JP2004206569A - Method and system for supporting process to determine specifications of new product - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To support a process to determine specifications for a new product, which encourage many consumers to accept the new product in a market, where various requirements for the product such as an automobile are in demand and they require many types of the products, while the products are already sold in the market. <P>SOLUTION: The system for supporting the process to determine the specifications for the new product, comprises a means 196 for storing a level of evaluation value for attributes of the new product, which the consumers evaluate as a product selection factor, a means 200 for storing the level for each attribute of competitive products, a means 208 for calculating a sales volume of the new product by estimation, a means 224 for calculating costs for the new product based on the levels set for each attribute of the new product, which are temporally determined, a means 228 for calculating a sales profit by the estimation from the calculated sales volume of the new product, a sale price, and the calculated cost for the new product, and a means 232 for modifying the level for each attribute of the new product to increase the sales profit calculated by the estimation. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

[0001]
BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technology for supporting a process of determining the specifications of a new product.
[0002]
2. Description of the Related Art Companies provide consumers with products having specifications required by the consumers, thereby satisfying the needs of the consumers and obtaining profits.
Consumer values are diversifying, and companies are struggling to understand the specifications of new products that consumers need. For products with a mix of practicality and hobbies, such as automobiles, it is difficult to know the product specifications required by consumers, and sales of products launched with high expectations have been disappointing. In some cases, products that did not expect much may be sold more than expected.
The product specifications referred to here include various characteristics that characterize a product from other products, and also include specifications of additional products added to the product body. For example, in the case of a car, the information includes a wide range of displacement, fuel consumption, sales price, the presence or absence of a navigation device, and the like.
If the specification of the product required by the consumer can be accurately grasped in advance, useless product development costs and the like can be suppressed, and the product required by the consumer can be provided at a low price. A technology for accurately grasping in advance the product specifications required by consumers is required not only by companies but also by consumers.
[0003]
A technology in which consumers participate in determining the specifications of a new product is described in Patent Document 1 and the like. If this technology works well, it is expected that consumers' wishes or necessity will be reflected in the planning stage of new products, and products with specifications required by consumers will be developed.
[0004]
[Patent Document 1]
JP 2002-92293 A [Patent Document 2]
Japanese Patent Application Laid-Open No. 2001-265991 [Patent Document 3]
JP 2002-73951 A [Patent Document 4]
JP 2001-142972 A [Patent Document 5]
JP 2000-132618 A
When one type of product specification is determined, a method in which consumers participate to determine the product specification is effective. However, in the case of products that require various types due to various demands, such as automobiles, for example, there are various limitations in a method in which consumers participate to determine specifications. If the same company offers a wide variety of products, it is necessary to prepare a group of types that can meet diverse demands. Need to be In a method in which consumers participate in determining product specifications, it is not possible to optimally set the entire type group so as to meet the diverse demands of many consumers.
In addition, the conventional specification support method is limited to the level of searching for the specifications that consumers need, and it is possible to estimate and calculate the profit obtained from it and evaluate the specification in terms of the estimated calculated profit. Did not.
[0006]
INDUSTRIAL APPLICABILITY The present invention is a product that requires many types due to various demands, such as an automobile, and is accepted by many consumers while many types of products are already provided on the market. It provides technology to support the process of determining new product specifications.
Another object of the present invention is to provide a technique for evaluating the validity of a specification in terms of estimated profits, and supporting a process of modifying the specification to obtain more profits.
According to the present invention, when a product required by a consumer can be provided accurately, useless product development costs and the like can be suppressed, and a product required by the consumer can be provided at a low price. Become like
[0007]
In order to assist the consumer in the process of determining the specifications of the product required, the method of claim 1 is shown schematically in FIG. At least, a step (194) of calculating an evaluation value possessed by a consumer with respect to a level for each attribute of a product which is a factor for selecting a product of the consumer and storing the evaluation value in a computer (194); A step (198) of temporarily determining a level for each attribute and storing it in a computer so that it can be used, and a step (202) of storing a level for each attribute of a competitive product in a computer so that it can be used are executed. Subsequent to the above information input step, the following steps are executed by the computer. That is, a step (206) of estimating and calculating the number of new products to be sold from the evaluation value that the consumer has for the level, the level for each attribute of the new product, and the level for each attribute of the competing product. On the other hand, a step (222) of calculating the cost of the new product based on the temporarily determined level of each attribute, the estimated number of sales of the new product, the selling price of the new product, and the cost of the new product The step (226) of estimating and calculating the operating profit of the new product and the step (230) of correcting the level for each attribute of the new product are performed to increase the estimated operating profit of the new product.
[0008]
The attribute here refers to a factor considered by a consumer in a product selection process. For example, in the case of a product such as an automobile, a purchase model is determined in consideration of factors such as a maker, a displacement, a fuel consumption, and a price. Therefore, the manufacturer, displacement, fuel efficiency, price, and the like are attributes.
The level indicates, for each attribute, a specific description of the attribute. For example, a level of 1.2 to 1.4 liters or a level of 1.4 to There is a level of 1.6 liters.
Consumers judge whether the attribute of the displacement is preferably a level of 1.2 to 1.4 liters or a level of 1.4 to 1.6 liters. It is determined whether the level of company is preferable or the level of company II is preferable. For the attribute of price, it is determined whether the level of 900 to 1.2 million yen is preferable or the level of 1.2 to 1.4 million yen is preferable. , And select one product after comprehensively evaluating them.
