Purpose of system analysis. The concept of "problem" in system analysis. Systematic cognitive activity

Let's see what meaning its authors put into system analysis, how they explain this concept.

The concept of "systemic" is used because this kind of research is based on the use of the category of system.

On the one hand, the system is the physical reality in relation to which it is necessary to make decisions (any natural and artificial objects).

On the other hand, in the process of system analysis, an abstract and conceptual system is created, described with the help of symbols or other means, which is a certain structural and logical device, the purpose of which is to serve as a tool for understanding, describing and possibly more fully optimizing the behavior of connections and relations of elements. real physical system. This kind of abstract system can be a mathematical, machine or verbal model or system of models, etc. In the physical and corresponding abstract systems, a one-to-one relationship between elements and their connections must be established. In this case, it is possible, without resorting to experiments on real physical systems, to evaluate various kinds of working hypotheses regarding the expediency of certain actions, using the corresponding abstract system, and develop the most preferable solution.

The term "analysis" is used to characterize the very procedure of conducting research, which consists in breaking the problem as a whole into its constituent parts, more accessible for solution, using the most appropriate special methods to solve individual subproblems, and finally combining particular solutions so that to develop a general solution to the problem. Obviously, the most effective analysis can be made only on the basis of a systematic approach, which provides not only an organic combination of the analytical division of problems into parts and the study of connections and relationships between these parts, but also places special emphasis on the consideration of goals and objectives common to all parts. , and in accordance with this, the synthesis of the general solution from particular solutions is carried out. In fact, in system analysis, the methods of analysis and synthesis are mutually intertwined, in the implementation of the analytical procedure, attention is constantly drawn to ways to combine individual results into a single whole and the influence of each of the elements on other elements of the system.

Today, "system analysis" as a whole is interpreted so broadly and vaguely that it practically cannot be implemented in specific studies. And apparently, it is no coincidence that today there is still no way to pick up a cross-cutting example of a fairly large completed systematic study. Let's try to understand this concept.

Referring to different points of view on the term "system analysis", experts distinguish two different approaches.

Supporters of the first of them emphasize mathematics, i.e. on the description of a complex system using formal means (block diagrams, networks, mathematical equations). Based on this kind of formal description, a mathematical problem is often posed to find the optimal design of the system or the best mode of its functioning, i.e., finding the maximum (or minimum) of the system’s objective function (for example, maximum profit, maximum number of disabled military facilities, minimum time operations, maximum reliability, etc.) under given restrictions on the values of controlled variables.

It should be emphasized that the compilation of block diagrams that characterize the relationship and sequence of operations performed is a stage preceding any calculations on a computer. Therefore, in many cases, system analysis began to be called any work of this kind performed by specialists directly involved in computer maintenance.

Another approach, which corresponds to the point of view of the RAND Corporation, puts the logic of systems analysis at the forefront. In this case, the inseparable connection of system analysis with decision-making is emphasized, and means the choice of a certain image or course of action among several possible alternatives. Here, system analysis is considered primarily as a methodology for clarifying and streamlining, or the so-called structuring of a problem to be solved with or without the use of mathematics and computers. At the same time, the concept of “structuring” is invested both in explaining the real goals of the system itself, alternative ways to achieve these goals and the relationships between the components in the process of implementing each alternative, and in achieving an in-depth understanding of the external conditions in which the problem arose, and hence the limitations and consequences of this or that a different course of action. Logical system analysis is supplemented to some extent by mathematical, statistical and logical methods, however, both the scope of its application and the methodology differ significantly from the subject and methodology of formal mathematical system research.

At first, systems analysis was based mainly on the application of complex mathematical techniques. After some time, scientists came to the conclusion that mathematics is ineffective in the analysis of broad problems with many uncertainties that are characteristic of the research and development of technology as a whole. Many leading system specialists speak about it. Therefore, the concept of such a system analysis began to be developed, in which the emphasis is mainly on the development of essentially new dialectical principles of scientific thinking, the logical analysis of complex objects, taking into account their interconnections and contradictory trends. With this approach, it is no longer mathematical methods that come to the fore, but the very logic of system analysis, streamlining the decision-making procedure. And apparently, it is no coincidence that in recent times a systemic approach is often understood as a certain set of systemic principles.

This approach, which we will primarily adhere to, corresponds to our following definition.

System analysis is an interconnected logical-mathematical and comprehensive consideration of all issues related not only to the design, development, production, operation and subsequent elimination of modern TS, but also to the methods of managing all these stages, taking into account social, political, strategic, psychological, legal , geographical, demographic, military and other aspects.

How is system analysis different from other methods?

Its main differences from other more or less formalized approaches in substantiating management decisions are as follows:

all theoretical possible alternative methods and means of achieving the goals of the TS life cycle (research, design, technological, operational, etc.), the correct combination and combination of these various methods and means are considered;
TS alternatives are necessarily assessed from a long-term perspective (especially for systems that have a strategic purpose);
there are no standard solutions;
different views are clearly stated when solving the same problem;
apply to problems for which cost or time requirements are not fully defined;
the fundamental importance of organizational and subjective factors in the decision-making process is recognized, and in accordance with this, procedures are developed for the widespread use of qualitative judgments in the analysis and coordination of various points of view;
special attention is paid to risk and uncertainty factors, their consideration and evaluation when choosing the most optimal solutions among the possible options.

The increased focus of systems engineers on risk and uncertainty stems directly from the extension of systems analysis to forward-looking problems. If the risk is understood as the potential variability of the objective characteristics of the analyzed TS, then the uncertainty expresses the lack of subjective knowledge about the form in which these phenomena will manifest themselves.

