KR101889824B1 - Method and apparatus for RnD quality verification - Google Patents

Abstract

The present invention provides a method and an apparatus for R&D quality evaluation which can quantitatively evaluate and compare the value of an R&D project designed to improve an existing product. According to an aspect of the present invention, the method for R&D quality evaluation evaluates the quality of R&D by an apparatus for R&D quality verification, and comprises: a step where the apparatus for R&D quality verification receives component characteristics and engineering characteristics of a product from a user′s terminal; a step where the apparatus for R&D quality verification receives information on a competitor′s product in a competitive relationship with the product from the user′s terminal; a step where the apparatus for R&D quality verification uses the component characteristics and the engineering characteristics to calculate the importance of the engineering characteristics; a step where the apparatus for R&D quality verification uses the importance of the engineering characteristics and the information on the competitor′s product to calculate an improvement contribution meaning an improved degree of quality of the product in comparison to the competitor′s product; a step where the apparatus for R&D quality verification calculates a user company comparison improvement contribution meaning the degree of improvement of the targeted product′s quality in comparison with the current quality of the product; and a step where the apparatus for R&D quality verification uses the improvement contribution, the user company′s comparison improvement contribution, and production costs of the product to calculate a value evaluation index (V-index) quantifying quality and the difficulty level of the R&D.

Description

TECHNICAL FIELD The present invention relates to a method and apparatus for verifying quality of RnD,

The present invention relates to a method and apparatus for evaluating and verifying the quality of a research and development (R & D) task. More particularly, the present invention relates to a method for evaluating and verifying an R & D task using a quality function deployment (QFD) and an apparatus for performing the method, in order to improve the quality of existing products.

Korea's R & D investment is 59.3 trillion won in 2013, ranking 6th in the world in terms of size and 4.15% of Gross Domestic Product (GDP). The success rate of the R & D investment is as high as 98%, but there is not enough technology available in the market among R & D investments. For example, 70% of patents acquired through R & D investment sleep in the wardrobe.

In order to solve these problems, it is necessary to examine the proposed tasks when selecting R & D investment and select marketable and competitive R & D tasks. Quality Function Deployment (QFD) is a type of quality management technique that can select marketable and competitive R & D tasks.

The development of the quality function was first proposed in 1972 at the Kobe shipyard of Mitsubishi Heavy Industries. Since then, the introduction of quality features was introduced to the United States in the early 1980s and was first introduced to Ford and other automotive and automotive industry partners. Now, not only global companies such as Proctor & Gamble, HP, IBM, Kodak, and Xerox, but also many domestic companies are utilizing quality function development from the design stage to develop products.

However, existing quality function development method has a disadvantage of not considering cost. This may lead to unrealistic conclusions. In other words, the development of quality functions that do not consider simultaneously meeting customer's continuous demand and reduction of production cost is hard to be reflected in reality, which can result in further increase of time and cost.

Therefore, from the pre-product development stage, it is possible to consider the quality and cost at the same time, and to set the target unit price of the parts to be reasonable. In the stage after product development, the R & D quality can be verified by comparing the developed product with the original R & A quality function development method is required.

KR 10-1484450 B1 "Quality Function Deployment development (QFD) - System and method for automatic quality evaluation of facilities based on HOQ (House of Quality)" (2015.01.13) KR 10-2008-0039524 A "How to Support Product Development Process Support System and Product Development Process" (2008.05.07) KR 10-2012-0017385 A "How to prioritize R & D, set priorities for R & D and develop business model" (2012.02.28) KR 10-2014-0046895 A "Technology planning automation system and method for scenario identification and timely change management implementation through matrix prioritization and matrix linkage based on business strategy" (2014.04.21)

SUMMARY OF THE INVENTION The present invention provides a method for quantitatively evaluating and comparing values of R & D tasks designed to improve existing products, and an apparatus for performing the method.

Another technical problem to be solved by the present invention is to provide a method of quantitatively verifying and comparing the performance of R & D tasks using information on prototypes produced as a result of R & D tasks, and an apparatus for performing the method.

The technical problems of the present invention are not limited to the above-mentioned technical problems, and other technical problems which are not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided a method for evaluating the quality of an R & D quality, the method comprising the steps of: The method comprising the steps of: receiving a component specification and an engineering characteristic; inputting information on a competitive product in competition with the product from the terminal of the user; The verification apparatus includes a step of calculating a degree of importance of the quality characteristic by using the part specification and the quality characteristic and a step of comparing the importance of the quality characteristic and the information on the competitive product, Calculating an improvement contribution degree indicating a degree of improvement of the quality of the product with respect to the product; Calculating a contribution contribution degree to the company, which means a degree of improvement in quality aimed at the commodity relative to a current quality of the commodity; and calculating the improvement contribution degree based on the improvement contribution degree, And a step of calculating a value evaluation index (V-Index) in which the quality and difficulty of the R & D are quantified by using the production unit price of the product.

According to another aspect of the present invention, there is provided a method for evaluating the quality of an R & D quality, the method comprising the steps of: (R & D) quality verification apparatus comprising: receiving information about a competitive product that is in competition with the product from the user's terminal; Wherein the step of calculating the importance of the quality characteristic using the part specification and the quality characteristic and the R & D quality verification device further comprises the steps of: calculating, by using the quality characteristic importance and the information on the competitive good, Calculating an improvement contribution degree indicating a degree of improvement of the quality of the product with respect to the highest quality of the R & The quality verification apparatus may further include a step of calculating an improvement contributory to the third party, which means an improvement in the quality of the product compared to the lowest quality of the competitive product, and a step of determining whether the quality of the improvement contribution, And a step of calculating a value evaluation index (V-Index) in which the quality and the degree of difficulty of the R & D are quantified by using the production unit price of the product.

The effects according to the embodiment of the present invention are as follows.

According to the present invention, it is possible to systematically and scientifically verify the global merchantability of an idea or developed technology recognized as having commercial value. In addition, it can expect economic performance through technology commercialization or IP business.

It is also possible to quantitatively assess and verify the proposed tasks for R & D investment. In the first stage of product planning for IP application or R & D investment, the quality and price aimed by SMEs can be compared with benchmarking benchmarks of global top level to evaluate and compare commercial value. Next, after the R & D investment, the prototype can be verified and compared with the target quality before R & D investment, so that SMEs can confirm the possibility of success of the product developed through R & D investment.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood to those of ordinary skill in the art from the following description.

1 is a diagram for explaining the development of a conventional quality function.
FIG. 2 is a diagram illustrating a process of applying a conventional quality function development from a design stage to a production stage in a cascade fashion.
FIG. 3 is a diagram for explaining a comparison between a conventional quality function development and a quality function development according to an embodiment of the present invention.
4 is a flowchart of a method for evaluating the merchantability of the R & D task according to an embodiment of the present invention.
5 to 13B are views for explaining a R & D quality evaluation method according to an embodiment of the present invention.
FIG. 14 is a flowchart of a method for verifying the quality of an R & D task according to an embodiment of the present invention.
FIGS. 15 to 21B are diagrams for explaining a R & D quality verification method according to an embodiment of the present invention.
22A and 22B are views for explaining a method of analyzing a V-Index according to an embodiment of the present invention.
23A to 24B are diagrams for illustrating visualization of a QFD chart according to an embodiment of the present invention.
25 is a hardware block diagram of an R & D quality verification apparatus according to an embodiment of the present invention.
26A to 31B are diagrams for explaining a method of evaluating the possibility of achieving the R & D task according to an embodiment of the present invention in advance.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention, and the manner of achieving them, will be apparent from and elucidated with reference to the embodiments described hereinafter in conjunction with the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

Unless defined otherwise, all terms (including technical and scientific terms) used herein may be used in a sense that is commonly understood by one of ordinary skill in the art to which this invention belongs. Also, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise. The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification.

It is noted that the terms "comprises" and / or "comprising" used in the specification are intended to be inclusive in a manner similar to the components, steps, operations, and / Or additions.

Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings.

1 is a diagram for explaining the development of a conventional quality function.

Referring to FIG. 1, the QFD chart 100 according to the conventional development of the quality function can be confirmed. The appearance of a house looks like a house of quality (HOQ). The QFD chart 100 may be somewhat different depending on the method of developing the quality function, but most of the QFD chart 100 has a form similar to the example of Fig. The QFD chart (100) is divided into nine areas, which are referred to as 9 rooms.

First, area 1 (1 Objective) is the step of determining the customer. It is a step to determine the answer to "who will buy the product". That is, it is the process of confirming what the goal is. If you are making a bike, you may have a bike rider, a bike shop seller, or a bike ranger.

Next, the second area (2 Customer Needs) is a step to determine the customer's needs. Customers' needs are gathered through surveys and Voice of Customers (VOC). It also confirms relevant government standards such as Ministry of Environment, Ministry of Health and Welfare, Ministry of Public Administration and Security.

3 Requirements Characterization and Verification is a step that determines the relative importance of each requirement. Weights are applied to each requirement. In general, the total sum is set to 100, and the measurement is expressed as a relative measurement value.

4 Competitive Analysis is a step to identify and evaluate competitors' products. We will investigate how customers feel about competitors 'products, and investigate the performance, design, and manufacturing methods of competitors' products that are launched in the market and reflect them in improving their products.

Section 5 (5 Supplier Responses) is the step of creating design variables. In general, customer requirements are qualitative indicators that are unclear and difficult to measure. It is a step that defines the quantitative goals to solve these customer needs.

6 Relationship Matrix is a step to identify the degree of correlation between customer requirements and design variables. The relationship between the design variables (How) to achieve the customer's requirements (What) is expressed in symbols or numerical values. In general, the symbol ⊙ or the numeral 9 is a strong correlation, the symbol ∘ or the numeral 3 is a general correlation, the symbol △ or the numeral 1 is a weak correlation. If it is blank, it means there is no relation.

Area 7 (7 Interactions, Leverage and Conflict) is a step to identify the relationship between design variables. Because design variables are engineered, they are highly relevant. For example, engines with high output are mostly heavy engines. This association is displayed on the top of the matrix as a roof.

In general, the symbol ▲ or +9 means a strong positive correlation, the symbol △ or +3 means a general positive correlation, and the symbol + or +1 means a weak positive correlation. In addition, the symbol ▼ or -9 indicates a strong negative correlation, the symbol ∇ or -3 indicates a general negative correlation, and the symbol -1 indicates a weak negative correlation. Similarly, if it is blank, it means that there is no relation.

