All economic processes of enterprises are interconnected and interdependent. Some of them are directly related to each other, some appear indirectly. Thus, an important issue in economic analysis is the assessment of the influence of a factor on a particular economic indicator, and for this purpose factor analysis is used.

Factor analysis of the enterprise. Definition. Goals. Kinds

Factor analysis refers in the scientific literature to the section of multivariate statistical analysis, where the assessment of observed variables is carried out using covariance or correlation matrices.

Factor analysis was first used in psychometrics and is currently used in almost all sciences, from psychology to neurophysiology and political science. The basic concepts of factor analysis were defined by the English psychologist Galton and then developed by Spearman, Thurstone, and Cattell.

You can select 2 goals of factor analysis:
— determination of the relationship between variables (classification).
— reducing the number of variables (clustering).

Factor analysis of the enterprise– a comprehensive methodology for systematically studying and assessing the impact of factors on the value of the performance indicator.

The following can be distinguished types of factor analysis:

1. Functional, where the effective indicator is defined as a product or an algebraic sum of factors.
2. Correlation (stochastic) – the relationship between the performance indicator and the factors is probabilistic.
3. Direct / Reverse – from general to specific and vice versa.
4. Single-stage/multi-stage.
5. Retrospective/prospective.

Let's look at the first two in more detail.

In order to be able to carry out factor analysis is necessary:
— All factors must be quantitative.
— The number of factors is 2 times greater than the performance indicators.
— Homogeneous sample.
— Normal distribution of factors.

Factor analysis carried out in several stages:
Stage 1. Factors are selected.
Stage 2. Factors are classified and systematized.
Stage 3. The relationship between the performance indicator and the factors is modeled.
Stage 4. Assessing the influence of each factor on the performance indicator.
Stage 5. Practical use of the model.

Methods of deterministic factor analysis and methods of stochastic factor analysis are distinguished.

Deterministic factor analysis– a study in which factors influence the performance indicator functionally. Methods of deterministic factor analysis - the method of absolute differences, the method of logarithm, the method of relative differences. This type of analysis is the most common due to its ease of use and allows you to understand the factors that need to be changed to increase/decrease the performance indicator.

Stochastic factor analysis– a study in which factors influence the performance indicator probabilistically, i.e. when a factor changes, there may be several values ​​(or a range) of the resulting indicator. Methods of stochastic factor analysis - game theory, mathematical programming, multiple correlation analysis, matrix models.

Factor analysis is understood as a method of complex and systematic study and measurement of factors for the value of effective indicators.

The following types of factor analysis are distinguished: deterministic (functional)

stochastic (probabilistic)

Deterministic factor analysis – this is a technique for assessing the influence of factors whose connection with the performance indicator is functional in nature, i.e. the effective indicator can be presented as a product, quotient or algebraic sum of factors.

Methods of deterministic factor analysis:

chain substitution method

index

integral

absolute differences

relative differences, etc.

Stochastic Analysis – a methodology for studying factors whose connection with an effective indicator, unlike a functional one, is incomplete, probabilistic.

Methods of stochastic factor analysis:

correlation analysis

regression analysis

dispersive

component

modern multivariate factor analysis

discriminant

Basic methods of deterministic factor analysis

THE CHAIN ​​SUBSTITUTION METHOD is the most universal; it is used to calculate the influence of factors in all types of factor models: addition, multiplication, division and mixed.

This method allows you to determine the influence of individual factors on changes in the value of the performance indicator by replacing the base value of each factor indicator with the actual value in the reporting period. For this purpose, a number of conditional values ​​of the performance indicator are determined, which take into account the change in one, then two, three, etc. factors, assuming that the rest do not change.

Comparing the value of an effective indicator before and after changing the level of one or another factor allows us to exclude the influence of all factors except one and determine its impact on the increase in the effective indicator.

The algebraic sum of the influence of factors must necessarily be equal to the total increase in the effective indicator. The absence of such equality indicates mistakes have been made.

INDEX METHOD is based on relative indicators of dynamics, spatial comparisons, plan implementation (indices), which are defined as the ratio of the level of the analyzed indicator in the reporting period to its level in the base period (or to the planned or other object).

Using indices, you can identify the influence of various factors on changes in performance indicators in multiplication and division models.

The INTEGRAL METHOD is a further logical development of the considered methods, which have a significant drawback: when using them, they assume that the factors change independently of each other. In fact, they change together, are interconnected, and from this interaction an additional increase in the effective indicator is obtained, which is added to one of the factors, usually the last one. In this regard, the magnitude of the influence of factors on the change in the performance indicator changes depending on the place in which one or another factor is placed in the model under study.

When using the INTEGRAL method, the error in calculating the influence of factors is distributed equally between them, and the order of substitution does not matter. The error distribution is carried out using special models.

Types of finite factor systems, the most frequently encountered in the analysis of economic activity:

additive models

multiplicative models

;

multiple models

;
;
;,

Where y– effective indicator (initial factor system);

x i– factors (factor indicators).

In relation to the class of deterministic factor systems, the following are distinguished: basic modeling techniques.

,

those. multiplicative model of the form
.

3. Factor system reduction method. Initial factor system
. If we divide both the numerator and denominator of the fraction by the same number, we get a new factor system (in this case, of course, the rules for selecting factors must be followed):

.