The trend towards systematic analysis of large problems appears only when their scale increases to such an extent that solutions become complex, time-consuming and costly. When substantiating such decisions, which become the subject of system analysis, factors calculated ahead for a 10-15-year period are becoming increasingly important. Factors of this kind include, above all, the huge increase in investment in large programs covering a long period, and the increasing dependence of these programs on the results of scientific research and technical developments.

Another important reason for the need to take into account the long term is the strategic nature of the goals themselves, which are set before the system analysis and which predetermine the policy of the government (or organization) for a long period.

It is important to note that the more general and important problems arise for managers at various levels, the more the importance of system analysis for their solution increases.

Where can and should systems analysis be applied?

Its application is determined by the type of problems that we will consider.

All problems, depending on the depth of their knowledge, are divided into three classes:

well-structured or quantified problems in which the essential dependencies are clarified so well that they can be expressed in numbers and symbols, which eventually receive numerical estimates;
unstructured or qualitatively expressed problems containing only a description of the most important resources, features and characteristics, the quantitative relationships between which are completely unknown;
loosely structured or mixed problems that contain both qualitative and quantitative elements, with the qualitative obscure and undefined aspects of the problem tending to dominate.

Operations research (OR) methodology is used to solve well-structured problems. It consists in applying mathematical models and methods (linear, non-linear, dynamic programming, queuing theory, game theory, etc.) to find the optimal strategy for managing purposeful actions. The main problem of applying the methods of operations research is to correctly select a typical or develop a new mathematical model, collect the necessary initial data and make sure, by analyzing the initial prerequisites and results of mathematical calculation, that this model reflects the essence of the problem being solved.

In unstructured problems, the heuristic method is traditional, which consists in the fact that an experienced specialist collects a maximum of various information about the problem being solved, gets used to it and, based on intuition and judgment, makes suggestions for appropriate measures.

With this approach, there is no ordered logical procedure for finding a solution, and the specialist who puts forward certain proposals cannot clearly state the way on the basis of which he arrived at the final recommendations from a set of disparate initial information. When solving a problem, such a specialist relies on his own experience, on the experience of his colleagues, on professional readiness, on studying similar problems by the method of situations, but not on a clearly formulated methodology.

The weakly structured problems that systems analysis is intended to solve include most of the most important economic, technical, political, and military-strategic problems of a large scale.

Typical problems of this kind are those that:

scheduled for future resolution;
faced with a wide range of alternatives;
depend on the current incompleteness of technological advances;
require large investments of capital and contain elements of risk;
internally complex due to the combination of resources needed to solve them;
for which cost or time requirements are not fully defined.

When carrying out a system analysis in the process of structuring the problem, some of its elements-subtasks receive a quantitative expression, and the relationships between all the elements become more and more definite. Based on this, in contrast to the application of IO methods, when using system analysis, an initial clear and exhaustive statement of the problem is not at all necessary, this clarity should be achieved in the process of the analysis itself and is considered as one of its main goals. The tasks of IO methods can be posed in quantitative form and solved on a computer. In contrast to this, strategic problems, consisting in the development of long-term policy in the field of production, as a rule, cannot be formulated as tasks of IO. Problems of this kind are the subject of system analysis. Strategic objectives are not easily qualified (i.e. quantified) due to the lack of an unambiguous criterion of optimality for the firm as a whole and require the involvement of subjective judgments of experienced managers and experts when developing solutions.

Let's sum up some results in essence of the system analysis.

System analysis is concerned with making the best decision from many possible alternatives.
Each alternative is evaluated from a long-term perspective.
SA is considered as a methodology for in-depth understanding (understanding) and ordering (structuring) of the problem.
In SA, the emphasis is on the development of new principles of scientific thinking, taking into account the interconnection of the whole and contradictory tendencies. More specifically, systematically at all stages of the life cycle of any TS, alternatives are compared, if possible in a quantitative form, based on a logical sequence of steps.
The intuition of specialists is aggravated.
It is used primarily to solve strategic problems.

So, SA is a set of methods and means for developing, making and justifying decisions (in the study, creation and management of the TS, in particular).

What is the novelty of system analysis, its main advantages and disadvantages?

The novelty of systems analysis lies in the fact that it considers the problem as a whole, with a constant emphasis on the clarity of the analysis, on quantitative methods and on the identification of uncertainty. Also new are schemes or models where the relationships cannot be adequately expressed using a mathematical model.

The advantage of systems analysis is that it allows you to systematically and effectively combine the judgments and intuitions of experts in their respective fields.

Systems analysis should not be seen as opposed to subjective judgments, but as a structural framework that ensures that the judgments of experts in various fields are used to obtain results that surpass any individual judgments. This is his goal, and he provides the opportunity for this.

But the subjectivity of judgments, the inaccuracy of knowledge, the intuitiveness of estimates, and the uncertainty of information about the nature and actions of other people lead to the fact that on the basis of research one can achieve no more than an estimate of some advantage of choosing one alternative over another.

The limitations of system analysis are due to:

the inevitable incompleteness of the analysis;
approximate measure of efficiency;
no way to accurately predict the future.

Some socio-political factors should play an important role in the development and selection of alternatives. However, at present there are no even approximate ways to measure these factors, and one has to take them into account intuitively.

It is extremely important to focus on immeasurable factors the attention of a responsible leader who makes decisions.

The disadvantages of system analysis are as follows. Many factors of fundamental importance cannot be quantified and may be left out of consideration or deliberately left for later consideration and then forgotten. Sometimes they may be given the wrong weight in the analysis itself or in a decision based on such an analysis.

