KR101717538B1 - Market Prediction Method - Google Patents

Abstract

The present invention relates to a market prediction method, and more particularly, to a method for predicting a market size and a growth possibility of an industry corresponding to an item.
The market forecasting method according to the embodiment of the present invention includes: (a) classifying the types of related industries having high relevance to the items (products) to be market predicted by the market forecast server and deriving growth models for the classified industries To predict the market; .
In addition to this, (b) the correlation between the industrial technology classification code, the standard industrial classification code, the item code, and the IPC code for all industries is scored by referring to the previous research, existing mapping data, Searching and determining related industries having a high degree of association with an item (product) to be market forecasted; In advance,
In the step (a), the type of the determined industry can be classified.

Description

Market Prediction Method

The present invention relates to a market prediction method, and more particularly, to a method for predicting a market size and a growth possibility of an industry corresponding to an item.

A modern economy can be defined as a knowledge-based economy, and a knowledge-based economy means an economy in which intangible assets such as technology, knowledge, and information become the key infrastructure factor of wealth creation and employment.

As the knowledge-based economy becomes more and more widespread, the financial system, which becomes the core of the economy, is required to be transformed from the old paradigm of large corporations and mortgages, to a technology finance system capable of supporting innovative technologies and ideas based on future values.

In order to revitalize technology finance, it is necessary to establish a virtuous cycle ecosystem centered on technology evaluation system that can evaluate technology and ideas properly. Reliable and timely technology and market information for technology evaluation should be established in the basis of this ecosystem.

R & D and commercialization of technological achievements tend to depend on market needs and attributes. Therefore, estimating future market size and growth potential is very important factor in evaluating technology value and technology success.

Especially, as the intensifying competition between companies and accelerating the emergence of alternative and competitive technologies, the technology life cycle is shortened and the need for market trends and prediction is increasing more and more.

Conventionally, the method of predicting market size and growth potential can be divided into qualitative method and quantitative method. Survey method and scenario method such as Delphi expert are mainly used as qualitative methods. However, Costs can be high, and limitations and consistency problems can arise in sample research. There is a limit to quantitatively measuring and forecasting the market.

As a quantitative method, a method of extrapolating past values is mainly used. Practically, a compound annual growth rate (CAGR), which simply extrapolates recent values, is mainly used.

The CAGR has a merit that can easily predict future market growth, but it does not reflect the specificity of the technology and the dynamics of the market, and has a problem that estimation distortion due to recent events may occur.

As a quantitative market forecasting method that extrapolates past values in addition to the above CAGR, a method using a conventional growth model (Logistic, Bass, Gompertz, etc.) is attempted, but a product limited to a specific industry having the same property or reflecting a detailed technical characteristic And it does not reflect the volatility due to changes in the external environment, resulting in poor practical utilization.

Registration No. 10-1265976 (Registration date May 14, 2013) Publication No. 10-2014-0053444 (Published date May 08, 2014)

SUMMARY OF THE INVENTION The present invention has been made in order to solve the problems described above, and it is an object of the present invention to provide a market forecasting method capable of predicting the market size and market growth rate by reflecting the technical characteristics of the product unit and the volatility of the external environment based on the growth model .

Another object of the present invention is to provide a market prediction method that can be used universally and practically throughout the entire industry.

Another object of the present invention is to provide a market forecasting method capable of supporting technical innovation of SMEs and promoting technology commercialization by using market information on market size and growth potential for various purposes such as technology evaluation, .

According to an aspect of the present invention, there is provided a market prediction method,

And the market is predicted by classifying the type of industry in the market prediction server and deriving a growth model for each type of the classified industry.

According to the solution of the above-mentioned problem, the market size and the market growth performance can be predicted by reflecting the technical characteristics of the product unit and the volatility of the external environment based on the growth model.

In addition, market forecasts can be used universally and practically across all industries.

In addition, market information on future market size and growth potential can be used for various purposes such as technology evaluation, technology finance, investment, etc. to support technological innovation of SMEs and promote technology commercialization.

