The essence and purpose of the method of relative differences. Scope of its application. Algorithm for calculating the influence of factors in this way
The method of relative differences, like the previous one, is used to measure the influence of factors on the growth of a performance indicator only in multiplicative models and combined types Y = (a - b)c. It is much simpler than chain substitutions, which makes it very effective under certain circumstances. This primarily applies to those cases when the source data contains previously determined relative deviations of factor indicators in percentages or coefficients.
Let us consider the methodology for calculating the influence of factors in this way for multiplicative models of type.
;
;
.
The deviation of the effective indicator due to each factor is determined as follows.
To calculate the influence of the first factor, it is necessary to multiply the basic (planned) value of the effective indicator by the relative increase of the first factor, expressed as a percentage, and divide the result by 100:
.
To calculate the influence of the second factor, you need to add the change in it due to the first factor to the planned value of the effective indicator and then multiply the resulting amount by the relative increase in the second factor as a percentage and divide the result by 100:
.
The influence of the third factor is determined in a similar way: to the planned value of the effective indicator, it is necessary to add its increase due to the first and second factors and multiply the resulting amount by the relative increase of the third factor, etc.:
.
Let us consolidate the considered methodology using the example given in Table 7.1:
As you can see, the calculation results are the same as when using the previous methods
The method of relative differences is convenient to use in cases where it is necessary to calculate the influence of a large set of factors (8-10 or more). Unlike previous methods, the number of calculations is significantly reduced.
Methods of proportional division and equity participation.
Essence, purpose and scope of application of methods of proportional division and equity participation, procedure and calculation algorithms
In a number of cases, to determine the magnitude of the influence of factors on the growth of a performance indicator, it can be used proportional division method
. This applies to cases where we are dealing with additive Y type models = 
And mixed type 
.
IN first In the case when we have a single-level model of type Y=a+b+c, the calculation is carried out as follows:
;
;
.
For example, the level of profitability (R) decreased by 8% due to an increase in the enterprise’s capital by 200 thousand rubles. At the same time, the value of fixed capital (a) increased by 250 thousand rubles, and working capital (b) decreased by 50 thousand rubles. This means that, due to the first factor, the level of profitability decreased, and due to the second, it increased:
Calculation method for mixed models somewhat more complicated. The relationship of factors in the combined model is shown in Fig. 7.1.
Performance indicator
First level factors
Second level factors
Fig 7.1 Scheme of interaction of factors
When known 
and 
, then to determine 
,
,
, you can use the method of proportional division" which is based on the proportional distribution of the increase in the effective indicator Y due to a change in factor B between the second level factors D, N and M according to their value. The proportionality of this distribution is achieved by determining a constant for all factors proportionality coefficient (K
)
which shows the amount of change in the effective indicator Y due to a change in factor B by one.
The value of the proportionality coefficient (K 
) is defined as follows:
.
By multiplying this coefficient by the absolute deviation B due to the corresponding factor, we find the deviations of the effective indicator:
;
;
.
For example, the cost of 1 t/km due to a decrease in the average annual production of a car (C 
) increased by 180 rubles. At the same time, it is known that the average annual production of a vehicle (GV) has decreased due to:
A) above-planned machine downtime -5000 t/km;
B) above-plan idle runs -4000 t/km;
B) incomplete use of carrying capacity -3000 t/km
Total -12000 t/km
From here you can determine the change in cost under the influence of second-level factors:
Total:+180rub
To solve this type of problem you can also use equity participation method (Table 7.3) .
Table.7.3
Calculation of the influence of factors on the performance indicator using the equity method
WITHAt the beginning, the share of each factor in the total amount of their increases is determined, which is then multiplied by the total increase in the effective indicator:
;
;
.
5.2.4 Method of relative differences
The method of relative differences, like the previous one, is used to measure the influence of factors on the growth of an effective indicator only in multiplicative models and combined ones of the type Y = (a - b) c. It is much simpler than chain substitutions, which makes it very effective under certain circumstances. This primarily applies to those cases when the source data contains previously determined relative deviations of factor indicators in percentages or coefficients.
Let us consider the methodology for calculating the influence of factors in this way for multiplicative models of the type Y = A * B * C. First, it is necessary to calculate the relative deviations of factor indicators:
Then the deviation of the effective indicator due to each factor is determined as follows:
According to this rule, to calculate the influence of the first factor, it is necessary to multiply the basic (planned) value of the effective indicator by the relative increase in the first factor, expressed as a percentage, and divide the result by 100.