Another reason is that a study may appear on the surface to be so scientific and quantitatively accurate that it may be given a wholly unjustified validity, despite the fact that it involves many subjective judgments. In other words, we may be so fascinated by the attractiveness and precision of numbers that we overlook the simplifications made to achieve this precision, overlook the analysis of qualitative factors, and exaggerate the importance of abstract calculations in the decision process. But without analysis, we face an even greater danger of missing out on improvements in certain considerations and incorrectly "weighing" individual factors.

What is the main meaning of system analysis?

The main and most valuable result of system analysis is not a quantitatively defined solution to the problem, but an increase in the degree of its understanding and possible solutions among specialists and experts participating in the study of the problem, and, most importantly, among responsible persons who are provided with a set of well-developed and evaluated alternatives.

The usefulness of new methods of analysis and management and, first of all, system analysis is as follows:

in greater understanding and insight into the essence of the problem: practical efforts to identify relationships and quantitative values will help to discover hidden points of view behind certain decisions;
in greater accuracy: a clearer formulation of goals, objectives ... will reduce, although not eliminate, the inevitable ambiguous aspects of multifaceted goals;
more comparably: analysis (policy) can be carried out in such a way that plans for one country or area can be usefully linked and compared with plans and policies for other areas; it is possible to identify common elements;
in greater utility, efficiency: the development of new methods should lead to the distribution of monetary resources ... in a more orderly manner and should help to test the value of intuitive judgments.

Let us illustrate the significance of the methods of system analysis with one example. But first, remember that the main tasks of systems analysis are to identify the entire set of alternatives for solving a problem and compare them in terms of cost and effectiveness in achieving a certain goal. Any complex problem involves many different factors that cannot be covered by one discipline. Therefore, it is advisable to create interdisciplinary teams of specialists with knowledge and qualifications in various fields. At the same time, it is more important that the problem looks different in the eyes of an economist, biologist, engineer, etc., and the different approaches inherent in them can better contribute to finding solutions.

There is a need to look at the problem from different points of view in order to find out which approach or which combination of "ad hoc approaches" is the best. Let's explain this with an example: The manager of a large administrative building received an increasing stream of complaints from employees who worked in this building. Complaints indicated that it took too long to wait for the elevator. The manager asked for help from a company specializing in lifting systems. The engineers of this firm conducted timing, which showed that the complaints are well founded. It was found that the average waiting time for the elevator exceeds the accepted norms. The experts told the manager that there were three possible ways to solve the problem: increasing the number of elevators, replacing existing elevators with high-speed ones, and introducing a special mode of operation of elevators, i.e. transfer of each elevator to serve only certain floors. The manager asked the firm to evaluate all of these alternatives and provide him with estimates of the estimated costs for implementing each of the options.

After some time, the company complied with this request. It turned out that the implementation of the first two options required costs, which, from the point of view of the manager, were not justified by the income generated by the building, and the third option, as it turned out, did not provide a sufficient reduction in waiting time. The manager was not satisfied with any of these proposals. He postponed further negotiations with this firm for some time to consider all options and make a decision.

When a manager is faced with a problem that seems to him insoluble, he often finds it necessary to discuss it with some of his subordinates. The group of employees approached by our manager included a young psychologist who worked in the recruitment department that maintained and renovated this large building. When the manager presented the essence of the problem to the assembled employees, this young man was very surprised at the very posing of it. He said he couldn't understand why office workers, who were known to waste a lot of time every day, were unhappy about having to wait minutes for an elevator. Before he had time to express his doubt, the thought flashed through him that he had found an explanation. Although employees often uselessly waste their working hours, they are busy at this time with something, albeit unproductive, but pleasant. But waiting for the elevator, they just languish from idleness. At this guess, the face of the young psychologist lit up, and he blurted out his proposal. The manager accepted it, and a few days later the problem was solved at the most minimal cost. The psychologist suggested hanging large mirrors on each floor by the elevator. These mirrors, of course, gave the women waiting for the elevator something to do, but the men, who were now absorbed in looking at the women, pretended not to pay any attention to them, ceased to be bored.

No matter how true the story is, but the point it illustrates is extremely important. The Psychologist was looking at exactly the same problem as the engineers, but he approached it from a different perspective, determined by his education and interests. In this case, the approach of the psychologist proved to be the most effective. Obviously, the problem was solved by changing the goal, which was reduced not to reduce the waiting time, but to create the impression that it had become less.

Thus, we need to simplify systems, operations, decision-making procedures, etc. But this simplicity is not so easy to achieve. This is the hardest task. The old saying, "I'm writing you a long letter because I don't have time to make it short" can be paraphrased as "I'm making it complicated because I don't know how to make it simple."

System analysis solves this problem!

The central procedure in system analysis is the construction of a generalized model (or models) that reflects all the factors and relationships of the real situation that may appear in the process of implementing the decision. The resulting model is investigated in order to find out the closeness of the result of applying one or another of the alternative options for action to the desired one, the comparative cost of resources for each of the options, the degree of sensitivity of the model to various undesirable external influences. System analysis is based on a number of applied mathematical disciplines and methods widely used in modern management activities: operations research, peer review method, critical path method, queuing theory, etc. The technical basis of system analysis is modern computers and information systems.

The methodological means used in solving problems using system analysis are determined depending on whether a single goal or a certain set of goals is pursued, whether a decision is made by one person or several, etc. When there is one fairly clearly defined goal, the degree of achievement of which can be evaluated on the basis of one criterion, methods of mathematical programming are used. If the degree of achievement of the goal must be assessed on the basis of several criteria, the apparatus of utility theory is used, with the help of which the criteria are ordered and the importance of each of them is determined. When the development of events is determined by the interaction of several persons or systems, each of which pursues its own goals and makes its own decisions, the methods of game theory are used.

