The relationship of economic phenomena. Introduction to factor analysis. Types of factor analysis, its main tasks.

All phenomena and processes of economic activity of enterprises are interconnected, interdependent and conditional. Some of them are directly related, others indirectly. For example, the value of gross output is directly affected by such factors as the number of workers and the level of productivity of their labor. All other factors affect this indicator indirectly.

Each phenomenon can be considered as a cause and as a consequence. For example, labor productivity can be considered, on the one hand, as the cause of a change in the volume of production, the level of its cost, and on the other hand, as a result of a change in the degree of mechanization and automation of production, improvement in the organization of labor, etc.

Each performance indicator depends on numerous and varied factors. The more detailed the influence of factors on the value of the effective indicator is studied, the more accurate the results of the analysis and assessment of the quality of the work of enterprises. Hence, an important methodological issue in the analysis of economic activity is the study and measurement of the influence of factors on the magnitude of the studied economic indicators. Without a deep and comprehensive study of the factors, it is impossible to draw reasonable conclusions about the results of activities, identify production reserves, justify plans and management decisions.

Under factor analysis refers to the methodology of complex and systematic study and measurement of the impact of factors on the magnitude of performance indicators.

There are the following types of factor analysis:

deterministic and stochastic;

direct and reverse;

single-stage and multi-stage;

static and dynamic;

retrospective and prospective (forecast).

Deterministic factor analysis is a methodology for studying the influence of factors whose relationship with the performance indicator is functional in nature, i.e. when the performance indicator is presented as a product, quotient or algebraic sum of factors.

Stochastic analysis is a methodology for studying factors whose relationship with the performance indicator, in contrast to the functional one, is incomplete, probabilistic (correlation). If with a functional (full) dependence, a corresponding change in the function always occurs with a change in the argument, then with a correlation relationship, a change in the argument can give several values ​​of the increase in the function, depending on the combination of other factors that determine this indicator. For example, labor productivity at the same level of capital-labor ratio may not be the same at different enterprises. It depends on the optimal combination of other factors affecting this indicator.

At direct factor analysis research is conducted in a deductive way - from the general to the particular. Inverse factor analysis carries out a study of cause-and-effect relationships by the method of logical induction - from private, individual factors to general ones.

Factor analysis can be single stage And multistage. The first type is used to study the factors of only one level (one stage) of subordination without detailing them into their constituent parts. For example, at = A X b. In multistage factor analysis, the factors are detailed A And b into constituent elements in order to study their behavior. Detailing the factors can be continued further. In this case, the influence of factors of different levels of subordination is studied.

It is also necessary to distinguish static And dynamic factor analysis. The first type is used when studying the influence of factors on performance indicators for the corresponding date. Another type is a methodology for studying cause-and-effect relationships in dynamics.

Finally, factor analysis can be retrospective which studies the reasons for the increase in performance indicators for past periods, and promising which examines the behavior of factors and performance indicators in the future.

The main tasks of factor analysis are the following.

1. Selection of factors that determine the studied performance indicators.

2. Classification and systematization of factors in order to provide an integrated and systematic approach to the study of their impact on the results of economic activity.

3. Determining the form of the relationship between the factors and the performance indicator.

4. Modeling the relationship between performance and factor indicators.

5. Calculation of the influence of factors and assessment of the role of each of them in changing the value of the effective indicator.

6. Working with a factor model (its practical use for managing economic processes).

Selection of factors for analysis this or that indicator is carried out on the basis of theoretical and practical knowledge acquired in this industry. In this case, they usually proceed from the principle: the larger the complex of factors studied, the more accurate the results of the analysis will be. At the same time, it must be borne in mind that if this complex of factors is considered as a mechanical sum, without taking into account their interaction, without highlighting the main determining ones, then the conclusions may be erroneous. In AHD, an interconnected study of the influence of factors on the value of effective indicators is achieved through their systematization, which is one of the main methodological issues of this science.

An important methodological issue in factor analysis is determination of the form of dependence between factors and performance indicators: functional or stochastic, direct or inverse, rectilinear or curvilinear. It uses theoretical and practical experience, as well as methods for comparing parallel and dynamic series, analytical groupings of initial information, graphical, etc.

Modeling economic indicators (deterministic and stochastic) is also a complex methodological problem in factor analysis, the solution of which requires special knowledge and practical skills in this industry. In this regard, this issue is given a lot of attention in this course.

The most important methodological aspect in AHD is influence calculation factors on the value of effective indicators, for which the analysis uses a whole arsenal of methods, essence, purpose, the scope of which and the calculation procedure are discussed in the following chapters.

And finally, the last stage of factor analysis - practical use of the factor model to calculate the reserves for the growth of the effective indicator, to plan and predict its value when the production situation changes.

5.2. Classification of factors in the analysis of economic activity

The value of the classification of factors. The main types of factors. The concept and difference between different types of factors in AHD.

The classification of factors is their distribution into groups depending on common characteristics. It allows you to better understand the reasons for the change in the phenomena under study, more accurately assess the place and role of each factor in the formation of the value of effective indicators.

The factors studied in the analysis can be classified according to different criteria (Fig. 5.1).