1 is a schematic flowchart of a market prediction method according to the present invention.
FIG. 2 is a flowchart of the inter-code correlation degree scoring step shown in FIG.
Figs. 3A to 3F are exemplary views of the respective steps shown in Fig.
4 is a flowchart of the step of deriving the market attractiveness index shown in FIG.
Figs. 5A to 5E are exemplary diagrams for explaining each step shown in Fig. 4. Fig.
6A and 6B are views for explaining a market prediction model.
7 is a flowchart of the market prediction step shown in FIG.
Figs. 8A and 8B are exemplary diagrams for explaining each step shown in Fig. 7. Fig.
9A to 9C are market size prediction result reports according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

It is to be noted that the same components of the drawings are denoted by the same reference numerals and symbols as possible even if they are shown in different drawings.

In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Also, when a part is referred to as "including " an element, it does not exclude other elements unless specifically stated otherwise.

1 is a schematic flow chart of a market prediction method according to an embodiment of the present invention.

Each step performed below is performed by the market forecast server.

As shown in FIG. 1, the market prediction step S30 is a step of classifying the type of the determined industry, deriving a growth model for each classified type of industry, and predicting the market based on the characteristics of the detailed items of the industry and the external environmental variables .

In addition to this, it is necessary to correlate the relevance of industrial technology classification codes, standard industrial classification codes, item codes, and IPC codes for all industries with reference to previous studies, existing mapping data, and expert evaluations, (S10) for searching and determining an associated business type having a high degree of association with an item (product) to be performed.

In addition, the data of the type of industry determined in the step S10 may be collected, and the step S20 may be performed to derive the market attractiveness index for each type of industry by reflecting the specificity of the industry and the type of industry by analyzing the long-term and short-

Fig. 2 is a flow chart of the inter-code correlation score scoring step shown in Fig. 1, and Figs. 3a to 3f are exemplary diagrams of the respective steps shown in Fig.

First, the industry technical classification code, the standard industry classification code, the item code and the IPC code are linked, but the industry technical classification code and the IPC code are linked with each other by the classification code and the item code of the standard industrial classification, and a spreadsheet program such as Excel is used And stores it as an original sheet (S11).

When a code or a name is input using the code search box (FIG. 3A) or the name search box (FIG. 3B) of the next search sheet, only the necessary data among the data of the original sheet is configured and stored as a first preparation procedure sheet (S12 ).

The necessary data is composed of a standard industrial classification code (subcategory classification), an item code, an industrial technical classification code (up / down), and an IPC code (up / down).

At this time, the subcategories of the standard industrial classification are composed of 6 digits codes, the item codes are composed of 9 digits codes, the industrial technical classification codes are composed of 6 digits, and the IPC codes are composed of 4 digits.

Here, it is classified according to the degree of association between the upper and lower codes.

FIG. 3C illustrates a first preparation procedure sheet of an industrial technical classification when the industrial technical classification code 400301 is retrieved, in which the associated standard industry classification, item code and association degree are outputted up / down, and the IPC code It can be seen that the upper and lower sections are divided and output.

Even in the case of the name search, the first preparation procedure sheet can be obtained in the same manner.

Based on the results obtained in the next first preparation procedure sheet, the score (degree of association) related to the code association is added up and stored in the second preparation procedure sheet (S13).

Here, the score related to the code linkage is a value indicating how much the association between the industrial technology classification code, the standard industry classification code, the item code, and the IPC code is related to the previous research, existing mapping data, and expert evaluation.

In other words, the score related to the code linkage is a weighted value obtained by dividing the degree of association between codes up / down.

FIG. 3D illustrates a second preparation procedure sheet of an industrial technical classification item when the industrial technical classification code 400301 is searched. When the result of adding the scores for each item code, each IPC code, and each standard industry classification code is an item The IPC score, and the standard industry score.

Even in the case of the name search, the second preparation procedure sheet can be obtained in the same manner.

The result obtained in the second preparation procedure sheet is rearranged to ultimately appear on the search sheet and stored as a result sheet (S14).