The effectiveness of the study of control systems is largely determined by the chosen and used research methods. To facilitate the choice of methods in real decision-making conditions, it is necessary to divide the methods into groups, characterize the features of these groups and give recommendations on their use in the development of models and methods of system analysis.

The whole set of research methods can be divided into three large groups: methods based on the use of knowledge and intuition of specialists; methods of formalized representation of control systems (methods of formal modeling of the processes under study) and integrated methods.

As already noted, a specific feature of system analysis is the combination of qualitative and formal methods. This combination forms the basis of any technique used. Let's consider the main methods aimed at using the intuition and experience of specialists, as well as methods of formalized representation of systems.

Methods based on the identification and generalization of the opinions of experienced experts, the use of their experience and non-traditional approaches to the analysis of the organization's activities include: the "Brainstorming" method, the "scenarios" type method, the method of expert assessments (including SWOT analysis), the " Delphi", methods such as "tree of goals", "business game", morphological methods and a number of other methods.

The above terms characterize one or another approach to enhancing the identification and generalization of the opinions of experienced experts (the term "expert" in Latin means "experienced"). Sometimes all these methods are called "expert". However, there is also a special class of methods that are directly related to the questioning of experts, the so-called method of expert assessments (since it is customary to put down marks in points and ranks in polls), therefore, these and similar approaches are sometimes combined with the term "qualitative" (specifying the convention of this name, since when processing the opinions received from specialists, quantitative methods can also be used). This term (although somewhat cumbersome) more than others reflects the essence of the methods that specialists are forced to resort to when they not only cannot immediately describe the problem under consideration by analytical dependencies, but also do not see which of the methods of formalized representation of systems considered above could help get the model.

Brainstorming methods. The concept of brainstorming has become widespread since the early 1950s as a "method of systematically training creative thinking" aimed at "discovering new ideas and reaching agreement among a group of people based on intuitive thinking."

Methods of this type pursue the main goal - the search for new ideas, their broad discussion and constructive criticism. The main hypothesis is the assumption that among a large number of ideas there are at least a few good ones. Depending on the rules adopted and the rigidity of their implementation, there are direct brainstorming, the method of exchange of opinions, methods such as commissions, courts (when one group makes as many proposals as possible, and the second tries to criticize them as much as possible), etc. Recently, sometimes brainstorming is carried out in the form of a business game.

Scenario type methods. Methods for preparing and coordinating ideas about a problem or an analyzed object, set out in writing, are called scenarios. Initially, this method involved the preparation of a text containing a logical sequence of events or possible solutions to a problem, deployed over time. However, the obligatory requirement of time coordinates was later removed, and any document containing an analysis of the problem under consideration and proposals for its solution or for the development of the system, regardless of the form in which it is presented, began to be called a scenario. As a rule, in practice, proposals for the preparation of such documents are written by experts individually at first, and then an agreed text is formed.

The role of system analysts in the preparation of the scenario is to help the leading specialists of the relevant fields of knowledge to be involved in identifying the general patterns of the system; analyze external and internal factors influencing its development and formation of goals; identify the sources of these factors; analyze the statements of leading experts in the periodical press, scientific publications and other sources of scientific and technical information; create auxiliary information funds (better automated) that contribute to the solution of the corresponding problem.

The scenario allows you to create a preliminary idea of the problem (system) in situations where it is not possible to immediately display it with a formal model. But still, a script is a text with all the ensuing consequences (synonymy, homonymy, paradoxes) associated with the possibility of its ambiguous interpretation by different specialists. Therefore, such a text should be considered as the basis for developing a more formalized view of the future system or problem being solved.

Methods of expert assessments. The basis of these methods is various forms of expert survey followed by evaluation and selection of the most preferred option. The possibility of using expert assessments, the justification of their objectivity is based on the fact that an unknown characteristic of the phenomenon under study is interpreted as a random variable, the reflection of the distribution law of which is an individual assessment of the expert on the reliability and significance of an event.

It is assumed that the true value of the characteristic under study is within the range of estimates received from the group of experts and that the generalized collective opinion is reliable. The most controversial point in these methods is the establishment of weighting coefficients according to the assessments expressed by experts and the reduction of conflicting assessments to some average value.

Expert survey This is not a one-time procedure. This way of obtaining information about a complex problem, characterized by a high degree of uncertainty, should become a kind of "mechanism" in a complex system, i.e. it is necessary to create a regular system of work with experts.

One of the varieties of the expert method is the method of studying the strengths and weaknesses of the organization, the opportunities and threats to its activities - the method of SWOT analysis.

This group of methods is widely used in socio-economic research.

Delphi type methods. Initially, the Delphi method was proposed as one of the brainstorming procedures and should help reduce the influence of psychological factors and increase the objectivity of expert assessments. Then the method began to be used independently. It is based on feedback, familiarizing the experts with the results of the previous round and taking these results into account when assessing the significance of the experts.

In specific methods that implement the "Delphi" procedure, this tool is used to varying degrees. So, in a simplified form, a sequence of iterative brainstorming cycles is organized. In a more complex version, a program of sequential individual surveys is developed using questionnaires that exclude contacts between experts, but provide for their acquaintance with each other's opinions between rounds. Questionnaires from tour to tour can be updated. To reduce factors such as suggestion or accommodation to the opinion of the majority, sometimes it is required that experts substantiate their point of view, but this does not always lead to the desired result, but, on the contrary, may increase the effect of adjustment. In the most advanced methods, experts are assigned weight coefficients of the significance of their opinions, calculated on the basis of previous surveys, refined from round to round, and taken into account when obtaining generalized assessment results.