By their nature, the factors are divided into natural-climatic, socio-economic and production-economic. Natural and climatic factors have a great impact on the results of activities in agriculture, in the extractive industry, forestry and other industries. Accounting for their influence allows more accurate assessment of the results of the work of business entities.

TO socio-economic factors include the living conditions of workers, the organization of mass cultural, sports and recreational work at the enterprise, the general level of culture and education of personnel, etc. They contribute to a more complete use of the enterprise's production resources and increase the efficiency of its work.

Production and economic factors determine the completeness and efficiency of the use of the enterprise's production resources and the final results of its activities.

According to the degree of impact on the results of economic activity, the factors are divided into primary and secondary. TO main factors that have a decisive influence on the performance indicator. Minor those that do not have a decisive impact on the results of economic activity in the current conditions are considered. Here it should be noted that the same factor, depending on the circumstances, can be both primary and secondary. The ability to identify the main determining factors from a variety of factors ensures the correctness of the conclusions based on the results of the analysis.

Of great importance in the study of economic phenomena and processes and the assessment of the results of enterprises' activities is the classification of factors domestic And external, that is, on factors that depend and do not depend on the activities of the enterprise. The main attention in the analysis should be given to the study of internal factors that the enterprise can influence.

At the same time, in many cases, with developed production ties and relations, the performance of each enterprise is largely influenced by the activities of other enterprises, for example, the uniformity and timeliness of the supply of raw materials, materials, their quality, cost, market conditions, inflationary processes, etc. Often the results of the work of enterprises are reflected in changes in the field of specialization and industrial cooperation. These factors are external. They do not characterize the efforts of a given team, but their study makes it possible to more accurately determine the degree of influence of internal causes and, thereby, to more fully reveal the internal reserves of production.

For a correct assessment of the activities of enterprises, factors must be divided into objective And subjective Objective ones, such as a natural disaster, do not depend on the will and desires of people. Unlike objective, subjective reasons depend on the activities of legal entities and individuals.

According to the degree of prevalence factors are divided into are common And specific. General factors include factors that operate in all sectors of the economy. Specific are those that operate in a particular sector of the economy or enterprise. Such a division of factors makes it possible to more fully take into account the characteristics of individual enterprises and branches of production and to make a more accurate assessment of their activities.

According to the period of impact on the results of economic activity, factors are distinguished permanent And variables. Constant factors affect the phenomenon under study continuously, throughout the entire time. The impact of variable factors is manifested periodically, for example, the development of new equipment, new types of products, new production technology, etc.

Of great importance for assessing the activities of enterprises is the division of factors according to the nature of their action into intense And extensive. Extensive factors include those that are associated with a quantitative, rather than a qualitative, increase in the result 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 the degree of effort, the intensity of labor in the production process, for example, an increase in crop yields, animal productivity, and the level of labor productivity.

If the analysis aims to measure the impact of each factor on the results of economic activity, then they are divided into quantitative And quality, sophisticated And simple, straight And indirect, measurable And immeasurable.

quantitative factors are considered that express the quantitative certainty of phenomena (the number of workers, equipment, raw materials, etc.). quality factors determine the internal qualities, signs and characteristics of the objects under study (labor productivity, product quality, soil fertility, etc.).

Most of the studied factors are complex in composition, consisting of several elements. However, there are also those that are not decomposed into component parts. In this regard, the factors are divided into complex (complex) And simple (elemental). An example of a complex factor is labor productivity, and a simple one is the number of working days in the reporting period.

As already mentioned, some factors have a direct impact on the performance indicator, others indirectly. According to the level of subordination (hierarchy), factors of the first, second, third and subsequent levels of subordination are distinguished. TO first level factors are those that directly affect performance. Factors that determine the performance indicator indirectly, with the help of first-level factors, are called second level factors etc. On fig. 5.2 shows that the factors of the first level are the average annual number of workers and the average annual output per worker. The number of days worked by one worker and the average daily output are second-level factors relative to gross output. The factors of the third level include the length of the working day and the average hourly output.

The impact of individual factors on the performance indicator can be quantified. At the same time, there are a number of factors whose influence on the performance of enterprises cannot be directly measured, for example, the provision of staff with housing, child care facilities, the level of training of personnel, etc.

5.3. Systematization of factors in the analysis of economic activity

Necessity and importance of systematization of factors. The main ways of systematizing factors in deterministic and stochastic analysis.

A systematic approach in AHD necessitates an interconnected study of factors, taking into account their internal and external relationships, interaction and subordination, which is achieved through systematization. Systematization as a whole is the placement of the studied phenomena or objects in a certain order with the identification of their relationship and subordination.

One way to systematize factors is to create deterministic factor systems. Create a factor system - means to represent the phenomenon under study in the form of an algebraic sum, a quotient or a product of several factors that determine its magnitude and are functionally dependent on it.

For example, the volume of gross output of an industrial enterprise can be represented as a product of two first-order factors: the average number of workers and the average annual output per worker per year, which in turn depends directly on the number of days worked by one worker on average per year and the average daily output per worker. . The latter can also be decomposed into the length of the working day and the average hourly output (Fig. 5.2).

The development of a deterministic factor system is achieved, as a rule, by detailing complex factors. Elemental (in our example - the number of workers, the number of days worked, the length of the working day) are not decomposed into factors, since they are homogeneous in content. With the development of the system, complex factors are gradually detailed into less general ones, which, in turn, into even less general ones, gradually approaching elemental (simple) ones in their analytical content.

