Situational modeling in project management. Intelligent project management and simulation modeling. Tutorial Imprint
One of the features of modern management science is the use of models. As noted by M. Mescon, M. Albert and F. Hedouri, the most notable and perhaps most significant contribution of the school of scientific management is the development of models that make it possible to make objective decisions in situations that are too complex for a simple causal assessment of alternatives.
According to the definition of R. E. Shannon, "a model is a representation of an object, system or idea in some form other than the whole itself." In this sense, all management theories, in fact, are models of the work of the organization or any of its subsystems. The main characteristic of the model is the simplification of the real situation to which it is applied. After the model is created, the variables are assigned quantitative values. This allows you to objectively compare and describe each variable and the relationships between them.
Reasons for the active use of the modeling method:
The natural complexity of many organizational situations;
The impossibility of conducting experiments in real life, even when they are necessary;
Leadership orientation for the future.
Thus, situation modeling is a powerful analytical tool to overcome many of the problems associated with decision-making in complex situations.
The main stages of building a model:
1. Refinement of the problem statement.
2. Formulation of laws relating the main parameters of the object.
3. Recording in mathematical expressions of the formulated regularities.
4. Study of the model based on a comparison of actual performance indicators with those calculated according to the model (theoretical and / or experimental analysis).
5. Accumulation of data on the object under study and correction of the model in order to introduce additional factors, restrictions and criteria.
6. Application of the model to solve the problems of object management.
7. Development and improvement of the model.
When modeling a managerial situation, three basic types of models can be used: physical, analog and mathematical models.
Physical model allows you to explore something with the help of an enlarged or reduced description of an object or system. For example, a designer's drawing reduced to a certain scale.
analog model represents the object under study as an analogue that behaves like a real object, but does not look like one. For example, a graph illustrating the relationship between production volume and costs, or an organizational chart of an enterprise.
A mathematical (symbolic) model uses symbols to describe properties or characteristics of an object or event. This type of model is probably most often used in organizational decision making.
In the 1930s 20th century at the intersection of mathematics, statistics and economic theory arose new section economics- econometrics. Methods of econometric analysis were quickly demanded by management theory.
Econometrics- a scientific discipline, the subject of which is the study of the quantitative side of economic phenomena and processes by means of mathematical and statistical analysis.
The main tool of econometrics is the econometric model, the task of which is to check economic theories on the actual material using the methods of mathematical statistics. Among her final applied tasks in management, two are distinguished: a forecast of the development of a managerial situation and an imitation of various possible scenarios for its development.
When constructing an econometric model, such analysis methods as regression analysis, time series analysis, systems of simultaneous equations, as well as other methods and tools of probability theory and economic statistics are used.
In the most general form, any econometric model built as a system of linear equations can be written as follows:
where y is the vector of current values of endogenous model variables;
А – matrix of coefficients of interactions between the current values of the endogenous variables of the model;
Z is a matrix of coefficients of influence of lagging (lag) variables of the model on the current values of endogenous and simulated indicators;
C is the matrix of coefficients of external influences;
х – vector of values of exogenous indicators of the model;
t is the index of the time period;
I – delay index (lag);
p is the duration of the maximum lag.
The number of different specific models used in management is as great as the number of problems for which they were developed. The most common types of models used in the analysis, solution development and forecasting of the development of the management process are: game theory, queuing theory model, inventory management model, linear programming model and simulation modeling.
Game theory is a method for modeling impact assessment decision on competitors. This is a mathematical method for studying optimal strategies in games, or the analysis of optimal decision making in conflict conditions. In this case, the conflict and the game are a kind of mathematical synonyms. A game is understood as a process in which two or more parties participate in the struggle for the realization of their interests.
An American mathematician made a great contribution to the development of game theory John Nash. Before J. Nash, mathematicians were engaged in the so-called zero-sum games, in which the gain of one side is equal to the loss of the other. J. Nash developed a methodology for analyzing games with a non-zero sum - a class of games in which the sum of the winning participants is not equal to the sum of the losses of the losing participants. An example of a non-zero-sum game would be a wage increase negotiation between a union and company management. Such a conflict situation can end either in a long strike in which both sides suffer, or in reaching a mutually beneficial agreement. Also, J. Nash mathematically modeled a situation in which both sides use an ideal strategy, which leads to the creation of a stable equilibrium.
The practical application of game theory allows, on the one hand, to predict the actions of the competitors of the organization, and on the other hand, it makes it possible to overcome intra-organizational conflicts by modeling them, taking into account all the components. Since real management situations are very complex and change quickly, game theory is not used as often as other models described. Nevertheless, it is necessary when it is required to determine the most important factors that need to be taken into account in a decision-making situation in a competitive environment.
Queue theory model, or optimal service model, is used to determine the optimal number of service channels in relation to the need for them. The queuing models are a tool for determining the optimal number of service channels to have in order to balance the overhead in cases of too few and too many. Situations in which this model is applicable include, for example, bank customers waiting for a free teller, waiting in line for machine data processing, equipment repairmen, etc.
Inventory management model used to determine the time of placing orders for resources and their quantities, as well as the mass of finished products in warehouses. The purpose of this model is to minimize the negative effects of inventory accumulation, expressed in certain costs. There are three main types of these costs: ordering, storage, and inventory losses.
Linear programming model used to determine the best way to allocate scarce resources in the presence of competing needs. Linear programming is commonly used by staff members to solve production problems.
According to surveys, linear programming and inventory management models are the most popular among practicing managers.
