Price transition prediction device, and price transition prediction method

Abstract

To consider grades of products and relations of generation, predict transition of a price after sales on the basis of a predetermined parameter, predict revenue during a predetermined period from the transition of the predicted price, and present a prediction parameter in which the revenue is stabilized and maximized.SOLUTION: A price transition prediction device predicts transition of a sales price until a predetermined period is elapsed from start of sales of a product. The price transition prediction device comprises a price curve generation processing unit which determines a price curve for each product grade from candidates of a volatility curve of a price, and a price curve correction processing unit which corrects both price curves at a switching period from an old product to a new product.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a price trend prediction device and a price trend prediction method for predicting the price trends of a product taking into account the relationships between product grades and generations. [Background technology]

[0002] Product comparison sites and social media have made it easier for consumers to compare products by researching their own design, functions, prices, and reviews, and consumers now have a wider range of product options to choose from. Manufacturers are releasing different grades and generations of products to meet the diverse needs of consumers.

[0003] Under these circumstances, with an open pricing system, manufacturers can indicate a reference price before the product is released, but it is difficult for them to directly control the price of the product after it is released. On the other hand, in order to plan a sales strategy to bring a product to market in a way that stabilizes and maximizes profits, manufacturers need to predict the price trend after the product is released.

[0004] Conventionally, an invention of this type is described in International Publication No. 2017 / 135322 (Patent Document 1). This publication describes an information processing device that calculates an optimal solution for the prices of multiple products based on a sales forecast of the multiple products so as to maximize the total sales amount of the multiple products. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2017 / 135322 Summary of the Invention [Problem to be solved by the invention]

[0006] Patent Document 1 does not take into account cases where manufacturers cannot directly control prices, such as retail sales under an open price system. Therefore, the effect is limited to cases where the manufacturer sells the product themselves and sets the price.

[0007] The present invention aims to realize the creation of more effective sales strategies by predicting the price transition after launch based on predetermined parameters, taking into account the relationship between product grades and generations, predicting revenues for a specified period from the predicted price transition, and presenting prediction parameters that stabilize and maximize revenues. [Means for solving the problem]

[0008] In order to solve the above problems, a representative price trend prediction device of the present invention is a price trend prediction device that predicts the trend in sales price from the launch of a product until a specified period of time has elapsed, and has a price curve generation processing unit that determines a price curve for each product grade from candidate price fluctuation curves, and a price curve correction processing unit that corrects the price curves of both the old product and the new product when switching from the old product to the new product. Effect of the Invention

[0009] According to this invention, in a situation where the manufacturer cannot directly control the price, such as an open pricing system, it is possible to predict the price transition after the product is released based on predetermined parameters taking into account the relationship between the product grade and generation, predict the profits for a specified period from the predicted price transition, and present prediction parameters that will stabilize and maximize the profits, thereby enabling the creation of a more efficient sales strategy.

[0010] Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing a configuration of a price transition prediction device according to an embodiment of the present invention. [Diagram 2]2 is a process flowchart according to an embodiment of the present invention. [Diagram 3] 4 is an example of a price curve parameter information DB according to an embodiment of the present invention. [Figure 4] 13 is an example of a screen used to edit or refer to price curve parameter information according to an embodiment of the present invention. [Diagram 5] 13 is an example of a price curve generation logic information DB according to an embodiment of the present invention. [Figure 6] 13 is an example of a graph showing a ratio of a predicted price Pt to a reference price P1 according to an embodiment of the present invention. [Figure 7] 1 is an example of a group of price curves for only the same generation according to an embodiment of the present invention. [Figure 8] 13 is an example of a price curve correction logic information DB according to an embodiment of the present invention. [Figure 9] 13 is an example of a set of price curves before correction for an old product and a new product of the same grade according to an embodiment of the present invention. [Figure 10] 13 is an example of a group of adjusted price curves of an old product and a new product of the same grade according to an embodiment of the present invention. [Figure 11] 2 is an example of a production price curve information DB according to an embodiment of the present invention. [Figure 12] 1 is an example of a corrected price curve information DB according to an embodiment of the present invention. [Figure 13] 2 is an example of a price curve revenue information DB according to an embodiment of the present invention. [Figure 14] 2 is an example of a revenue evaluation information DB according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. EXAMPLES