Methods of the "tree of goals" type. The term "tree" implies the use of a hierarchical structure obtained by dividing the general goal into subgoals, and these, in turn, into more detailed components, which can be called subgoals of lower levels or, starting from a certain level, functions.

The "tree of goals" method is focused on obtaining a relatively stable structure of the goals of problems, directions, i.e. a structure that has changed little over a period of time with the inevitable changes that occur in any developing system.

To achieve this, when constructing the initial version of the structure, one should take into account the patterns of goal formation and use the principles of forming hierarchical structures.

Morphological methods. The main idea of the morphological approach is to systematically find all possible solutions to the problem by combining the selected elements or their features. In a systematic form, the method of morphological analysis was first proposed by the Swiss astronomer F. Zwicky and is often called the "Zwicky method".

business games- the simulation method has been developed for making managerial decisions in various situations by playing a group of people or a person and a computer according to the given rules. Business games allow, with the help of modeling and imitation of processes, to analyze, solve complex practical problems, ensure the formation of a thinking culture, management, communication skills, decision-making, instrumental expansion of managerial skills.

Business games act as a means of analyzing management systems and training specialists.

To describe management systems in practice, a number of formalized methods are used, which to varying degrees provide the study of the functioning of systems in time, the study of management schemes, the composition of units, their subordination, etc., in order to create normal working conditions for the management apparatus, personalization and clear information management

One of the most complete classifications based on a formalized representation of systems, i.e. on a mathematical basis, includes the following methods:

- analytical (methods of both classical mathematics and mathematical programming);
- statistical (mathematical statistics, probability theory, queuing theory);
- set-theoretic, logical, linguistic, semiotic (considered as sections of discrete mathematics);

graphic (graph theory, etc.).

The class of poorly organized systems corresponds in this classification to statistical representations. For the class of self-organizing systems, the most suitable models are discrete mathematics and graphical models, as well as their combinations.

Applied classifications are focused on economic and mathematical methods and models and are mainly determined by the functional set of tasks solved by the system.

Consider examples of system analysis:

Example . Let's consider a simple task - to go to classes in the university in the morning. This problem, often solved by a student, has all aspects:

- material, physical aspect - the student needs to move a certain mass, for example, textbooks and notebooks to the required distance;
- energy aspect - the student needs to have and spend a specific amount of energy to move;
- information aspect - information is needed about the route of movement and the location of the university, and it needs to be processed along the way of one's movement;
- human aspect - movement, in particular, movement by bus is impossible without a person, for example, without a bus driver;
- organizational aspect - suitable transport networks and routes, stops, etc. are needed;
- spatial aspect - moving a certain distance;
- time aspect - time will be spent on this movement (during which there will be corresponding irreversible changes in the environment, in relations, in connections).

All types of resources are closely related and intertwined. Moreover, they are impossible without each other, the actualization of one of them leads to the actualization of the other.

Types of thinking

A special type of thinking is systemic, inherent in an analyst who wants not only to understand the essence of the process, phenomenon, but also to control it. Sometimes it is identified with analytical thinking, but this identification is not complete. An analytical mindset can be, and a systems approach is a methodology based on systems theory.

Subject (subject-oriented) thinking is a method (principle) with the help of which it is possible to purposefully (usually for the purpose of studying) identify and update, learn cause-and-effect relationships and patterns in a number of private and general events and phenomena. Often this is a technique and technology for studying systems.

Systemic (system-oriented) thinking is a method (principle) with the help of which it is possible to purposefully (usually for the purpose of management) identify and update, learn cause-and-effect relationships and patterns in a number of general and universal events and phenomena. It is often a systems research methodology.

In systems thinking, a set of events, phenomena (which may consist of various constituent elements) is updated, studied as a whole, as one event organized according to general rules, a phenomenon whose behavior can be predicted, predicted (as a rule) without clarifying not only the behavior of the constituent elements, but also the quality and quantity of themselves. Until it is understood how the system as a whole functions or develops, no knowledge of its parts will give a complete picture of this development.

What is an elephant or why is system analysis needed?

One day, six blind men asked what an elephant was. And kind people led them to the elephant. One, touching his side, said: I know the elephant is a wall. Another, touching his leg, said: this is a pillar. The third, holding on to the trunk, is a snake ... They all left in full confidence that they know what an elephant is.

With this parable, I begin the first lesson in the discipline "Theory of Systems and System Analysis". It allows us to clearly and concisely outline several important aspects of this very interesting and useful discipline.

At the first lesson, it allows you to designate the position in which we are with the students at the beginning of the study of the discipline. With each new group, we, together with the students, are like blind men from a parable, and in front of us is an “elephant” - this is the discipline “Systems Theory and System Analysis”. Everyone has their own idea of this discipline, and in order to work effectively further, we need all of us to equally understand what we are going to talk about. And the main task of the first lesson is to define terms and come to a common understanding of what we will study next. And then, in the words of one of my students “the realization will come that “system analysis” is not just a bunch of words, but a necessary discipline in my profession.”

So what is systems analysis? "System analysis is a logically connected set of theoretical and empirical provisions from the field of mathematics, natural sciences and the experience of developing complex systems, which provides an increase in the validity of solving a specific problem."

System analysis allows you to divide a complex task into a set of simple tasks, to divide a complex system into elements, taking into account their relationship.

In many other subjects there are a lot of techniques and methods that are difficult to apply to other disciplines. In systems analysis there is a system of methods that are applied in all other subjects.

One of the objectives of the discipline "Systems Theory and Systems Analysis" is to learn as many methods of systems analysis as possible. Knowledge of methods and the ability to apply them to any task allow us to more effectively and efficiently solve problems that arise both in professional activities and in the personal sphere.