The level (importance) of the level played by consumers in the process of selecting products is not constant, and many consumers select products with a focus on the level of 1.4 to 1.6 liters. While the level of 2 to 1.4 liters is not preferred, it may not be important to determine whether the manufacturer is I or II. In this case, the level of 1.4 to 1.6 liters is a level that works strongly favorably in selecting products, and the level of 1.2 to 1.4 liters is a level that works strongly disadvantageously in selecting products. It can be said that the level of the product and the level of the product made by Company II are neutral levels. The significance with which a level works in selecting a product can be quantified, for example, a positive level can indicate an advantageous level, a zero value a neutral level, and a negative value a negative level. In this case, a large absolute value is given according to the degree of strong working. The criterion for quantification can be set arbitrarily, and the most disadvantageous level may be set to zero, and a neutral level, an advantageous level, and a strongly advantageous level may be positively large. In the present specification, a value obtained by quantifying the importance of a level when selecting a product is referred to as an evaluation value or a partial utility coefficient.
The evaluation value or partial utility coefficient β is conceptually displayed in the evaluation value database 196 of FIG. 19, and the evaluation value β is determined for each combination of the attribute (indicated by alphabet) and the level (indicated by numeral). Has been given. The evaluation value β can be said to be a value obtained by quantifying the evaluation that the consumer has with respect to the level for each attribute of the product.
The attribute in which the evaluation value β greatly varies depending on the level, that is, the attribute of the displacement in the previous example, can be said to be an important attribute when selecting a product. An attribute whose evaluation value β does not fluctuate much depending on the level, that is, an attribute of a maker in the above example, can be said to be an attribute that is not important in selecting a product. The importance of attributes in product selection can also be quantified. Importance can also be shown in%. Here, a value obtained by quantifying the importance of an attribute in product selection is referred to as a contribution ratio. In the previous example, the contribution rate of the attribute of the displacement is high, and the contribution rate of the attribute of the maker is low.
The words of attribute, level, contribution rate, and partial utility coefficient are words used in the method of conjoint analysis, and are described in detail in, for example, "Chapter 9 of the Product Planning Seven Tools, Editors: Noriaki Kanda, Nihon Co., Ltd. It is described in "Science Technology Publishing Company."
The terms of attribute, level, contribution ratio, and partial utility coefficient as used herein are not limited to those based on conjoint analysis, but include concepts and indices that are homogeneous in function or quality. In particular, the word of the evaluation value should be interpreted most widely, and means an index that quantifies the magnitude of the value perceived by the consumer in some way.
[0009]
Products have various attributes, and when compared with competing products existing in the market, the level of each attribute is widely distributed. There are products made by Company I that cost 1.5 million yen for 1.5 liters, and products made by Company II that cost 1.3 million yen for 1.3 liters. Consumers select one product from competing products according to their values.
When a well-adjusted consumer value survey is conducted on the consumer, the evaluation value β possessed by the consumer with respect to the level of each attribute described above can be known, and the contribution ratio of the attribute can be known. The details are described in the above-mentioned book, and will not be repeated here. By conducting a consumer value survey using a known method, for example, an evaluation value β of a level of 1.4 to 1.6 liters, an evaluation value β of a level of 1.2 to 1.4 liters, It is possible to know the evaluation value β of the level of the product manufactured by the company II and the evaluation value β of the level of the product manufactured by the company II, and to know the contribution ratio of the attribute of the displacement and the attribute of the manufacturer. As described above, it is possible to investigate a consumer's values regarding product selection.
By conducting a consumer value survey on the level of each attribute as a factor for the product selection by the consumer, the evaluation value β possessed by the consumer with respect to the level can be calculated for each attribute of the product. The evaluation value of the level for each attribute is stored in a computer so that it can be used (194).
[0010]
Competing products are offered in the market, and the level of each product attribute can be known. In addition, the company can tentatively determine the level for each attribute of a new product to be put on the market. Therefore, the level for each attribute of a new product can be temporarily determined and stored in a computer so that it can be used (198), or the level for each attribute of a competing product can be stored in a computer so that it can be used (202).
Thus, the data input step in the present method is completed. When the data input process is completed, the values of the consumers are stored in the database 196, the specifications of the competitor models (setting of the level for each attribute) are stored in the database 204, and the specifications of the provisionally determined new product are stored in the database 200. Is done.
[0011]
When consumer values, specifications of competing models (setting of the level for each attribute), and specifications of newly determined new products are stored in a computer so that they can be used in a computer, the computer estimates the number of new products sold. (206). That is, from the evaluation value of each attribute level and the level of each attribute of competing products, estimate the number of sales when a new product with provisionally determined specifications (level setting for each attribute) is put on the market. Can be.
When the sales quantity of the new product is estimated and calculated, the computer estimates the sales amount of the new product in consideration of the sales price of the new product (set as one of the levels). The computer also calculates the cost of the new product (222). The computer then estimates the operating profit of the new product by subtracting the calculated cost of the new product from the estimated calculated sales of the new product (226).
If the operating profit is estimated and calculated, the computer can analyze the relationship between the new product specification and the operating profit, and can correct the new product specification to increase the operating profit (230). By repeating this, the specifications of the new product are modified so that the operating profit increases, and a specification that can ensure the maximum operating profit can be obtained. Companies can know the specifications of new products that can secure satisfactory operating profits, eliminating the need to spend unnecessary costs on developing products that can not be accepted in the market. Products at low prices.