[0013] 1 is an example of a block diagram showing the configuration of a price trend prediction device 100 according to an embodiment of the present invention. The price trend prediction device 100 is composed of an operation unit 110 that accepts user operations such as a keyboard, mouse, or touch panel, a data input / output unit 120 that takes in input data related to price prediction obtained from peripheral systems, spreadsheet software, or the like, and outputs intermediate data related to demand forecast and the result of price prediction as output data, a calculation unit 130 that generates, corrects, and evaluates data related to price prediction, a storage unit 140 that stores the taken in input data, intermediate data for price prediction, and the result of price prediction, and a display unit 150 such as a display device or projector that presents the result of price prediction to a user.

[0014] The calculation unit 130 is composed of a price curve generation processing unit 131, a price curve correction processing unit 132, and a revenue evaluation processing unit 133. The processing content of each processing unit will be described later, but the calculation unit 130 is, for example, a processing unit (CPU) of a computer device, and each processing unit is realized by the CPU executing a program in which the processing is described. Furthermore, in the following explanation, unless a specific entity is specified in the processing performed by the price trend prediction device 100, it is assumed that the processing is performed by the CPU of the calculation unit 130 executing a predetermined program. Furthermore, each processing unit may be realized by a dedicated integrated circuit device or device.

[0015] The storage unit 140 is composed of a price curve parameter information database (DB) 141, a price curve generation logic information DB (142), a price curve correction logic information DB (143), a generated price curve information DB (144), a corrected price curve information DB (145), a price curve revenue information DB (146), and a revenue evaluation information DB (147). The contents of each DB will be described later, but they may be composed of a storage device built into the same computer device as the above-mentioned calculation unit, or all or part of each DB may be composed of another storage device, etc.

[0016] Next, the configuration and function of each part in Fig. 1 will be described in detail in the order of the processing steps in the processing flowchart of the price transition prediction device 100 of this embodiment shown in Fig. 2. Note that here, product grades are classified into three types, high-end (H), middle (M), and low-end (L), in order of price, and it is assumed that a new product is released while the old product is on sale, and both are sold side by side for a certain period of time.

[0017] <Step S201> A user starts the price trend prediction device 100 through the operation unit 110. The price trend prediction device 100 may be started in batches by specifying a date and time, or may be started with an event as a trigger.

[0018] <Step S202> The price curve parameter information is read from the data input / output unit 120 and stored in the price curve parameter information DB (141) of the storage unit 140.

[0019] Here, the price curve parameter information is information (parameter set) consisting of a collection of parameters that specify how the product will be put on the market and how the price will change after release. Fig. 3 shows an example of a parameter set stored in the price curve parameter information DB (141), and includes a parameter set name 141a, usage logic 1 (for generation) 141b, usage logic 2 (for correction) 141c, product grade 141d, product generation 141e, release date 141f, input parameter 1 (reference price) 141g, input parameter 2 (expected time) 141h, and input parameter 3 (expected ratio) 141i.

[0020] The logic 1 (for generation) 141b specifies the logic (formulas, constraints) used to generate a price curve, and the logic 2 (for correction) 141c specifies the logic (formulas, constraints) used to correct the price curve.

[0021] The product grade 141d indicates the grade of the product, with "H" indicating a high-grade product, "M" indicating a mid-range product, and "L" indicating a low-end product.

[0022] Product generation 141e indicates the generation (newness) of the product, and indicates the order of product generations from old products to new products, for example, corresponding to alphabetical order from A to Z. In other words, if product generation "A" is an old product, product generation "B" indicates a new product, which is one generation after the old product.

[0023] The release date 141f is the release date of the product, and the input parameter 1 (reference price) 141g is the reference price at the time of release. The input parameter 2 (estimated time) 141h is the expected time after the release date, and the input parameter 3 (estimated rate) 141i is the rate of price fluctuation during that time.

[0024] The price curve parameter information 141 can be input (set) by using the operation unit 110 from, for example, an input screen 400 of FIG.