FIG. 3E shows a result sheet of an industrial technical classification item when the industrial technical classification code 400301 is searched. The result shows a score corresponding to a standard industrial classification code, item code, IPC code, Rank) is given.

Even in the case of the name search, the result sheet can be obtained in the same manner.

Then, the degree of association between the codes is standardized on the basis of 100 points and finally displayed in the search result display field of the search sheet (S15).

FIG. 3F illustrates the result of searching the industrial technology classification code 400301, and it can be seen that the standard industrial classification, the item, the IPC search result, and the code, name, and standardized association degree of each item are shown.

FIG. 4 is a detailed flowchart of the step of deriving the market attractiveness index shown in FIG. 1, and FIGS. 5A to 5E are views for explaining each step shown in FIG.

First, data for each type of business is collected (S21).

At this time, the long-term and short-term related indicators are collected, and the data of the small classification level is collected for the manufacturing industry and the data of the middle classification level for the service industry.

FIG. 5A is an illustration of major macro / market / technology related indicators and research institutes thereof, through which data of respective industries can be collected.

By analyzing the following long-term and short-term factors by industry, we reflect the specificity by industry / sector (S22).

In this case, the main variables for reflecting the specificity of industry / industry are basically manufacturing value, and service industry focuses on sales change.

In addition, employment growth rate, wage change rate, price change rate, and interest rate change rate are the main variables.

The rate of change of each major variable is calculated by the following equation (1).

Where X _{i, t} is the rate of change of the main variable (Y _i ) at time t.

In addition, long-term data are used for annual data and short-term data are used for monthly and quarterly data. Annual data on which monthly and quarterly data are accumulated are reflected in long-term trends. Monthly and quarterly data are reflected in short-term economic fluctuations.

For example, as shown in Figure 5b, the influence of major variables on the overall manufacturing industry is strongly influenced by changes in employment (+), interest rate (-) and price (+) in the long term, Has a limited effect.

In the long term, the interest rate (-) has a strong influence on the overall service industry. In the short term, the change in employment (+) has a strong influence.

In addition, the market attractiveness index is calculated by reflecting short-term and long-term evaluations for each industry considering the variables (S23).

In more detail, the growth rate F is standardized according to the following equation (2) using the rate of change of the variables of Equation (1).

In this case, F _j ^t is the standardized growth forecast for each time (t) of j industry _, and it is compared with the average growth level during past period (T) Is normalized by the standard deviation (? _Xj ) for each variable and is then calculated as the sum of these.

In the case of the standardized growth forecast by the long term viewpoint in the above equation (2), whether or not the standardized growth forecast between the last 1, 3, and 5 years (t = 1, 3, 5) If the value is +, the industry is likely to show a steady increase (growth), which is likely to increase in the future.

In the case of the standardized growth forecast by the short-term viewpoint in the above equation (2), the standardized growth forecast between the last 2, 4 and 6 (t = 2, 4, 6) .

Through the above procedure, the estimates derived for each period are standardized to have values of 0 to 100 using Equation (3).

That is, V _j ^t is a value obtained by normalizing the growth forecast (F _j ^t ) for each time point ^t from 0 to 100.

The long-term and short-term market attractiveness indexes are quantified by Equations (4) and (5) by applying weighted averages of the standardized forecasts to each of the above-mentioned points by applying a high weight to recent estimates.

Ω is a weight for each point of view, and for each of the long-term, long-term, short-term, and long-term markets, 2, 4, It is derived every 6 quarters.

At this point, the closer to 100 the market attractiveness index, the more likely it is to grow.

In order to analyze the contribution of the market attractiveness index to the growth rate by sector, we calculate the relative level of the market attractiveness index relative to the industry (manufacturing or service industry) to which each industry j belongs, ), We will quantify the impact on the future growth of the sector.

For example, if standardized growth forecasts are shown for each code as shown in FIG. 5c, the C101 code has grown by 8.34% over the past 10 years, but has been growing for 5, 3, and 1 years in the past. And the market attractiveness index is low at 17.63.