[0012]
There is a possibility that the evaluation value β of the level for each attribute calculated by the value survey deviates from the true value. In addition, it cannot be denied that the attribute having a large contribution rate is actually missing from the consideration. Therefore, there is a need to guarantee the accuracy of the number of sales estimated and calculated from the evaluation value β of the level for each attribute.
For this purpose, it is preferable to compare the estimated sales quantity of the new product introduced in the past with the actual sales quantity (212). For example, the estimated number of sales three years ago for a product introduced three years ago is compared with the actual number of sales obtained three years thereafter.
If the two match, it is assured that the correct estimation is calculated using the correct evaluation value. If they do not match, the cause is searched. For example, if a competing product of another company that was not known three years ago is put on the market, the effect is recalculated taking into account the effect. As a result of the recalculation, the number of sales approximate to the actual number of sales may be estimated and calculated. In this case, it is guaranteed that the estimation calculation is correctly performed using the correct evaluation value β. On the other hand, if the evaluation value β of the level for each attribute is not corrected, it may not be possible to estimate and calculate the number of sales approximate to the actual number of sales. In this case, the evaluation value β of the level stored in the database 196 so as to be available is corrected so that the estimated number of sales approximate to the actual number of sales can be calculated. By performing the correction, the number of sales can be accurately estimated and calculated using the correct evaluation value β (claim 2).
[0013]
In order to calculate the cost of the new product, it is preferable to store the cost for each attribute and level so that it can be used by the computer (for example, the database 220), and calculate the cost of the product based on the cost (claim 3).
For example, in the case of database 220, cost required to set the level of attribute A to 1 is the epsilon _{A 1,} in the case of setting the level 2 illustrates that the epsilon _{A 2.} By adding the cost corresponding to the level set for each attribute to the attribute, the cost of a new product having the provisionally determined specification can be calculated.
[0014]
If you set the selling price as one of the attributes of the new product, you can calculate the change in operating profit caused by changing the selling price level, and you can explore the selling price that produces the maximum operating profit (Claim 4).
When this method is adopted, a scientific method can be introduced in the stage of determining the selling price, and the selling price determined solely by empirical rules can be rationally determined.
[0015]
The computer can constitute an apparatus for supporting the process of determining a new product specification. As shown schematically in FIG. 19, this device stores in a computer an evaluation value possessed by a consumer with respect to a level for each attribute of a product, which is a factor for selecting a product by the consumer, in a computer. Means 196, means 200 for storing the level of each attribute of the tentatively determined new product in a computer, means 204 for storing the level of each attribute of a competing product in the computer, and means for use in the computer The information stored in the database 196, that is, the evaluation value that the consumer has for the level stored in the database 196, the level for each attribute of the new product stored in the database 200, and the information stored in the database 204 Means 208 for estimating and calculating the number of new products to be sold based on the level of each attribute of the competitor product, and a new product based on the level of each attribute temporarily determined for the new product. Means 224 for calculating the cost of the new product; means 228 for estimating and calculating the operating profit of the new product from the estimated number of sold new products, the selling price of the new product, and the calculated cost of the new product; Means 232 for correcting the level of each attribute of the new product is provided on the side that increases the calculated operating profit of the new product (claim 5).
[0016]
According to this device, the computer estimates and calculates the number of new products sold based on the values of consumers, the specifications of competing vehicle types (setting of the level for each attribute), and the specifications of the temporarily determined new products. The computer further estimates the sales amount of the new product by taking into account the selling price of the new product. The computer also calculates the cost of the new product. Then, the computer estimates the operating profit of the new product. Further, the computer corrects the specification of the new product (setting of the level for each attribute) so that the estimated operating profit of the new product increases.
With this device, companies can learn about new product specifications that will bring a lot of operating profit, avoid having to waste money developing products that are not acceptable to the market, Products with the required specifications can be obtained at low prices.
[0017]
In the case of a device that supports the specification determination stage, a database 210 of actual sales numbers is provided, and means 218 for comparing (214) with the estimated sales numbers and correcting the evaluation value when they do not match is provided. Is preferred. The method of claim 2 can be performed.
[0018]
Further, it is preferable to provide a database 220 for storing the product cost for each combination of the attribute and the level. In this case, the product cost of the new product can be easily calculated by determining the specifications of the new product, and the method of claim 3 can be executed.
[0019]
DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will be listed.
(Mode 1) The evaluation value database stores evaluation values for each consumer. For each consumer and product combination, for all consumers, and for all competing products, including their new products, calculate the value that the consumer perceives for the product, i.e., the overall evaluation value . An overall rating is calculated for all competing products for a particular consumer, and that consumer selects the product with the highest overall rating. By doing so for all consumers, we calculate the per-product sales share of our competitors.
(Mode 2) If the consumer whose evaluation value is stored is not representative of all consumers, it is corrected and the sales share is estimated and calculated. For example, consumers whose evaluation values are known are A and B, and in the actual market, consumers having values similar to those of consumer A have consumers of values similar to those of consumer B. If the number of products is double, the number of products selected by the consumer A is double the number of products selected by the consumer B, and the sales share is calculated.
[0020]
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The hardware configuration of the new product introduction plan planning support device of this embodiment is a normal computer device, and is not shown. The computer device includes a database (D / B) shown in FIG. 1 and an arithmetic device. Various databases store information for use by computers. Various arithmetic devices are realized by a computer operating according to a program. The device shown in FIG. 1 illustrates a case where the present invention is applied to a product called an automobile. The application target of the present invention is not limited to automobiles, and can be widely applied to products that require various types due to various demands.