In the process of mastering the theoretical and practical aspects of system analysis, applying the studied methods to solving problems, system thinking develops. Systems thinking is the second aspect that the parable of the elephant allows us to illustrate. To see systematically is to see “the whole elephant at once”, to see the situation as a whole when solving any problem, to understand all aspects and nuances.

In other words " systems thinking is the ability to think in such a way as to see a holistic picture, while relying on various theoretical models and a holistic intuitive vision of complex objects. In systems thinking, intuition often prevails so far. Systems thinking with a predominance of the factor of intuition can use both methods of inductive and deductive thinking.

Every person has a systematic way of thinking, but not everyone uses it. The study of systems analysis allows you to develop systems thinking and see the benefits of its use in solving any problems of any level of complexity in any field of activity. The development of systems thinking is the main task of the discipline "Theory of Systems and System Analysis".

My task as a teacher is for students to try the methods of system analysis, applying them in their practical activities, primarily professional, and maybe somewhere else, and see their effectiveness.

The task of students is to listen, write down their thoughts, ask as many questions as possible, and express their opinion.

And then the study of the discipline "Systems Theory and System Analysis" will be interesting, useful and allow you to move further on the path of becoming an effective professional.

SYSTEM ANALYSIS- a set of methods and tools used in the study and design of complex and super-complex objects, primarily methods for developing, making and justifying decisions in the design, creation and management of social, economic, man-machine and technical systems . In the literature, the concept of system analysis is sometimes identified with the concept systems approach , but such a generalized interpretation of systems analysis is hardly justified. Systems analysis emerged in the 1960s. as a result of the development of operations research and systems engineering. The theoretical and methodological basis of system analysis is a systematic approach and general systems theory . The system analysis is applied hl.o. to the study of artificial (arising with the participation of man) systems, and in such systems an important role belongs to human activity. The use of system analysis methods for solving research and management problems is necessary, first of all, because in the decision-making process one has to make choices under conditions of uncertainty, which is associated with the presence of factors that cannot be rigorously quantified. The procedures and methods of system analysis are aimed at putting forward alternative solutions to the problem, identifying the extent of uncertainty for each of the options and comparing the options according to certain performance criteria. According to the principles of system analysis, one or another complex problem that arises before society (primarily the problem of management) should be considered as something whole, as a system in the interaction of all its components. To make a decision about the management of this system, it is necessary to determine its goal, the goals of its individual subsystems and the many alternatives for achieving these goals, which are compared according to certain efficiency criteria, and as a result, the most appropriate management method for a given situation is selected. The central procedure in system analysis is the construction of a generalized model (or models) that reflects all the factors and relationships of the real situation that may appear in the process of implementing the solution. The resulting model is investigated in order to find out the closeness of the result of applying one or another of the alternative options for action to the desired one, the comparative cost of resources for each of the options, the degree of sensitivity of the model to various undesirable external influences. System analysis is based on a number of applied mathematical disciplines and methods widely used in modern management activities. The technical basis of system analysis is modern computers and information systems. Systems analysis widely uses methods of system dynamics, game theory, heuristic programming, simulation modeling, program-targeted control, etc. An important feature of system analysis is the unity of the formalized and non-formalized means and methods of research used in it.

Literature:

1. Gvishiani D.M. Organization and management. M., 1972;

2. Cleland D.,King W. System analysis and target management. M., 1974;

3. Nappelbaum E.L. System analysis as a research program - structure and key concepts. - In the book: System Research. Methodological problems. Yearbook 1979. M., 1980;

4. Larichev O.I. Methodological problems of practical application of system analysis. - There; Blauberg I.V.,Mirsky E.M.,Sadovsky V.N. System approach and system analysis. - In the book: System Research. Methodological problems. Yearbook 1982. M., 1982;

5. Blauberg I.V. The problem of integrity and a systematic approach. M., 1997;

6. Yudin E.G. Methodology of science. Consistency. Activity. M., 1997.

7. See also lit. to Art. System , Systems approach.

V.N.Sadovsky

System Analysis- a scientific method of cognition, which is a sequence of actions to establish structural relationships between variables or elements of the system under study. It is based on a set of general scientific, experimental, natural science, statistical, and mathematical methods.

To solve well-structured quantifiable problems, the well-known methodology of operations research is used, which consists in constructing an adequate mathematical model (for example, linear, nonlinear, dynamic programming problems, problems of queuing theory, game theory, etc.) and applying methods to find the optimal control strategy targeted actions.

System analysis provides the following system methods and procedures for use in various sciences, systems:

abstraction and specification

analysis and synthesis, induction and deduction

Formalization and concretization

composition and decomposition

Linearization and selection of non-linear components

Structuring and restructuring

· prototyping

reengineering

algorithmization

simulation and experiment

software control and regulation

Recognition and identification

clustering and classification

expert evaluation and testing

verification

and other methods and procedures.

It should be noted the tasks of studying the system of interactions of the analyzed objects with the environment. The solution to this problem involves:

- drawing a boundary between the system under study and the environment, which determines the maximum depth

the influence of the interactions under consideration, to which the consideration is limited;

- determination of the real resources of such interaction;

– consideration of the interactions of the system under study with a higher level system.

Tasks of the following type are associated with the design of alternatives for this interaction, alternatives for the development of the system in time and space. An important direction in the development of systems analysis methods is associated with attempts to create new possibilities for constructing original solution alternatives, unexpected strategies, unusual ideas and hidden structures. In other words, speech here about the development of methods and means strengthening the inductive possibilities of human thinking, in contrast to its deductive possibilities, to which, in fact, the development of formal logical means is aimed at strengthening. Research in this direction has begun only quite recently, and there is still no single conceptual apparatus in them. Nevertheless, several important areas can be distinguished here, such as the development the formal apparatus of inductive logic, methods of morphological analysis and other structural and syntactic methods for constructing new alternatives, syntactic methods and organization of group interaction in solving creative problems, as well as the study of the main paradigms of search thinking.