To calculate the influence of the second factor, you need to add the change in it due to the first factor to the planned value of the effective indicator and then multiply the resulting amount by the relative increase in the second factor as a percentage and divide the result by 100.
The influence of the third factor is determined in a similar way: to the planned value of the effective indicator it is necessary to add its increase due to the first and second factors and multiply the resulting amount by the relative increase of the third factor, etc.
Let us consolidate the considered methodology using the example given in Table 15:
As you can see, the calculation results are the same as when using the previous methods.
The method of relative differences is convenient to use in cases where it is necessary to calculate the influence of a large set of factors (8-10 or more). Unlike previous methods, the number of calculations is significantly reduced.
5.2.5 Method of proportional division and equity participation.
In some cases, to determine the magnitude of the influence of factors on the growth of a performance indicator, the method of proportional division can be used. This applies to those cases when we are dealing with additive models of the type Y = ∑Х i and mixed ones of the type
In the first case, when we have a single-level model of the type Y = a + b + c, the calculation is carried out as follows:
For example, the level of profitability decreased by 8% due to an increase in the enterprise’s capital by 200 million tenge. At the same time, the value of fixed capital increased by 250 million tenge, and working capital decreased by 50 million tenge. This means that, due to the first factor, the level of profitability decreased, and due to the second, it increased:
The calculation method for mixed models is somewhat more complicated.
When ∆Вd are known; ∆Bn and ∆Bm as well as ∆Yb then to determine ∆Yd, ∆Yn, ∆Ym you can use the method of proportional division, which is based on the proportional distribution of the increase in the effective indicator Y due to a change in factor B between the second level factors D, N and M, respectively their size. The proportionality of this distribution is achieved by determining a constant coefficient for all factors, which shows the amount of change in the effective indicator Y due to a change in factor B by one.
The value of the coefficient (K) is determined as follows:
By multiplying this coefficient by the absolute deviation B due to the corresponding factor, we find the deviations of the effective indicator:
∆Yb=К*∆Bd; ∆Yn=К*∆Bn; ∆Ym=К*∆Bm
For example, the cost of 1 t/km increased by 180 rubles due to a decrease in the average annual production of a car. It is known that the average annual production of a car has decreased due to:
a) extra-planned machine downtime - 5000 t/km
b) above-plan idle runs - 4000 t/km
c) incomplete use of carrying capacity - 3000 t/km
Total - 12000 t/km
From here you can determine the change in cost under the influence of second-level factors:
Table 18 - Calculation of the influence of factors on the performance indicator using the equity method
To solve this type of problem, you can also use the equity method. To do this, the share of each factor in the total amount of their increases is first determined, which is then multiplied by the total increase in the effective indicator:
There are many similar examples of the application of this method in ACD, as you can see in the process of studying the industry course in the analysis of economic activity at enterprises.
5.2.6 Method of logarithm in the analysis of economic activity.
The logarithm method is used to measure the influence of factors in multiplicative models. In this case, the calculation result, as with integration, does not depend on the location of factors in the model and, compared to the integral method, higher calculation accuracy is ensured. If, during integration, the additional increase from the interaction of factors is distributed equally between them, then using logarithm, the result of the joint action of the factors is distributed in proportion to the share of the isolated influence of each factor on the level of the performance indicator. This is its advantage, and the disadvantage is the limited scope of its application.
In contrast to the integral method, when taking logarithms, not absolute increases in indicators are used, but growth (decrease) indices.
Mathematically, this method is described as follows: Let us assume that the effective indicator can be represented as a product of three factors: F = xyz. Taking logarithms of both sides of the equality, we get
Considering that the same relationship between the indices of changes in indicators remains as between the indicators themselves, we will replace their absolute values with indices:
It follows from the formulas that the total increase in the effective indicator is distributed among the factors in proportion to the ratio of the logarithms of the factor indices to the logarithm of the effective indicator. And it does not matter which logarithm is used - natural or decimal.
By comparing the obtained results of calculating the influence of factors using different methods using this factor model, one can be convinced of the advantage of the logarithm method. This is reflected in the relative simplicity of calculations and increased accuracy of calculations.