[0021]
The database 2 stores attributes and levels of products (in this case, automobiles). FIG. 2 exemplifies the storage contents of the database 2. The attribute stores a list of factors to be considered when a consumer selects and purchases a product, such as a maker, a displacement, a fuel consumption, and a driving method. I have. The level is stored for each attribute, and the manufacturer attribute stores the levels of companies I, II, III, etc. Have been.
[0022]
The arithmetic unit 4 creates a virtual product group combining levels for each attribute in order to investigate consumer values. In order to calculate the evaluation value that is recognized by consumers with respect to many types of levels with as few questions as possible (sometimes simply the evaluation value of the level or a partial utility coefficient), an experimental design The method of the orthogonal table of the method is adopted. The details are explained in "Item 9.4 in Chapter 9 of the Seven Items of Product Planning".
Here, it is assumed that two virtual products are used for the paired comparison method which is easy to be used in the investigation of the virtual negotiation method using the Internet. An example is shown in FIG. 3, which creates products 44 and 46, products 50 and 52, etc. that the consumer chooses alternatively. The level of the product to be created is selected using an orthogonal table method so that the evaluation value (partial utility coefficient) of the level that exists in a large number with a small number of products is calculated.
In process 6, the consumer's sense of value is investigated by a virtual business negotiation method using the Internet. The consumer who accesses the Internet uses the products 44 and 46 illustrated in FIG. 3 and clicks in the response column 48 to obtain a response. Next, the products 50 and 52 are shown, and a response is obtained by clicking the response column 54. They are executed one after another for a set of products set in advance.
[0023]
When answers are obtained from a considerable number of consumers, a table shown at 56 in FIG. 4 is created. Β in Table 56 indicates the evaluation value or partial utility coefficient of the level for each attribute, and the partial utility coefficient of the level that works advantageously when the consumer selects a product is a positive value. The partial utility coefficient at a level that does not work favorably or disadvantageously when consumers select a product is zero, and the partial utility coefficient that works disadvantageously when consumers select a product is negative and disadvantageous. The smaller the effect, the smaller the partial utility coefficient (the larger the absolute value). The partial utility coefficient at the level that is most disadvantageous may be set to zero, and all other partial utility coefficients may be set to zero or more. Some attributes have a partial utility coefficient widely distributed depending on the level, and some attributes have a narrow distribution range. It can be seen that the attribute in which the partial utility coefficient is widely distributed depending on the level is an important attribute that has a great effect on the consumer's product selection. This attribute is given a large contribution rate α. It can be seen that the attribute in which the partial utility coefficient does not change much depending on the level is an insignificant attribute that does not significantly affect the consumer's product selection. This attribute is given a small contribution.
From the list of attributes and levels shown in FIG. 2, a set of products to be used in the pairwise comparison method shown in FIG. 3 is created. From the obtained answers, the partial utility coefficient for each attribute and level is calculated, and the attribute contribution rate Software for executing the process up to the calculation of is available on the market, and that software can be used. For example, a program for adaptive conjoint analysis can be obtained from Sawtooth. In the calculation step 8 in FIG. 1, based on the answer obtained from the survey in the processing 6, the partial utility coefficient β for each attribute and each level and the contribution rate α of the attribute, which are exemplified in the vote 56 in FIG. Store in database.
[0024]
FIG. 4 shows another method of investigating the values of consumers, and simulates a plurality of types of products to be ordered by consumers. Here, a combination of virtual product levels is calculated using an orthogonal table. The virtualized product is indicated by a card indicating a combination of levels. This card is usually called an attribute profile card. Consumers rank a plurality of attribute profile cards according to their values. This research method can also calculate the partial utility coefficient for each attribute and level, and the contribution rate for each attribute, as shown in Table 56 of FIG. The process of calculating the value using the attribute profile card and calculating the partial utility coefficient for each attribute and level and the contribution rate for each attribute are described in detail in "Chapter 9 of the Product Planning Seven Tools".
[0025]
As illustrated in FIG. 5, the database 10 in FIG. 1 stores data on the number of consumers by region or country. The database 10 also stores the population for each attribute of the consumer (age, number of family members, household income) and the level of each attribute (15 to 20 years for the age, one or two for the number of family members). Have been. In the case of FIG. 5, it is illustrated that, in the area or area under consideration, there are γ12 people aged 15 to 20 years old, and there is only γ33 with a household annual income of $ 8000 to 12,000. This database 10 is prepared for each region or country, and covers all regions where products are supplied.
[0026]
Consumers who surveyed values do not necessarily represent all consumers. In particular, when conducting a value survey using the Internet, surveys can be conducted quickly and inexpensively, but on the other hand, the survey target is biased toward consumers who are familiar with the Internet. The values obtained in the survey cannot be averaged for all consumers.
The arithmetic unit 22 in FIG. 1 corrects the attribute contribution rate and the level partial utility coefficient in consideration of the bias of the survey subjects. An example of the correction process is shown in FIG.
In FIG. 6, it is assumed that 80% of consumers according to the value survey using the Internet are men and 20% are women. In this case, the male accepts a partial utility coefficient of 0.7 against the level of company I, while the female accepts a partial utility coefficient of 0.4, and the The example illustrates a case where a male recognizes only a partial utility coefficient of 0.3, while a female recognizes a partial utility coefficient of 0.6.
