximize the right-hand side of the above mathematical formulas (6) and (7). Here, maximizing means making the value as large as possible, and may include locally maximizing using, for example, an optimization method.

Further, the parameter calculation unit 116 may calculate the parameter estimated value by maximizing the above mathematical formulas (6) and (7) sequentially once, or repeatedly maximizing the parameter estimated values. It may be calculated. When it is repeatedly maximized, for example, it may be repeated a predetermined number of times, or it may be repeated until the difference due to the repetition becomes equal to or less than a predetermined threshold value.

The explanation will be continued by returning to FIG. As described above, the parameter calculated by the parameter calculation unit 116 in step S110 is stored in the parameter storage unit 118 (S112).

<3-3. Consumables Demand Forecast Step>
FIG. 9 is a flowchart showing a more detailed flow of the consumables demand forecast step S20. First, the acquisition unit 108 obtains the main unit sales actual number, the consumables sales actual number, and the main body sales from each of the main body sales actual unit accumulation unit 102, the consumables sales actual unit accumulation unit 104, and the main unit sales plan unit accumulation unit 106. Acquire data on the planned number of units (S202).

Subsequently, the data processing unit 110 performs aggregation processing on each of the data acquired in step S202 to total the number of units for each time unit (for example, month or day) for which prediction is to be performed (S204).

Subsequently, the data processing unit 110 combines the data of the main unit sales actual unit subjected to the aggregation processing in step S204 and the data of the main unit sales planned number unit subjected to the aggregation processing (S206). FIG. 10 is a table showing an example of data on the number of combined main units.

Subsequently, the forecast value calculation unit 120 calculates the forecast value of the demand number of consumables based on the demand forecast model, the parameter estimation value, the main body sales actual number, the consumables sales actual number, and the main body sales planned number (the main body sales actual number). S208).

In step S208, the forecast value calculation unit 120 reads out the demand forecast model stored in the demand forecast model storage unit 114 in step S104 of the model construction step S10 described above from the demand forecast model storage unit 114. Further, in step S208, the predicted value calculation unit 120 reads the parameter estimated value calculated in step S110 of the model construction step S10 described above and stored in the parameter storage unit 118 in step S112 from the parameter storage unit 118. Further, in step S208, the predicted value calculation unit 120 receives data from the data processing unit 110 that has been processed for the number of main unit sales, the number of consumables sold, and the number of main unit sales planned in steps S204 and S206. May be provided.

In step S208, the forecast value calculation unit 120 adds the parameter estimated value, the main unit sales actual number, the consumables sales actual number, and the main unit sales plan unit obtained as described above to the demand forecast model obtained as described above. May be input to calculate the predicted value of the demand for consumables.

Subsequently, the calculated predicted value of the demand number of consumables is output by the output unit 122 (S210). FIG. 11 is a table showing an example of a predicted value of the demand number of consumables output by the output unit 122.

<< 4. Modification example >>
The embodiment of the present invention has been described above. Hereinafter, some modifications of the above embodiment will be described. In addition, each modification described below may be applied to the said embodiment alone, or may be applied to the said embodiment in combination. Further, each modification may be applied in place of the configuration described in the above embodiment, or may be additionally applied to the configuration described in the above embodiment.

<4-1. Modification 1>
In the above embodiment, the mathematical formula (1) and the mathematical formula (2) have been described as examples of the demand forecast model, but the present technology is not limited to such an example. An example of another demand forecast model will be described below.

For example, in the formula (1), the demand forecast model may be expressed as the following formula (8) by removing the second term on the right side of the formula (1-1) representing the first model.

Similarly, in the formula (2), the demand forecast model can be expressed as the following formula (9) by removing the second term on the right side of the formula (2-1) representing the first model. good.

Further, in the above, the demand forecast model is expressed by simultaneous differential equations, but the present technology is not limited to such an example. For example, the demand forecast model is a simultaneous difference of the first model, the second model, the equation (10-1), and the equation (10-2), as in the following equation (10). It may be expressed by an equation.