In this case we have a finite factor system of the form
.

Thus, the complex process of forming the level of the studied indicator of economic activity can be decomposed using various techniques into its components (factors) and presented in the form of a model of a deterministic factor system.

Modeling the return on capital indicator of an enterprise ensures the creation of a five-factor profitability model, which includes all indicators of intensification of the use of production resources.

We will conduct a profitability analysis using the data in the table.

CALCULATION OF KEY INDICATORS FOR THE ENTERPRISE FOR TWO YEARS

Indicators	Legend	First (base) year (0)	Second (reporting) year (1)	Deviation, %
1. Products (sales at selling prices without indirect taxes), thousand rubles.
2. a) Production personnel, people
b) Remuneration with accruals, thousand rubles.
3. Material costs, thousand rubles.
4. Depreciation, thousand rubles.
5. Fixed production assets, thousand rubles.
6. Working capital in inventory, thousand rubles.	E 3
7. a) Labor productivity (page 1:page 2a), rub.	λ R
b) Products worth 1 rub. wages (line 1: line 2b), rub.	λ U
8. Material productivity (page 1: page 3), rub.	λ M
9. Depreciation return (page 1: page 4), rub.	λ A
10. Capital productivity (page 1: page 5), rub.	λ F
11. Turnover of working capital (line 1:line 6), number of revolutions	λ E
12. Cost of sales (line 2b+line 3+line 4), thousand rubles.	S P
13. Profit from sales (page 1 + page 12), thousand rubles.	P P

Based on the basic indicators, we will calculate the indicators of intensification of production resources (rub.)

Indicators	Legend	First (base) year (0)	Second (reporting) year (1)
1. Payment intensity (labor intensity) of products
2. Material consumption of products
3 Depreciation capacity of products
4. Capital intensity of production
5. Working capital consolidation ratio

Five-factor model of return on assets (advanced capital)

.

We will illustrate the methodology for analyzing the five-factor model of return on assets using the method of chain substitutions.

First, let's find the profitability value for the base and reporting years.

For base year:

For the reporting year:

The difference in the profitability ratios of the reporting and base years was 0.005821, and as a percentage - 0.58%.

Let's look at how the five factors mentioned above contributed to this increase in profitability.

In conclusion, we will compile a summary of the influence of factors on the deviation of profitability of the 2nd year compared to the 1st (base) year.

Total deviation, % 0.58

Including due to the influence of:

labor intensity +0.31

material consumption +0.28

depreciation capacity 0

Total cost: +0.59

capital intensity −0.07

working capital turnover +0.06

Total advance payment −0.01

The goal of an enterprise's economic activity is always a certain result, which depends on numerous and varied factors. Obviously, the more detailed the influence of factors on the magnitude of the result is studied, the more accurate and reliable the forecast about the possibility of achieving it will be. Without a deep and comprehensive study of factors, it is impossible to draw informed conclusions about the results of operations, identify production reserves, justify a business plan and make management decisions. Factor analysis, by definition, is a methodology that includes unified methods for measuring (constant and systemic) factor indicators, a comprehensive study of their impact on the value of performance indicators, and the theoretical principles underlying forecasting.

The following are distinguished: types of factor analysis:

– analysis of functional dependencies and correlation analysis (probabilistic dependencies);

– direct and reverse;

– single-stage and multi-stage;

– static and dynamic;

– retrospective and prospective.

Factor analysis of functional dependencies is a technique for studying the influence of factors in the case when the resulting indicator can be presented in the form of a product, quotient or algebraic sum of factors.

Correlation analysis is a technique for studying factors whose connection with an effective indicator is probabilistic (correlation). For example, labor productivity at different enterprises at the same level of capital-labor ratio may also depend on other factors, the impact of which on this indicator is difficult to predict.

In direct factor analysis, the research is conducted from the general to the specific (deductively). Reverse factor analysis carries out research from particular, individual factors to general ones (using the induction method).

Single-stage factor analysis is used to study factors of only one level (one level) of subordination without detailing them into their component parts. For example, y = А·В. In multi-stage factor analysis, factors are detailed A And IN: dividing them into their component elements in order to study interdependencies.

Static factor analysis is used to study the influence of factors on performance indicators as of the corresponding date. Dynamic - is a technique for studying the relationships between factor indicators in dynamics.

Retrospective factor analysis studies the reasons for changes in performance indicators over past periods, while prospective factor analysis predicts the behavior of factors and performance indicators in the future.

The main tasks of factor analysis are the following:

– selection, classification and systematization of factors that influence the studied performance indicators;

– determination of the form of dependence between factors and the performance indicator;

– development (application) of a mathematical model of the relationship between the result and factor indicators;

– calculation of the influence of various factors on the change in the value of the effective indicator and comparison of this influence;

– making a forecast based on a factor model.

From the point of view of impact on the results of financial and economic activities of the enterprise, factors are divided into basic and secondary, internal and external, objective and subjective, general and specific, constant and variable, extensive and intensive.

The main factors include those that have the most significant impact on the result. Others call them minor. It should be noted that, depending on the circumstances, the same factor can be both primary and secondary.

Internal factors are factors that an enterprise can influence. They should be given the most attention. However, external factors (market conditions, inflation processes, conditions of supply of raw materials, materials, their quality, cost, etc.) certainly affect the results of the enterprise. Their study makes it possible to more accurately determine the degree of influence of internal factors and provide a more reliable forecast of production development.