Tasks of the third type consist in constructing a set simulation models describing the influence of one or another interaction on the behavior of the object of study. It should be noted that system studies do not pursue the goal of creating some kind of supermodel. We are talking about the development of private models, each of which solves its own specific issues.

Even after such simulation models have been created and studied, the question of bringing various aspects of the system's behavior into a single scheme remains open. However, it can and should be solved not by building a supermodel, but by analyzing the reactions to the observed behavior of other interacting objects, i.e. by studying the behavior of objects - analogues and transferring the results of these studies to the object of system analysis. Such a study provides a basis for a meaningful understanding of situations of interaction and the structure of relationships that determine the place of the system under study in the structure of the supersystem, of which it is a component.

Tasks of the fourth type are associated with the design decision making models. Any system study is connected with the study of various alternatives for the development of the system. The task of system analysts is to choose and justify the best development alternative. At the stage of development and decision-making, it is necessary to take into account the interaction of the system with its subsystems, combine the goals of the system with the goals of the subsystems, and single out global and secondary goals.

The most developed and at the same time the most specific area of scientific creativity is associated with the development of the theory of decision making and the formation of target structures, programs and plans. There is no lack of work and actively working researchers here. However, in this case, too many results are at the level of unconfirmed inventions and discrepancies in understanding both the essence of the tasks and the means to solve them. Research in this area includes:

a) building a theory for evaluating the effectiveness of decisions made or plans and programs formed;

b) solving the problem of multi-criteria in the evaluation of decision or planning alternatives;

c) study of the problem of uncertainty, especially associated not with statistical factors, but with the uncertainty of expert judgments and deliberately created uncertainty associated with the simplification of ideas about the behavior of the system;

d) development of the problem of aggregating individual preferences on decisions affecting the interests of several parties that affect the behavior of the system;

e) study of specific features of socio-economic criteria of efficiency;

f) creation of methods for checking the logical consistency of target structures and plans and establishing the necessary balance between the predetermination of the action program and its readiness for restructuring when a new one arrives

information about both external events and changes in ideas about the execution of this program.

The latter direction requires a new awareness of the real functions of the target structures, plans, programs and the definition of those that they should perform, as well as the links between them.

The considered tasks of system analysis do not cover the full list of tasks. Listed here are those that present the greatest difficulty in solving them. It should be noted that all the tasks of systemic research are closely interconnected with each other, cannot be isolated and solved separately, both in time and in terms of the composition of performers. Moreover, in order to solve all these problems, the researcher must have a broad outlook and possess a rich arsenal of methods and means of scientific research.

ANALYTICAL AND STATISTICAL METHODS. These groups of methods are most widely used in the practice of design and management. True, graphical representations (graphs, diagrams, etc.) are widely used to present intermediate and final results of modeling. However, the latter are auxiliary; the basis of the model, the proofs of its adequacy, are those or other directions of analytical and statistical representations. Therefore, despite the fact that independent courses of lectures are given in universities in the main areas of these two classes of methods, we will still briefly characterize their features, advantages and disadvantages from the point of view of the possibility of using them in system modeling.

Analytical in the classification under consideration, methods are named that display real objects and processes in the form of points (dimensionless in strict mathematical proofs) that make any movements in space or interact with each other. The basis of the conceptual (terminological) apparatus of these representations is the concepts of classical mathematics (value, formula, function, equation, system of equations, logarithm, differential, integral, etc.).

Analytical representations have a long history of development, and they are characterized not only by the desire for rigor of terminology, but also by assigning certain letters to some special quantities (for example, doubling the ratio of the area of a circle to the area of a square inscribed in it p» 3.14; the base of the natural logarithm – e» 2.7, etc.).

On the basis of analytical representations, mathematical theories of varying complexity have arisen and are developing - from the apparatus of classical mathematical analysis (methods of studying functions, their type, methods of representation, searching for extrema of functions, etc.) to such new sections of modern mathematics as mathematical programming (linear, non-linear, dynamic, etc.), game theory (matrix games with pure strategies, differential games, etc.).

These theoretical directions have become the basis of many applied ones, including the theory of automatic control, the theory of optimal solutions, etc.

When modeling systems, a wide range of symbolic representations is used, using the "language" of classical mathematics. However, these symbolic representations do not always adequately reflect real complex processes, and in these cases, generally speaking, they cannot be considered rigorous mathematical models.

Most of the areas of mathematics do not contain the means of setting the problem and proving the adequacy of the model. The latter is proved by experiment, which, as the problems become more complex, also becomes more and more complex, expensive, not always indisputable and realizable.

At the same time, this class of methods includes a relatively new area of mathematics - mathematical programming, which contains the means of setting the problem and expands the possibilities of proving the adequacy of models.

Statistical ideas were formed as an independent scientific direction in the middle of the last century (although they arose much earlier). They are based on the display of phenomena and processes using random (stochastic) events and their behavior, which are described by the corresponding probabilistic (statistical) characteristics and statistical patterns. Statistical mappings of the system in the general case (by analogy with analytical ones) can be represented as if in the form of a “blurred” point (fuzzy area) in n-dimensional space, into which the system (its properties taken into account in the model) is transferred by the operator F. “Blurred” point should be understood as a certain area characterizing the movement of the system (its behavior); in this case, the boundaries of the region are given with a certain probability p (“blurred”) and the movement of the point is described by some random function.