[0025] For example, the user inputs "PS1" 401 as the name of the parameter set to be set. Previously used parameter sets may be stored and recalled and reused by inputting their names. Then, "LC1C" 402 and "LC1U" 403 are set as usage logic 1 (for generation) and usage logic 2 (for correction), respectively. The processing contents of these are stored in advance in the price curve generation processing unit 131 and the price curve correction processing unit 132 of the calculation unit 130, and the user can specify these.

[0026] Furthermore, "H" 404, which indicates a high grade, is specified as the product grade, and "A" 410, which indicates an older product, is specified as the product generation. Also, "2024 / 6" and "1000000" are set as the release date and input parameter 1 (reference price) 411, respectively, and "12 months later" and "80%" are specified as input parameter 2 (expected timing) and input parameter 3 (expected ratio) 414, respectively. With the above, the information of record 141-1 in FIG. 3 is input (set).

[0027] In addition, because the way products are introduced to the market, such as the release date, reference price, and expected ratio in the expected time frame, is different between the old product and the new product, the new product with product generation "B" 420 is set with the release date "2025 / 6", the reference price at the time of release "1,100,000" 421, the expected time frame "12 months later", and the expected ratio "85%" 424. These settings are reflected in the information of record 141-2 in FIG. 3.

[0028] <Step S203> Based on the information stored in the price curve parameter information DB (300) in step S202, price curve generation logic information is generated and stored in the price curve generation logic information DB (142) of the storage unit 140. The price curve generation logic information is information (data, parameters) regarding the logic of mathematical expressions and constraint conditions used when the price curve generation processing unit 131 described later generates a price curve.

[0029] 5 shows an example of information stored in the price curve generation logic information DB (142). Here, the data items (values) include the name of the generation logic (generation logic code) 142a, an explanation 142b of the formula in the generation logic code 501 and an explanation 142c of its constraint conditions, an estimated time point 142d, an estimated ratio 142e, a combination number i (142f), an intermediate parameter a (142g), and an intermediate parameter b (142h). Here, the estimated time point is a monthly bucket with the release date as "time point 1," and "time point 13" indicates the estimated time 12 months later.

[0030] The formula used in the generation logic code "LC1C" is stored in the price curve generation processing unit 131 as described above, and its contents are described as formula explanation 502. For example, this formula is as follows, for a predicted price Pt at time t (t is an integer equal to or greater than 1) of a monthly bucket: <Formula 1>: Pt=P1(1 / t+a-(t-1)b) / (1+a-(t-1)b) Here, "P1" indicates the input parameter 1 (reference price) 141g in FIG. 3, "a" indicates the intermediate parameter a (142g) in FIG. 5, and "b" indicates the intermediate parameter b (142h) in FIG.

[0031] Formula 1 is used to extract candidates for the fluctuation trend (fluctuation curve) when the reference price (P1) fluctuates over time by substituting multiple combinations of the intermediate parameter a (142g) and the intermediate parameter b (142h). In this embodiment, the combinations of the intermediate parameter a (142g) and the intermediate parameter b (142h) are narrowed down (limited) to realistic ones based on past experience, etc.

[0032] In this embodiment, since the estimated ratio at the estimated time point 142d is specified, when the intermediate parameter a (142g) is specified, the intermediate parameter b (142h) is also determined accordingly. <Formula 2>:Pt / P1=(1 / t+a-(t-1)b) / (1+a-(t-1)b) However, (Pt / P1) on the left side is the expected ratio (price fluctuation rate) of the expected price Pt to the reference price P1 at a certain point in time t. For example, if the expected ratio at point 13 is 80%, substituting "0.8" for (Pt / P1) and "13" for (t) gives the following transformation: <Formula 3>: b = a / 12-47 / 156 The following relationship can be obtained.

[0033] Using this relational expression, when intermediate parameter a (142g) is substituted, for example, from 4.0 to 8.0 in increments of 1.0, intermediate parameter b changes to 0.032, 0.115, 0.199, 0.282, and 0.365. The five candidates for combinations of a and b thus obtained are assigned combination numbers i (1, 2, 3, 4, 5) 142f, as shown in records 142-1 to 142-5 in FIG. 5, and stored in the price curve generation logic information DB (142).