Objective factors do not depend on the will and desires of people (in contracts, the term force majeure is used to refer to these factors; this could be a natural disaster, an unexpected change of political regime, etc.). Unlike objective reasons, subjective reasons depend on the activities of individuals and organizations.

Common factors are characteristic of all sectors of the economy. Specific are those that operate in a particular industry or enterprise. This division of factors allows us to more fully take into account the characteristics of individual enterprises and make a more accurate assessment of their activities.

Constant and variable factors are distinguished by the duration of their impact on production results . Constant factors influence the phenomenon under study continuously throughout the entire period under study (reporting period, production cycle, product life, etc.). The impact of variable factors is one-time, irregular.

Extensive factors include factors that are associated with a quantitative rather than a qualitative increase in the performance indicator, for example, an increase in the volume of production by expanding the sown area, increasing the number of livestock, the number of workers, etc. Intensive factors characterize qualitative changes in the production process, for example, an increase in crop yields as a result of the use of new types of fertilizers.

Factors are also divided into quantitative and qualitative, complex and simple, direct and indirect. Quantitative factors, by definition, can be measured (number of workers, equipment, raw materials, labor productivity, etc.). But often the process of measuring or searching for information is difficult, and then the influence of individual factors is characterized qualitatively (more - less, better - worse).

Most of the factors studied in the analysis consist of several elements. However, there are also those that cannot be broken down into their component parts. In this regard, factors are divided into complex (complex) and simple (single-element). An example of a complex factor is labor productivity, and a simple one is the number of working days in the reporting period.

Factors that have a direct impact on the performance indicator are called direct (factors of direct action). Indirect ones influence through the mediation of other factors. Depending on the degree of indirect influence, factors of the first, second, third and subsequent levels of subordination are distinguished. Thus, direct action factors — first level factors. Factors that determine the performance indicator indirectly, using first-level factors, are called second level factors etc.

Any factor analysis of indicators begins with modeling a multifactor model. The essence of building a model is to create a specific mathematical relationship between factors.

When modeling functional factor systems, a number of requirements must be met.

1. Factors included in the model must actually exist and have a specific physical meaning.

2. Factors that are included in the system of factor analysis of indicators must have a cause-and-effect relationship with the indicator being studied.

3. The factor model must provide measurement of the influence of a specific factor on the overall result.

In factor analysis of indicators, the following types of the most common models are used.

1. When the resultant indicator is obtained as an algebraic sum or the difference of the resulting factors, apply additive models, for example:

,

where is the profit from product sales,

- revenues from sales,

– production cost of goods sold,

– business expenses,

– administrative expenses.

Multiplicative models are used when the resulting indicator is obtained as a product of several resulting factors:


,


where is return on assets,


– profitability of sales,


– return on assets,


– the average value of the organization’s assets for the reporting year.


3. When the effective indicator is obtained by dividing one factor by another, apply multiples models:


Various combinations of the above models give mixed or combined models:


;


;


etc.


In the practice of economic analysis, there are several ways to model multifactor models: lengthening, formal decomposition, expansion, reduction and dismemberment of one or several factor indicators into component elements.


For example, using the expansion method, you can build a three-factor model of the organization’s return on assets as follows:


;


,


where is the organization’s equity capital turnover,


– independence coefficient or the share of equity capital in the total assets of the organization,


– the average cost of the organization’s equity capital for the reporting period.


Thus, we have obtained a three-factor multiplicative model of the organization's return on assets. This model is widely known in the economic literature as the Dupont model. Considering this model, we can say that the profitability of an organization’s assets is influenced by the return on sales, equity turnover and the share of equity in the total assets of the organization.


Now consider the following return on assets model:


=;


where is the share of revenue per 1 rub. full cost of production,


– share of current assets in the formation of assets,


– share of inventories in the formation of current assets,


– inventory turnover.


The first factor of this model speaks about the pricing policy of the organization; it shows the basic markup that is included directly in the price of the products sold.


The second and third factors show the structure of assets and current assets, the optimal value of which makes it possible to save working capital.


The fourth factor is determined by the volume of production and sales of products and speaks of the efficiency of use of inventories; physically it expresses the number of revolutions that inventories make during the reporting year.


Equity method used when it is difficult to establish the dependence of the analyzed indicator on private indicators. The method is that the deviation according to the general indicator is proportionally distributed among the individual factors under the influence of which it occurred. For example, you can calculate the impact of changes in book profit on the level of profitability using the formula:


R i = R·(  i / b) ,


where  R i- change in the level of profitability due to an increase in profit under the influence of a factor i, %;


R-change in the level of profitability due to changes in balance sheet profit,%;


 b - change in balance sheet profit, rub.;


 i- change in balance sheet profit due to the factor i.


Chain substitution method allows you to measure the influence of individual factors on the result of their interaction - generalizing ( target) indicator, calculate deviations of actual indicators from standard (planned) indicators.


Substitution is the replacement of a basic or standard value of a particular indicator with an actual one. Chain substitutions are sequential replacements of the basic values ​​of particular indicators included in the calculation formula with the actual values ​​of these indicators. Then these influences (the influence of the replacement made on the change in the value of the general indicator being studied) are compared with each other. The number of substitutions is equal to the number of partial indicators included in the calculation formula.