Fixing all the parameters of this area, except for one, you can get a cut along the line a - b, the meaning of which is the impact of this parameter on the behavior of the system, which can be described by a statistical distribution for this parameter. Similarly, you can get two-dimensional, three-dimensional, etc. statistical distribution patterns. Statistical regularities can be represented as discrete random variables and their probabilities, or as continuous dependences of the distribution of events and processes.

For discrete events, the relationship between the possible values of a random variable xi and their probabilities pi is called the distribution law.

Brainstorming method

A group of researchers (experts) develops ways to solve the problem, while any method (any thought expressed aloud) is included in the number of considered ones, the more ideas, the better. At the preliminary stage, the quality of the proposed methods is not taken into account, that is, the subject of the search is the creation of as many options for solving the problem as possible. But to be successful, the following conditions must be met:

the presence of an inspirer of ideas;

· a group of experts does not exceed 5-6 people;

· the potential of researchers is commensurable;

the environment is calm;

equal rights are observed, any solution can be proposed, criticism of ideas is not allowed;

· Duration of work no more than 1 hour.

After the "flow of ideas" stops, the experts carry out a critical selection of proposals, taking into account organizational and economic limitations. The selection of the best idea can be carried out according to several criteria.

This method is most productive at the stage of developing a solution for the implementation of the goal, when revealing the mechanism of the system's functioning, when choosing a criterion for solving the problem.

The method of "concentration of attention on the goals of the problem"

This method consists in selecting one of the objects (elements, concepts) associated with the problem being solved. At the same time, it is known that the object accepted for consideration is directly related to the ultimate goals of this problem. Then the connection between this object and some other, chosen at random, is examined. Next, the third element is selected, just as randomly, and its relationship with the first two is examined, and so on. Thus, a certain chain of interconnected objects, elements or concepts is created. If the chain breaks, then the process resumes, a second chain is created, and so on. This is how the system is explored.

Method "inputs-outputs of the system"

The system under study is necessarily considered together with the environment. In this case, special attention is paid to the restrictions that the external environment imposes on the system, as well as the restrictions inherent in the system itself.

At the first stage of studying the system, possible outputs of the system are considered and the results of its functioning are evaluated according to changes in the environment. Then the possible inputs of the system and their parameters are investigated, which allow the system to function within the limits of the accepted restrictions. And, finally, at the third stage, acceptable inputs are chosen that do not violate the system's limitations and do not bring it into conflict with the goals of the environment.

This method is most effective at the stages of understanding the mechanism of the system functioning and decision-making.

Scenario Method

The peculiarity of the method is that a group of highly qualified specialists in a descriptive form represents the possible course of events in a particular system - starting from the current situation and ending with some resulting situation. At the same time, artificially erected, but arising in real life, restrictions on the input and output of the system (on raw materials, energy resources, finance, and so on) are observed.

The main idea of this method is to identify the links between various elements of the system that manifest themselves in a particular event or constraint. The result of such a study is a set of scenarios - possible directions for solving the problem, from which, by comparing according to some criterion, the most acceptable ones could be chosen.

Morphological method

This method involves the search for all possible solutions to the problem by exhaustive census of these solutions. For example, F.R. Matveev identifies six stages in the implementation of this method:

the formulation and definition of the constraints of the problem;

search for possible decision parameters and possible variations of these parameters;

Finding all possible combinations of these parameters in the resulting solutions;

Comparison of decisions in terms of the goals pursued;

Choice of solutions

· in-depth study of selected solutions.

Modeling methods

A model is a system created to represent a complex reality in a simplified and understandable form, in other words, a model is an imitation of this reality.

The problems solved by models are many and varied. The most important of them:

· with the help of models, researchers try to better understand the course of a complex process;

· with the help of models, experimentation is carried out in the case when this is not possible on a real object;

· with the help of models, the possibility of implementing various alternative solutions is evaluated.

In addition, models have such valuable properties as:

reproducibility by independent experimenters;

· variability and the possibility of improvement by introducing new data into the model or modifying relationships within the model.

Among the main types of models, symbolic and mathematical models should be noted.

Symbolic models - diagrams, diagrams, graphs, flowcharts and so on.

Mathematical models are abstract constructions that describe in mathematical form the connections, relationships between the elements of the system.

When building models, the following conditions must be observed:

have a sufficiently large amount of information about the behavior of the system;

Stylization of the functioning mechanisms of the system should take place within such limits that it would be possible to accurately reflect the number and nature of the relationships and connections existing in the system;

The use of automatic information processing methods, especially when the amount of data is large or the nature of the relationship between the elements of the system is very complex.

However, mathematical models have some disadvantages:

the desire to reflect the process under study in the form of conditions leads to a model that can be understood only by its developer;

On the other hand, simplification leads to a limitation of the number of factors included in the model; consequently, there is an inaccuracy in the reflection of reality;

· the author, having created a model, "forgets" that he does not take into account the action of numerous, maybe insignificant factors. But the combined effect of these factors on the system is such that the final results cannot be achieved on this model.

In order to level these shortcomings, the model must be checked:

How realistically and satisfactorily does it reflect the real process?

· whether changing the parameters causes a corresponding change in the results.

Complex systems, due to the presence of many discretely functioning subsystems, as a rule, cannot be adequately described using only mathematical models, so simulation modeling has become widespread. Simulation models have become widespread for two reasons: firstly, these models allow the use of all available information (graphic, verbal, mathematical models ...) and, secondly, because these models do not impose strict restrictions on the input data used. Thus, simulation models allow you to creatively use all the available information about the object of study.