8 Targets and Gap Analysis is the step of setting target values of the design variables. The specific target value of each design variable is indicated by the quantity. For example, the energy transfer efficiency of a bicycle to be made is set to at least 90%.

Finally, Section 9 (9 Importance) identifies the design variables that should be considered in order to satisfy the customer's demand among the design variables. In other words, the importance of the design variable projected by the customer's demand can be confirmed through the correlation matrix of the area 6. This allows you to see which design variables need to be improved first.

FIG. 2 is a diagram illustrating a process of applying a conventional quality function development from a design stage to a production stage in a cascade fashion.

Figure 1 shows the process of identifying the importance of design variables based on customer requirements. This is the application of Quality Function Deployment (QFD) in the product planning stage (HOQ # 1). In order to satisfy the customer's demand, it is to know which function should be improved first.

Next, when the quality function development (QFD) is applied (HOQ # 2) in the part designing step 103, it is possible to grasp what parts should be improved first in order to improve the core function selected in the product planning step 101. That is, the result of the product planning step (101) can be input again, and the importance of the projected parts can be confirmed by the customer's request.

Next, when applying the quality function development (QFD) (HOQ # 3) in the process planning step 105, it is possible to grasp which process should be preferentially improved in order to improve the core parts selected in the part designing step 103. [ That is, the result of the part designing step 103 is input again, and the importance of the process in which the customer's request is projected can be confirmed.

Finally, applying Quality Function Deployment (QFD) (HOQ # 4) at the production planning stage 107 can identify which process variables should be preferentially improved in order to improve the core process selected at the process planning stage 105 . That is, it is possible to confirm the importance of the projected process variables by inputting the result of the process planning step 105 again.

As shown in FIG. 2, when a plurality of quality function developments are sequentially applied, such as a chain process of the quality function development, it is possible to confirm which items should be preferentially processed in each step to satisfy the customer's requirement. This enables us to achieve customer satisfaction and position ourselves in a competitive advantage in an infinite competitive environment.

Generally, as illustrated in FIG. 2, quality function development over four stages is used, but quality function development (QFD) can be further applied even after the fourth stage. For example, a quality function development may be applied in a sales planning stage (not shown) for selling a product produced after the production planning stage 107 to establish a sales plan in which the customer's demand is projected.

Up to now, the development of the conventional quality function has been described with reference to Figs. The conventional quality function development has the advantage of being able to apply the customer's demands from the design stage to the production stage, but it has the disadvantage that unrealistic conclusions can be drawn due to the development of the quality function without consideration of the cost. In order to solve this problem, it is necessary to consider the price when calculating the importance of the quality characteristic element in which the customer's demand is projected using the quality functional development.

However, there are times when it is difficult to apply the quality function development by simply considering the price. In order to apply the conventional quality function development, the customer's needs (VOC: Voice of Customer) should be grasped as input. This can be done through surveys or market surveys, and in some cases it may be difficult to summarize customer requirements. Let's take a look at this.

"Choosing and distributing R & D budget 19 without budgeting, producing hyenas only" This is the title of the news on October 14, 2016. According to the article, despite the world 's top R & D investment weight, it is said that there is no achievement due to cash dispenser and distributing budget support as it supports pennies anywhere without expertise or aptitude.

Even if they receive venture companies' R & D tasks to implement their budgets, they will not be able to foster a proper net venture company unless they can quantitatively assess and fairly compare them. Although it is possible to utilize quality function development to evaluate R & D tasks, it is not easy to utilize quality function development as it requires customer input as input to apply quality function development.

The customer's request is generally "I want durability." Or "I wish the weight was light." Or "I have to look good". To quantify this, the size of the population should be some. In other words, we need to conduct surveys on many customers.

However, if the R & D tasks of various venture companies are diverse, it is not easy for the budgeting government to assess the R & D tasks of several venture companies by collecting the voices of the customers in each field. However, it is not easy for small and medium-sized companies or venture companies to identify customer needs and apply R & D to apply quality function development. It is because the cost and the time are not enough for the professional manpower to manage the quality to quantify the demand of the customers from small and medium enterprises or venture companies and apply quality function development.

Therefore, it is necessary to provide an objective and easy indicator to replace the requirement of the customer. In the present invention, it is proposed to use information of each component constituting a product as an input instead of a requirement of a customer used in the conventional development of a quality function. In general, information on parts can be measured quantitatively. In addition, in consideration of the price in the development of the quality function, the price of the components constituting the product must be essentially reflected.

Next, we propose to utilize information of competitors to benchmark in quality function development process. Most of the competitors that have achieved some success in the market are succeeding by putting the products reflecting the requirements of the customers into the market, so that it is possible to indirectly grasp the requirements of the customers from the information of the competitors.

Of course, we also compared our products with competitors' products through the area of No. 4 in the development of the conventional quality function. However, it did not actively utilize the quality function development process in selecting the core functional requirements. Therefore, if information of competitors is used in the process of judging the importance of core functions by inputting information on parts, it will be possible to evaluate R & D tasks quantitatively easily and easily in place of the needs of customers.

According to the present invention, it is possible to replace the process of selecting a core part in which a customer's requirement is projected by one quality function development through the development of a quality function ranging from step 1 to step 2 shown in FIG. In other words, we can select the core functions that indirectly reflect the customer's requirements and reflect the price with the development of one quality function. On the other hand, core parts can be selected. This is shown in FIG.

FIG. 3 is a diagram for explaining a comparison between a conventional quality function development and a quality function development according to an embodiment of the present invention.

Referring to FIG. 3, a conventional quality functional development is illustrated at the top. Identify customer needs, analyze the correlation with the quality characteristics of the product, and design the product. When the actual product is developed, it is evaluated whether it satisfies the customer's demand by examining how well the quality characteristic is satisfied.

When the principle of the valuation method among the Value Innovation (VI) technique is applied to the development of the conventional quality function, the configuration illustrated in the abort of FIG. 3 is the same. In other words, it grasps the needs of the customers, analyzes the correlation with the quality characteristics of the products, and designs the products by reflecting the prices. When the actual product is developed, it is examined how well the quality characteristics are satisfied, and it is evaluated whether it meets the customer's demand considering the actual production cost.

Here, the input of the quality function development is replaced with the component information, and the configuration shown in the lower part of FIG. 3 is the same as that of the competitor information. In other words, instead of the customer's request, we identify the components that make up the product, analyze the correlation with the quality characteristics of the product, and design the product reflecting the competitor's information and price. When the actual product is developed, it is examined how well the quality characteristics are satisfied, and the parts information and competitor information are compared and evaluated in consideration of the actual production cost.

As shown in FIG. 3, by using the quality function development according to an embodiment of the present invention, it is possible to quantitatively and easily evaluate the merchantability of the R & D task in the design stage and to quantitatively verify the competitiveness of the product developed in the production stage have. That is, the quality function development proposed by the present invention can be utilized for evaluating and verifying R & D tasks.

Through such a process, it is possible to secure a fundamental competitiveness in the market, and to create high value-added luxury goods by improving the quality and function of existing products. This can be confirmed by sales performance through market analysis, product launch, and customer response evaluation of developed products. In other words, it is possible to construct a market-oriented technology commercialization ecosystem rather than a technology-oriented technology commercialization ecosystem.

4 is a flowchart of a method for evaluating the merchantability of the R & D task according to an embodiment of the present invention.

Referring to FIG. 4, information on quality characteristics is input from a user who is to evaluate the R & D task (S1100). Next, information on the part specification is input from the user (S1200). It is possible to calculate the index (V-Index) which quantitatively evaluates the value considering the price through the part specification and the quality through the quality characteristic. The process of receiving the quality characteristic and the part specification from the user will be described in more detail with reference to FIG. 5 to FIG.

In order to calculate the V-Index from the part specification and the quality characteristic, the correlation between the quality characteristics is inputted from the user (S1300), and the correlation between the part specification and the quality characteristic is inputted (S1400). However, the step of inputting the correlation between the quality characteristics (S1300) and the step of receiving the correlation between the parts specification and the quality characteristics (S1400) may be automated by uploading data related to the goods. The process of receiving the correlation between the user and the quality characteristic or the correlation between the part specification and the quality characteristic will be described in more detail with reference to FIGS. 9A to 10C.

Lastly, the benchmarking information is input to replace the requirement of the customer who used the input in the improvement of the conventional quality function (S1500). When the input of necessary information is completed, it is possible to apply the development of the quality function and to generate and interpret the merchandise property evaluation table of the R & D task (S1600). The process of receiving benchmarking information from a user and generating and analyzing a merchantability evaluation table will be described in more detail with reference to FIGS. 11A to 13B.

The quality characteristics information, part specification information, and benchmarking information inputted at this time can be reused when verifying the R & D task using the information on the prototype product produced when the prototype product according to the R & D task is produced later. This will be described in more detail with reference to FIG.

Figure 4 shows how we can evaluate R & D tasks by applying quality function development in the process of establishing R & D tasks. Here, the user can be the government to implement the R & D budget. Or a small business or a venture company that would like to propose an R & D assignment. Or it could be a general company that wants to improve its products and check its marketability.

5 to 13B are views for explaining a R & D quality evaluation method according to an embodiment of the present invention.

Referring to FIG. 5, the user screen 200 of the R & D quality verification program implementing the quality function development proposed by the present invention can be confirmed. In the left area, menus for verifying R & D quality are provided. In the right area, contents related to the menu are provided when each menu is selected.

When the R & D quality verification program is executed, a screen as shown in FIG. 5 is provided on the home screen. In the right area, two buttons are provided with guidance for the R & D quality verification program. One is a new button 211, and the other is an open button 213. [ These functions are also accessible by clicking on the file (F) in the toolbar at the top.

The new button 211 is a function mainly used in the process of initially using the R & D quality verification program, that is, in the process of establishing R & D tasks and evaluating the merchantability. On the contrary, the open button 213 is a function mainly used in the process of developing the product according to the previously established R & D task, that is, in the process of verifying the quality of the R & D task using the information about the prototype.

When clicking the New button 211 in Fig. 5, a screen of Fig. 6 is provided. Referring to FIG. 6, menus for merchandise evaluation and menus for quality verification are provided in the form of a tree menu in the menu area 210 on the left side. You can move to each screen through the tree menu.