However, it should be noted that the development of factor systems to the required depth is associated with some methodological difficulties and, above all, with the difficulty of finding factors of a general nature that could be represented as a product, particular or algebraic sum of several factors. Therefore, usually deterministic systems cover the most common factors. Meanwhile, the study of more specific factors in AHD is much more important than general ones.

It follows from this that the improvement of the method of factor analysis should be aimed at the interconnected study of specific factors that are, as a rule, in a stochastic relationship with performance indicators.

Of great importance in the study of stochastic relationships is structural and logical analysis of the relationship between the studied indicators. It allows you to establish the presence or absence of causal relationships between the studied indicators, to study the direction of the relationship, the form of dependence, etc., which is very important when determining the degree of their influence on the phenomenon under study and when summarizing the results of the analysis.

Analysis of the structure of the relationship of the studied indicators in the AHD is carried out using the construction structural-logical block diagram, which allows you to establish the presence and direction of the relationship not only between the studied factors and the performance indicator, but also between the factors themselves. Having built a flowchart, one can see that among the studied factors there are those that more or less directly affect the performance indicator, and those that affect not so much the performance indicator as each other.

For example, in fig. 5.3 shows the relationship between the unit cost of crop production and factors such as crop yields, labor productivity, the amount of fertilizer applied, seed quality, and the degree of mechanization of production.

First of all, it is necessary to establish the presence and direction of the relationship between the cost of production and each factor. Of course, there is a close relationship between them. In this example, only the yield of crops has a direct impact on the cost of production. All other factors affect the cost of production not only directly, but also indirectly, through crop yields and labor productivity. For example, the amount of fertilizer applied to the soil contributes to an increase in crop yields, which, other things being the same, leads to a decrease in the unit cost of production. However, it must also be taken into account that an increase in the amount of fertilizers applied leads to an increase in the amount of costs per hectare of sowing. And if the amount of costs increases at a higher rate than the yield, then the cost of production will not decrease, but increase. This means that the relationship between these two indicators can be both direct and inverse. Similarly, it affects the cost of production and the quality of seeds. The purchase of elite, high-quality seeds causes an increase in the amount of costs. If they increase to a greater extent than the yield from the use of higher quality seeds, then the cost of production will increase, and vice versa.

The degree of mechanization of production affects the cost of production both directly and indirectly. An increase in the level of mechanization causes an increase in the cost of maintaining the fixed assets of production. However, at the same time, labor productivity increases, productivity increases, which helps to reduce the cost of production.

A study of the relationships between factors shows that of all the factors studied, there is no causal relationship between the quality of seeds, the amount of fertilizers and the mechanization of production. There is also no direct inverse relationship between these indicators and the level of crop yield. All other factors directly or indirectly influence each other.

Thus, the systematization of factors allows a deeper study of the relationship of factors in the formation of the value of the indicator under study, which is very important at the next stages of the analysis, especially at the stage of modeling the studied indicators.

5.4. Deterministic modeling and transformation of factor systems

Essence and value of modeling, requirements to it. The main types of factorial deterministic models. Methods for transforming factor models. Modeling rules.

One of the tasks of factor analysis is to model the relationship between performance indicators and the factors that determine their value.

Modeling - this is one of the most important methods of scientific knowledge, with the help of which a model (conditional image) of the object of study is created. Its essence lies in the fact that the relationship of the studied indicator with the factorial ones is transmitted in the form of a specific mathematical equation.

In factor analysis, there are deterministic models (functional) and stochastic (correlation). With the help of deterministic factor models, the functional relationship between the performance indicator (function) and factors (arguments) is investigated.

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

1. The factors included in the model, and the models themselves must have a definite character, really exist, and not be invented abstract quantities or phenomena.

2. The factors included in the system should not only be necessary elements of the formula, but also be in a causal relationship with the indicators being studied. In other words, the constructed factorial system should have cognitive value. Factor models that reflect cause-and-effect relationships between indicators have a much greater cognitive value than models created using mathematical abstraction techniques. The latter can be illustrated as follows. Let's take two models:

1)VP=CR X GV:

2) HV=VP/CR, Where VP - gross output of the enterprise; CR - the number of employees in the enterprise; GV - average annual output per worker.

In the first system, the factors are in a causal relationship with the performance indicator, and in the second - in a mathematical relationship. This means that the second model, built on mathematical dependencies, has less cognitive value than the first.

3. All indicators of the factorial model must be quantifiable, i.e. must have a unit of measurement and the necessary information security.

4. The factor model should provide the ability to measure the influence of individual factors, which means that it should take into account the proportionality of changes in the performance and factor indicators, and the sum of the influence of individual factors should be equal to the overall increase in the performance indicator.

In deterministic analysis, the following types of the most common factorial models are distinguished.

1. Additive models:

They are used in cases where the performance indicator is an algebraic sum of several factorial indicators.

2. Multiplicative models:

This type of model is used when the performance indicator is the product of several factors.

3. Multiple Models:

They are used when the effective indicator is obtained by dividing one factor indicator by the value of another.