The method of chain substitutions consists in determining a number of intermediate values ​​of the generalizing indicator by sequentially replacing the basic values ​​of the factors with the reporting ones. This method is based on elimination. Eliminate means to eliminate, exclude the influence of all factors on the value of the effective indicator, except one. Moreover, based on the fact that all factors change independently of each other, i.e. First, one factor changes, and all the others remain unchanged. then two change while the others remain unchanged, etc.


In general, the application of the chain production method can be described as follows:





where a 0, b 0, c 0 are the basic values ​​of factors influencing the general indicator y;


a 1 , b 1 , c 1 —
actual values ​​of factors;


y a , y b , —
intermediate changes
the resulting indicator associated with changes in factors a, b, respectively.


The total change  y=y 1 –y 0 consists of the sum of changes in the resulting indicator due to changes in each factor with fixed values ​​of the other factors:


The algorithm of the chain substitution method can be demonstrated by the example of calculating the impact of changes in the values ​​of partial indicators on the value of the indicator, presented in the form of the following calculation formula: F = a· b· c· d.


Then the base value F will be equal F 0 = a 0 · b 0 · c 0 · d 0 ,


and the actual one: F 1 = a 1 · b 1 · c 1 · d 1 .


Total deviation of the actual indicator from the basic indicator  F (F=F 1 –F 0) is obviously equal to the sum of deviations obtained under the influence of changes in particular indicators:


F = F 1 +F 2 +F 3 +F 4 .


And changes in private indicators are calculated by successive substitutions into the formula for calculating the indicator F actual parameter values a, b, c, d instead of basic ones:


The calculation is checked by comparing the balance of deviations, i.e. the total deviation of the actual indicator from the basic indicator should be equal to the sum of deviations under the influence of changes in private indicators:


F 1 –F 0 = F 1 +F 2 +F 3 +F 4 .


The advantages of this method: versatility of application, ease of calculations.


The disadvantage of the method is that, depending on the chosen order of factor replacement, the results of factor decomposition have different meanings. This is due to the fact that as a result of applying this method, a certain indecomposable residue is formed, which is added to the magnitude of the influence of the last factor. In practice, the accuracy of factor assessment is neglected, highlighting the relative importance of the influence of one or another factor. However, there are certain rules that determine the substitution sequence:

if there are quantitative and qualitative indicators in the factor model, the change in quantitative factors is considered first;


if the model is represented by several quantitative and qualitative indicators, the substitution sequence is determined by logical analysis.


In analysis, quantitative factors are understood as those that express the quantitative certainty of phenomena and can be obtained by direct accounting (number of workers, machines, raw materials, etc.).


Qualitative factors determine the internal qualities, signs and characteristics of the phenomena being studied (labor productivity, product quality, average working hours, etc.).


A variation of the method of chain substitutions is the method of calculation using absolute differences. The target function in this case, as in the previous example, is presented in the form of a multiplicative model. The change in the value of each factor is determined in comparison with the base value, for example, the planned one. Then these differences are multiplied by the remaining partial indicators - the factors of the multiplicative model. But, note, when moving from one factor to another, a different value of the multiplier is taken into account. The factors that appear after the factor (on the right) by which the difference is calculated remain in the value of the base period, and all those remaining before it (on the left) are taken in the values ​​of the reporting period.


The absolute difference method is a modification of the chain substitution method. The change in the effective indicator due to each factor using the method of differences is defined as the product of the deviation of the factor being studied by the basic or reporting value of another factor, depending on the selected substitution sequence:





We will show this using the example of the influence of individual factors on the amount of materials costs TS m, which are formed under the influence of three factors: volume of production in physical terms Q, norms of material consumption per accounting unit of production m and prices for materials Pm.


TS m = Q· m· Pm.


First, the change in each factor compared to the plan is calculated:


change in production volume  Q= Q 0 – Q 1 ;


change in material consumption rates per accounting unit  m = m 0 – m 1 ;


change in price per unit of material  Pm = Pm 1 – Pm 0 .


Next, the influence of individual factors on the general indicator is determined, i.e. the amount of costs for materials. In this case, the partial indicators that stand before the indicator by which the difference is calculated are left in their actual value, and all those following it are left in the basic value.


In this case, the impact of changes in production volume  Q the amount of materials costs will be:


TS mQ = Q· m 0 · Pm 0 ;


influence of changes in material consumption rates  TS mm:


TS mm = Q 1 · m· Pm 0 ;


impact of changes in prices for materials  TS mp:


TS mp = Q 1 · m 1 · Pm.


The total deviation of the amount of costs for materials will be equal to the sum of the deviations of the influence of individual factors, i.e.


TS m = TS mQ + TS mm + TS mp.


However, in practice there are more often situations when one can only assume the existence of a functional dependence (for example, the dependence of revenue ( TR) from the number of products produced and sold ( Q): TR = TR(Q)). To test this assumption, use regression analysis, with the help of which a function of a certain type is selected ( Fr(Q)). Then, on the set of function definition (on the set of values ​​of the factor indicator), the set of function values ​​is calculated.