In the right area, a function for creating a new file is provided. If the name and storage position of the file are inputted and the next button 215 is selected, the merchandise evaluation process using the quality function development proposed in the present invention can be performed. Conversely, if the user selects the cancel button 217, the screen returns to the home screen. The file extension used in the R & D quality verification program is "qfd". In order to facilitate the understanding of the following, the electric toothbrush product will be examined for the development of the quality function.

When the user creates the "BBC toothbrush product .qfd" file on the screen of Fig. 6, the screen of Fig. 7A is provided. In the menu area 210 on the left side, [1.1 Quality Characteristic Input] is selected, and a user screen for inputting quality characteristics related to the electric toothbrush product is provided. The menu will be displayed with [].

The items to be input as quality characteristics are entered into the quality classification, name, measurement unit, characteristic classification, and specification of the target quality to be achieved through the R & D task. The quality distinction or characteristic distinction can be provided in the same form as a select input. Here, items other than those illustrated in FIG. 7A can be input as the quality characteristics. The input items illustrated in FIG. 7A are merely examples for facilitating understanding of the invention.

Quality classification can be largely basic function, reliability, environmental, and palatability. Of course, you can also manage similar quality characteristics through separate menus that manage quality classification. Next, the property classification can have a value of the tower, the moon, and the mesh. The tower is used when the quality is better when the value is higher, when the quality is better when the value is smaller, when the quality is better when the value falls within a certain range.

Referring to FIG. 7A, power consumption is illustrated as one of quality characteristics inputted by the user. In the case of power consumption, it is the quality characteristic corresponding to the basic function. It is measured in W unit, and the lower the power consumption, the better the quality. We would like to develop electric toothbrush products that do not exceed 1100W, no matter how high the power consumption.

In the upper part of the input screen of FIG. 7A, a - button and a + button that can add or delete items are illustrated. 7B, when the remaining quality characteristics of the electric toothbrush are input as shown in FIG. 7A. Referring to FIG. 7B, it can be seen that the quality characteristics such as the number of revolutions, B10 life, durability, noise, vibration, handle size, and weight are inputted in addition to the power consumption.

The quality characteristics belonging to the basic functions are the power consumption and the number of revolutions. The quality characteristics corresponding to the reliability are B10 life and durability. The quality characteristics corresponding to the environment are the noise and vibration, and the quality characteristics corresponding to the preference are the size and weight to be. A total of eight quality attributes were entered in relation to the electric toothbrush. Of course, it is also possible to input quality characteristics other than the quality characteristics illustrated in Fig. 7B. The quality characteristics illustrated in Fig. 7B are merely examples for the purpose of assisting the understanding of the invention.

After entering information on the quality characteristics, you must enter information about the part specification. Referring to FIG. 8, a screen in which the [1.2 Part specification input] menu is selected can be seen. The items to be entered in the parts specification are the parts classification, price, parts characteristic, measurement unit, characteristic classification, and specification of the target component.

Referring to FIG. 8, it can be seen that information on the parts constituting the electric toothbrush is inputted. In the example shown in FIG. 8, only information about the three parts, that is, the rotor core, the magnet, and the case, is input to facilitate understanding. There are two or three component characteristics for each component, and information on a total of seven components is input.

Referring to FIG. 8, the rotor core has thickness variations and BH as component characteristics, and the magnet has magnetic flux density, plating thickness, and eccentricity as component characteristics with respect to three components constituting the electric toothbrush, As component characteristics.

The price here is the unit price of the electric toothbrush that is currently produced, and the target price is the expected unit price of the improved product. The production unit price is set for each part. Referring to FIG. 8, the production cost of the electric toothbrush was 4,500 won in the past, and it is intended to lower the production cost to 3,300 won through R & D.

In the example of FIG. 8, the sum of the prices at the bottom is painted with a dark color. This is because the production unit price of a product is automatically calculated if the product unit price of each component is all added. Among the values that the user should input in the following screen, the area painted in dark color is a value that is automatically calculated in the R & D verification program.

Parts specifications and quality characteristics (Engineering Characteristic) were inputted through FIGS. 7A to 8. Next, we need to enter the correlation between them. That is, it is necessary to input the correlation between the part specification and the quality characteristics and the correlation between the quality characteristics.

Referring to FIG. 9A, a screen in which the [1.3 Quality attribute relation input] menu is selected can be seen. The correlation between the quality characteristics can be inputted through the screen as shown in FIG. 9A. The correlation between the quality characteristics entered here constitutes the roof of the House of Quality.

As can be seen in FIG. 9A, the correlation between quality characteristics is sufficient for only half of the 8 * 8 size matrix produced due to the 8 quality characteristics. That is, the correlation value 9 when the row is B10 life and the column is the power consumption is the same as the correlation value when the row is power consumption and the heat is B10 life. The correlation between quality characteristics is bi-directional.

It is sufficient for the user to input the correlation only in half of the triangular area painted white in Fig. 9A. This triangular area serves as a roof in a quality home. There is also no correlation between the same quality characteristics. For example, the correlation is not significant when the row is power consumption and the heat is power consumption.

Correlation values generally use values of -9, -3, -1, 0, +1, +3 and +9. The sign is determined according to whether there is a positive correlation or a negative correlation, and the number is determined depending on whether the correlation is strong or weak. If the correlation is strong, it has a value of 9; in general, it has a value of 3; when the correlation is weak, it has a value of 1.

Referring to FIG. 9A, the number of revolutions and noise have a value of -9, which is a strong negative correlation. That is, the higher the number of revolutions, the larger the noise is, which means that the quality is adversely affected. Similarly, when we look at the weight and the number of revolutions, we can see that it has a positive positive correlation value of +3. The lighter the weight, the better (= roots), and the lighter the weight, the higher the number of revolutions.

When the user inputs the correlation between the quality characteristics, the R & D quality verification program automatically calculates the weight of the quality characteristic. The weight of the quality characteristic is a numerical value that calculates which quality characteristic is more important in consideration of the correlation between the quality characteristics. The formula for calculating the weight of the quality characteristic can be found in Fig. 9B.

(Numerator) of the correlation index R with another quality characteristic correlated with the first quality characteristic is divided by a value (denominator) obtained by squaring the correlation index R between all the quality characteristics, and the square root of this value It is possible to add one to drunk. The weight of the quality characteristic has a value between 1 and 2. When the weight of the quality characteristic is 1, there is no correlation with other quality characteristics, that is, when the number of molecules is zero. On the contrary, when the weight of the quality characteristic is 2, the denominator and the molecule are the same, that is, there is a correlation only with a specific quality characteristic.

Referring to FIG. 9A, the sum of the squares of the correlation indices of power consumption is 90, which is the square of 9 plus 3. Likewise, the sum of the squares of the correlation indices of the number of revolutions is the square of -9 plus 90 as the square of 3. In this way, the sum of the squares of the correlation indices is obtained for all the quality characteristics, and the sum of squares of the correlation indices of all the quality characteristics is added to obtain a value of 90 + 90 + 90 + 1 + 81 + 9 + 10 + Can be obtained.

Next, the sum of the squares of the correlation indices of each quality characteristic is divided by 382, the square root is taken, and 1 is added to the value to obtain a weight value of each quality characteristic. Table 1 shows the process of obtaining the weight of the quality characteristic for 8 quality characteristics.

Quality characteristics Sum of Correlation Exponents Weight of quality characteristic Power Consumption 9 ^ 2 + 3 ^ 2 = 90 1 + sqrt (90/382) = 1.49 Revolutions (-9) ^ 2 + 3 ^ 2 = 90 1 + sqrt (90/382) = 1.49 B10 Life 9 ^ 2 + (-3) ^ 2 = 90 1 + sqrt (90/382) = 1.49 durability 1 ^ 1 = 1 1 + sqrt (1/382) = 1.05 noise (-9) ^ 2 = 81 1 + sqrt (81/382) = 1.46 vibration (-3) ^ 2 = 9 1 + sqrt (9/382) = 1.15 Handle size 3 ^ 2 + (-1) ^ 2 = 10 1 + sqrt (10/382) = 1.16 weight 3 ^ 2 + 1 ^ 2 + (-1) ^ 2 = 11 1 + sqrt (11/382) = 1.17

Referring to FIG. 9A, it can be seen that the power consumption, the number of revolutions, and the B10 life are quality characteristics highly correlated with other quality characteristics. When improving a product, improving the performance of the quality characteristic affects other quality characteristics. Therefore, it is desirable to improve such quality characteristics in priority.

Although the correlation index is received from the user in FIG. 9A, the correlation index can be automatically calculated by using actual product data, instead of directly receiving the correlation index from the user. We previously mentioned that the value of the correlation index could be -9, -3, -1, 0, +1, +3, +9.

Referring to FIG. 9C, it can be seen that the process of judging the degree of correlation according to the value of the correlation coefficient r as a result of performing a regression analysis between two items can be seen.

Referring to the lower part of FIG. 9C, when the correlation coefficient r has a value of -1, the two items have a strong negative correlation. At this time, it can be seen that the two items are distributed in a straight line having a negative slope. When the correlation coefficient r has a value between -1 and 0, it has a negative correlation. There is no correlation with the correlation coefficient r of 0, so that the two items are randomly distributed. When the correlation coefficient r has a value of 0 and 1, it has a positive correlation. Finally, when the correlation coefficient r has a value of 1, it has a strong positive correlation. At this time, it can be seen that the two items are distributed in a straight line having a positive slope.

The conversion table at the top of FIG. 9c can be used to find the correlation index R based on the correlation coefficient r obtained from the regression analysis. When the value of the correlation coefficient r is less than 0.1, the value of the correlation coefficient R is zero. When the value of the correlation coefficient r is 0.1 or more but less than 0.3, it has a weak correlation and the value of the correlation index R is 1. [

When the value of the correlation coefficient r is 0.3 or more and less than 0.5, it has a weak correlation and the value of the correlation index R is 1. [ When the value of the correlation coefficient r is 0.5 or more and less than 0.7, it has a general correlation, and the value of the correlation index R is 3. When the value of correlation coefficient r is 0.7 or more, it has a strong correlation and the value of correlation index R is 9. The correlation coefficient R has the same sign as the correlation coefficient r.