If the ratio of men and women in the region is 60% and 40%, the weighted average partial utility coefficient is calculated by the formula shown in FIG. 6, and the average consumer in the region is made by company I. And a partial utility coefficient of 0.42 for the level of II.
The arithmetic unit 22 shown in FIG. 1 corrects the attribute contribution rate and the partial utility coefficient of the level in consideration of the bias of the surveyees, and corrects it to that of the average consumer.
[0027]
The database 12 of FIG. 1 stores the attribute-specific levels of all products of the company and other companies. An example is shown in FIG. 7, and it is stored that the product X is manufactured by Company II, the displacement is at a level of 1.2 to 1.4, and the fuel consumption is at a level of 16 to 19 km / liter. I have. This database also stores changes over time in the actual sales number of products. This database stores all products, regardless of their products.
[0028]
In the case of a car, it is difficult to determine which car type and which car type compete, and which car type and which car type do not compete.
In the arithmetic processing unit 14 in FIG. 1, positioning is performed for each product. Positioning is performed from various viewpoints. In one positioning method, the product is positioned on a two-dimensional map by the top two attributes having the highest contribution rate α shown in Table 56 of FIG.
FIG. 8 shows an example of such a case, in which the horizontal axis represents the product characteristic 1 (the characteristic combining ride comfort, operability, stability, interior and size attributes) showing the highest contribution rate (45.6%). The vertical axis indicates the product characteristic 2 (the characteristic that combines the price, fuel efficiency, insurance cost, and affordability attribute obtained by dividing the price by the function) that showed a high contribution rate (16.1%) An example in which a product is positioned on a two-dimensional map is shown. In the conjoint analysis, not only the contribution ratio of an attribute but also the contribution ratio of a combined attribute combining attributes can be calculated. The highest contribution is rarely obtained with one attribute, and the combined attribute often results in a higher contribution.
In the example illustrated in FIG. 8, the product group is spread over a range 84, and shows an example in which a new product of the company whose introduction plan is being considered is positioned at the point 80. In this case, if a product positioned within a range 82 close to the point 80 is extracted, a product group competing with the company's new product becomes apparent.
[0029]
Competing products can also be extracted from the level distribution of product owners. As shown in FIG. 9, δ11 is 15 years or less among the owners of the product (for example, product X), and it can be sorted out what percentage of the population is 15 years or less. If the population ratio is calculated for each attribute level, the attribute whose population ratio changes the most depending on the level becomes clear. For example, it becomes clear that the ratio of population by level of family changes significantly while the ratio of population by age does not change significantly.
Therefore, it is possible to position the product on a two-dimensional map using two attributes whose population ratio changes most depending on the level. FIG. 10 shows an example. In this case, the attribute (horizontal axis) in which the age and the household annual income whose contribution rate is the maximum (48.9%) is combined, and the contribution rate is second (34.6%) An example is shown in which a product is positioned on a two-dimensional map of an attribute (vertical axis) obtained by combining a certain number of family members and household annual income. In the example illustrated in FIG. 10, the product group is widely spread on the two-dimensional map, and a car type favored by a large-income family is located at the upper right of the map, and a car model favored by a small-income family at the lower right. The vehicle type is located, and the lower left is a vehicle type favored by low-income small families, and the upper left is a vehicle type favored by low-income large families. The company's new product whose introduction plan is being considered is positioned at point 100. In this case, if a product positioned within the close range 102 is extracted, a product group competing with the company's new product is obtained. It becomes clear.
[0030]
In the database 16 of FIG. 1, data on the attribute and level of the competing product extracted from the positioning result of the arithmetic unit 14 are stored. The database 16 corresponds to a partial database of the database 12.
[0031]
If the data of the attribute and the level of the competing product are stored in the database 16 of FIG. 1, the share of each product in the competitor product group can be calculated from the database 16 and the database 8. Now, as shown in FIG. 11, assuming that the partial utility coefficient of the level j of the attribute i is βi, j and the level of a certain product is the level indicated by a circle in the figure, the number of sales of the product is Is known to be proportional to the exponential of the sum of the partial utility coefficients marked with a circle. By calculating the sales volume of all competitor products, it is possible to calculate the market share of each competitor product group. The calculation unit 24 of FIG. 1 performs the above calculation to calculate the share of each product in the competitor product group.
[0032]
Various measures other than the above can be taken for the estimation calculation of the number of sales. When storing the partial utility coefficient for each attribute level in the database 8, it is possible to store the partial utility coefficient for each attribute level for each consumer. As shown in FIG. 20, the partial utility coefficient β for each attribute of the consumer 1 is stored, the partial utility coefficient の for each attribute of the consumer 2 is stored, and the attribute of the consumer 3 for each attribute is stored. If the partial utility coefficient λ is stored, by inputting the attribute and the level setting of the specific product, it is possible to calculate a numerical value of the value recognized by the consumer for the specific product. For example, the total evaluation value that the consumer 1 recognizes for the product 1 and the total evaluation value that the consumer 1 recognizes for the product 2 can be known. The total evaluation value can be calculated by the equation in FIG. In FIG. 2, only some consumers and some products are shown, but actually, information on many consumers and products is stored.
Calculating the value of each of the competing products for a single consumer reveals the products that the consumer will value the most, and knows which products the consumer will choose when choosing a product. it can. By executing this for all the consumers stored in the database 8, it is possible to calculate the product-specific share among all the consumers stored in the database 8.