The above mathematical formula (10) is another expression of the same mechanism as the mathematical formula (1) and the mathematical formula (2).

Further, the demand forecast model is not limited to the above-mentioned example. For example, a mathematical formula obtained by transforming a simultaneous equation obtained by combining the first model and the second model described above as a demand forecast model is also considered to be a demand forecast model based on the first model and the second model. obtain. A mathematical formula transformed in advance into a format suitable for calculating a parameter estimated value and a predicted value of a demand number of consumables may be input as a demand forecast model and stored in the demand forecast model storage unit 114. Further, although the example in which the demand forecast model is expressed as a mathematical formula is shown above, the present technology is not limited to such an example, and may be expressed in another format that can be processed by the demand forecast device 1.

<4-2. Modification 2>
In the above embodiment, the configuration of the demand forecasting device 1 has been described with reference to FIG. 1, but the configuration of the embodiment according to the present technology is not limited to the above example.

For example, it is desirable that the data stored in the main unit sales record unit storage unit 102, the consumables sales record unit storage unit 104, and the main unit sales plan unit storage unit 106 described above be updated at any time, and the demand forecasting device 1 may be used. It may be configured to allow such data to be updated.

For example, the demand forecasting device 1 includes a communication unit (not shown in FIG. 1), and the communication unit obtains new data regarding the actual sales volume of the main body, the actual sales volume of consumables, and the planned sales volume of the main body from an external server (not shown) or the like. You may receive it. Then, the new data received by the communication unit may be accumulated in the main body sales actual number accumulating unit 102, the consumables sales actual number accumulating unit 104, and the main body sales planned unit accumulating unit 106.

Alternatively, using the input unit 112 shown in FIG. 1, the user may be able to input new data regarding the actual number of units sold by the main unit, the actual number of units sold by consumables, and the planned number of units sold by the main unit. Then, new data input by the user using the input unit 112 may be accumulated in the main body sales actual number accumulating unit 102, the consumables sales actual number accumulating unit 104, and the main body sales planned number accumulating unit 106.

Further, FIG. 1 shows an example in which the demand forecasting device 1 includes a main body sales actual number accumulating unit 102, a consumables sales actual number accumulating unit 104, and a main body sales planned number accumulating unit 106, but the present invention is not limited to this. For example, the main body sales actual number accumulating unit 102, the consumables sales actual number accumulating unit 104, and the main body sales planned number accumulating unit 106 may be provided in the external server. In such a case, the acquisition unit 108 acquires (receives) data from the main unit sales record unit storage unit 102, the consumable sales record unit storage unit 104, and the main unit sales plan unit storage unit 106 provided in the external server. You may.

Further, the configuration shown in FIG. 1 may be distributed and provided in a plurality of devices. For example, a model building apparatus including a configuration for executing the model building step S10 described with reference to FIG. 6 and a configuration for executing the consumables demand forecasting step S20 described with reference to FIG. 9 are provided. An information processing system including a consumables demand forecasting device is also feasible.

In such a case, the model building apparatus is, for example, the main body sales record unit storage unit 102, the consumable sales record unit storage unit 104, the acquisition unit 108, the data processing unit 110, the input unit 112, and the demand forecast model storage unit 114 shown in FIG. , The parameter calculation unit 116, and the parameter storage unit 118 may be provided. Further, in such a case, the consumables demand forecasting device is, for example, the main body sales actual number accumulating unit 102, the consumable sales actual number accumulating unit 104, the main body sales planned number accumulating unit 106, the acquisition unit 108, and the data processing unit shown in FIG. It may have functions corresponding to 110, a demand forecast model storage unit 114, a parameter storage unit 118, a forecast value calculation unit 120, and an output unit 122. Then, the demand forecast model and the parameter estimation value may be provided (transmitted) from the model building device to the consumables demand forecasting device.

<< 5. Hardware configuration >>
Each embodiment of the present invention has been described above. Information processing such as the above-mentioned aggregation processing, combination processing, parameter calculation processing, and predicted value calculation processing is realized by the cooperation between the software and the hardware of the demand forecasting device 1. Hereinafter, a hardware configuration example of the demand forecasting device 1 which is the information processing device according to the embodiment of the present invention will be described.