Since the correlation coefficient R obtained from the regression analysis can be used to obtain the correlation index R, instead of receiving the correlation between the quality characteristics directly from the user, the quality characteristic information about the current product to be improved And uploading it, automatically performs regression analysis, and the correlation coefficient can be obtained to obtain a correlation.

Referring to FIG. 9D, a process of collecting the measured values of the quality characteristics of 100 actual products from data 1 to data 100 in the form of Excel is uploaded. When a total of 100 data are uploaded, the result is a regression analysis for each quality characteristic and can automatically establish a correlation as shown in FIG. 9a.

Although FIG. 9D illustrates an example of uploading a total of 100 data, it is not necessary to upload 100 data. The larger the number of data, the higher the accuracy of the correlation obtained as a result of the analysis, but the number of data to be uploaded is not limited. As a result of the actual experiment, we could find a significant correlation between 10 data.

After inputting the correlation between the quality characteristics, the correlation between the parts specification and the quality characteristics should be input. Referring to FIG. 10A, a 7 * 8 size relationship matrix made up of 7 parts specifications and 8 quality characteristics can be seen. At this time, under the name of the quality characteristic, the weight of the quality characteristic obtained in FIG. 9A is automatically displayed.

The user can also display the correlation between the part specification and the quality characteristic using the numerical values of 1, 3, and 9 as well. Or symbols of?,?, And?. Symbols and numbers used here are the numerical values and symbols commonly used in QFD charts. The correlation between quality characteristics can have both positive and negative correlation, but most of the correlation between parts specification and quality characteristics have a positive correlation.

Referring to FIG. 10A, since the thickness deviation and power consumption of the rotor core have a strong positive correlation, the value of 9 is shown. The rotor core is a part used in a motor. The smaller the thickness variation of the rotor core (rust), the lower the power consumption (rust).

When a correlation is input to a matrix of 7 * 8 in this manner, the importance of the quality characteristic reflecting the part specification and the importance of the part specification reflecting the quality characteristic are automatically calculated as a result. This value can be confirmed by the item of score in FIG. 10A. The formula for calculating this score can be seen in Figure 10B.

Referring to FIG. 10B, the formula for calculating the score can be confirmed. Here, the score means importance. To calculate the score, the correlation coefficient between the part specification and the quality characteristic is multiplied by the weight of the quality characteristic in the correlation matrix. In order to calculate the cost, the target production cost of the product to be improved by the R & D task is multiplied by the ratio (%) of the score.

Referring to FIG. 10A, the formula for calculating the score of FIG. 10B and the formula for calculating the cost are taken as 9 * 1.49 + 1 * 1.49 to obtain the power consumption score. Of course, actually, since the weight of the quality characteristic of power consumption obtained before is rounded off, it is 1.49, which is actually 1.485, so that the power consumption score can be obtained as 14.85 as a result of the formula 9 * 1.485 + 1 * 1.485. The results are shown in Table 2 below.

Quality characteristics score score(%) Power Consumption (9 + 1) * 1.49 = 14.85 14.85 / 126.81 = 11.71 Revolutions (9 + 1 + 3 + 1) * 1.49 = 20.80 20.80 / 126.81 = 16.40 B10 Life (3 + 9) * 1.49 = 17.82 17.82 / 126.81 = 14.06 durability (3 + 1 + 9) * 1.05 = 13.67 13.67 / 126.81 = 10.78 noise (1 + 9 + 3) * 1.46 = 18.99 18.99 / 126.81 = 14.97 vibration (1 + 3 + 9) * 1.15 = 15.00 15.00 / 126.81 = 11.83 Handle size (3 + 3) * 1.16 = 6.97 6.97 / 126.81 = 5.50 weight (3 + 3 + 9 + 1) * 1.17 = 18.72 18.72 / 126.81 = 14.76

The total score of all 8 quality characteristics is 126.81, and the ratio of each score is obtained by using this value as shown in Table 2 above. The scores obtained by the quality characteristics indicate how important the quality is in relation to the parts. That is, it is the importance of the quality characteristics reflecting the parts specifications.

For example, in the case of handle size, the score is only 5.50%. That is, even if the handle size is improved, the influence on the quality of the electric toothbrush is relatively low. Instead, the number of revolutions is 16.40%. This means that the quality characteristic that most affects the quality of the electric toothbrush is the number of revolutions of the motor.

Similarly, in terms of parts specifications, scores that reflect quality characteristics can be obtained. This is indicated on the right side in the table of Fig. 10A. In other words, when the component specifications are shown horizontally and the quality characteristics are shown vertically and then crossed, the importance of the component specification reflecting the quality characteristics can be known on the right side and the importance of the quality characteristics reflecting component specifications can be grasped below. Table 3 shows the scores for the parts specifications as same as the score for quality characteristics.

Part Specifications score Thickness deviation (9 * 1.49) + (3 * 1.49) + (1 * 1.46) + (3 * 1.16) = 22.77 BH (9 * 1.49) + (3 * 1.05) + (1 * 1.15) + (3 * 1.17) = 21.18 Magnetic flux density (1 * 1.49) + (9 * 1.46) + (3 * 1.17) = 18.14 Plating Thickness (3 * 1.49) + (1 * 1.05) + (3 * 1.15) + (9 * 1.17) = 19.50 Eccentricity (1 * 1.49) + (9 * 1.05) = 10.95 Inner diameter (1 * 1.49) + (9 * 1.15) + (3 * 1.16) = 15.35 Concentricity (9 * 1.49) + (3 * 1.46) + (1 * 1.17) = 18.92

Scoring ratio (%) of parts specification The process of obtaining is not different from the process of obtaining score ratio of quality characteristic, so it is omitted in space in Table 3. Referring to FIG. 10A, the eccentric amount of the magnet in the parts specification is 8.63%, which is the smallest value. This means that the eccentricity of the magnet in the parts has the lowest effect on the quality of the electric toothbrush. Instead, the thickness deviation of the rotor core is the highest with a score of 17.96%. This means that the thickness variation of the rotor core among the components has the greatest effect on the quality of the electric toothbrush.

In FIG. 10A, the weight of the quality characteristic is indicated by two decimal places. This is indicated by rounding up the indication. Actually, in the process of calculating the score according to the quality characteristic or the part specification, the value calculated by the formula of FIG. , The results of FIG. 10A, Table 2, and Table 3 may be slightly different at the decimal point.

After obtaining the score for each quality characteristic and obtaining the score for each part specification, the cost for each quality characteristic can be obtained. In FIG. 8, the information on the part specification is inputted, and the target production unit price to be lowered for each part is inputted. The rotor core was priced at 1,100 won, the magnet at 1,200 won, and the case at 1,000 won. The total is 3,300 won and the previous production cost is 4,500 won.

Multiply the target production cost by 3,300 won for each quality characteristic, it is possible to know how much the cost should be produced for each quality characteristic. The price depends on the parts, but if the correlation between the parts specification and quality characteristics is reflected, the cost by quality characteristics can be grasped. The result is shown at the bottom of FIG. 10A.

9D, the process of automatically calculating the correlation between the quality characteristics using the information of the product currently being produced, rather than receiving the correlation directly from the user has been described. Likewise, the correlation between part specifications and quality characteristics can be calculated automatically from actual raw data. Instead, the data format shown in Fig. 10C should be used instead of the data format used in Fig. 9D.

Referring to FIG. 10C, a total of about 100 data from data 1 to data 100 are illustrated. This is the actual data of the product to be improved at present, the part characteristic is displayed on the left side of the circle of the circle and the quality characteristic is displayed on the right side of the result line. By collecting and uploading raw data in this format, you can automatically generate a first correlation between quality characteristics and a second correlation between the part specification and quality characteristics.

The purpose of the development of the conventional quality function is to grasp what quality characteristics should be improved with priority in order to reflect the demand of the customer. Therefore, all processes are terminated by calculating the importance of the quality characteristic, that is, the score according to the quality characteristic. However, since the quality function development proposed by the present invention is aimed at quantitatively evaluating the R & D task, there is an additional process after calculating the score for each quality characteristic.

Referring to FIG. 11A, a screen of the [1.5 Benchmarking Information Input] menu can be viewed. In the present invention, in order to more easily apply the quality function development, the benchmarking information of the competitor will be utilized instead of the requirement of the customer. By using the benchmarking information of competitors, customers' needs can be grasped indirectly. Competitors should choose the company with the highest market share in the field of electric toothbrush. A total of three competitors are illustrated in FIG. 11A.

Referring to FIG. 11A, it can be seen that the competitors c1, c2, and c3 and the quality characteristics of their products are listed. Currently, we are producing and selling PC-240 models, and we want to develop PC-250 models by improving functions through R & D. In this case, the quality characteristics of the PC-250 model to be developed by R & D are compared with the SSQ-6 model of competitor c1, the BOSH11 model of c2, and the PJM97 model of c3 as shown in FIG.

As can be seen from Figure 11, customer requirements are qualitative and difficult to assemble and quantify, but information about competitors' products is quantitative as well as quality, and information is easy to assemble. Therefore, in the present invention, the information of the competitor is used to calculate the V-Index instead of the requirement of the customer.

In the lower part of FIG. 11A, an improvement coefficient comparing a product of a competitor with a PC-250 model to be developed is displayed. The arithmetic factor is a measure of the degree of quality improvement achieved through R & D compared to competitors' products. The formula for calculating the improvement coefficient can be found in Fig. 11B.

The improvement coefficient is calculated differently depending on whether the quality characteristics are minnow, tower, or mesh. For example, if the quality characteristic is a tower, the higher the value, the better the quality. At this time, the target quality is obtained by dividing the target quality by the highest value among the quality characteristics of the competitor. On the contrary, when the quality characteristic is small, the smaller the value is, the better the quality is. In this case, the smallest value among competitors' quality characteristics is divided by the target quality.

In case of mesh, the quality should be included in the range so that the quality is excellent. In this case, when the range is smaller, the improvement coefficient is calculated in a similar manner to that in the case where the quality is better, and when the quality is better, the improvement coefficient is calculated similarly to the tower.

Referring to FIG. 11B, the improvement contribution degree means a degree of contribution to the degree of improvement in quality that is desired to be achieved through R & D for each quality characteristic. The contribution contribution is the value obtained by dividing the score of the relevant quality characteristics by the sum of the scores of the total quality characteristics multiplied by the improvement coefficient. That is, the ratio (%) of the score according to the quality characteristic is the product of the improvement coefficient.