The method of relative differences is used to measure the influence of factors on the growth of an effective indicator in multiplicative and mixed models of the form y = (a – c) . With. It is used in cases where the source data contains previously determined relative deviations of factor indicators in percentages.


For multiplicative models like y = a . V . The analysis technique is as follows:


find the relative deviation of each factor indicator:





determine the deviation of the performance indicator at due to each factor





The integral method allows you to avoid the disadvantages inherent in the chain substitution method and does not require the use of techniques for distributing the indecomposable remainder among factors, because it has a logarithmic law of redistribution of factor loads. The integral method makes it possible to achieve a complete decomposition of the effective indicator into factors and is universal in nature, i.e. applicable to multiplicative, multiple and mixed models. The operation of calculating a definite integral is solved using a PC and is reduced to constructing integrand expressions that depend on the type of function or model of the factor system.


You can also use already formed working formulas given in specialized literature:


1. Model view:





2. View model :





3. View model:





4. View model:





A comprehensive analysis of the financial condition involves a broad and complete study of all factors that influence or may influence the final financial results of the organization, which, ultimately, are the main goal of the organization.


The results of the analysis should be used to make correct management decisions by the organization's administration and informed investment decisions by shareholders-owners.


TASK 2


It is known that during the reporting period the average number of workers on the payroll increased from 500 to 520 people, the average number of hours worked per working day - from 7.4 to 7.5 hours; the average number of days worked by workers per year decreased from 290 to 280 days; the average hourly output of a worker decreased from 26.5 rubles to 23 rubles. The volume of production decreased from 28434.5 tr. up to 25116 tr. Using the method of relative differences, evaluate the influence of factors on changes in production volume. Draw reasoned conclusions.


SOLUTION


Relative difference method used to measure the influence of factors on the growth of a performance indicator only in multiplicative and additive-multiplicative models.


Table 1


Initial data for calculation


Index	Designation	Base year	Reporting year	Deviations (+;-)
Average number of workers, people.
Average number of hours worked by one worker per day, hours.
Average number of days worked by a worker per year, days
Average hourly output, rub.		26,5
Product output volume, t.r.	VP	28434,5	25116	3318,5

We have a model of the form


VP = H*t*N*F,


In this case, the change in the performance indicator 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 of the first factor, expressed as a decimal fraction.


To calculate the influence of the second factor, you need to add the change in it due to the first factor to the planned (basic) value of the effective indicator and then multiply the resulting amount by the relative increase in the Second factor.


The influence of the third factor is determined in a similar way: it is necessary to add its growth due to the first and second factors to the planned value of the effective indicator and multiply the resulting amount by the relative growth of the third factor.


The influence of the quadruple factor is similar





Let’s summarize the factors that influenced the formation of revenue in the reporting year:


increase in the number of workers 1137.38 thousand rubles.


increasing the number of hours worked by one worker


per day 399.62 tr.


changes in the number of working days -1033.5 tr.


Changes in average hourly output -3821.95 tr.


Total -3318.45 t.r.


Thus, based on the method of relative differences, it was found that the total influence of all factors amounted to -3318.45 tr, which coincides with the absolute dynamics of the volume of production according to the conditions of the problem. A small discrepancy is determined by the degree of rounding in the calculations. A positive impact was exerted by an increase in the average number of workers by 20 people in the amount of 1137.8 thousand rubles, a slight increase in the working day of one worker by 0.1 hours led to an increase in output by 399.62 thousand rubles. A negative impact was caused by a decrease in the average hourly work per worker by 3.5 rubles. per hour, which resulted in a decrease in production volume by -3821.5 tr. A decrease in the average number of days worked by one worker per year by 10 days led to a decrease in production volumes by -1033.5 tr.


TASK 3


Using the economic information of your enterprise, assess its financial stability based on the calculation of relative indicators.


SOLUTION


Joint-stock company "KRAITEKHSNAB", registered by the Registration Chamber of the Krasnodar City Hall No. 10952 dated May 14, 1999, OGRN 1022301987278, hereinafter referred to as the "Company", is a closed joint-stock company.


The Company is a legal entity and operates on the basis of the Charter and legislation of the Russian Federation. The Company has a round seal containing its full corporate name in Russian and an indication of its location, stamps and forms with its name, its own emblem, as well as a duly registered trademark and other means of visual identification.


Full corporate name of the Company in Russian:
Closed joint-stock company "KRAITECHSNAB". The abbreviated corporate name of the Company in Russian is ZAO KRAITECHSNAB.


Location (mailing address) of the Company: 350021, Russian Federation, Krasnodar region, Krasnodar, Karasun administrative district, st. Tramway, 25.


Closed joint stock company "KRAITECHSNAB" was created without any limitation on the period of activity.


The main subject of the Company's activity is trade and purchasing activities, intermediary, brokerage.