[0034] Here, the range and interval of the value of the intermediate parameter a are selected based on the experience of the price fluctuation trends of similar products of the company and other companies in the past, but other ranges and intervals may be used. For example, such calculations may be performed in a peripheral system or spreadsheet software, and the candidates for the combination of a and b and their combination number i may be read from the data input / output unit 120 and stored in the price curve generation logic information DB (142). In addition to this method, for example, the calculations for the candidates for the combination of a and b and their combination number i may be built in as part of the price curve generation processing unit 131 described later.

[0035] The user can check the contents of the price curve generation logic information DB (142) shown in FIG. 5 by displaying it on the display unit 150 as necessary.

[0036] <Step S204> Based on the information stored in the price curve parameter information DB (141) in step S202, price curve correction logic information is generated and stored in the price curve correction logic information DB 143 of the storage unit 140.

[0037] The price curve correction logic information is information on the logic of mathematical expressions and constraint conditions used when the price curve correction processing unit 133, which will be described later, corrects the price curve generated by the price curve generation processing unit 131 etc., taking into account the influence of generational change. An example of the price curve correction logic information DB 143 is shown in Fig. 8. The data items (values) include a name of the correction logic (correction logic code) 143a, a mathematical expression explanation 143b, a constraint condition explanation 143c, a reference price of the old product 143d, a reference price of the new product 143e, and a correction price c 143f.

[0038] The formula used in the correction logic code "LC1U" is stored in the price curve correction processing unit 132, and its contents are described as the formula explanation 143b.

[0039] For example, in record 143-1 of the table in Fig. 8, the reference price of the old product "1000000" corresponds to input parameter 1 (reference price) "1000000" of product generation "A" which is an old product of product grade "H" in record 141-1 of the price curve parameter information DB (141) in Fig. 3. Similarly, the reference price of the new product "1100000" corresponds to input parameter 1 (reference price) "1100000" of product generation "B" which is a new product of product grade "H" in record 141-2 in Fig. 3.

[0040] In addition, the correction price c (806) is used when correcting the forecast price at a specific time so as to reduce the price gap between the old product and the new product when the generational change occurs. In this case, when the reference price 143d of the old product of the same grade is P1A and the reference price 143e of the new product is P1B, the correction price c (143f) is expressed as follows: <Formula 4>: c = P1B - P1A and calculate.

[0041] The method of selecting the time point to be corrected by the correction price c (143f) is built into the price curve correction processing unit 132 as a constraint condition of the correction logic code (LC1U), and the contents are described as the constraint condition explanation 143c. Here, the constraint conditions are as follows: For new products, this applies only to the month following the release of the new product and the start of the generation changeover. For older products, the new product will be released and the month after the new product is released and the changeover begins. is stated.

[0042] The logic of the formula and constraint conditions is not limited to the above example. For example, a formula may be generated from past sales prices using techniques such as machine learning or deep learning, and then adjusted as necessary for use. Alternatively, such calculations may be performed in a peripheral system or spreadsheet software, and stored in the price curve correction logic information DB (143) via the data input / output unit 120.

[0043] Furthermore, the user can check the contents of the price curve correction logic information DB (143) shown in FIG. 8 by displaying it on the display unit 150 as necessary.

[0044] <Step S205> The price curve generation processing unit 131 substitutes multiple intermediate parameters a and b into the formula (Formula 2) specified by the specified generation logic code (LC1C) to generate multiple price fluctuation rate (Pt / P1) curve candidates.

[0045] 6 shows an example of a graph of the ratio (Pt / P1) of the predicted price Pt to the reference price P1 at time t, calculated using Formula 2. This graph shows the results for parameters 5141 to 5145 with combination numbers i (506) of 1 to 5 in the price curve generation logic information DB 500 of FIG.

[0046] In both curves, the expected ratio (Pt / P1) is 100% (611) at time point 1 (time of release (year and month of release)) 601, and 80% (614) at time point 13 (expected time point (12 months later)) 604, but between time points 2 and 12, the curves differ depending on the combination 620 of intermediate parameter a and intermediate parameter b. It can be seen that the smaller the value of intermediate parameter a is, the more the curve changes at the initial point in time.