Returning to FIG. 11a, if we look at the improvement coefficient, in the case of power consumption, we intend to reduce the power consumption to 1,100W through R & D. In other words, the lower the power consumption, the better the quality. I would like to do R & D so that the power consumption of the electric toothbrush PC-250 model does not exceed 1,100W. If you look at the competitors' products at this time, c1's SSQ-6 model has the best quality with 1,150W of power consumption. Since we want to improve the power consumption to 1,100W, the improvement coefficient of power consumption is 1150/1100 = 1.05.

Let's look at the case where the quality characteristic is the tower because the power consumption differs in the characteristic division. For the number of revolutions, the unit of measurement is RPM, and the higher the value, the better the quality. We want to increase the number of revolutions to 5,000 RPM through R & D. That is, the higher the number of revolutions is, the better the quality is. The R & D of the electric toothbrush PC-250 model is desired to exceed 5,000 RPM even if the number of revolutions is small. At this time, if you look at the competitors' products, the c3's PJM97 model has the best quality at 4,900 RPM. Since we want to improve the number of revolutions to 5,000 RPM, the number of revolutions of the improvement coefficient is 5000/4900 = 1.02.

Let's look at the case where the quality characteristic is mesh. In the case of the handle size, the measurement unit is mm. The higher the value is, the better the quality is. We want to adjust the handle size range from 8 to 10mm by R & D. At this time, since the handle size is uniformly produced as the handle size is narrower, the range of the handle size corresponds to the knockdown. If you look at the competitors' products, the c1's SSQ-6 model has a range of 10-7 to 3mm, and the other competitors have a range of 4mm. Since we want to improve the handle size range to 2mm, the enhancement factor of the handle size is 3/2 = 1.5.

The improvement coefficient and the contribution contribution for each quality characteristic are obtained through this process as shown in Table 4 below.

Quality characteristics Improvement coefficient Improvement Contribution Power Consumption 1150/1100 = 1.05 1.05 * 11.71 / 100 = 0.12 Revolutions 5000/4900 = 1.02 1.02 * 16.40 / 100 = 0.17 B10 Life 700/690 = 1.01 1.01 * 14.06 / 100 = 0.14 durability 16 / 15.9 = 1.01 1.01 * 10.78 / 100 = 0.11 noise 88/87 = 1.01 1.01 * 14.97 / 100 = 0.15 vibration 0.39 / 0.35 = 1.11 1.11 * 11.83 / 100 = 0.13 Handle size 3/2 = 1.5 1.5 * 5.50 / 100 = 0.08 weight 3.7 / 3.5 = 1.06 1.06 * 14.76 / 100 = 0.16

As shown in Table 4, the improvement coefficient of the handle size is 1.5, which is 1.5 times that of the competitor. However, the effect of the handle size on the overall quality of the product is very low at 5.5 (%), so that the size of the handle improves the quality of the actual product is only 0.08.

On the other hand, in the case of the number of revolutions, the improvement coefficient is 1.02, and the quality of the number of revolutions is improved by 1.02 times as compared with that of the competitors, so that the quality of the revolutions itself is insignificant. However, the effect of the number of revolutions on the overall quality of the product is very high at 16.40 (%), so that the degree of improvement of the actual product quality is 0.17, which is very large.

(S1200), a step of receiving a first correlation between quality characteristics (S1300), a step of receiving a second correlation between a part specification and a quality characteristic A step of receiving a relation (S1400), and a step of receiving a benchmarking information (S1500).

After inputting such information from the user, various necessary indices are automatically calculated to quantitatively evaluate the commerciality of the R & D task. Referring to FIG. 12, it can be seen that the menu [1.6 Analysis of Productivity Evaluation Table] is selected. Referring to FIG. 12, the V-Index suggested by the present invention can be confirmed.

First, the contribution contribution degree (N) is a sum of the improvement contributions of each quality characteristic automatically calculated in FIG. 11A. The improvement contribution (N) can be obtained from the equation 0.12 + 0.17 + 0.14 + 0.11 + 0.15 + 0.13 + 0.08 + 0.16 = 1.06. In other words, the PC-250 model we intend to make through R & D is 1.06 times better than our competitors. If the value of improvement contribution (N) is 1, it means that the quality of the product of the competitor is the same. Therefore, the improvement contribution of 1.06 means that the quality of the improvement is about 6% as compared with that of the competitor.

Next, the cost ratio (C) is the target price divided by the current price. Cost ratio (C) means how much lower the cost of production to enter the product. Generally, if a product is of the same quality, the lower the price, the more competitiveness it has. Therefore, it is reflected in the formula for calculating the V-Index.

Referring to FIG. 12, the target price of the electric toothbrush PC-250 to be developed through R & D is 3,300 won, and the production cost is 3300/4500 = 0.73, that is, 27,700 compared with the conventional PC- % Of the cost. Competitiveness will increase because the unit price is lowered.

Where necessary, the current price can be based on the unit price of the competitor's product, not the current unit price of the company's product. In other words, the production cost of the competitors c1, c2, c3 can be used instead of the PC-240 model, which is a PC-250 model to be developed through R & D, at a production cost of 4,500 won.

Since the production unit price has the characteristics of the net, the cost ratio (C) can be obtained by using the unit price of the product to be developed through the R & D as the denominator with the lowest unit cost among the products of c1, c2 and c3. Alternatively, the cost ratio (C) can be obtained by using the average value of the production cost of the products of c1, c2, c3 as the denominator and the product cost of the product to be developed through R & D as the numerator.

Finally, the valuation (V) can be obtained by dividing the value of the improvement contribution (N) by the value of the cost ratio (C). The higher the value of the valuation (V), the higher the commercial value of the R & D task. On the other hand, the larger the value of the valuation (V), the lower the commercial value of the R & D task. In other words, the V-Index can be used to quantitatively evaluate the commerciality of the R & D project in advance. In the following text, the V-Index is used in the same sense as the valuation (V).

Suppose that the value of the valuation (V) is 1. The value of the improvement contribution degree (N) is 1, and the cost ratio (C) is also 1. This is the case when the quality is the same as that of the competitor's product and the production unit price is not lowered. At this time, the competitiveness is weak.

To increase the value of the valuation (V), it is necessary to increase the numerator or decrease the denominator. That is, it is necessary to increase the improvement contribution N indicating the quality as a molecule, or to reduce the cost ratio C indicating the denominator price. If R & D can develop better quality products or lower the production cost even if products of the same quality are developed, the R & D task can be considered to be productive.

In the present invention, the valuation index (V) can be simply computed with quantitative items by omitting the customer's request and using the information of the competitor. In other words, conventional quality function development using customer's requirements must make a customer survey in order to create a QFD chart (100). In contrast, in the quality function development proposed in the present invention, it is sufficient to collect product information of a competitor.

Referring to FIG. 12, the improvement contribution (N) is 1.06, but the cost ratio (C) is excellent as 0.73, and the value of the valuation (V) is 1.06 / 0.73 = 1.449. The value of the valuation (V) is about 1.45, and it can be seen that the merchantability is good. The grade according to the value of the valuation (V) will be described in more detail in Figs. 22A to 22B.

Referring to FIG. 11A, the values of the improvement contribution (N), the cost ratio (C), and the valuation (V) are automatically calculated, and the interpretation of these values is shown at the bottom. This interpretation is automatically generated according to the value of improvement contribution (N), cost ratio (C), value evaluation (V), and can be modified by the user as needed.

"The quality level is 6% higher than competitors and the cost is 27% lower." And "Value (V) is 1.449, which is competitive and 44.9% more competitive than competitors." And "the thickness variation of the rotor core among the major components is influencing the 17.96% occupancy rate" can be automatically generated and displayed together in the QFD chart (220).

Referring to FIG. 13A, it can be seen that all of the information up to now is collected and displayed in one QFD drawing 220. In the second area of the QFD drawing 220 suggested in the present invention, the parts specification entered in FIG. 8 is shown instead of the customer requirement. The quality characteristics input in FIG. 7A to FIG. 7B are shown in the fifth field of the QFD drawing 220.

The first correlation between the quality characteristics entered in FIGS. 9A-9D is shown in field 7 of the QFD plot 220. And region 6 shows a second correlation between the parts specification and quality characteristics entered in Figures 10a-10c. In the fourth area of the QFD drawing 220, scores indicating the importance of the parts specification reflecting the quality characteristics are shown. Part-specific scores can be used to select components to improve the V-Index in the future.

Next, in the area 8 of the QFD drawing 220, the scores of the quality characteristics indicating the importance of quality characteristics reflecting parts specifications are shown. 11A to 11B show the benchmarking information inputted at the lower end thereof. In the present invention, by applying the quality function development using the benchmarking information, it is possible to replace the customer requirement used in the conventional development of the quality function.

 Next, information related to the valuation (V) shown in FIG. 12 is shown on the lower right side of the QFD drawing 220. 13A, the overall contents of the R & D task can be reviewed at a glance, and the merit of the R & D task can be evaluated simply by confirming the value of the value evaluation (V) at the lower right.

Referring to FIG. 13B, [2. Productivity Evaluation Result] menu is selected. It can be seen that the QFD chart 220 shown in FIG. 13A is partially displayed on the screen. The user can look at the QFD chart 220 on the user screen 200 of the R & D quality verification program through the navigation menu 221 at the top and the enlargement / reduction menu 223 beside it.

Thus, the R & D quality evaluation method proposed by the present invention has been described with reference to FIGS. 4 to 13B. The R & D quality evaluation method proposed by the present invention can be utilized in the design process of the R & D task or in the review process. In addition, it can be used as a data for selecting excellent R & D tasks among a plurality of R & D tasks by using a V-Index.

For example, if the government intends to enforce the R & D budget, it will be able to identify the QFD diagram (220) of the proposed R & D task and support R & D budget by selecting only those R & D tasks with high valuation . When a small company such as a small company or a venture company wants to develop a new product, a QFD drawing 220 proposed in the present invention is created without a customer survey, and which quality characteristic is to be developed first, It can be used as an indicator to decide whether to develop.

FIG. 14 is a flowchart of a method for verifying the quality of an R & D task according to an embodiment of the present invention.

Referring to FIG. 14, it can be seen that the R & D quality evaluation method shown in FIG. 4 selects excellent R & D tasks and verifies R & D quality using information on prototypes developed according to the selected R & D tasks. That is, if the R & D quality evaluation method of FIG. 4 is applied before product development, the R & D quality verification method of FIG. 14 is applied after developing the product according to the R & D task.