4. Mixed (combined) models is a combination in various combinations of previous models:

Modeling multiplicative factor systems in AHD is carried out by successive division of the factors of the original system into factors-factors. For example, when studying the process of forming the volume of production (see Figure 5.2), you can use such deterministic models as:

These models reflect the process of detailing the original factor system of a multiplicative type and expanding it by dividing complex factors into factors. The degree of detail and expansion of the model depends on the purpose of the study, as well as on the possibility of detailing and formalizing indicators within the established rules.

In a similar way, modeling of additive factor systems by dividing one or more factor indicators into constituent elements.

As you know, the volume of sales of products is equal to:

VRP =VBP -VAND,

Where VBP - volume of production; VAND - the volume of on-farm use of products.

On the farm, the products were used as seeds (C) and feed (TO). Then the given initial model can be written as follows: VRP =VBP - (C + K).

To the class multiple models the following methods of their transformation are used: lengthening, formal decomposition, expansion and reduction.

First method provides for lengthening the numerator of the original model by replacing one or more factors by the sum of homogeneous indicators. For example, the cost of a unit of production can be represented as a function of two factors: changes in the amount of costs (3) and the volume of output (VBP). The initial model of this factorial system will have the form

If the total amount of costs (3) is replaced by their individual elements, such as wages (3P), raw materials (SM), depreciation of fixed assets (A), overheads (HP) etc., then the deterministic factorial model will look like an additive model with a new set of factors:

Where X 1 - labor intensity of products; X 2 - material consumption of products; X 3 - capital intensity of production; X 4 - overhead level.

Formal decomposition method factor system provides for lengthening the denominator of the original factor model by replacing one or more factors by the sum or product of homogeneous indicators. If IN = L+ M + N + P, then

As a result, we got the final model of the same type as the original factorial system (multiple model). In practice, such a decomposition occurs quite often. For example, when analyzing the indicator of profitability of production (R):

where P - the amount of profit from the sale of products; 3 - the amount of costs for the production and sale of products. If the sum of costs is replaced by its individual elements, the final model as a result of the transformation will take the following form:

The cost of one ton-kilometer depends on the amount of costs for the maintenance and operation of the vehicle (3) and on its average annual output (GV). The initial model of this system will look like: C tkm = 3 / GV. Considering that the average annual production of a car, in turn, depends on the number of days worked by one car per year (D) shift duration (P) and average hourly output (CV), we can extend this model considerably and decompose the cost increment into more factors:

The expansion method involves expanding the original factorial model by multiplying the numerator and denominator of the fraction by one or more new indicators. For example, if the original model

introduce a new indicator, the model will take the form

The result is a final multiplicative model in the form of a product of a new set of factors.

This method of modeling is very widely used in analysis. For example, the average annual output of products by one worker (an indicator of labor productivity) can be written as follows: GV \u003d VP / CR. If you enter such an indicator as the number of days worked by all employees (D), then we get the following model of annual output:

Where DV - average daily output; D - number of days worked per employee.

After introducing the indicator of the number of hours worked by all employees (D), we will obtain a model with a new set of factors: average hourly output (CV), number of days worked per employee (D) and the duration of the working day (I):

The reduction method is the creation of a new factorial model by dividing the numerator and denominator of the fraction by the same indicator:

In this case, we get the final model of the same type as the original one, but with a different set of factors.

Again, a practical example. As you know, the economic profitability of the enterprise is calculated by dividing the amount of profit ( P) on the average annual cost of fixed and working capital of the enterprise (KL):

R=P/K.L.

If we divide the numerator and denominator by the volume of product sales (turnover), we get a multiple model, but with a new set of factors: return on sales and capital intensity of products:

And one more example. Return on assets (FR) is determined by the ratio of gross ( VP) or marketable products ( TP) to the average annual cost of fixed production assets (OPF):

Dividing the numerator and denominator by the average annual number of workers (CR), we will obtain a more meaningful multiple model with other factor indicators: the average annual output of products by one worker (GW), characterizing the level of labor productivity, and capital-labor ratio (FV):

It should be noted that in practice several methods can be successively used to transform the same model. For example:

Where FO - capital productivity; RP - the volume of products sold (revenue); C - cost of goods sold; P- profit; OPF-average annual cost of fixed production assets; OS - average working capital balances.

In this case, to transform the original factorial model, which is built on mathematical dependencies, the methods of lengthening and expansion are used. As a result, a more meaningful model was obtained, which has a greater cognitive value, since it takes into account cause-and-effect relationships between indicators. The resulting final model allows us to explore how the profitability of fixed assets of production, the ratio between fixed and working capital, as well as the turnover ratio of working capital affect the return on assets.

Thus, performance indicators can be decomposed into constituent elements (factors) in various ways and presented in the form of various types of deterministic models. The choice of modeling method depends on the object of study, the goal, as well as on the professional knowledge and skills of the researcher.

The process of modeling factor systems is a very complex and crucial moment in AHD. The final results of the analysis depend on how realistically and accurately the created models reflect the relationship between the studied indicators.

I think many of us, at least once, were interested in artificial intelligence and neural networks. In the theory of neural networks, factor analysis is far from the last place. It is designed to highlight the so-called hidden factors. This analysis has many methods. Standing apart is the method of principal components, a distinctive feature of which is a complete mathematical justification. To be honest, when I started reading the articles on the links above, I felt uncomfortable because I did not understand anything. My interest subsided, but, as it usually happens, understanding came by itself, unexpectedly.