[0033]
If the consumer group stored in the database 8 is representative of all consumers in the area, it can be used as a share for each product. If the consumers stored in the database 8 are skewed from all the consumers in the area, it is possible to estimate and calculate the market share by product by correcting the skew. For example, the consumers whose evaluation values are known are 1 and 2, and in the actual market, consumers having values similar to consumer 1 have consumers having values similar to consumer 2. If there are twice, the number of products selected by the consumer 1 is double the number of products selected by the consumer 2, and the sales share is calculated.
If the value perceived by a consumer for a product is quantified for each combination of consumer and product, it is also possible to estimate and calculate the share of each product by processing that value as the probability of the consumer selecting the product. .
[0034]
The comparison operation unit 28 in FIG. 1 compares the share calculated from the partial utility coefficient with the actual share, and if they do not match, corrects the attribute contribution rate and the level partial utility coefficient (processing unit 26). Of the estimation calculation (operation unit 24) is redone. Until the share calculated from the partial utility coefficient and the actual share match well, the process of correcting the partial utility coefficient of the attribute contribution rate and level is repeated.
Various mathematical methods can be used for the correction. For example, the evaluation value of the level belonging to a specific attribute is multiplied by k so that the contribution ratio for the specific attribute is k times. The value of k at which the sales quantity estimated and calculated based on the evaluation value multiplied by k well matches the actual sales quantity is taken. Alternatively, the evaluation value of the level belonging to the attribute A is multiplied by kA, the evaluation value of the level belonging to the attribute B is multiplied by kB, the evaluation value of the level belonging to the attribute C is multiplied by kC, and the evaluation is multiplied by kA, kB, kC. KA, kB, and kC at which the sales quantity estimated and calculated based on the value well matches the actual sales quantity may be obtained.
The process of correcting the evaluation value may be applied uniformly to all consumers, but if there is likely to be a large deviation in a specific group, it can be corrected for consumers belonging to that specific group. In some cases, sales numbers that closely match actual sales numbers may be calculated. In this case, the correction process may be performed only on the consumers belonging to a specific group.
By the above-described comparison and correction processing, an error generated between the value survey and the product selection at the time of actual product purchase is corrected.
[0035]
The database 18 of FIG. 1 stores a known new product introduction plan of another company. The specifications of the products to be introduced (level settings, which are the same as those illustrated in FIG. 7), the time of introduction, and the like are stored. New product launch information from other companies is legally available from various sources.
[0036]
The database 20 of FIG. 1 stores a company's new product introduction plan for which an introduction plan is to be considered. The validity of the plan is evaluated by tentatively determining the specifications of the product to be launched and the timing of launch, etc. If the validity of the plan is verified, the plan is decided as the launch plan, but problems with the plan are apparent Revise the plan if it becomes possible.
[0037]
The database 16 of FIG. 1 stores attribute and level data of competing products, the database 18 stores attribute and level data of competing products introduced by other companies, and the database 20 stores the attribute and level of products introduced by the company. Since the data is stored and the corrected contribution ratio of the attribute and the partial utility coefficient of the level are stored in the database 8, the calculation unit 30 estimates the sales number of the company's product using these data. . Here, the share of the company's new product is calculated according to the calculation formula described in FIG. 11 or the estimation method described in FIG. 20, and the sales number of the company's product is estimated and calculated based on the calculated share and the total sales prospect of the competitor product group. The partial utility coefficient used for the share estimation calculation is modified to a coefficient that provides a share that matches well with the actual share, so that the estimation calculation has high reliability.
The number of sales is calculated over time from the time of introduction of a new product, and is estimated and calculated up to the temporal change of the number of sales.
[0038]
The computer estimates and calculates the production cost of the company's product by the arithmetic unit 32. Here, as shown in FIG. 12, the production cost per product is obtained by dividing the test research cost and the capital investment amount by the number of production units, and adding the operation cost per unit (direct production cost). Yes, it can be calculated from the estimated number of sales.
Here, if the new vehicle uses an already developed platform, a low cost is calculated. Also, the more units that are shared with other products, the cheaper the cost. Further, the cost is calculated by using the resource utilization ratio between the group companies to be consolidated.
If the cost is stored for each combination of attribute and level as in a database 220 illustrated in FIG. 19 to calculate the cost of a new product, the product cost can be calculated based on the stored cost.
For example, in the case of database 220, cost required to set the level of attribute A to 1 is the epsilon _{A 1,} in the case of setting the level 2 illustrates that the epsilon _{A 2.} By adding the cost corresponding to the level set for each attribute to the attribute, the cost of a new product having the provisionally determined specification can be calculated.
Further, if the database 220 is prepared, a change in cost due to a change in specifications can be easily calculated. If you change the level of attribute A from 1 to 2, the cost is seen immediately be changed by ε _{A 1} -ε _{A 2.}
[0039]
The calculation unit 34 estimates and calculates the company's operating profit obtained from the new product. That is, based on the number of sales estimated by the arithmetic unit 30, the selling price (set as one type of level), and the product cost estimated by the arithmetic unit 32, the operating profit of the company obtained from the new product is calculated. Estimate and calculate.
[0040]
The computer analyzes variously the relationship between the estimated operating profit and the setting of the level of the new product. Analyze the change in operating profit that occurs when the level is changed for each attribute, and correct the level to increase operating profit.