Referring to FIG. 14, the evaluation information of the prototype developed according to the R & D task is inputted from the user (2100). The evaluation information of the prototype is information such as the quality characteristics of the development cost or the prototype and the parts specification. The remaining information besides the evaluation information of the prototype reuses the information entered in the R & D quality evaluation method.

Next, the evaluation information of the prototype is analyzed (S2200). The process of analyzing the evaluation information of the prototype is a process of obtaining a first correlation between quality characteristics using the evaluation information of the prototype and obtaining a second correlation between the specification of parts and the quality characteristics.

After analyzing the prototype evaluation information, a final verification overall quality table is generated (S2300). This is the result of verifying the R & D quality, which will be described in more detail in FIG. Finally, a final quality verification result is generated (S2400). This is a QFD chart 220 similar to the QFD chart 220 of FIG. 13A. This will be described in more detail with reference to FIG. 21A.

FIGS. 15 to 21B are diagrams for explaining a R & D quality verification method according to an embodiment of the present invention.

Referring to FIG. 15, a screen when the open button 213 is selected in the user screen of FIG. 5 can be seen. In Fig. 15, a previously created quality function development file (qfd file) can be opened through a searcher. We want to verify the quality of R & D by opening the file "BBB_ toothbrush product .qfd".

Referring to FIG. 16, it can be seen that the [1.1 Development Cost Input] menu is selected. In accordance with the R & D task of the existing "BBB_ toothbrush product .qfd", a new model PC-250 is developed, a prototype is manufactured, and then the actual development cost used to produce the prototype is entered.

Referring to FIG. 16, the R & D task required a production cost of KRW 1,100 for the rotor core, KRW 1,200 for the magnet, and KRW 1,000 for the case. However, as the actual prototype was produced, the production cost was further reduced. 1,000 won and 800 won in case.

In the screen of FIG. 16, it is sufficient for the user to input only the production unit price of the developed prototype, and the production unit price (target) of the PC-250 model predicted in the production cost (present) or R & D quality evaluation process of the existing model PC- It is automatically loaded with the previously stored values. Also, the total below that is also automatically entered.

Referring to the following FIG. 17, it can be seen that the menu [1.2 Input trial product evaluation information] is selected. At this time, the user can upload raw data similar to that shown in FIG. 10C. In other words, the parts characteristics and quality characteristics of actual prototype can be uploaded in Excel form. In Fig. 17, a total of 100 source data is inputted from data 1 to data 100.

Once you have measured and uploaded the source data for the prototype, the subsequent process will proceed automatically. Referring to FIG. 18A, it can be seen that the menu [1.3.1 Quality characteristic relation] is selected. The user automatically sets the first correlation between quality characteristics based on the data uploaded in FIG. In Fig. 18A, since there is no information to be input by the user, all of the input areas are displayed in dark color as compared with Fig. 9A.

Referring to FIG. 18A, most of the correlations are the same as the values input in FIG. 9A, but only the correlation related to the weight is somewhat changed. The correlation between weight and B10 life was changed from the previous 0 to 1, and the correlation between weight and durability was changed from 1 to 0 in the past. As shown in FIG. 18A, the changed value among the values in the R & D quality evaluation process can be expressed together with the () display, and the user can be guided to what portion has been changed.

Referring to FIG. 18B, it can be seen that the [1.3.2 Parts-Quality Relationship] menu is selected. As in FIG. 18A, the user automatically sets a second correlation between the part specification and the quality characteristic based on the data uploaded in FIG. In comparison with FIG. 10A, it can be seen that the values have changed in various items.

Although not shown in FIG. 18B, similarly to FIG. 18A, when the value of an item is changed, it can be expressed together with the () display, and the user can be guided to what portion has been changed. In FIG. 18B, the cost is a product obtained by multiplying the production unit price of the prototype by 2,700 won and the score (%) of each quality characteristic.

Referring to FIG. 19, it can be seen that the [1.3.3 Benchmarking Information] menu is selected. Similarly in FIG. 19, the user automatically sets benchmarking information based on the data uploaded in FIG. At this time, average and standard deviation can be obtained from the information about a total of about 100 prototypes uploaded by the user, and the process information such as bench process capability index (Cpb) and bench sigma index (Zb) can be managed.

The weight of the quality characteristic in FIG. 18A, the score and cost in FIG. 18B, the improvement coefficient in FIG. 19, and the improvement contribution level are calculated using the formula used in the R & D quality evaluation process. After calculating these values, the V-Index is calculated. Referring to FIG. 20, it can be seen that the menu [1.4 Analysis of quality verification table] is selected. Through the menu, you can compare the objectives of the R & D task with the indicators of the actual developed product.

Referring to FIG. 20, the improvement contribution (N) of the R & D task for the electric toothbrush development was aimed at 1.06, but when the actual prototype was developed, the improvement contribution (N) increased by 1.44. In other words, they produced prototypes of better quality than those of the original R & D task. In addition, although the cost ratio (C) was aimed at 0.73, when the actual prototype was developed, the cost ratio (C) decreased to 0.60. In other words, they produced goods at less cost than the original R & D task.

The value of the valuation (V) will increase as the quality of the original R & D task is improved and the cost is reduced. In fact, the valuation (V) of the developed PC-250 model is 1.44 / 0.60 = 2.397. The target valuation (V) rose by one near 1.449. In other words, it can be seen that the developed prototype has superior competitiveness.

If the value of the V-Index of the developed prototype is smaller than the target value, it means that the competitiveness of the produced prototype is lower than the original target value, so the R & D task has not been carried out as planned Able to know. Conversely, if the value of the V-Index of the prototype developed is larger than the target value, it can be seen that the R & D task has been carried out as planned. In other words, the R & D quality can be verified by comparing the target value evaluation index of the R & D task with the value evaluation index of the developed prototype.

As shown in FIG. 20, as a result of the R & D quality verification, "the quality level is improved by 38% to 44% compared to 6% of the target" and "the cost level is -27% 40% to 13% more savings "and" Valuation is improved by 94.8% to 139.7% compared to 44.9% of goals ". Which is later displayed in the QFD chart 220 according to the R & D quality verification.

Referring to FIG. 21A, a QFD diagram 220 corresponding to FIG. 13A is shown. In this way, in the present invention, the source data regarding the prototype product uploaded by the user is automatically analyzed in FIG. 17, and the QFD diagram 220 of FIG. 21A is generated and provided to the user. This enables us to verify R & D tasks at a glance.

Referring to FIG. 21B, [2. Quality Verification Result] menu is selected. As in FIG. 13B, a QFD chart 220 can be searched for the R & D quality in the user screen 200 of the R & D quality verification program.

We have examined the evaluation method in the design phase and the verification method in the production stage of the R & D project. According to an embodiment of the present invention, the importance is calculated on the basis of the quality characteristics and the parts specification, the improvement contribution (N) is calculated by reflecting the competitor information, the price information is compared to calculate the cost ratio (C) (V-Index) that can easily and quantitatively compare R & D tasks.

22A and 22B are views for explaining a method of analyzing a V-Index according to an embodiment of the present invention.

The R & D task can be evaluated and verified according to the V-Index. In order to simplify the R & D task, it is possible to classify R & D tasks according to the V-Index. FIG. 22A shows an example in which grades are divided into five sections, and FIG. 22B shows an example in which grades are divided into seven sections. However, this is merely an example to assist the understanding of the invention, and is not intended to limit the invention.

Referring to FIG. 22A, the value index is divided into five sections based on 1.00, 1.33, 1.67, and 2.00, and the lowest section has a score of 50 and the highest section has a score of 100. In addition, the value of each valuation index is given to each section. For example, if the value of the valuation index is more than 2, it can be interpreted as "global competitiveness is very high".

Referring to FIG. 22B, the value of the valuation index is divided into seven sections based on 1.00, 1.33, 1.50, 1.67, 1.83, and 2.00, and the lowest section has a score of 40, and the highest section has a score of 100 Respectively. In addition, the value of each valuation index is given to each section. For example, if the value of the valuation index is 2 or more, it can be interpreted as "global competitiveness is the best".

For example, the value evaluation index calculated in the R & D quality evaluation stage of FIG. 12 is 1.449, and a score of 70 points is obtained in 5 intervals and 60 points in 7 intervals. The value evaluation index calculated in the R & D quality verification step of FIG. 20 is 2.397, and a score of 100 points in five sections and a score of 100 points in seven sections can be obtained. By comparing the valuation indexes in this way, it is possible to compare them more easily.

23A to 24B are diagrams for illustrating visualization of a QFD chart according to an embodiment of the present invention.

Referring to FIG. 23A, it can be seen that the score according to the parts specification is visually expressed using a horizontal bar graph. You can see at a glance which part specifications are more important by expressing the bar graph according to the score (%) for each part specification.

Similarly, referring to FIG. 23B, it can be seen that the scores according to quality characteristics are visually expressed using a vertical bar graph. It is possible to confirm at a glance which quality characteristic is more important if it is expressed by a bar graph according to the score (%) for each quality characteristic. The bar graphs of FIGS. 23A and 23B can be included in the area 4 or 8 of the QFD chart 220 to further enhance user convenience.

Next, referring to FIG. 24A, a regression analysis graph between the weight of the quality characteristic and the improvement contribution degree can be confirmed. Also, referring to FIG. 24B, a regression analysis graph between the cost and the contribution contribution obtained through the price * score (%) can be confirmed.

These two graphs can be used to determine which weighted quality attributes have influenced the quality of the overall product and whether the quality characteristics with a certain production cost have influenced the overall quality of the product. Similarly, the graphs of FIGS. 24A and 24B can be included in the results analysis portion of the QFD chart 220 to further enhance user convenience.

As described above, a method of evaluating R & D quality proposed in the present invention and a method of verifying R & D quality have been described. The quality function development (QFD) proposed in the present invention has an advantage that it is easy to select a concept of a product to be improved because the price is considered from the design stage of the R & D task.

In addition, it can speed up decision-making on whether to proceed with R & D tasks by reviewing the calculated V-Index. Further, there is an advantage that the quality of each part can be managed by managing the quality of each part by managing the quality of each part.