This low market attractiveness index is expected to reduce the growth rate of the C101 code sector by about 1.88% p.

In addition, the C103 code is expected to grow in the future due to an increase in standardized growth forecasts of 0.24, 0.29, and 0.90 over the past 5, 3, and 1 years, The sector's growth rate is expected to increase by 2.02% p.

If we compare the C103 and C112 codes based on the derived market attractiveness index, we believe that the growth will continue over the long term. However, we expect the C112 code to grow more strongly in the future.

Using the standardized growth forecasts and the market attractiveness index, the short and long-term growth types of each industry (industry) are classified into four types (PP, PN, NP, NN).

First, the PP type shows a high market attractiveness index with a long-term growth trend and a high market attractiveness index due to sufficient demand in the short term.

Although the PN type has a high market attractiveness index due to its long-term growth trend, it seems to have a low market attractiveness index due to lack of short-term demand due to economic factors.

The NP type shows a low market attractiveness index due to a downward trend. However, it can be seen as a market attractive index because it can secure short-term demand due to economic factors.

We believe the NN type has a low market attractiveness index with a long-term downward trend and a low market attractiveness index due to lack of short-term demand.

For example, if the short- and long-term growth prospects and the market attractiveness index are derived as shown in Figure 5d in the basic chemical manufacturing industry, the long-term prospects for the global petrochemical market are reflected in 2011, ), And it has been shifting to positive growth in favor of the recent one year, but it is only 0.16 times the standard deviation of the manufacturing industry.

Also, in the short term, we are confronted with the problem of short-term shortage of demand due to economic factors, the demand shortage is continuing, and the deterioration rate is rapidly progressing.

As a result, the industry is in the PN type, but the long-term market attractiveness index is still maintained. However, the current technology is mature and the supply shortage is expected to continue to decline in the near future.

If the long-term growth prospects and the market attractiveness index are derived as shown in Figure 5e in the light bulb and lighting manufacturing industry, the LED business, which is a representative business segment, will continue to grow (production, sales and employment) As a result, operating profit at leading Korean companies has been deteriorating.

From a short-term point of view, we believe that the short-term outlook is negative due to continued deterioration in operating profit since 2012. In addition,

As a result, it can be concluded that the long-term market attractiveness index is maintained as a PN-type industry in the industry, and now corresponds to the growth period of technology and short-term unstable factors.

If the code or name of the business type and the market attractiveness index are known, it is convenient to check the market attractiveness index in other items using the degree of association in the step S10.

Some of the various market growth or demand forecasting models will be discussed first in order to understand the market forecasting steps described below.

First, the revised exponential curve growth model predicts the market assuming that cumulative sales volume up to the present time (t) follows the expotential curve form Y _t = a [1-exp (-bt)]

Second, the logistic growth model estimates market growth assuming that Y _t = L / [1 + exp (a-bt)], the cumulative function of the normal distribution.

Third, the growth model of Bass is a combination of exponential curve growth model and logistic growth model considering so - called 'innovation effect' and 'imitation effect'.

Fourth, the Gompertz growth model is basically similar to the logistic growth model, but it has an asymmetric S shape instead of symmetrical shape centering on the inflection point.

Fifth, the Weibull growth model shows various probability distributions by generalizing the exponential distribution, and it is often used to predict the life of a product or a part.

The cumulative sales amount Y _t from the logistic growth curve curve shown in FIG. 6A up to the current time instant _t is expressed by Equation (7).

)이다.Where L is the saturation market size (potential market), α _F is the position parameter in the FP (Fisher-Pry), α _P is the position parameter in the PR (Pearl-Reed), β is the shape parameter or growth parameter

)to be.

The logistic function can be variously expressed by Mansfield-Blackman, PR, FP, etc. depending on the function expression method. This is because the convenience of the variable processing is weak and disadvantageous according to each function. In the model of the present invention, Since the positional parameters represent an average, the calculation was performed based on the Mansfield-Blackman function and the FP function.