[0047] Note that while Figure 6 shows an example where data up to the 13th month is plotted, calculations are made in the same way for subsequent months. The same applies to Figure 7, which will be described later.

[0048] Furthermore, the user can check the graph of the transition of the estimated ratio shown in FIG. 6 by displaying it on the display unit 150 as necessary.

[0049] <Step S206> Next, the price curve generation processing unit 131 multiplies the curve of the price fluctuation rate (Pt / P1) generated in step S205 by the respective reference prices P1 for each product grade (H, M, L) to obtain multiple price curve Pt candidates for each product grade, and selects an appropriate price curve (P t H , P t M , P t L ) is selected.

[0050] In other words, in order to maintain the hierarchy of prices between product grades, regardless of the time (t), <Formula 5>: P t H > P t M > P t L While satisfying the above relationship, we select products whose prices change more significantly in the early stages of sales as their product grades increase. Specifically, we apply a price fluctuation rate (Pt / P1) curve in which the value of intermediate parameter 1 "a" decreases as the product grade increases while maintaining the ranking between product grades.

[0051] In addition, if multiple patterns of price curve groups that satisfy the above constraints are obtained, each may be assigned a different identification number (the "price curve group number" described below) and the processing described below may be performed on each of them, or one of the multiple patterns may be selected based on predetermined criteria, such as selecting the product grade "H" with the smallest value of the intermediate parameter a, or the product grade "L" with the largest value of the intermediate parameter a.

[0052] In addition, the logic of the formulas and constraints is not limited to the above, and may be automatically generated from past sales prices using techniques such as machine learning and deep learning, and then adjusted as necessary.

[0053] FIG. 7 shows an example of generating a group 720 of price curves for only the same generation using the formula 2 of the generation logic code "LC1C" and the constraint conditions. In FIG. 7, the predicted price (P t H , P t M , P t L ) shows a group of price curves starting from a reference price 711 at time 1 (release time (year and month of release)) 601 and ending at an expected price 714, which is the reference price multiplied by the expected rate, at time 13 (expected time (12 months later)) 604. The higher the product grade, the greater the change in price at the initial stage, and the up-down relationship between prices between product grades is maintained.

[0054] The user can check the graph of the price curve group shown in FIG. 7 by displaying it on the display unit 150 as necessary.

[0055] <Step S207> Furthermore, the price curve generation processing unit 131 stores the selected price curve group in the generated price curve information DB (144). At this time, various information is added and the time axis is also adjusted based on the price curve parameter information DB (141) in Fig. 3. In addition, the price curve data is interpolated as necessary, for example, assuming use in later data analysis.

[0056] Fig. 11 is an example of the generated price curve information DB (144), which has, as data items (values), a parameter set 144a, a product grade 144b, a product generation 144c, a release date 144d, a price curve group number 144e, a generation logic code 144f, a combination number i (144g), a week start date 144h, and a predicted price 144i, and shows a price curve group number 144e (intermediate parameters a and b (144g) of combination number "i", a week start date 144h, and a predicted price 144i) that identifies a group of price curves generated by the parameter set 144a (product grade 144b, product generation 144c, release date 144d, and generation logic code 144f). Also, in Fig. 11, the price curve generated in the monthly bucket is linearly interpolated to the price curve in the week bucket starting on Monday.

[0057] For example, records 144-1 to 144-8 in Figure 11 relate to product grade "H" and product generation "A," and record 144-1 stores the start date of the first week of the release date "2024 / 6" as "2024 / 6 / 3," and the value of input parameter 1 (reference price) of record 141-1 in Figure 3, "1000000," as the predicted price at that time.

[0058] Record 144-2 stores the start date of the second week of the release date "2024 / 6" ("2024 / 6 / 10") and the predicted price at that time ("999702") obtained by linear interpolation. The same is true for records 144-3 to 144-7.

[0059] Record 144-8 stores the start date of the first week of the expected period (12 months after June 2024), "June 2, 2025," and the predicted price, which is the product of input parameter 1 (reference price) "1,000,000" of record 141-1 in Fig. 3 multiplied by input parameter 3 (expected ratio) "80%." Records 144-8 to 144-9 store predicted prices for the period after record 144-8.