So, let's look at Arabic numerals from 0 to 9. In this case, 5x7 format, which were taken from the project for LCD from Nokia 3310.

Black pixels correspond to 1, white - 0. Thus, we can represent each digit in the form of a 5x7 matrix. For example the matrix below:

matches the picture:

Let's sum the pictures for all the digits, and normalize the resulting one. This means getting a 5x7 matrix whose cells contain the sum of the same cells for different digits divided by their number. As a result, we get a picture:

Matrix for her:

The darkest areas immediately catch your eye. There are three of them, and they correspond to the value 0.9 . This is what they look like. Something that is common to all numbers. The probability of meeting a black pixel in these places is high. Let's look at the lightest areas. There are also three of them, and they correspond to the meaning 0.1 . But again, this is what all the numbers are like, what is common to all of them. The probability of meeting a white pixel in these places is high. How do they differ? And the maximum difference between them is in places with a meaning 0.5 . The color of the pixel in these places is equally probable. Half of the numbers in these places will be black, half will be white. Let's analyze these places, since we have only 6 of them.

The position of a pixel is determined by column and row. The countdown starts from 1, the direction for a row is from top to bottom, for a column it is from left to right. In the remaining cells, the pixel value for each digit in a given position is entered. Now let's select the minimum number of positions at which we can still distinguish between numbers. In other words, for which the values ​​in the columns will be different. Since we have 10 digits, and we encode them binary, mathematically at least 4 combinations of 0 and 1 are required (log(10)/log(2)=3.3). Let's try to select 4 out of 6 that would satisfy our condition:

As you can see, the value in columns 0 and 5 are the same. Consider another combination:

There are also matches between columns 3 and 5. Consider the following:

And here there are no collisions. Bingo! And now I will tell you why all this was started:

Suppose from each pixel, of which we have 5x7 = 35, the signal enters a certain black box, and the output is a signal that corresponds to the input digit. What happens in the black box? And in the black box, out of all 35 signals, those 4 are selected that are fed to the input of the decoder and allow you to uniquely determine the number at the input. Now it is clear why we were looking for combinations without matches. After all, if 4 signals of the first combination were selected in the black box, then the numbers 0 and 5 for such a system would simply not be distinguishable. We minimized the task, because instead of 35 signals, it is enough to process only 4. Those 4 pixels are the minimum set of hidden factors that characterize this array of numbers. This set has a very interesting feature. If you look closely at the values ​​in the columns, you can see that the number 8 is the opposite of the number 4, 7 - 5, 9 - 3, 6 - 2, and 0 - 1. An attentive reader will ask, what does neural networks have to do with it? A feature of neural networks is that it itself is able to highlight these factors, without the intervention of a reasonable person. You just periodically show her the numbers, and she finds those 4 hidden signals and switches it to one of her 10 outputs. How can those similar signals that we discussed at the beginning be applied? And they can serve as a label for a set of numbers. For example, Roman numerals will have their own set of highs and lows, and letters will have their own. By similarity signals, you can separate numbers from letters, but you can only recognize characters within a set by the maximum difference.

In order to find out how profitable or unprofitable an enterprise is, it is not enough just to count the money. To understand this for sure, and most importantly, to help increase profits, you need to regularly carry out the work of the enterprise as a whole. And for this you need to have some skills in the accounting field and certain information. It is worth considering that the company worked both at the time of inflation and during the crisis. The prices changed constantly. Now you understand why the banal counting of money does not make it possible to objectively assess the situation with profit or costs? After all, you need to take into account the price factor.

So, many find it difficult to make an example of our analysis, we hope it will help them make their own - by analogy, this type of diagnosis is compiled extremely quickly. It is in the form of a table. First, let's make a header for our factor analysis. We draw a table with 5 columns and 9 rows. Make the first column wider - it will contain the names of the articles of the enterprise, not numbers. It will be called - "Indicators", which you should write in the first line of the column. In it, fill in all the lines according to the sample: 1 - the name, 2 - put the number 1 - the numbering of the columns, in the 3rd line write down - "Sales revenue", 4 - "Cost". In the fifth line of the first column, put the item - "Business expenses". In 6, write - "Expenses for managing the process." The seventh line is called - and 8 - "Index of price changes", and the last line, 9 - "Sale at comparable prices."