FIG. 12 is an explanatory diagram showing a hardware configuration of the demand forecasting device 1 according to the embodiment of the present invention. As shown in FIG. 12, the demand forecasting device 1 includes a CPU (Central Processing Unit) 1001, a ROM (Read Only Memory) 1002, a RAM (Random Access Memory) 1003, an input device 1004, and an output device 1005. , A storage device 1006 and a communication device 1007.

The CPU 1001 functions as an arithmetic processing unit and a control device, and controls the overall operation in the demand forecasting device 1 according to various programs. Further, the CPU 1001 may be a microprocessor. The ROM 1002 stores programs, arithmetic parameters, and the like used by the CPU 1001. The RAM 1003 temporarily stores a program used in the execution of the CPU 1001 and parameters that are appropriately changed in the execution. These are connected to each other by a host bus composed of a CPU bus or the like. Mainly, by collaboration between CPU 1001, ROM 1002 and RAM 1003 and software, functions such as data processing unit 110, demand forecast model storage unit 114, parameter calculation unit 116, parameter storage unit 118, and prediction value calculation unit 120 are realized. Will be done.

The input device 1004 includes an input means for the user to input information such as a mouse, a keyboard, a touch panel, a button, a microphone, a switch, and a lever, and an input control circuit that generates an input signal based on the input by the user and outputs the input signal to the CPU 1001. It is composed of such things. By operating the input device 1004, the user of the demand forecasting device 1 can input various data to the demand forecasting device 1 and instruct the processing operation. The input device 1004 corresponds to the input unit 112.

The output device 1005 includes, for example, a liquid crystal display (LCD) device, an OLED device, and a display device such as a lamp. Further, the output device 1005 includes an audio output device such as a speaker and headphones. For example, the display device displays an captured image, a generated image, or the like. On the other hand, the voice output device converts voice data and the like into voice and outputs it. The output device 1005 corresponds to the output unit 122.

The storage device 1006 is a device for storing data. The storage device 1006 may include a storage medium, a recording device for recording data on the storage medium, a reading device for reading data from the storage medium, a deleting device for deleting data recorded on the storage medium, and the like. The storage device 1006 stores programs and various data executed by the CPU 1001. The storage device 1006 corresponds to the main body sales actual number accumulating unit 102, the consumables sales actual number accumulating unit 104, and the main body sales planned number accumulating unit 106.

The communication device 1007 is a communication interface composed of, for example, a communication device for connecting to a communication network. Further, the communication device 1007 may include a wireless LAN (Local Area Network) compatible communication device, an LTE (Long Term Evolution) compatible communication device, a wire communication device for wired communication, or a Bluetooth (registered trademark) communication device.

<< 5. Conclusion >>
As described above, according to the embodiment of the present invention, it is possible to predict the demand amount for consumables with high accuracy while reducing the human burden.

Although the preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, the present invention is not limited to these examples. It is clear that a person having ordinary knowledge in the field of technology to which the present invention belongs can come up with various modifications or modifications within the scope of the technical ideas described in the claims. , These are also naturally understood to belong to the technical scope of the present invention.

For example, each step in the above embodiment does not necessarily have to be processed in chronological order in the order described as a flowchart. For example, each step in the processing of the above embodiment may be processed in an order different from the order described in the flowchart, or may be processed in parallel.

Further, according to the above embodiment, it is possible to provide a computer program for causing hardware such as the CPU 1001, ROM 1002, and RAM 1003 to exhibit the same functions as the above-mentioned configuration of the demand forecasting device 1. Also provided is a recording medium on which the computer program is recorded.