Let us analyze the financial stability indicators of the organization under study (Table 2).


table 2


Analysis of financial stability indicators of Kraytekhsnab CJSC in absolute terms


Indicators	2003	2004	2005	2005 to 2003
				(+,-)	Growth rate, %
1. Sources of own funds	7371212,4	6508475,4	7713483,3	342 270,9	1004,6
2. Non-current assets	1339265,0	1320240,0	1301215,0	38 050,0	97,2
3. Sources of own working capital for the formation of reserves and costs	6031947,4	5188235,4	6412268,4	380 321,0	1006,3
4. Long-term loans and borrowings
5. Sources of own funds, adjusted for the amount of long-term borrowed funds	6031947,4	5188235,4	6412268,4	380 321,0	106,3
6. Short-term credit and borrowed funds	1500000,0	2000000,0	1500000,0
7. The total amount of sources of funds, taking into account long-term and short-term borrowed funds	7531947,4	7188235,4	7912268,4	380 321,0	105,0
8. The amount of inventories and costs circulating in the balance sheet asset	9784805,7	10289636,4	11152558,8	1367753,1	114,0

End of table 2


Indicators	2003	2004	2005	2005 to 2003
				(+,-)	Growth rate, %
9. Excess sources of own working capital	3752858,3	5101401,1	4740290,4	987432,2	126,3
10. Excess of sources of own funds and long-term borrowed sources	3752858,3	5101401,1	4740290,4	987432,2	126,3
11. Surplus of the total value of all sources for the formation of reserves and costs	2252858,3	3101401,1	3240290,4	987 432,2	143,8
12. Three complex indicator (S) of the financial situation	(0,0,0)	(0,0,0)	(0,0,0)

Analyzing the type of financial stability of an enterprise over time, a noticeable decrease in the financial stability of the enterprise is observed.


As can be seen from Table 2, both in 2003, and in 2004, and in 2005, the financial stability of Kraytekhsnab CJSC according to the 3-complex indicator of financial stability can be characterized as “Crisis-unstable state of the enterprise”, since the enterprise does not have enough funds to form reserves and costs to carry out current activities.


Let's calculate the financial stability coefficients of Kraytekhsnab CJSC (Table 3).


Table 3


Financial stability ratios of Kraytekhsnab CJSC


Indicators	2003	2004	2005	(+,-)
				2004 2003	2005 to 2004
Autonomy coefficient	0,44	0,37	0,30	0,06	0,08
Debt to equity ratio (financial leverage)	1,28	1,67	2,34	0,39	0,67
Ratio of mobile and immobilized assets	11,56	13,32	18,79	1,76	5,47
Debt to equity ratio	0,78	0,60	0,43	0,18	0,17
Maneuverability coefficient	0,82	0,80	0,83	0,02	0,03
Inventory and cost coverage ratio with own funds	0,62	0,50	0,57	0,11	0,07
Industrial property ratio	0,66	0,61	0,48	0,05	0,13
Short-term debt ratio, %	15,9	18,4			10,1
Accounts payable ratio, %	84,1	81,6	91,7		10,1

An analysis of financial stability by relative indicators presented in Table 3 suggests that, according to the indicators presented in the table, compared with the base period (2003), the situation at Kraytekhsnab CJSC generally worsened in 2004 and improved slightly in the reporting year 2005 G.


The indicator “Autonomy coefficient” for the period from 2003 to 2004 decreased by -0.06 and in 2004 amounted to 0.37. This is below the standard value (0.5) at which borrowed capital can be compensated by the property of the enterprise. The indicator “Autonomy coefficient” for the period from 2004 to 2005 decreased by -0.08 and in 2005 amounted to 0.30. This is also below the standard value (0.5) at which borrowed capital can be compensated by the property of the enterprise.


The indicator “Ratio of debt and equity” (financial leverage) increased by 0.39 from 2003 to 2004 and amounted to 1.67 in 2004. The indicator for 2004 to 2005 increased by 0.67 and in 2005 amounted to 2.34. The more this ratio exceeds 1, the greater the enterprise's dependence on borrowed funds. The acceptable level is often determined by the operating conditions of each enterprise, primarily by the rate of turnover of working capital. Therefore, it is additionally necessary to determine the rate of turnover of inventories and receivables for the analyzed period. If accounts receivable turn over faster than working capital, which means a fairly high intensity of cash flow to the enterprise, i.e. the result is an increase in own funds. Therefore, with a high turnover of tangible working capital and an even higher turnover of accounts receivable, the ratio of equity and borrowed funds can greatly exceed 1.


The indicator “Ratio of mobile and immobilized assets” increased by 1.76 from 2003 to 2004 and amounted to 13.32 in 2004. The indicator for 2004 to 2005 increased by 5.47 and in 2005 amounted to 18.79. The standard value is specific to each individual industry, but all other things being equal, an increase in the coefficient is a positive trend.


Indicator "Maneuverability coefficient", for the period 2003 - 2004. decreased by -0.02 and at the end of Dec. 2004 was 0.80. This is higher than the standard value (0.5). The indicator for the period 2004 to 2005 increased by 0.03 and in 2005 amounted to 0.83. This is higher than the standard value (0.5). The agility coefficient characterizes what share of sources of own funds is in mobile form. The standard value of the indicator depends on the nature of the enterprise’s activities: in capital-intensive industries, its normal level should be lower than in material-intensive ones. At the end of the analyzed period, Kraytekhsnab CJSC had a light asset structure. The share of fixed assets in the balance sheet currency is less than 40.0%. Thus, the enterprise cannot be classified as a capital-intensive industry.


Indicator “Coefficient of coverage of inventories and costs with own funds”, for 2003 – 2004. decreased by -0.11 and in 2004 amounted to 0.50. The indicator for the period 2004–2005 increased by 0.07 and in 2005 amounted to 0.57. This is lower than the standard value (0.6 - 0.8), as in 2003, 2004 and 2005. The company lacks its own funds for the formation of reserves and costs, as shown by the analysis of financial stability indicators in absolute terms.