Next, we proceed to the design of 2 columns: in 1 line we write - "Previous period, thousand rubles." (you can write other monetary units - euro, dollar, etc. - depending on the currency in which you will carry out the calculations), and in the second line we write the number - 2. Go to the 3rd column - in it 1 line has the name - "Reporting period", thousand rubles. And the second one is filled with the number 3. Next, we draw up our factorial analysis of revenue and go to column 4. In the first line we enter - "Absolute change, thousand rubles", and the second line contains a small formula: 4 \u003d 3-2. This means that the numbers that you will write in subsequent rows will be the result of subtracting the numbers in the second column from the numbers in the third. We proceed to the design of the last - 5th column. In it, in 1 line, you need to write: "Relative changes%", which means that in this column all data will be written as a percentage. In the second line, the formula is: 5=(4/2)*100%. Everything, we have designed the header, it remains only to fill in each item of the table with the relevant data. We carry out factor analysis, an example of which we give you. First of all, we calculate the price change index - this is perhaps the most important figure in our calculations. We write the numbers of different periods in the corresponding columns. In columns 4 and 5 we carry out the necessary calculations. Factor analysis, of which you can view an example, assumes precision in numbers. Therefore, only reliable information should be written in 3 lines of each column. In 4 and 5, we again carry out calculations. As you understand, the factorial is mainly carried out in lines 5 and 6: try to add there the most real, not underestimated, numbers. In the 4th and 5th columns of these lines, again carry out calculations using formulas. Next, we perform a factor analysis of revenue in column 7 - profit. We write reliable numbers in columns 2 and 3, and in columns 4 and 5 we again consider everything according to the formulas. And the last column remains: we write the data, we calculate. Bottom line: the factor analysis, of which we give you an example, shows what is the impact of each of the factors described in the articles on profit or production costs. Now you see the weaknesses and can correct the situation in order to get as much profit as possible.

You have done all the calculations to perform factor analysis, but they will not help you in any way if you do not analyze the data thoroughly.

The purpose of the economic activity of the enterprise is always a certain result, which depends on numerous and diverse factors. It is obvious that 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 the factors, it is impossible to draw reasonable conclusions about the results of activities, identify production reserves, justify a business plan and make a management decision. 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 magnitude of performance indicators, and theoretical principles underlying forecasting.

There are the following 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 effective indicator can be represented as a product, quotient or algebraic sum of factors.

Correlation analysis is a technique for studying factors whose relationship with the performance indicator is probabilistic (correlation). For example, labor productivity at different enterprises with 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 study is conducted from the general to the particular (deductively). Reverse factor analysis carries out research from private, individual factors to general ones (by induction).

Single-stage factor analysis is used to study the factors of only one level (one stage) of subordination without their detailing into component parts. For example, y \u003d A B. In multistage factor analysis, the factors are detailed A And IN: dividing them into their constituent elements in order to study interdependencies.

Static factor analysis is used when studying the influence of factors on performance indicators for the corresponding date. Dynamic - is a technique for studying the relationship of factor indicators in dynamics.

Retrospective factor analysis studies the causes of changes in performance indicators for past periods, prospective - 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 affect the studied performance indicators;

– determination of the form of dependence between the 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 factorial model.

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

The main ones are the factors that have the most noticeable effect on the result. Others are called secondary. It should be noted that, depending on the circumstances, the same factor can be both primary and secondary.

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

Objective factors do not depend on the will and desires of people (in contracts, these factors are referred to as force majeure; it can be a natural disaster, an unexpected change in the political regime, etc.). Unlike objective, subjective causes depend on the activities of individuals and organizations.

General factors are characteristic of all sectors of the economy. Specific are those that operate in a particular industry or enterprise. Such a division of factors makes it possible to take into account the characteristics of individual enterprises more fully and to make a more accurate assessment of their activities.

Fixed and variable factors are distinguished by the period of impact on the results of production . Constant factors have an impact on 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 those that are associated with a quantitative, rather than qualitative, increase in the result 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 are not decomposed into component parts. In this regard, the 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 (direct action factors). Indirect ones influence through the mediation of other factors. Depending on the degree of mediation of 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, with the help of first-level factors, are called second level factors etc.

Any factorial analysis of indicators begins with the modeling of a multifactorial 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 observed.

1. The 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 causal relationship with the indicator under study.

3. The factor model should provide a measure of the influence of a particular factor on the overall result.

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

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

,

where is the profit from the sale of products,

- revenues from sales,

- production cost of goods sold,

- business expenses

- administrative expenses.

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


,


where is the return on assets,


- return on sales


- return on assets,


- the average value of the organization's assets for the reporting year.


3. When the performance 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 multifactorial models: lengthening, formal decomposition, expansion, reduction and division of one or more factor indicators into constituent elements.


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


;


,


where is the turnover of the company's own capital,


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


- the average cost of equity capital of the organization for the reporting period.


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


Now consider the following return on assets model:


=;


where - the share of revenue attributable to 1 rub. full cost of production


- the share of current assets in the formation of assets,


- the share of stocks 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 margin that is directly embedded in the price of 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 due to the magnitude of output and sales of products and speaks of the efficiency of the use of inventories, physically it expresses the number of turnovers that stocks make in the reporting year.


Equity method is used when it is difficult to establish the dependence of the analyzed indicator on private indicators. The method consists in the fact that the deviation according to the generalizing indicator is proportionally distributed among the individual factors under the influence of which it occurred. For example, you can calculate the impact of a change in balance sheet 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 profits under the influence of the 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.


Method of chain substitutions 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).


Substitution is the replacement of the basic or normative value of a particular indicator with an actual one. Chain substitutions are successive replacements of the base values ​​of particular indicators included in the calculation formula with the actual values ​​of these indicators. Then these influences (the influence of the replacement on the change in the value of the studied generalizing indicator) 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 successively replacing the basic values ​​of the factors with the reporting ones. This method is based on elimination. To eliminate means to eliminate, exclude the influence of all factors on the value of the effective indicator, except for one. At the same time, 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 rest remain unchanged, and so on.