The cumulative observation value Y _t from the bath growth model curve shown in FIG. 6B to the immediately preceding present time (t) is expressed by Equation (8).

Where S (t) is the observed value at the present time, L is the saturation market size, p is the innovation factor (initial purchase probability), and q is the imitation coefficient.

The innovation coefficient (p) is an external influence coefficient, which is an independent influence factor for users of existing products. The imitation coefficient (q) is an internal influence coefficient, which is a coefficient indicating the influence of existing product users on the next adopter.

FIG. 7 is a flowchart of the market prediction step shown in FIG. 1, and FIGS. 8A and 8B are views for explaining each step shown in FIG.

First, the item-specific specialist receives the related information, that is, the item classification, the total market period T and the current time t (S31).

Next, the entire growth model model of the corresponding industry is typed (S32).

For this purpose, the data for the sub-category or small-category industry corresponding to the industry is collected.

That is, the data for the sub-category or sub-category is collected using the GDP data of the Bank of Korea and the output or sales data of the National Statistical Office shown in FIG. 8A.

The typification is done through the following four steps.

In the first step, the data of all industrial categories collected in the above are standardized based on the initial year value.

In the second stage, graphs of the annual growth rate of each industry are sketched.

In the third step, the standard value of the sub-category is divided by the standard value of the corresponding upper category, and the relative size of each category is calculated.

In the fourth step, the type is classified again at each industrial classification level according to the given type classification standard.

At this time, sequential type classification is performed with the classification level, middle classification level, and small classification level to derive the market size growth type of each industrial classification.

The type classification at the above-mentioned classification level provides a macroscopic level based on the growth of the whole country, and a more detailed type can be derived when the classification is performed up to the level of the small classification.

In order to reflect the characteristics of the market, we derive the forecast curve of the market by taking into consideration the macroeconomic and industrial economic trends and the recent related industry trends in total (S33).

In other words, the market size is predicted by combining the long-term time series data trend and the recent time series data trend and applying it to the above-mentioned logistic or bath demand forecasting model to derive the forecast (growth) curve of the market.

At this time, it reflects the expert's score on the detailed item evaluation index and reflects the external environment variable.

FIG. 8B is a diagram illustrating the future prediction curve of the chemical and chemical product manufacturing (excluding medicines) derived through the above steps. The market cycle T is 30 years and the present time t is 10 years to be.

9A to 9C are market size prediction result reports according to an embodiment of the present invention.

Figure 9a is a report of the market size forecast for cheese in the standard industry classification C10501107, item name is liquid liquid milk and other dairy products.

FIG. 9B is a report of the market size prediction result for the item category code C15211102 and the item name for women's shoes (including shoe) in the standard industrial classification.

FIG. 9C shows a standard industry classification code of J58222, and a standard industry classification name is a market size prediction result report for application software development and supply.

Although the preferred embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.

In addition, it is a matter of course that various modifications and variations are possible without departing from the scope of the technical idea of the present invention by anyone having ordinary skill in the art.

S10: Inter-code relevance scoring step
S20: Step to derive market attractiveness index
S30: Market Forecast Phase

Claims (15)