[0060] Records 144-9 to 144-10 relate to product grade "H" and product generation "B", and record 144-9 stores the start date of the first week of the release date "2025 / 6", "2025 / 6 / 2", and the value of input parameter 1 (reference price) of record 141-2 in Figure 3, "1100000", as the predicted price at that time.

[0061] Record 144-10 stores the start date of the first week of the expected period (12 months after the release date "2025 / 6"), "2026 / 6 / 1", and the predicted price at that time, "935000", which is the value obtained by multiplying input parameter 1 (reference price) "1,100,000" of record 141-2 in FIG. 3 by input parameter 3 (expected ratio) "85%". Records 144-10 to 144-11 store predicted prices for the period after record 144-10. Records 144-9 to 144-11 show that the week before the record 144-9 to 144-11 used the combination of intermediate parameters a and b with combination number i "2" in the formula of the generation logic code "LC1C".

[0062] Record 144-11 is for product grade "M" and product generation "A", and stores the start date of the first week of release date "2024 / 6" as "2024 / 6 / 3" and the value of input parameter 1 (reference price) "900000" of record 141-3 in Figure 3 as the predicted price at that time. It also shows that the combination of intermediate parameters a and b of combination number i "4" was used in the formula of generation logic code "LC1C".

[0063] Record 144-12 is for product grade "L" and product generation "A", and stores the start date of the first week of release date "2024 / 6 / 3" as well as the value of input parameter 1 (reference price) "800000" of record 141-4 in Figure 3 as the predicted price at that time. It also shows that the combination of intermediate parameters a and b of combination number i "5" was used in the formula for generation logic code "LC1C".

[0064] In addition, the above records 144-1 to 144-12 store the price curve group number 144e specified by "FL1", but from record 144-13 onwards, the price curve group number 144e stored is specified by "FL2".

[0065] Furthermore, the user can check the contents of the generation price curve information DB (144) shown in FIG. 11 by displaying it on the display unit 150 as necessary.

[0066] <Step S208> The price curve correction processing unit 132 corrects the time-series price curve group for the group of price curves in the generated price curve information DB (144) generated in step S207 based on the price curve correction logic information DB (143) (FIG. 8) generated in step S204 so as to reduce the price difference between the old product and the new product at the time of generational change (switchover) from the old product to the new product, and stores the corrected price curve information DB (145). The specific contents of the correction process will be described below.

[0067] Figure 9 shows the price P tA A (911) and the price P of the new product of product generation B, which is the same product grade. tB B For the two groups of price curves in (921), we show the state before the price curve correction is applied.

[0068] In Figure 9, the time axis t B In (920), the release date of product generation B is time 1 (922), one month after the release date is time 2 (923), two months after is time 3 (924), and three months after is time 4 (925). The time axis t A In (910), 12 months after the release date of product generation A is time 13 (912), 13 months after is time 14 (913), 14 months after is time 15 (914), and 15 months after is time 16 (915). These correspond to the first April when the generational change began.

[0069] Here, in accordance with the constraint 803 of the correction logic code “LC1U” shown in FIG. 8, which is “For new products, apply only the month after the new product is released and the generational change begins. For old products, apply the month after the new product is released and the generational change begins and the month after that,” the price for the target month is corrected by adding the correction price c (806) (=100,000).

[0070] In other words, for product generation B, which is the new product, the correction price c is added only at time 2 (923), which is the month after the new product is released and the generational change begins, and for product generation A, which is the old product, the correction price c is added at time 14 (913) and time 15 (914), which are the month after and the month after that after the new product is released and the generational change begins.

[0071] FIG. 10 shows the state after the price correction has been applied to the price curve group in FIG. 9 as described above, and shows the corrected price P' of the product of product generation A, which is the old product. tA A (1011) and the adjusted price P' of the new product, product generation B tB B (1021) shows two families of price curves.

[0072] For example, the time axis t of the new product generation B B (920) is the predicted price P before correction for time 2 (923) that is the subject of price correction. 2 B Adding the correction price c to (1001) gives the corrected predicted price P' 2 B (1002). Similarly, the time axis t A (910) The uncorrected predicted price P at time 14 (913) and time 15 (914) which are the subject of price correction 14 A Adding the correction price c to (1003) gives the corrected predicted price P' 14 A (1004), uncorrected predicted price P 15 A Adding the correction price c to (1005) gives the corrected predicted price P' 15 A (1006).