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





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


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


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


The total change  y=y 1 -y 0 is the sum of the 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 effect 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 to F 0 = a 0 · b 0 · c 0 · d 0 ,


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


General deviation of the actual indicator from the baseline  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 in the formula for calculating the indicator F actual parameter values a, b, c, d instead of basic


The verification of the calculation is carried out by comparing the balance of deviations, i.e. the total deviation of the actual indicator from the baseline should be equal to the sum of deviations under the influence of changes in particular indicators:


F 1 –F 0 = F 1 +F 2 +F 3 +F 4 .


Advantages of this method: versatility of application, ease of calculation.


The disadvantage of the method is that, depending on the chosen order of factor replacement, the results of the factor expansion have different values. 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 assessing factors is neglected, highlighting the relative importance of the influence of one or another factor. However, there are certain rules that determine the sequence of substitution:

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


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


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


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


A variation of the method of chain substitutions is the method of calculation using absolute differences. In this case, the objective function, as in the previous example, is presented as 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 other partial indicators - multipliers of the multiplicative model. But, we note, when moving from one factor to another, a different value of the multiplier is taken into account. The multipliers after the factor (on the right), by which the difference is calculated, remain in the value of the base period, and all 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 by the difference method is defined as the product of the deviation of the studied factor by the base or reporting value of another factor, depending on the selected substitution sequence:





Let's show this on the example of the influence of individual factors on the amount of material costs TS m, which are formed under the influence of three factors: the volume of output in physical terms Q, consumption rates of materials per accounting unit of production m and material prices Pm.


TS m = Q· m· Pm.


First, the change in each factor in comparison with the plan is calculated:


change in output  Q= Q 0 – Q 1 ;


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


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


Next, the influence of individual factors on the generalizing indicator is determined, i.e. the cost of materials. At the same time, private indicators that precede the indicator by which the difference is calculated are left in their actual value, and all following it are in the base value.


In this case, the effect of a change in the volume of output  Q the cost of materials will be:


TS mQ = Q· m 0 · Pm 0 ;


the impact of changing material consumption rates  TS mm:


TS mm = Q 1  m· Pm 0 ;


the impact of price changes on materials  ts mp:


ts mp = Q 1 · m 1  Pm.


The total deviation of the amount of material costs 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, situations are more common when one can only assume the existence of a functional dependence (for example, the dependence of revenue ( TR) from the number of produced and sold products ( Q): TR = TR(Q)). To test this assumption, use regressive analysis, with the help of which a function of a certain type is chosen ( F r(Q)). Then, on the set of function definitions (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 the effective indicator in multiplicative and mixed models of the form y = (a - c) . With. It is used in cases where the initial data contain previously defined relative deviations of factor indicators in percent.


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


find the relative deviation of each factor indicator:





determine the deviation of the effective indicator at for each factor





The integral method avoids the disadvantages inherent in the chain substitution method and does not require the use of methods for distributing the irreducible remainder over factors, since it has a logarithmic law of redistribution of factor loadings. The integral method allows you to achieve a complete decomposition of the effective indicator by factors and is universal in nature, i.e. applicable to multiplicative, multiple, and mixed models. The operation of calculating a definite integral is solved with the help of a PC and is reduced to the construction of integrands that depend on the type of function or model of the factorial system.


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


1. View model:





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 affect or may affect 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 the right management decisions by the administration of the organization and reasonable 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 worker per day - from 7.4 to 7.5 hours; the average number of days worked by a worker per year was reduced from 290 to 280 days; the average hourly output of a worker decreased from 26.5 rubles to 23 rubles. The volume of output decreased from 28434.5 tr. up to 25116 tr. Using the method of relative differences, evaluate the influence of factors on the change in the volume of output. Draw reasoned conclusions.


SOLUTION


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


Table 1


Initial data for calculation


Index	Designation	Base year	Reporting year	Deviations (+;-)
Average payroll number of workers, pers.
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
Output volume, tr.	VP	28434,5	25116	3318,5

We have a view model


VP \u003d 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 base (planned) value of the effective indicator by the relative growth of the first factor, expressed as a decimal fraction.


To calculate the influence of the second factor, it is necessary to add the change 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 Proth factor.


The influence of the third factor is determined similarly: 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.


Similarly, the influence of the fourth factor





Let us summarize the factors that contributed to the formation of revenue in the reporting year:


increase in the number of workers 1137.38 t.


increasing the number of hours worked per worker


per day 399.62 t.


changes in the number of working days -1033.5 t.


Changes in average hourly output -3821.95 tr.


Total -3318.45 thousand rubles


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 output according to the condition of the problem. A slight discrepancy is determined by the degree of rounding in the calculations. The growth of the average payroll number of workers by 20 people in the amount of 1137.8 thousand rubles had a positive effect, 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 exerted by a decrease in the average hourly work of one worker by 3.5 rubles. per hour, which resulted in a decrease in output by -3821.5 tr. The decrease in the average number of days worked by one worker per year by 10 days led to a decrease in output by -1033.5 tr.


TASK 3


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


SOLUTION


Joint Stock Company "KRAITEHSNAB", registered by the Registration Chamber of the Mayor's Office of Krasnodar No. 10952 dated May 14, 1999, PSRN 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 the 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 trademark registered in the prescribed manner and other means of visual identification.