Also, since customer requirements are not directly used to apply quality function development, it is possible to omit the process of complicated and uncomfortable customer requirements such as surveying. Instead, customers' requirements can be indirectly reflected in the development of quality functions using competitor information.

In addition, the quality level of the product can be evaluated from the R & D task design stage to clarify the development purpose. And it is easy to analyze the correlation between the causal factor and the result factor. That is, the QFD chart (220) can be used to grasp information related to product development at a glance.

25 is a hardware block diagram of an R & D quality verification apparatus according to an embodiment of the present invention.

25, the R & D quality verification apparatus 10 may include one or more processors 510, a memory 520, a storage 560, and an interface 570. The processor 510, the memory 520, the storage 560, and the interface 570 transmit and receive data via the system bus 550.

The processor 510 executes a computer program loaded into the memory 520 and the memory 520 loads the computer program from the storage 560. [ The computer program may include an information input operation 521, a merchantability evaluation operation 523, and a quality verification operation 535. [

The information input operation 521 may receive information for applying the quality function development from the user's computer 20 via the interface 570. [ First, in order to evaluate the commerciality of the R & D task, it is necessary to input quality characteristic information and part specification information.

The information input operation 521 inputs the information through the network and stores the information in the quality information 561 and the part information 563 of the storage 560 via the system bus 550. At this time, the quality characteristic information and the parts specification information include the case of inputting through the raw data in addition to the case of directly inputting by the user.

Next, the information input operation 521 receives the competitor information from the user through the network. By gathering information about competitors' products and applying them to the development of quality donation, it is possible to replace the requirements of customers used in general quality function development.

The competitor information received from the user's computer 20 is stored in the competitor information 565 of the storage 560 via the system bus 550. The quality information 561, the part information 561 and the competitor information 561 stored in the storage 560 can be used in both the merchandise evaluation operation 523 and the quality verification operation 525. [

The merchandiseability evaluation operation 523 can be utilized in the step of evaluating the merchantability of R & D tasks before designing R & D tasks or executing costs. Fetches quality information 561 stored in storage 560 and receives a first correlation between quality characteristics through an information input operation 521. [ And also receives the part specification information 563 stored in the storage 560 and receives the second correlation between the part specification and the quality characteristic through the information input operation 521. [

Next, the merchandise evaluation operation 523 calculates the importance of the quality characteristic and the importance of the part specification, fetches the competitor information 535 of the storage 560, and uses it to calculate the improvement contribution N indicating the improvement degree of the quality, . Further, the cost ratio C, which is the ratio between the unit price of production, is calculated by referring to the price information included in the part information 563.

Next, the merchandise evaluation operation 523 calculates the value of the valuation (V) using the improvement contribution (N) and the cost ratio (C). The result thus generated is visualized in the QFD table 220 and stored as an evaluation result 567 of the storage 560 via the system bus 550.

Likewise, the quality verification operation 525 can be utilized in the step of verifying the quality of the R & D task before developing the prototype according to the R & D task and putting it on the market. The quality verification operation 525 receives quality characteristic information and part specification information about the prototype through the information input operation 521. [ At this time, the input process can receive raw data in the form of Excel.

The quality verification operation 525 then automatically calculates and analyzes a first correlation between quality characteristics and a second correlation between the quality characteristics and the part specification. Next, the competitor information 565 stored in the storage 560 is fetched, and the value of the value evaluation V is calculated using the contribution contribution degree N and the cost ratio C. The results thus generated are visualized in the QFD chart 220 and stored as verification results 569 of the storage 560 via the system bus 550.

Each component in FIG. 25 may refer to software or hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit). However, the components are not limited to software or hardware, and may be configured to be addressable storage media, and configured to execute one or more processors. The functions provided in the components may be implemented by a more detailed component, or may be implemented by a single component that performs a specific function by combining a plurality of components.

As described above, the R & D quality verification method proposed by the present invention has been described. Using the R & D quality verification method proposed in the present invention, it is possible to quantitatively evaluate and verify the competitiveness of proposed tasks for R & D investment as shown in FIG. As shown in FIG. 20, after the R & D development, the result of the R & D task can be evaluated posteriorly using the prototype produced.

Before conducting the R & D task, you can evaluate the competitiveness of the R & D task as a valuation index and evaluate the degree of achievement of the R & D task by calculating the R & D valuation index after carrying out the R & D task. In other words, you can judge how well you have accomplished the R & D task. However, in this process, there is a need for more ways to assess the possibility of achieving the goals of the R & D task in advance.

The R & D verification method proposed by the present invention is only to evaluate how competitiveness is in comparison with the technology of a competitor when developing a target product, and to what extent the actual proposed level is achievable I can not evaluate it. If a user maliciously sets the target quality to an excellent value in comparison to the competitor, the valuation index will be high, so that it will be well evaluated when evaluating the R & D task.

However, since the malicious input value will be difficult to achieve, it can only be post-mortem evaluated that the R & D task failed to be achieved by comparing with the valuation index calculated using the prototype developed afterwards. Therefore, there is a need for a method that can evaluate the quality of the target quality that the user has entered in advance. This will be described with reference to FIGS. 26A to 31B.

26A to 31B are diagrams for explaining a method of evaluating the possibility of achieving the R & D task according to an embodiment of the present invention in advance.

The extent to which the user-entered quality of the target is achievable can be viewed in two ways. One is based on oneself and the other is based on other companies. Referring to FIG. 11A, it can be seen that the person who made the PC-240 model entered the target quality in order to develop the PC-250 model through the R & D task. At this time, the first method is to compare the performance of the PC-240 model to the performance of the PC-250 model to be newly developed.

Referring to FIG. 26A, power consumption having characteristics of a network is shown as an example of quality characteristics. At this time, there are cases where A company that produced 1200W consumes 1100W by R & D, and 1100W that consumes 1400W when R & D consumes 1100W In the case of the present invention.

Both A and B set the target of power consumption of R & D task at 1100W, so that contribution contribution from competitors will be calculated the same. However, since company A has already consumed 1200W, it can reduce power consumption of about 100W. However, since company B was previously producing 1400W of consumed products, consumption of 300W is required to achieve R & D task. Power should be reduced through technology development.

In this way, even if the quality aimed at the R & D task is the same, it is possible to quantify and compare to what extent the quality aimed at the R & D task is achievable as compared with the previous production. In the case of company A, it is necessary to develop technology of 9% at 1200/1100 = 1.09, but in case of company B, it is required to develop technology of 27% at 1400/1100 = 1.27. have.

Similarly, referring to FIG. 26B, it can be seen that the target company A and the company B have different target quality. At this time, the quality of the previous product can be compared with the target quality, and the achievement can be quantified and compared.

A company that had previously consumed 1300W, proposed to develop 1200W of products through R & D, and a company that previously used 1400W of consuming products developed R & D to consume 1100W Let's compare the case of proposing to do so.

In this case, the lower the target power consumption, the higher the value evaluation index will be. Company B aims at 1100W and Company A aims at 1200W, so that B's valuation index can be higher. However, since company A already manufactured 1300W of products, it is necessary to develop 1300/1200 = 1.08 or 8% of technology, but since company B was producing 1400W, 1400/1100 = 1.27 or 27% There are differences in technology development. In other words, A's R & D task is more likely to be achieved.

In FIG. 11A, only the specifications of the PC-250 model of the target quality are focused. However, in order to evaluate the possibility of achieving the target, it is necessary to consider the specification of the PC-240 model that was previously produced. 27A, specifications of the PC-240 model and the PC-250 model are shown. The improvement coefficient and the improvement contribution degree of the corresponding quality characteristic are calculated according to whether the quality characteristic is the minnow, the tower or the mesh.

Here, the process of obtaining the erection count relative to the self is similar to the process of obtaining the improvement coefficient in comparison with the highest quality specification in the competitor in FIG. 11B. In this way, we can see how much quality of R & D task is needed by R & D task compared to the previous model.

The improvement contribution to the company can be calculated by multiplying the improvement coefficient by the weight (%) of each quality characteristic. 11A is similar to the process of calculating the contribution contribution improvement. Thus, the sum of the finally calculated contribution contributions has a value of + on the basis of 1 in most cases. And the larger the value over 1, the more technology development should be done and the target quality can be reached.

If the total contribution contribution to the company is 1, the target is a product with similar quality to the model currently being produced. If the contribution is higher than 1, it means that the technology improvement should be made in excess of the current production do. Therefore, the greater the contribution of improvement, the lower the likelihood of achievement. It is because the quality improvement of about 10% is generally easier than the quality improvement of 100%.

The following Table 5 summarizes the process for obtaining the contribution contribution to improvement in comparison with FIG. 27A.

Quality characteristics Improvement coefficient Improvement Contribution Power Consumption 1200/1100 = 1.09 1.09 * 11.71 / 100 = 0.13 Revolutions 5000/4000 = 1.25 1.25 * 16.40 / 100 = 0.20 B10 Life 700/650 = 1.08 1.08 * 14.06 / 100 = 0.15 durability 16/15 = 1.07 1.07 * 10.78 / 100 = 0.11 noise 90/87 = 1.03 1.03 * 14.97 / 100 = 0.15 vibration 0.5 / 0.35 = 1.41 1.41 * 11.83 / 100 = 0.17 Handle size 3/2 = 1.5 1.5 * 5.50 / 100 = 0.08 weight 4 / 3.5 = 1.14 1.14 * 14.76 / 100 = 0.17

Referring to FIG. 27A and Table 5, the total contribution contribution to the company is 1.17, which can be produced only by improving the PC-250 model by 17% compared to the PC-240 model currently being produced. That is, the value of 1.17 can be considered as the difficulty of the R & D task.

Referring to FIG. 27B, it is possible to confirm a specific formula for obtaining the improvement coefficient and the contribution contribution degree Si to the company. This is similar to the equation of FIG. 11B, but only in the quality of the previous model, not the highest specification of the competitor. Let's take a look at the process of reflecting the contribution contribution to R & D as a difficulty of the R & D task and reflecting it in the valuation index.

Referring to FIG. 28A, the companies A and B having the same valuation index of 1.45 can be seen. Since the valuation indices of A and B are the same, we will evaluate the competitiveness of R & D tasks as having 45% competitiveness. However, A has a contribution of 1.09 in comparison with the current production, and B has an improvement contribution of 1.27 in comparison with the current production. In other words, the R & D task of company A is likely to achieve its goal relatively.