Having considered the main techniques of deterministic factor analysis and the scope of their application, the results can be systematized in the form of the following matrix:
Table 19 - Deterministic factor techniques and models
|
| Multiplicative | Additive | Multiples | Mixed |
| Chain substitution | + | + | + | + |
| Index | + | - | + | - |
| Absolute differences | + | - | - | Y=a (b-c) |
| Relative differences | + | - | - | - |
| Proportional division (equity participation) | - | + | - | Y=a/Sxi |
| Integral | + | - | + | Y= a/Sxi |
| Logarithms | + | - | - | - |
ModelsBibliography
1. Bakanov M.I., Sheremet A.D., Theory of economic analysis. - M.: Finance and Statistics, 2000.
2. Savitskaya G.V. Analysis of the economic activities of an enterprise: Textbook. - Mn.: IP "Ecoperspective", 2000. - 498 p.
3. Methodology of economic analysis of an industrial enterprise (association) / Ed. A.I. Buzhinsky, A.D. Sheremet. - M.: Finance and Statistics, 1988
4. Muravyova A.I. Theory of economic analysis. - M.: Finance and Statistics, 1988.
The result of deterministic factor analysis is the decomposition of the increase in the effective indicator, due to the general influence or change in factor characteristics, into the sum of partial increases in the effective indicator, which are due to a change in only one factor. For this purpose, in economic analysis, in addition to index analysis, specially developed methods are used, which are sometimes called techniques. The main ones are the method of differences and the method of identifying the isolated influence of factors. In turn, the method of differences includes the techniques of chain substitutions, absolute (arithmetic) differences and relative (percentage) differences.
The method of chain substitutions is rightfully considered the main method of elimination. It is used in the study of functional dependencies and is intended to measure the impact of changes in factor characteristics on changes in the effective indicator while keeping the other values constant (fixed).
To do this, the basic values of each factor (planned, last period) are successively replaced with its actual data (reported). The obtained results of sequential replacement of each factor-indicator are compared. The difference between each subsequent and previous indicator characterizes the influence of the factor, provided that the influence of all other factors is eliminated.
Based on the above, the method of chain substitutions is often called the method of sequential, gradual isolation of factors.
When using the technique of chain substitutions, you should adhere to a clear order of replacing factors:
First of all, volumetric (quantitative) indicators are replaced;
Secondly - structural;
Thirdly, quality.
In cases where there are several quantitative or qualitative indicators in the analytical model, a priority is established among them - first, the main, primary (general) indicators are replaced, and then the secondary, derivative (partial) indicators are replaced (Fig. 11.2).
Rice. 11.2. The order of replacing indicators when using the technique of chain substitutions
Let's consider the general scheme for using chain substitutions using the example of a multiplicative multiplicative model:
where T is the effective indicator;
a, b, c, d - factor indicators, with a being a qualitative indicator; c - structural indicator; c, d - volumetric (quantitative) indicators and indicator d is primary relative to indicator c.
Let's compare the actual values of the indicators (index "1") with the planned ones (index "0"). The total deviation of the T indicator from the plan will be:
.
To carry out further calculations, we will rebuild our analytical model in the order necessary to replace the indicators. Then:
;. 
Let us determine the variation of the effective indicator due to changes in all factors and each individually:
General impact of factors;
Influence of factor d;
Influence of factor c;
Influence of factor b;
The influence of factor a;
Thus:
Example. Based on the data given in the table, calculate the influence of factors on the deviation in the cost of production in the reporting year compared to the previous one (Table 11.5).
1. Let us determine the general change in output:
(thousand UAH).
2. Let us calculate the influence of individual factors as a change in output:
a) the impact of changes in the number of workers on changes in output:
b) the impact of a change in the number of days worked by one worker on the change in output:
c) the impact of changes in the average shift duration on the dynamics of product output:
d) the impact of changes in labor productivity on changes in output:
Deviation balance:
Thus, in the reporting year compared to the previous year, product output increased by UAH 429.3 thousand. This was influenced by the following factors: changes in the number of workers, the number of days worked, the length of the work shift and average hourly output (labor productivity).
Thus, thanks to the increase in the number of workers, production output increased by 269.5 thousand UAH. Due to the reduction in the number of days worked, production output decreased by 64.68 thousand UAH. The increase in shift duration led to an increase in product output by 34.16 thousand UAH, and an increase in labor productivity - by 190.32 thousand UAH.