BIBLIOGRAPHY

1. The procedure for monitoring the financial condition of organizations and recording their solvency. Federal Service of Russia for Insolvency and Financial Recovery: Order No. 13-r dated March 31, 1999 // Economics and Life. 1999. No. 22.
   

2. Bakanov M.I., Sheremet A.D. Theory of economic analysis. –M.: Finance and Statistics, 2006.
   ASSESSMENT OF THE ECONOMIC INDICATORS OF THE ACTIVITY OF A TRADING ENTERPRISE USING THE EXAMPLE OF THE MAIN INDICATORS OF THE ENTERPRISE'S ACTIVITY SHOW THE USE OF 6 PARTIAL METHODS AND TECHNIQUES OF ECONOMIC ANALYSIS Financial condition of a trade organization and assessment of economic indicators

   2013-11-12

To analyze the variability of a trait under the influence of controlled variables, the dispersion method is used.

To study the relationship between values ​​- the factorial method. Let's take a closer look at the analytical tools: factorial, dispersion and two-factor dispersion methods for assessing variability.

Analysis of Variance in Excel

Conventionally, the goal of the dispersion method can be formulated as follows: to isolate 3 partial variations from the general variability of the parameter:

• 1 – determined by the action of each of the studied values;
• 2 – dictated by the relationship between the studied values;
• 3 – random, dictated by all unaccounted for circumstances.

In Microsoft Excel, analysis of variance can be performed using the “Data Analysis” tool (the “Data” tab - “Analysis”). This is a spreadsheet add-on. If the add-in is not available, you need to open Excel Options and enable the Analysis setting.

The work begins with the design of the table. Rules:

1. Each column should contain the values ​​of one factor under study.
2. Arrange the columns in ascending/descending order of the value of the parameter being studied.

Let's look at variance analysis in Excel using an example.

The company's psychologist analyzed the behavior strategies of employees in a conflict situation using a special technique. It is assumed that behavior is influenced by the level of education (1 – secondary, 2 – specialized secondary, 3 – higher).

Let's enter the data into an Excel table:

The significant parameter is filled in yellow. Since the P-Value between groups is greater than 1, Fisher's test cannot be considered significant. Consequently, behavior in a conflict situation does not depend on the level of education.



Factor analysis in Excel: example

Factorial analysis is a multidimensional analysis of relationships between the values ​​of variables. Using this method you can solve the most important problems:

• comprehensively describe the object being measured (and succinctly, compactly);
• identify hidden variable values ​​that determine the presence of linear statistical correlations;
• classify variables (identify relationships between them);
• reduce the number of required variables.

Let's look at an example of factor analysis. Let's say we know the sales of some goods over the last 4 months. It is necessary to analyze which titles are in demand and which are not.

Now you can clearly see which product sales are generating the main growth.

Two-way ANOVA in Excel

Shows how two factors influence the change in the value of a random variable. Let's look at two-factor analysis of variance in Excel using an example.

Task. A group of men and women were presented with sounds of different volumes: 1 – 10 dB, 2 – 30 dB, 3 – 50 dB. Response times were recorded in milliseconds. It is necessary to determine whether gender influences the response; Does volume affect response?

All phenomena and processes of economic activity of enterprises are interconnected and interdependent. Some of them are directly related to each other, others indirectly. Hence, an important methodological issue in economic analysis is the study and measurement of the influence of factors on the value of the economic indicators under study.

Under economic factor analysis is understood as a gradual transition from the initial factor system to the final factor system, the disclosure of a full set of direct, quantitatively measurable factors that influence the change in the performance indicator.

Based on the nature of the relationship between indicators, methods of deterministic and stochastic factor analysis are distinguished.

Deterministic factor analysis is a methodology for studying the influence of factors whose connection with the performance indicator is functional in nature.

The main properties of the deterministic approach to analysis:
· construction of a deterministic model through logical analysis;
· the presence of a complete (hard) connection between indicators;
· the impossibility of separating the results of the influence of simultaneously acting factors that cannot be combined in one model;
· study of relationships in the short term.

There are four types of deterministic models:

Additive Models represent an algebraic sum of indicators and have the form

Such models, for example, include cost indicators in relation to elements of production costs and cost items; an indicator of the volume of production in its relationship with the volume of output of individual products or the volume of output in individual departments.

Multiplicative models can be summarized by the formula

.

An example of a multiplicative model is a two-factor model of sales volume

,

Where H- average number of employees;

C.B.- average output per employee.

Multiple models:

An example of a multiple model is the indicator of the turnover period of goods (in days). T OB.T:

,

Where Z T- average stock of goods; O R- one-day sales volume.

Mixed models are a combination of the above models and can be described using special expressions:

Examples of such models are cost indicators per 1 ruble. commercial products, profitability indicators, etc.

To study the relationship between indicators and quantitatively measure the many factors that influenced the effective indicator, we present general model transformation rules in order to include new factor indicators.

To detail the generalizing factor indicator into its components, which are of interest for analytical calculations, the technique of lengthening the factor system is used.

If the initial factor model is , a , then the model will take the form .