1 Demand forecaster 102 Main unit sales record unit storage unit 104 Consumables sales record unit storage unit 106 Main unit sales plan unit unit storage unit 108 Acquisition unit 110 Data processing unit 112 Input unit 114 Demand forecast model storage unit 116 Parameter calculation unit 118 Parameter storage unit 120 Predicted value calculation unit 122 Output unit

Claims (5)

A demand forecaster that forecasts the demand for replaceable consumables used in products.
Expected value A (t) of cumulative shipments of the product at a predetermined time t, expected value B (t) of the number of operating units of the product at the predetermined time t, and number of shipments of the consumables at the predetermined time t. The expected value C (t), the parameter p representing the rate of suspension of use of the product, the parameter q representing the usage rate of the consumables, and the parameter r representing the simultaneous purchase rate of the consumables at the time of purchasing the product are used. Based on the first model in which the demand curve for consumables is expressed as a mathematical formula (F-1) and the second model in which the time change rate of the number of operating units of the product is expressed as a mathematical formula (F-2) . , The demand forecast model storage unit that stores the demand forecast model,
Based on the actual number of sales of the product and the actual number of sales of the consumables, the parameter p, the parameter q, and the parameter r stored in the demand forecast model storage unit are estimated, and the parameter p, the parameter p, and the parameter r are estimated. A parameter calculation unit that calculates a parameter q and a parameter estimated value that is an estimated value of the parameter r, and
A parameter storage unit that stores the parameter estimated value calculated by the parameter calculation unit, and a parameter storage unit.
The forecast value of the demand number of the consumables is calculated based on the demand forecast model stored in the demand forecast model storage unit, the parameter estimation value stored in the parameter storage unit, and the number of future product sales plans. Predicted value calculation unit and
A demand forecaster equipped with.

The forecast value calculation unit inputs the parameter estimated value, the number of sales results of the product, the number of sales records of the consumables, and the number of future product sales plans into the demand forecast model, and obtains the forecast value. The demand forecasting device according to claim 1, which is calculated. The demand forecasting device according to claim 1 or 2 , wherein the demand forecasting model is selected based on a user operation. A demand forecasting method for forecasting the demand for replaceable consumables used in products.
Expected value A (t) of cumulative shipments of the product at a predetermined time t, expected value B (t) of the number of operating units of the product at the predetermined time t, and number of shipments of the consumables at the predetermined time t. The expected value C (t), the parameter p representing the rate of suspension of use of the product, the parameter q representing the usage rate of the consumables, and the parameter r representing the simultaneous purchase rate of the consumables at the time of purchasing the product are used. Based on the first model in which the demand curve for consumables is expressed as a mathematical formula (F-1) and the second model in which the time change rate of the number of operating units of the product is expressed as a mathematical formula (F-2) . , Remembering the demand forecast model,
Based on the actual number of sales of the product and the actual number of sales of the consumables, the stored parameter p, the parameter q, and the parameter r are estimated, and the parameter p , the parameter q, and the parameter r are estimated. To calculate the parameter estimate , which is the estimate of
To store the calculated parameter estimates and
To calculate the forecast value of the demand number of the consumables based on the stored demand forecast model, the stored parameter estimate, and the number of future product sales plans.
Demand forecasting methods, including.

A program to enable a computer to realize the demand forecasting function of replaceable consumables used in products.
Expected value A (t) of cumulative shipments of the product at a predetermined time t, expected value B (t) of the number of operating units of the product at the predetermined time t, and number of shipments of the consumables at the predetermined time t. The expected value C (t), the parameter p representing the rate of suspension of use of the product, the parameter q representing the usage rate of the consumables, and the parameter r representing the simultaneous purchase rate of the consumables at the time of purchasing the product are used. Based on the first model in which the demand curve for consumables is expressed as a mathematical formula (F-1) and the second model in which the time change rate of the number of operating units of the product is expressed as a mathematical formula (F-2) . , The function to store the demand forecast model,
Based on the actual number of sales of the product and the actual number of sales of the consumables, the stored parameter p, the parameter q, and the parameter r are estimated, and the parameter p , the parameter q, and the parameter r are estimated. The function to calculate the parameter estimated value, which is the estimated value of
A function to store the calculated parameter estimates and
A function to calculate a forecast value of the demand number of the consumables based on the stored demand forecast model, the stored parameter estimate, and the number of future product sales plans.
A program to realize.

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