[0073] In this way, by carrying out the correction process, it is possible to express the state in which, after the new product is released and the generational change with the old product begins, the price gap between the new product and the old product is gradually reduced while affecting their prices. In particular, by carrying out more correction process on the old product than on the new product, the price gap is gradually reduced.

[0074] An example of the corrected price curve information DB (145) obtained by correcting the contents of the generation price curve information DB (144) as described above is shown in Fig. 12. It differs from the generation price curve information DB (145) in that a column for corrected forecast price 145a is added.

[0075] The records 145-1 and 145-2 in the corrected price curve information DB (145) correspond to the records 144-1 and 144-2 in the generated price curve information DB (144) in FIG. 11. The record 145-3 corresponds to the time axis t A Corresponding to the correction at time 14 (913) of (910), the value "880000" obtained by adding the correction price c "100000" to the predicted price "780000" for the week start date "2025 / 6 / 30" is stored as the corrected predicted price.

[0076] Similarly, record 145-4 corresponds to the time axis t of product generation A shown in FIG. 10 as the time to be subject to price correction. A Corresponding to the correction at time 15 (914) of (910), the value "860000" obtained by adding the correction price c "100000" to the predicted price "760000" for the week start date "8 / 4 / 2025" is stored as the corrected predicted price.

[0077] 12 correspond to records 144-9 and 144-10 in the generated price curve information DB (144) in FIG. 11. Record 145-6 corresponds to the time axis t BThe value "1,080,000" obtained by adding the correction price c "100,000" to the predicted price "980,000" for the start day of the week "2025 / 6 / 30" corresponding to time 2 (923) of (920) is stored as the corrected predicted price.

[0078] In addition, the user can display the pre-correction price curve group shown in Figure 9, the post-correction price curve group shown in Figure 10, and the contents of the corrected price curve information DB (145) shown in Figure 12 on the display unit 150 and check them as necessary.

[0079] <Step S209> The revenue evaluation processing unit 133 predicts revenue by multiplying the predicted value of the number of sales (predicted number of units) by the group of predicted price curves stored in the corrected price curve information DB (145), and stores the predicted revenue in the price curve revenue information DB (146). Fig. 13 is an example of the price curve revenue information DB (146), in which the parameter set 146a, product grade 146b, product generation 146c, release date 146d, price curve group number 146e, generation logic code 146f, correction logic code 146g, combination number i (146h), and week start date 146i are the same as the items of the same names in the corrected price curve information DB (145) shown in Fig. 12, and the predicted price 146j corresponds to the corrected predicted price 145a in the corrected price curve information DB (145).

[0080] Here, the predicted number of units 146k used to calculate the predicted revenue 146l by multiplying the predicted price 146j may be, for example, a predicted number of units determined empirically based on the product type, product grade, price range, release month, etc. and held in a separate database (not shown) and quoted, or may be imported from outside via the data input / output unit 120.

[0081] The user can check the contents of the price curve profit information DB (146) shown in FIG. 13 by displaying it on the display unit 150 as necessary.

[0082] <Step S210> The profit evaluation processing unit 133 extracts a group of price curves with the maximum or most stable predicted profits, and presents them to the data input / output unit 120 and stores them in the profit evaluation information DB (147).

[0083] 14 shows an example of the profit evaluation information DB (147), which includes a parameter set 147a, a price curve number 147b, a profit maximization ranking 147c, a profit total 147d, a profit stability ranking 147e, and a profit variance 147f. The user can display these contents on the display unit 150 and check them as necessary.

[0084] Here, the total revenue 147d is the sum of the forecast revenue 146l for a specified period in the price curve revenue information DB (146) for each parameter set 146a and price curve group number 146e, and the revenue maximization ranking 147c is the ranking in which the total revenue for each parameter set 146a and price curve group number 146e is sorted in descending order.