The reception of absolute (arithmetic) differences and the reception of relative differences is a modification of the reception of chain substitutions. It can be used to determine the influence of factor indicators on the results in multiplicative and mixed models. It is better to use the method of absolute differences when the source data already contains absolute deviations in factor indicators. However, this method is not practical for multiple models.
Let us consider the algorithm for calculating the influence of factors using the method of absolute differences using the example of the multiplicative multiplicative model, which was used above in the method of chain substitutions:
There are absolute deviations of the actual values of each factor indicator from the basic ones:
;
;
;
.
As a result:
Based on the data in the above example (Table 11.5), we determine the influence of factors on changes in product output using absolute differences.
1. General change in output:
(thousand UAH).
2. The influence of changes in individual factors on the dynamics of product output, namely:
a) number of employees:
(thousand UAH);
b) number of days worked by one worker:
(thousand UAH);
c) average shift duration:
(thousand UAH);
d) labor productivity:
(thousand UAH).
Deviation balance:
The example shows that the method of absolute differences gives the same results of the influence of factors as the method of chain substitutions.
The method of relative (percentage) differences is a variation of the method of chain substitutions, which is used in multiplicative models when the source data is presented in relative values. Determining the influence of factors using relative differences involves performing the following sequential actions:
To determine the influence of the first factor, the basic value of the effective indicator should be multiplied by the relative deviation (growth rate) of the first indicator, taken as a percentage, and divided by 100;
To calculate the influence of the second and subsequent factors, it is necessary to multiply the sum of the basic value of the effective indicator and the magnitude of the influence of previous factors by the relative deviation of the factor-indicator in question, expressed as a percentage, and divide by 100.
For example,. Then:
Deviation balance:
Based on the above example, we will determine the influence of factors on changes in product output using relative differences, first calculating the percentage deviation (growth rate) of the reporting year’s indicators from the previous year (column 5 of Table 11.5):
1. General change in output.
(thousand UAH).
2. Change in production output due to changes in the number of employees:
(thousand UAH).
3. Change in product output due to a change in the number of days worked:
(thousand UAH).
4. Change in product output under the influence of shift duration dynamics:
5. The influence of average hourly output on product output:
Deviation balance:
As you can see, we obtained the same results using the techniques of chain substitutions and relative differences.
It should be noted that it is advisable to use the method of relative differences when the initial data for analysis are presented in the form of relative values (for example, the percentage of plan completion).
Thus, the method of differences can be used when studying deviations of actual values of economic indicators from planned ones, as well as when studying the dynamics of indicators. Its advantage is its simplicity and versatility of use.
However, this method also has certain disadvantages. Thus, the result of decomposition of the influence of factors on an effective indicator depends on compliance with the order (sequence) of their replacement. In addition, this method is not additive in time, that is, the results of the work done, for example, for a year of analysis do not coincide with the corresponding data obtained by month or quarter.
The essence of factor analysis in economics
Definition 1
Factor analysis is a type of economic analysis that studies the influence of specific factors on economic indicators. Main types of factor analysis: deterministic and stochastic analysis.
The basis of deterministic analysis is the methodology for studying the influence of those factors that have a functional relationship with the general indicator.
In stochastic factor analysis, the influence of those factors that have a probabilistic relationship with the general indicator is studied, i.e. correlation.
The efficiency of an enterprise is influenced by many factors. They can be classified into internal, which depend on the activities of a given company, and external, which do not depend on a given enterprise.
The methods used in factor analysis can also vary. Deterministic factor analysis uses:
- Chain substitution method;
- Method of absolute and relative differences;
- Index method;
- Balance method;
- Integral method;
- Logarithmic method, etc.
Stochastic analysis uses:
- Correlation method;
- Regression method;
- Cluster analysis method;
- Dispersion method, etc.
The greatest completeness and depth of analytical research, the greatest accuracy of results is ensured through the use of economic and mathematical methods. These methods have a great advantage over statistical and traditional methods, since they allow a more accurate and detailed calculation of the influence of individual factors on the value of economic indicators, and they also help solve some analytical problems.
Relative difference method
Note 1
The method of relative differences is used in deterministic factor analysis to assess the influence of a specific factor on the growth of performance indicators. The most important advantage of the method under consideration is its simplicity. However, it can only be used in multiplicative and multiplicative-additive factor models.