Full corporate name of the Company in Russian:
Closed Joint Stock Company "KRAITEHSNAB". Abbreviated corporate name of the Company in Russian: CJSC KRAITEHSNAB.


Location (postal address) of the Company: 350021, Russian Federation, Krasnodar region, Krasnodar, Karasunsky administrative district, st. Tram, 25.


Closed Joint Stock Company "KRAITEHSNAB" was established without limitation of the period of activity.


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


Let's analyze the indicators of financial stability of the organization under study (table 2).


table 2


Analysis of indicators of financial stability of CJSC "Kraitekhsnab" 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 stocks 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 borrowings	6031947,4	5188235,4	6412268,4	380 321,0	106,3
6. Short-term loans and borrowings	1500000,0	2000000,0	1500000,0
7. The total value of sources of funds, taking into account long-term and short-term borrowings	7531947,4	7188235,4	7912268,4	380 321,0	105,0
8. The amount of stocks and costs circulating in the asset balance	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. Surplus 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)

When analyzing the type of financial stability of an enterprise in dynamics, a decrease in the financial stability of an enterprise is noticeable.


As can be seen from Table 2, in 2003, and in 2004, and in 2005, the financial stability of CJSC "Kraitekhsnab" in terms of a 3-complex indicator of financial stability can be characterized as "Crisis-unstable state of the enterprise", since the enterprise does not have enough funds for the formation of stocks and costs for the implementation of current activities.


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


Table 3


Financial stability ratios of CJSC "Kraitekhsnab"


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
The ratio of mobile and immobilized means	11,56	13,32	18,79	1,76	5,47
The coefficient of the ratio of own and borrowed funds	0,78	0,60	0,43	0,18	0,17
Agility factor	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

The analysis of financial stability in terms of relative indicators, presented in Table 3, indicates that, according to the indicators presented in the table, compared with the base period (2003), the situation at CJSC “Kraitekhsnab” as a whole worsened in 2004 and slightly improved in the reporting 2005 G.


The indicator "Coefficient of autonomy" for the period from 2003 to 2004 decreased by -0.06 and in 2004 amounted to 0.37. This is below the normative value (0.5) at which the borrowed capital can be compensated by the property of the enterprise. The indicator "Coefficient of autonomy" for the period from 2004 to 2005 decreased by -0.08 and in 2005 amounted to 0.30. It is also below the normative value (0.5) at which borrowed capital can be compensated by the property of the enterprise.


The indicator "Ratio of borrowed and own funds" (financial leverage), for the period from 2003 to 2004 increased by 0.39 and in 2004 amounted to 1.67. 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 company's dependence on borrowed funds. The permissible level is often determined by the operating conditions of each enterprise, primarily by the speed of turnover of working capital. Therefore, it is additionally necessary to determine the turnover rate of inventories and receivables for the analyzed period. If accounts receivable turn around faster than working capital, which means a rather high intensity of cash flow to the enterprise, i.e. The end result is an increase in equity. Therefore, with a high turnover of material working capital and an even higher turnover of accounts receivable, the ratio of own and borrowed funds can be much higher than 1.


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


Indicator "Coefficient of maneuverability", 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 coefficient of maneuverability characterizes what proportion of sources of own funds is in a mobile form. The normative value of the indicator depends on the nature of the enterprise's activity: in capital-intensive industries, its normal level should be lower than in material-intensive ones. At the end of the analyzed period CJSC "Kraitekhsnab" has a light structure of assets. 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 production.


Indicator "Coefficient of provision of reserves 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 below the standard value (0.6 - 0.8), as in 2003, 2004 and 2005. The enterprise lacks its own funds for the formation of reserves and costs, which was also shown by the analysis of financial stability indicators in absolute terms.


BIBLIOGRAPHY

1. The procedure for monitoring the financial condition of organizations and accounting for their solvency. Federal Service of Russia for Insolvency and Financial Recovery: Order No. 13-r of 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.
   Evaluation of the economic performance of a trading enterprise ON THE EXAMPLE OF THE MAIN PERFORMANCE INDICATORS OF THE ENTERPRISE SHOW THE USE OF 6 PRIVATE METHODS AND RECEPTIONS OF ECONOMIC ANALYSIS Financial condition of a trade organization and assessment of economic indicators

   2013-11-12

All business processes of enterprises are interconnected and interdependent. Some of them are directly related to each other, some are manifested 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, 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 the 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.

Can be distinguished 2 goals of factor analysis:
- determination of the relationship between variables (classification).
— reduction of the number of variables (clustering).

Factor analysis of the enterprise- a comprehensive methodology for systematic study and assessment of the impact of factors on the value of the effective 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 factors is probabilistic.
3. Direct / Reverse - from general to specific and vice versa.
4. Single stage / multi stage.
5. Retrospective / prospective.

Let's take a closer look at the first two.

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

Factor analysis carried out in several stages:
Stage 1. Selected factors.
Stage 2. Factors are classified and systematized.
Stage 3. The relationship between the performance indicator and factors is modeled.
Stage 4. Evaluation of 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 singled out.

Deterministic factor analysis- a study in which factors affect 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 effective indicator.

Stochastic factor analysis- a study in which factors affect 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.