If there is room to increase the operating profit by correcting the level, step 36 becomes NO, and step 40 corrects the level setting of the new product. When the maximum operating profit is obtained by correcting the level setting, the result of step 36 is YES, and the specifications of the new product are determined.
When searching for a specification that provides the maximum operating profit, the change in operating profit resulting from changing the level of the selling price is calculated, and the selling price that provides the maximum operating profit is searched for. Thereby, the optimum selling price is also searched.
In addition to the process of correcting the specifications to obtain the maximum operating profit, the estimated operating profit may be visualized and displayed. At this time, various evaluation indices are displayed together. For example, a graph showing the past operating profit performance of similar products of the same company and a graph showing the operating profit amount of competing products of other companies are displayed so as to be able to be compared. Further, a result of comparison with expected operating profit may be displayed together. This makes it easier for those who decide new product launch plans to evaluate the amount of operating profit itself.
Alternatively, the temporal transition of the estimated operating profit, particularly, the transition of the operating profit when the demand fluctuates, is displayed in detail. A solid line 150 in FIG. 14 shows an example of a fluctuating demand, and a broken line 152 shows a change in operating profit. The further apart the two graphs indicate that the new product launch plan cannot respond quickly to demand fluctuations. Since a graph showing demand fluctuations and operating profit fluctuations is output to the comparison observation, it is easy for a person who decides a new product introduction plan to evaluate the extent to which he can follow the changing demands.
Anticipated uncontrollable changes, such as sudden changes in exchange rates and changes in laws and regulations, are also graphically displayed over time. A solid line 154 in FIG. 15 shows an example of an expected exchange rate, and a broken line 156 shows a change in operating profit. It is understood that the lower the operating profit at a change in the rate, the more sensitive to disturbance. Preferably, the plan is insensitive to uncontrollable disturbances. Since a graph showing operating profit fluctuations when uncontrollable disturbances such as changes in exchange rates and laws and regulations occur is output to the comparison observation, those who decide on the launch plan of new products are uncontrollable disturbances It is easier to evaluate the degree of the plan's strength.
Looking at the temporal change of the operating profit, the wave is repeated as shown in FIG. Busy season and off season are repeated. At this time, due to inventory adjustment or the like, there may be a case where it is possible to respond without a large wave at the production site, or there is a case where demand fluctuation is not attenuated and directly connected to the production site. The computer graphically displays the waves of operating profit and the waves that must be addressed at the production site. Anyone who decides on a new product launch plan can easily assess the difference between the peak season and the off season.
The computer also calculates the total operating profit for all of its products, summing the operating profit. Even if the operating profit of the new product itself is strong, the operating profit of other proprietary products will decrease, so the total operating profit may fall. The computer visually displays both the new product's operating profit and the total operating profit to help humans evaluate the adequacy of the plan.
[0041]
If the operating profit estimated and calculated by the arithmetic unit 34 can be evaluated highly, the specification of the company's new product is determined as shown in a process 38. On the other hand, if the operating profit estimated by the arithmetic unit 34 cannot be highly evaluated, the specifications of the company's new product are corrected by the process 40. Here, the problem of the operating profit is analyzed and the plan is revised so as to solve the problem. This specification correction process is executed by a computer, but may be executed by a human.
At the stage of the correction processing, the correction is performed while taking various constraints into account. As shown in step 170 of FIG. 17, the plan cannot be modified to violate legal norms such as regulations. If the plan is modified to violate the legal norms, the modification is further modified to a plan that does not violate the legal norms (step 182). As shown in step 172, it cannot be modified to a plan that cannot be handled by the available product development resources. As shown in step 174, it cannot be modified to a plan that cannot be handled by available product production resources. As shown in step 176, it cannot be modified to a plan that cannot be handled by available sales resources. However, as to the plan that is not allowed due to the limitation of the input resources, as shown in Step 178, it is examined whether or not it can be dealt with by changing the development base, the production base, and the sales base (Step 178). If the plan can be dealt with by changing the base, an operating profit calculation process is executed (step 180), and the validity of the plan is evaluated.
[0042]
As the above process is repeated, it becomes possible to understand the change in the operating profit obtained by correcting the specification, and the direction of correcting the specification to generate satisfactory operating profit becomes clear.
For example, if four colors are set as compared with the case where three colors are set for the attribute of product color, the sales amount increases more than the cost increases, while the cost increases when four colors are increased to five colors. It turns out that the sales amount does not tend to increase as the number increases. By repeating the correction and evaluation, a group of types that can be manufactured at low cost and meet various demands will be planned. The left side of FIG. 13 shows the relationship between the diversity and the number of types of platforms (or units) when the conventional planning method is used, and the number of types of platforms (or units) increases to meet various demands. The right side shows the relationship between the diversity obtained when planning a new product using the planning support technology of the present invention and the number of types of platforms (or units). You can respond with numbers.
It is possible to provide a large number of consumers with various demands at low cost with products having specifications required by the consumers.
When searching for the specification that produces the greatest operating profit and the specification that produces the greatest sales, the two may not match. This is equivalent to an increase in production cost if the production is performed with specifications that produce the maximum sales. In this case, if a product that produces the maximum sales can be manufactured at the production cost that produces the maximum operating profit, it means that the operating profit further increases. If cost reduction activities are performed with the goal of achieving maximum operating profit, costs can be reduced while securing maximum sales, and more profit can be secured.