The basis of this method is the elimination method. Elimination means eliminating the impact of other factors, i.e. all other factors become static. The main idea of the method is the independent change of all factors. First, the base value of one factor changes to the reporting value, while the other factors are static, and then the second, third, etc. change.
To calculate the magnitude of the impact of the first factor on the effective one, you should multiply the basic value of the effective indicator by the relative increase in the first factor in % and divide by 100. To calculate the degree of influence of the second factor, you need to add the basic value of the effective indicator and its increase from the first factor, and the resulting multiply the amount by the relative increase in the next factor, etc.
When using this method, the order of factors in the model and, consequently, the sequence of changes in their values is of great importance, since this determines the quantitative assessment of the influence of each individual factor.
Using the method of relative differences involves the use of a correctly constructed deterministic factor model and adherence to a certain order in the arrangement of factors.
Factors can be both quantitative and qualitative. Qualitative factors reflect the internal properties, signs and characteristics of the objects under study. For example, labor productivity, milk fat content, product quality. Quantitative factors characterize the quantitative certainty of a phenomenon. Quantitative factors have both cost and physical expression. Quantitative factors can characterize the volumes of production and sales of goods, and the value of such factors can be expressed both in money and in pieces, etc.
If during the analysis there are several quantitative and qualitative indicators, then first of all the magnitude of factors that are at the first level of subordination changes, and then at a lower one.
Factors of the first level are factors that directly influence the performance indicator, and factors that indirectly affect the performance indicator belong to a lower level (second, third, etc.)
The calculation algorithm using the relative difference method is presented in Figure 1.
The sum of the quantities $∆X_A$, $∆X_B$ must be identical to the difference between $X_1$ and $X_0$.
Example of using the relative difference method
Let's consider the use of the relative difference method using a specific example. The volume of production for the year depends on the average annual number of workers (N) and the average annual output per worker (B). A two-factor multiplicative model is built, in which the number of workers is a quantitative factor, so it is in first place, and production is a qualitative factor, and is located behind the quantitative one.
$OP = H V$
All data that will be used is presented in the table (Figure 2).
At the first step, the relative increase in factors is calculated (Figure 3).
Figure 3. Calculation of the relative increase in factors. Author24 - online exchange of student works
At the second step, the degree of influence of the first factor on the performance indicator is determined (Fig. 4)
Figure 4. Calculation of the degree of influence of a factor. Author24 - online exchange of student works
From the data obtained it follows that with an increase in the average annual number of employees by 2 people, production volume will increase by 400 thousand rubles.
At the third step, the sequential consideration of the model factors continues (Fig. 5)
According to the data obtained, we can conclude that by increasing the average annual output of one worker, the volume of production increased by 810 thousand rubles.
At the fourth step, the calculations are checked (Figure 6).
Thus, the calculations performed are correct.
Also applicable to multiplicative models and mixed models of the same type as the absolute difference method.
The method of relative differences is used in cases where the source data already contains previously determined relative deviations of factor indicators in percentages or in coefficients.
According to this rule, to calculate the influence of the first factor, it is necessary to multiply the basic effective indicator by the relative increase in this factor in the form of a decimal fraction.
The influence of the second factor is determined by adding to the base value of the effective indicator the magnitude of its change due to the first factor and multiplying the resulting amount by the relative increase in the second factor.
Example
The total change in the effective indicator consists of the sum of changes in the effective indicator due to changes in each factor, with other factors fixed.
As a result of using this method, an indecomposable residue can be formed, which is added to the magnitude of the influence of the last factor.
Index method
Based on the construction of factor (aggregated) indices.
Using indexes in analysis, the following tasks are solved:
1) Assessment of changes in the level of the phenomenon
2) Identification of the influence of individual factors on changes in the resulting characteristic
3) Assessing the influence of the structure of the population on the dynamics of the phenomenon
Economic analysis uses simple and analytical indices.
The index simply represents the ratio of the level of the attribute in the reporting period compared to the base one.
Indicated by a small letter i if they talk about prices
An analytical index always consists of two elements: the indexed feature (the dynamics of which is being studied) and the weight element, which serves as a co-measurer.
Using analytical indices, the dynamics of a complex economic phenomenon, the individual elements of which are not comparable, are studied.
Indicated by a capital letter I
The central problem of analytical indices is the problem of weighting. It is important to first determine the weight attribute, and then select the level at which the weight attribute is taken.