[0085] In addition, the revenue variance 147f is the value obtained by calculating the standard deviation of the monthly total of the forecast revenue 146l in the price curve revenue information DB (146) for each parameter set 146 and price curve group number 146e, and the revenue stability ranking 147e is the ranking in ascending order of the revenue variance for each parameter set 146a and price curve group number 146e.

[0086] By referring to the parameters used to predict the desired revenue evaluation item, users can use the data as reference when planning sales strategies, such as setting reference prices for each product grade and the timing of new product launches.

[0087] In the case of Fig. 14, when the magnitude of profit is prioritized, the most effective selection candidate is "parameter set 147a is "PS1" and price curve group number 147b is "FL1"" of record 147-1, in which profit maximization rank 147c is "1". Also, when the stability of profit is prioritized, the most effective selection candidate is "parameter set 147a is "PS1" and price curve group number 147b is "FL2"" of record 147-2, in which profit stability rank 147e is "1".

[0088] As described above, according to this embodiment, even in cases where the manufacturer cannot directly control the price, such as in retail sales under an open price system, it is possible to predict the price transition after release based on predetermined parameters taking into account the relationship between the product grade and generation, predict the profits for a predetermined period from the predicted price transition, and present prediction parameters that stabilize and maximize the profits, thereby enabling the creation of a more efficient sales strategy.

[0089] It should be noted that the present invention is not limited to the above-mentioned embodiment, but includes various modified examples. For example, the above-mentioned embodiment has been described in detail to explain the present invention in an easily understandable manner, and the present invention is not necessarily limited to the embodiment having all of the described configurations.

[0090] In addition, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD. [Explanation of symbols]

[0091] 100: Price trend forecasting device 110:Operation unit 120: Data input / output section 130: Arithmetic section 131: Price curve generation processing unit 132: Price curve correction processing unit 133: Revenue evaluation processing unit 140: Storage section 141: Price curve parameter information DB 142: Price curve generation logic information DB 143: Price curve correction logic information DB 144: Generated price curve information DB 145: Corrected price curve information DB 146: Price curve revenue information DB 147: Revenue evaluation information DB 150: Display section

Claims

1. A price transition prediction device for predicting a change in sales price of a product from the start of its release until a predetermined period of time has elapsed, comprising: a price curve generation processing unit that determines a price curve for each product grade from candidates of the price fluctuation rate curve; a price curve correction processing unit that corrects the price curves of both the old product and the new product at the time of switching from the old product to the new product; A price trend prediction device comprising:

2. 2. The price transition prediction device according to claim 1, The price curve generation processing unit: generating multiple candidate price volatility curves by applying multiple different parameter combinations to a formula that represents the rate of price change from the launch date until the elapse of a predetermined period of time; multiplying the fluctuation rate curve candidates by a reference price at the time of launch for each product grade to obtain a plurality of price curve candidates for each product grade, and selecting a price curve that satisfies a first constraint condition that is predetermined for each product grade from the price curve candidates; A price trend prediction device characterized by:

3. The price transition prediction device according to claim 2, The price curve correction processing unit: correcting the prices of the old product and the new product so that the price difference between the two products in the price curves decreases when switching from the old product to the new product; A price trend prediction device characterized by:

4. The price transition prediction device according to claim 3, The first constraint condition is to maintain a price hierarchy among the product grades, and to change the price more significantly at an early stage of sales for a higher product grade. A price trend prediction device characterized by:

5. The price transition prediction device according to claim 4, the price curve correction processing unit performs a correction process more frequently on the old product than on the new product; A price trend prediction device characterized by:

6. The price transition prediction device according to claim 5, a revenue evaluation processing unit that calculates a predicted revenue for a predetermined period by multiplying the predicted sales price change by a predicted number of units sold; A price trend prediction device characterized by:

7. The price transition prediction device according to claim 6, The profit evaluation processing unit presents a combination of the parameters that maximizes profits or a combination of the parameters that most stabilizes profits. A price trend prediction device characterized by:

8. A price trend forecasting method for forecasting a sales price trend from a product launch until a predetermined period of time has elapsed, comprising: determining a price curve for each product grade from the candidates of the price fluctuation rate curve; A step of correcting the price curves of both products at the time of switching from the old product to the new product; A price trend forecasting method comprising the steps of:

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