(a) classifying the types of industries corresponding to the products (products) to be market predicted by the market forecast server and deriving the growth models for the classified industry types to predict the market; , ≪ / RTI &
The step (a) includes the steps of (a-1) inputting an item classification, a total market period T and a current time t for each type of business,
(A-2) collecting a middle class or small class industry data corresponding to the business type and classifying the whole growth model model of the business type,
(A-3) of applying the growth model model to the demand forecast model to derive a forecast (growth) curve of the market and forecasting the market size. delete The method according to claim 1,
In the step (a-2), the data for each industry classification is standardized based on the initial year value, the relative value graph for each industry classification is drawn to grasp the market size growth characteristic of the industry, The method according to claim 1, wherein the method further comprises the steps of: calculating a relative size of each business type by a standard value of the upper classification; and classifying the type at each industry classification level according to a given classification standard. The method according to claim 1,
Wherein the expert evaluation score and the external environment variable of the detailed item evaluation index are reflected in the step (a-3). (b) Scoring the correlation between the industry technical classification code, the standard industrial classification code, the item code and the IPC code for the entire industry with reference to the previous research, the existing mapping data, and the expert evaluation in the market prediction server, Searching for an industry corresponding to an item (product) to be market predicted using the diagram;
(a) classifying a type of business type corresponding to an item (product) to be market predicted in the step (b) in the market forecast server and deriving a growth model for each classified business type to predict a market; A market forecast method comprising: 6. The method of claim 5,
In the step (b), the industrial technology classification code, the standard industry classification code, the item code, and the IPC code are linked, but the spreadsheet program is linked by linking the industrial technical classification code and IPC code for each of the sub- (B-1) storing the original sheet by using the received data,
(B-2) storing only the required data corresponding to the inputted code or name in the data of the original sheet when the code or the name is inputted and storing the data in the first preparation procedure sheet,
(B-3) accumulating scores (degree of association) related to code linkage on the basis of the results obtained in the first preparation procedure sheet and storing them in a second preparation procedure sheet,
(B-4) organizing the results obtained in the second preparation procedure sheet and storing the results as a result sheet,
The degree of association between the codes is standardized on the basis of 100 points and the step (b-5) shown in the search result display column of the search sheet is performed,
And searching for a business type of the item to determine the market. The method according to claim 6,
The necessary data of the step (b-2) includes six digits of a standard industrial classification code (subcategory), nine digits of an item code, six digits of an industrial technical classification code (upper / 4 digits,
(Upper / lower), IPC description (upper / lower) when the name search is performed.
Here, it is classified according to the degree of association between the upper and lower codes. 6. The method of claim 5,
(c) collecting data on an industry determined by the market forecast server, and deriving a market attractiveness index reflecting long-term and short-term factor analysis by sector type specificity; To
Wherein the step of performing the market prediction further comprises: 9. The method of claim 8,
The step (c) includes: (c-1) collecting data including long-term and short-term related indices of each industry type;
(C-2) reflecting the specific characteristics of each industry / sector by analyzing the major variables of the industry by long-term and short-term factor analysis,
And (c-3) calculating a market attractiveness index by reflecting the long-term short-term evaluation by industry. 10. The method of claim 9,
The method of predicting the market according to the above (c-2), wherein the main variables of the manufacturing industry are based on the production amount and the main variables of the service industry are based on sales, wherein the employment increase rate, the wage change rate, the price change rate and the rate change rate are added. 10. The method of claim 9,
In step (c-3), standardized growth estimates for each viewpoint are calculated using the rate of change of the major variables,
The growth forecast

Standardize from 0 to 100 using the formula,
Using the standardized growth estimates,

We quantify long-term market attractiveness index by formula,

A market forecasting method characterized by quantifying short term market attractiveness index by formula.
Where F is the standardized growth forecast for each time point, V is the standardized value of F as an index of 0 to 100, ω is the time weighted value for each long term, and 1, 3, , 4th and 6th quarters. delete 12. The method of claim 11,

Through the formulas, we derive the relative level of the market attractiveness index relative to the industry (manufacturing or service industry) to which each industry (j) belongs and evaluate it based on the growth rate of the corresponding industry (manufacturing or service industry) Quantifying the market forecast. 12. The method of claim 11,
After the step (c-3), the short-term growth type of each industry (industry) is classified into four types using the standardized growth forecast and the market attractiveness index to perform comprehensive evaluation (c-4) The market forecast method comprising the steps of: The method according to claim 1,
In the step (a-2), the data for each industry classification is standardized based on the initial year value, the relative value graph for each industry classification is drawn to grasp the market size growth characteristic of the industry, It is divided into standard values of upper classification, and relative size is calculated with respect to the upper classification of each industry. Classification is again performed at each industrial classification level according to the given type classification standard,
Wherein the expert evaluation score and the external environment variable of the detailed item evaluation index are reflected in the step (a-3).

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