The first problem is solved by finding a system of related features, the product of which gives an economically understandable indicator.
For qualitative indicators, it takes quantitative weight and vice versa.
A sign directly related to the phenomenon being studied and characterizing it is called primary or quantitative. Primary signs can be summarized. Features that relate to the phenomenon under study not directly, but through one or more other features and characterize the qualitative side of the phenomenon being studied are called secondary or quality. They are always relative indicators and, as a rule, cannot be directly summed up.
There is the following rule for choosing a weight attribute when constructing analytical indices:
When constructing analytical indices based on primary characteristics, it is recommended to take weight at the level of the base period, and for secondary characteristics at the level of the reporting period.
It is advisable to use the index method when each factor is a complex indicator.
Improving the method of differences in modern analysis. Logarithmic and integral methods
Correlation analysis
Correlation analysis – is a method of establishing a connection and measuring its closeness between observations that can be considered random and selected from a population distributed according to a multivariate normal law.
A correlation relationship is a statistical relationship in which different values of one variable correspond to different average values of another.
Distinguish steam room And multiple correlation. In pairwise correlation, a connection occurs between two indicators, one of which is a factor and the other a result.
Multiple correlation occurs when several factors influence an effective indicator.
The closeness of the connection in statistics can be determined using various coefficients. In economic analysis, a linear correlation coefficient is more often used. The values change [-1;1]. A value of -1 indicates the presence of a strictly determined inversely proportional relationship between factors. A value of 1 indicates a strictly determined directly proportional relationship. When the correlation coefficient is 0, there is no connection between the factors. For other values of the correlation coefficient, there is a stochastic relationship. The closer the value r
to unity, the stronger the connection.
|r|<3 – слабая связь
3<|r|<7 – средняя теснота
|r|>7 – close connection
Carrying out correlation analysis includes the following steps:
1) Collection of information and its primary processing
At this stage, grouping, exclusion of anomalous observations, and checking the normality of the univariate distribution are carried out.
2) Preliminary characterization of relationships. Construction of analytical groupings and graphs
3) Elimination of multicollinearity and refinement of the set of indicators by calculating pairwise correlation coefficients.
4) Study of factor dependence and verification of its significance.
5) Evaluating the results of the analysis and preparing recommendations for their practical use.
Regression analysis
This is a method for establishing an analytical expression for the stochastic dependence between the characteristics under study.
The regression equation shows how on average Y changes when any of their X changes
If there is only one independent variable X, we have a simple regression analysis. If there are 2 or more independent variables, then this is a multivariate analysis.
During regression analysis, 2 main tasks are solved:
1) Construction of a regression equation (finding the type of relationship between the performance indicator and independent factors).
2) Assessing the significance of the resulting equation, i.e. determining how much selected factor characteristics explain the variation in trait Y.
Regression analysis, unlike correlation analysis, provides a formalized expression of the relationship, and does not simply determine the presence of correlation.
Correlation analysis studies any relationship between factors, while regression analysis studies only one-sided dependence, i.e. such a connection that shows how a change in factor characteristics affects the effective characteristic.
Regression analysis uses only linear models.
To find the parameters of the equation, the least squares method is most often used.
Analysis of variance
A method that allows you to confirm or refute the hypothesis that 2 data samples belong to the same population.
In relation to the analysis of the activities of an enterprise, analysis of variance makes it possible to determine whether groups of different observations belong to the same set of data or not. (are the differences between groups significant)
Analysis of variance is often used in conjunction with grouping methods and its task in this case is to assess the significance of differences between groups. To do this, group variances are determined, and then the significance of differences between groups is checked using the Student-Fisher statistical tests.
Cluster analysis
One of the methods of multivariate analysis intended for grouping (clustering) a population whose elements are characterized by many characteristics. The value of each feature serves as the coordinates of each unit of the population under study in the multidimensional space of features.
Each observation, characterized by the values of several indicators, can be represented as a point in the space of these indicators, the values of which are considered as coordinates in a multidimensional space.
The differences between clusters should be more significant than between observations assigned to the same cluster.
HEURISTIC METHODS IN ECONOMICS
They have become widespread in the study of commercial activities due to the high degree of uncertainty of the driving factors of activity.
These include search and evaluation methods that allow you to obtain a solution to a creative problem in conditions of incompleteness or unreliability of the source data.
Heuristic methods can be divided into 2 classes: search and evaluation