[0043]
FIG. 18 exemplifies the contents output by the new product introduction plan planning support device, and sets the level for each attribute of a new product that brings a highly evaluated operating profit, the introduction time of each new product by region, and the new product. Platforms and units used, new product development bases and development schedules, new product production bases and time series of production quantities, parts supply chains for production bases, or sales bases for produced products Is output.
From the above, those who develop, produce and sell new products will be able to understand what and when to do it, reduce unnecessary development costs, and enable consumers to obtain the products they need at low cost. Become.
[0044]
According to the present invention, specifications of a new product that can secure a large operating profit can be obtained, so that there is no need to spend unnecessary costs on the development of products that cannot be accepted in the market. Will be able to obtain products with the required specifications at low prices.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating a system configuration of a new product introduction plan planning support apparatus according to an embodiment.
FIG. 2 is a diagram illustrating attributes and levels of a product called an automobile.
FIG. 3 is a diagram exemplifying a method of calculating a contribution ratio of an attribute and a partial utility coefficient of a level.
FIG. 4 is a diagram showing another method of calculating the attribute contribution rate and the level partial utility coefficient.
FIG. 5 is a diagram showing an example of a configuration number database for each attribute / level of a consumer.
FIG. 6 is a diagram showing a process of correcting the average consumer's values from the survey results.
FIG. 7 is a diagram showing an example of an attribute / level-based database of products of a company / other company.
FIG. 8 is a map in which a product is positioned according to the characteristics (bonding characteristics) of the product.
FIG. 9 is a diagram illustrating an example of a database for each attribute / level of a product owner.
FIG. 10 is a map in which a product is positioned according to a property (combining property) of a product owner.
FIG. 11 is a diagram showing a process of calculating the number of product sales from a level partial utility coefficient.
FIG. 12 is a diagram showing a process of calculating a product cost.
FIG. 13 is a diagram showing the relationship between diversity and the number of required platforms as viewed from consumers.
FIG. 14 is a diagram showing the follow-up characteristics of demand fluctuation and operating profit fluctuation.
FIG. 15 is a diagram showing the ability to follow exchange rate fluctuations and operating profit fluctuations.
FIG. 16 is a diagram illustrating a wave of operating profit.
FIG. 17 is a procedure diagram for correcting a new product introduction plan based on constraints.
FIG. 18 is a diagram exemplifying the contents output by the new product introduction plan planning support device of the embodiment.
FIG. 19 is a diagram schematically showing an outline of a support method and a support device according to the present invention.
FIG. 20 is a diagram showing an example of a process of estimating a sales share from individual consumer values.
[Explanation of symbols]
2: Product attributes / levels that cause consumers to purchase products Database 6: Virtual negotiations over the Internet (value survey)
8: Attribute contribution ratio and level partial utility coefficient calculation unit and its database 10: Consumer attribute / level database 16: Competitive product attribute / level database 18: Competitor's new product launch plan database 20: Company's new Product launch plan database 22: Attribute contribution rate and level partial utility coefficient correction unit 30: Sales number estimation calculation unit 34 for our own new product: Operating profit calculation unit 36 for our product: Evaluation index calculation unit 40 for operating profit In-house new product launch plan revision department

Claims (5)

Calculating an evaluation value possessed by the consumer with respect to the level of each attribute of the product which is a factor for selecting the product of the consumer and storing it in a computer so as to be usable;
Temporarily determining a level for each attribute of the new product and storing it in a computer so that it can be used;
Storing the levels for each attribute of the competing product in a computer for use;
Estimate the number of new products sold by computer from the consumer's evaluation value for the level, the level for each attribute of the new product, and the level for each attribute of the competing product stored in the computer Calculating,
Calculating a cost of the new product by the computer based on the level of each attribute temporarily determined for the new product;
Estimating the operating profit of the new product by computer from the number of sales of the new product estimated by the computer, the selling price of the new product, and the cost of the new product calculated by the computer;
On the side where the operating profit of the new product estimated by the computer is increased, the step of correcting the level of each attribute of the new product by the computer,
To assist in the process of determining new product specifications. Further comprising the step of modifying the evaluation value of the consumer with respect to the level, which is stored in a computer so that the estimated number of sales of the products put in the past and the actual number of sales coincide with each other. 2. The method according to claim 1, further comprising the steps of: 3. The method according to claim 1 or 2, wherein the cost is stored for each attribute and level so that it can be used in a computer, and the cost of the product is calculated based on the cost. One of the attributes of the new product is the selling price, and it has a process of calculating the change in operating profit caused by changing the selling price level and searching for the selling price that maximizes the operating profit 4. The method according to claim 1, wherein the new product specification is determined. Means for storing, in a computer, an evaluation value possessed by a consumer with respect to a level for each attribute of a product that is a factor for selecting a product of the consumer;
Means for storing the level of each temporarily determined new product attribute in a computer in a usable manner;
Means for storing in a computer the levels for each attribute of the competitor product;
A means for estimating and calculating the number of new products sold from the evaluation values held by consumers for the levels, the levels for each attribute of new products, and the levels for each attribute of competing products stored in a computer. When,
Means for calculating the cost of the new product based on the level of each attribute temporarily determined for the new product;
Means for estimating the operating profit of the new product from the estimated number of sales of the new product, the selling price of the new product, and the calculated cost of the new product;
Means for correcting the level of each attribute of the new product to increase the estimated operating profit of the new product;
An apparatus for supporting the process of determining new product specifications, comprising:

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