To identify a certain number of new factors and construct the factor indicators necessary for calculations, the technique of expanding factor models is used. In this case, the numerator and denominator are multiplied by the same number:

.

To construct new factor indicators, the technique of reducing factor models is used. When using this technique, the numerator and denominator are divided by the same number.

.

The detail of factor analysis is largely determined by the number of factors whose influence can be quantitatively assessed, therefore multifactorial multiplicative models are of great importance in the analysis. Their construction is based on the following principles:
· the place of each factor in the model must correspond to its role in the formation of the effective indicator;
· the model should be built from a two-factor complete model by sequentially dividing factors, usually qualitative, into components;
· when writing a formula for a multifactor model, factors should be arranged from left to right in the order of their replacement.

Building a factor model is the first stage of deterministic analysis. Next, determine the method for assessing the influence of factors.

Chain substitution method consists in determining a number of intermediate values ​​of the generalizing indicator by sequentially replacing the basic values ​​of the factors with the reporting ones. This method is based on elimination. Eliminate- means to eliminate, exclude the influence of all factors on the value of the effective indicator, except one. Moreover, based on the fact that all factors change independently of each other, i.e. First, one factor changes, and all the others remain unchanged. then two change while the others remain unchanged, etc.

In general, the application of the chain production method can be described as follows:

where a 0, b 0, c 0 are the basic values ​​of factors influencing the general indicator y;

a 1, b 1, c 1 - actual values ​​of factors;

y a, y b, are intermediate changes in the resulting indicator associated with changes in factors a, b, respectively.

The total change D у = у 1 – у 0 consists of the sum of changes in the resulting indicator due to changes in each factor with fixed values ​​of the remaining factors:

Let's look at an example:

table 2

Initial data for factor analysis

Indicators	Legend	Basic values	Actual values	Change
				Absolute (+,-)	Relative (%)
Volume of commercial products, thousand rubles.
Number of employees, people
Output per worker,

We will analyze the impact of the number of workers and their output on the volume of marketable output using the method described above based on the data in Table 2. The dependence of the volume of commercial products on these factors can be described using a multiplicative model:

Then the effect of a change in the number of employees on the general indicator can be calculated using the formula:

Thus, the change in the volume of marketable products was positively influenced by a change in the number of employees by 5 people, which caused an increase in production volume by 730 thousand rubles. and a negative impact was had by a decrease in output by 10 thousand rubles, which caused a decrease in volume by 250 thousand rubles. The combined influence of two factors led to an increase in production volume by 480 thousand rubles.

The advantages of this method: versatility of application, ease of calculations.

The disadvantage of the method is that, depending on the chosen order of factor replacement, the results of factor decomposition have different meanings. This is due to the fact that as a result of applying this method, a certain indecomposable residue is formed, which is added to the magnitude of the influence of the last factor. In practice, the accuracy of factor assessment is neglected, highlighting the relative importance of the influence of one or another factor. However, there are certain rules that determine the substitution sequence:
· if there are quantitative and qualitative indicators in the factor model, the change in quantitative factors is considered first;
· if the model is represented by several quantitative and qualitative indicators, the substitution sequence is determined by logical analysis.

Under quantitative factors in analysis they understand those that express the quantitative certainty of phenomena and can be obtained by direct accounting (number of workers, machines, raw materials, etc.).

Qualitative factors determine the internal qualities, signs and characteristics of the phenomena being studied (labor productivity, product quality, average working hours, etc.).

Absolute difference method is a modification of the chain substitution method. The change in the effective indicator due to each factor using the method of differences is defined as the product of the deviation of the factor being studied by the basic or reporting value of another factor, depending on the selected substitution sequence:

Relative difference method used to measure the influence of factors on the growth of a performance indicator in multiplicative and mixed models of the form y = (a – c) . With. It is used in cases where the source data contains previously determined relative deviations of factor indicators in percentages.

For multiplicative models like y = a . V . The analysis technique is as follows:

· find the relative deviation of each factor indicator:

· determine the deviation of the performance indicator at due to each factor

Example. Using the data in table. 2, we will analyze using the method of relative differences. The relative deviations of the factors under consideration will be:

Let's calculate the impact of each factor on the volume of commercial output:

The calculation results are the same as when using the previous method.

Integral method allows you to avoid the disadvantages inherent in the chain substitution method, and does not require the use of techniques for distributing the indecomposable remainder among factors, because it has a logarithmic law of redistribution of factor loads. The integral method makes it possible to achieve a complete decomposition of the effective indicator into factors and is universal in nature, i.e. applicable to multiplicative, multiple and mixed models. The operation of calculating a definite integral is solved using a PC and is reduced to constructing integrand expressions that depend on the type of function or model of the factor system.
1. What management problems are solved through economic analysis?
2. Describe the subject of economic analysis.
3. What distinctive features characterize the method of economic analysis?
4. What principles underlie the classification of techniques and methods of analysis?
5. What role does the method of comparison play in economic analysis?
6. Explain how to construct deterministic factor models.
7. Describe the algorithm for using the simplest methods of deterministic factor analysis: the method of chain substitutions, the method of differences.
8. Characterize the advantages and describe the algorithm for using the integral method.
9. Give examples of problems and factor models to which each of the methods of deterministic factor analysis is applied.

This may be of interest (selected paragraphs):