The R & D task can be evaluated by dividing the calculated value evaluation index by the improvement contribution ratio in order to reflect this in the value evaluation index before performing the R & D task. In other words, the valuation can be performed considering the difficulty of technology development. In the example of FIG. 28A, a company A has a value of 1.45 / 1.09 = 1.33, whereas a company B has a value of 1.14 / 1.24, which is 1.45 / 1.29. Considering the difficulty of technology development, the R & D task of A company can be evaluated as a better task.

Similarly, FIG. 28B illustrates an example in which the valuation indices of the two companies are different. A company's valuation index is 1.35, and B's valuation index is 1.45. If the company produces products according to the target quality as proposed by the R & D task, company B is 10% more competitive than company A can see.

However, in case of A company, it is necessary to make 8% improvement of technology compared to the current production, whereas in case of B company, 27% of technology improvement should be achieved more than the current production. When the value evaluation index is recalculated to reflect the difficulty of the technology development, the value of A is 1.35 / 1.08 = 1.25, while that of B is 1.45 / 1.27 = 1.14.

In other words, the value-added index is 1.45 for B and 1.35 for A compared to competitors' superior quality. However, when considering the technological development power of the two companies at present, A's valuation index 1.25, which is higher than B's valuation index of 1.14. In this case, if only one company should invest in R & D, it would be better to choose A company.

Among the two methods of evaluating the degree to which the target quality inputted by the user is feasible, the case where the user himself or herself is referred to was examined in FIGS. 26A to 28B. The other method, which is based on the other company, is a method of utilizing the lowest specification of the competitor that is not used when calculating the improvement coefficient in FIG. 11A.

Referring to FIG. 11A, competitors c1, c2, and c3 are supposed to be competitors that produce the best quality products in the field of R & D. In this case, a company that intends to invest in R & D is actually a late runner. Hence, rather than catching the best quality among competitors, it may be the first thing to catch up with the lowest quality among competitors.

For example, suppose you have three competitors: A, B, and C. Suppose that A produces the highest quality product, B the next highest quality product, and finally C the lowest quality product. Let us assume that we are currently producing products with less quality than that of C and want to achieve quality improvement through R & D.

At this time, the degree of difficulty of technology development is evaluated based on the difference in quality between A company producing the highest specification product and C company producing the lowest specification product. For example, let's say that there is a big difference in the quality of products from company C and company A. So, even though the company C has not caught up with the quality of company A, it is unlikely that the company, which is a latecomer than company C, has set a goal to catch up with company A.

Suppose, on the other hand, that the difference in the quality of the products of Company C and Company A is small. Then, since C company almost caught up with A company, it is relatively easy to catch up with the quality of A company, which is a late runner than C company. Therefore, it should be quantified to the difficulty of technology development.

Referring to FIG. 29A, the c1 company and the c2 company are illustrated. c1 is currently producing 1,150W of products for power consumption with mining characteristics. And c2 company produces 1190W products. In this case, if we aim at 1100W quality, we can calculate improvement coefficient for c1 company and improvement coefficient for c2 company respectively.

The target power of 1100W is aimed at improving the quality of 1150/1100 = 1.05 or 5% compared to that of the c1 company, and the quality of 1190/1100 = 1.08 or 8% .

This time the same c1 company produces the highest quality, but let's assume that the c4 company produces the lowest quality. The power consumption of the products produced by the c4 company is 1300W. In this case, compared with the quality produced by the c4 company, the technical improvement should be 1300/1100 = 1.18 or 18%.

29A and 29B, the difference between the highest specification and the lowest specification of a competitor is small in FIG. 29A, but the difference between the highest specification and the lowest specification of a competitor is large in FIG. 29B. The greater the difference, the greater the difference in technology among competitors. The greater the difference in technology among competitors, the lower the chance of achieving the R & D task aimed at higher quality than the best specification among competitors.

Referring to FIG. 30B, it can be seen that the process of obtaining the improvement coefficient and the contribution contribution of improvement to the lowest-ranked company is obtained. In the equation of Fig. 11B, the target quality is compared with the competitor's highest specification. In Fig. 30B, the enhancement coefficient is calculated by comparing the target quality and the competitor's lowest specification. The contribution to improvement of Oi compared to other companies can be calculated by multiplying the improvement factor calculated based on the minimum specification by the weight (%) of each quality characteristic.

The following Table 6 summarizes the process of finding the contribution contribution to the third party in FIG. 30A or the contribution contribution to the lowest customer.

Quality characteristics Improvement coefficient Improvement Contribution Power Consumption 1190/1100 = 1.08 1.08 * 11.71 / 100 = 0.13 Revolutions 4500/4000 = 1.11 1.11 * 16.40 / 100 = 0.18 B10 Life 660/650 = 1.06 1.06 * 14.06 / 100 = 0.15 durability 6/15 = 1.07 1.07 * 10.78 / 100 = 0.11 noise 90/87 = 1.03 1.03 * 14.97 / 100 = 0.15 vibration 0.45 / 0.35 = 1.29 1.29 * 11.83 / 100 = 0.15 Handle size 4/2 = 2 2 * 5.50 / 100 = 0.11 weight 3.9 / 3.5 = 1.14 1.11 * 14.76 / 100 = 0.16

In FIG. 11A, the total sum of improvement contribution is 1.06, but in FIG. 30A, the total sum of improvement contribution is 1.15. This means that the performance of the target quality is improved by about 6% when compared with the best quality of the competitor, but the quality of the target is improved by about 15% when the quality of the competitor is compared with the quality of the lowest quality. The target quality is more difficult than that. Let's take a look at the process that reflects the contribution contribution of the R & D project to other companies as the degree of difficulty of the R & D task and reflects it in the valuation index.

Referring to FIG. 31A, the companies A and B having the same valuation index of 1.45 can be seen. Since the valuation indices of A and B are the same, we will evaluate the competitiveness of R & D tasks as having 45% competitiveness. However, in case of A company, it has contribution contribution of 1.08 in comparison with the lowest specification product of competitors, and in case of B company, it has contribution contribution of 1.18 in comparison with the lowest specification product of competitors. In other words, the R & D task of company A is likely to achieve its goal relatively.

The R & D task can be evaluated by dividing the calculated value evaluation index by the contribution contribution to improvement in comparison with other companies, in order to reflect this in the value evaluation index before performing the R & D task. In other words, the valuation can be performed considering the difficulty of technology development. In the example of FIG. 31A, the value of A is 1.45 / 1.08 = 1.34, while the value of B is 1.45 / 1.18, which is 1.23. Considering the difficulty of technology development, the R & D task of A company can be evaluated as a better task.

Similarly, FIG. 31B illustrates an example in which the valuation indices of the two companies are different. A company's valuation index is 1.35, and B's valuation index is 1.45. If the company produces products according to the target quality as proposed by the R & D task, company B is 10% more competitive than company A can see.

However, company A needs to improve technology by about 5% compared to the lowest specification of competitors, while company B needs to improve technology by 18% more than its competitors. When the value evaluation index is calculated again to reflect the difficulty of technology development, the value of A is 1.35 / 1.05 = 1.29, while that of B is 1.45 / 1.18 = 1.23.

In other words, the value-added index is 1.45 for B and 1.35 for A compared to competitors' superior quality. However, when considering the technological development power of the two companies at present, A's valuation index 1.29, which is higher than B's valuation index of 1.23. In this case, if only one company should invest in R & D, it would be better to choose A company.

26a to 31b, the process of evaluating the valuation index by reflecting the degree of difficulty of the target quality when the R & D task is evaluated by using the value index is described. One examines two examples, one based on our existing model and the other based on a competitor's lowest specification.

This can be seen as a consideration of the distribution of the quality of the product. In the present invention, only about 3 competitors were used to input data when applying the quality function development. However, if there is enough information on the quality characteristics of the product, that is, if you have a database of products from various companies related to electric toothbrushes, you can get a distribution of quality.

In this distribution, I can confirm that the target quality is the R & D task with some degree of difficulty, considering where the current location is, where the target is, and where the competitors are located. Therefore, we can estimate the degree of difficulty by using the distribution function of quality characteristics and the distribution function of quality characteristics when these data are fully equipped.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

Claims (7)

delete delete delete In a method for evaluating the quality of R & D,
Wherein the R & D quality verification apparatus comprises: inputting, from a user terminal, a component characteristic and an engineering characteristic of a product to be R & D subject;
Wherein the R & D quality verification device comprises: receiving information on a competitive product in competition with the product from the user terminal;
The R & D quality verification apparatus comprising: a first correlation between each quality characteristic from the user terminal and a second correlation between the quality specification and the part specification;
Wherein the R & D quality verification apparatus calculates the importance (I _i ) of each quality characteristic by using the part specification and the quality characteristic

According to a formula of equation (1);
Wherein the R & D quality verification device includes an improvement contribution degree (B) indicating an improvement degree of the quality of the commodity relative to a highest quality of the competitive commodity, using the importance (I _i ) of the quality characteristic and the information on the commodity _i )

According to a formula of equation (1);
Wherein the R & D quality verification device calculates an improvement contribution to a third party, which indicates a degree of improvement of the quality of the product compared to the lowest quality of the competitive product;
The R & D quality verification apparatus divides the improvement contribution (B _i ) by the cost ratio (target price / current price) indicated by the production unit price of the product, and calculates a value evaluation index (V-Index) Dividing the calculated value evaluation index by the contribution contribution degree to the third party and calculating a final value evaluation index adjusted so that the calculated value evaluation index reflects the degree of difficulty of the R &
If the calculated value evaluation index is the same, the calculated final value evaluation index decreases as the difference between the highest quality and the lowest quality of the competitive product increases,
The step of calculating the contribution contribution to the third party,
Comparing the corresponding lowest quality characteristic of the competitive product with the quality characteristic to calculate a third party relative improvement coefficient indicating a degree of improvement of the quality characteristic with respect to the lowest quality characteristic of the competitive product;
Calculating a value obtained by multiplying a percentage (%) of the importance of the quality characteristic by an improvement coefficient with respect to the third person, with the contribution contribution degree of the quality characteristic to the third person; And
Calculating a contribution value of the quality characteristic to the third party by adding all the improvement contribution levels of the quality characteristic to the third party;
R & D quality assessment method. delete delete A computer program stored in a storage medium coupled to a computer to perform the method of claim 4.

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