Since all the considered models are "substitutes for reality", they imply the use of imitation. But imitation as a method modeling denotes the process of creating a model and its experimental application to determine changes in a real situation. As a rule, imitation is used in those situations that are too complex for mathematical methods type of linear programming. This is due to a large number of variables, the difficulty of mathematical analysis of certain relationships between variables, or a high level of uncertainty.
One of the forms of building a model is economic analysis. Break-even analysis is considered a typical "economic model".
A specific modeling method is neuro-linguistic modeling. At the same time, NLP is not exactly a quantitative method. It is based on the mechanisms and methods of modeling the subjective experience of people. The main tasks of NLP are to model specific or exceptional abilities for their subsequent assimilation by other people. NLP modeling is often used in personnel management, for example, when building effective communications.
Decision-making methods. Decision-making theory aims to increase the rationality of managerial decisions. This theory can be seen as a further development of operations research. The subject of the theory of managerial decisions is the decision-making process itself, the formation of the principles of choice, the development of evaluation criteria and methods for choosing decisions that are most relevant to the goals set.
Almost any decision-making method used in management can technically be considered as a kind of modeling. Traditionally, however, the term "model" refers only to methods of a general nature. In addition to modeling, there are a number of methods to help you make an objectively informed decision to choose among several alternatives.
the one that most contributes to the achievement of the organization's goals. In this sense, the main decision-making methods are the payoff matrix and the decision tree.
Payment matrix is one of the methods of statistical decision theory. This method helps the manager in choosing one of several solutions. For example, in choosing a strategy that is most conducive to achieving goals.
A decision tree is a method used to select the best course of action from available options. A decision tree is a schematic representation of a decision problem. Like the payoff matrix, the decision tree gives the manager the opportunity to "consider various courses of action, correlate financial results with them, adjust them according to the probability assigned to them, and then compare alternatives." From this point of view, an integral part of the decision tree method is the concept of expected value. To the greatest extent, this tool is applicable for making consistent decisions.
It must be emphasized that the methods presented in this chapter are by no means a complete list of quantitative research methods used in modern management science. However, they give a general idea of the various classes (types) of research methods and decision-making methods.
Thus, the quantitative approach to management consists in the application of statistical methods, optimization models, information models and computer simulation methods. The use of various methods developed within the framework of the quantitative approach can significantly improve the quality of decisions made based on the use of a scientific approach, situation modeling and systemic research orientation.
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Meskon M., Albert M., Hedouri F. Fundamentals of management: per. from English. Moscow: Delo, 2005, p. 226.
Ayvazyan S. A. Fundamentals of econometrics. Moscow: UNITI, 2001, pp. 19–20.
Meskon M., Albert M., Hedouri F. Fundamentals of management: per. from English. Moscow: Delo, 2005, p. 236.
Meskon M., Albert M., Hedouri F. Fundamentals of management: per. from English. Moscow: Delo, 2005, pp. 241–242.
Tutorial output:
History of management: textbook / E. P. Kostenko, E. V. Mikhalkina; South Federal University. - Rostov-on-Don: Southern Federal University Press, 2014. - 606 p.
History of occurrence. Situational management methodarose in connection with the need to model processes during
making decisions in systems with an active element (human). IN
It is based on three main premises.
The first premise is psychology, which began the study
to describe the principles and models of human decision-making in opera
active situations. Known are the works of Soviet psychologists in this
region - V.N. Pushkin, B.F. Lomov, V.P. Zinchenko and others. V.N. Pushkin formulated the so-called model theory
riu of thinking. He showed that the psychological mechanism
661 regulation of acts of human behavior is closely related to the construction
in the structures of the brain of the information model of the object and
external world, within which the process of
management based on human perception of information from outside and
existing experience and knowledge. The basis for building a model
are conceptual representations of objects and relationships
between them, reflecting the semantics of the selected sphere of activity
human (subject area). The object model has
multilevel structure and defines that information
the context against which management processes take place. How
the richer such an information model of an object is and the higher it is capable of
knowledge manipulation, the higher the quality
decisions, more diverse human behavior. V.N. Pushkin
first identified three important features of the adoption process
decisions: the possibility of classifying situations in co
compliance with standard management solutions; principal
naya openness of large systems; significant limitation
language for describing the state space and solutions of the object
management.
The second premise of the method of situational management
were the ideas obtained in studies on semio
tiki, the science of sign systems. These are the works of Yu.A. Schreide-
ra, Yu.D. Apresyan. A three-dimensional structure has been defined
sign in any sign system: the name of the sign, reflecting its syn
taxi aspect; the content of the sign, expressing its seman
tic aspect; the purpose of the sign, which determines its pragmatism
logical aspect (Frege's triangle). In applied semiotics
signs, variants of which are words, sentences, tech
hundred, began to be considered as systems that replace real
objects, processes, events of the external world. Aggregates
signs with relationships between them thus became fashionable
pseudophysical analogues of real systems of func
shareholding and management. That is why the situational
management was also called semiotic modeling,
since the sign language is sufficient for the description and processes
functioning of the object with the required degree of approximation.
The third premise is related to developments in the field of information
mathematical search engines and attempts to create a formal
description and presentation language technical sciences for the purpose of auto
662 matizatsiya works on summarizing scientific publications and organizing
nization of the processes of searching, storing and presenting information
mation. As part of these studies, E.F. There was a time
the language was worked out and studied, which later received the name of the language
gh codes . This language found its implementation in the information
but the BIT search engine, which has been successful and for quite a long time
operated at the Institute of Cybernetics of the Academy of Sciences of the Ukrainian SSR.
On the basis of the model theory of thinking V.N. Pushkin, languages
ka gKh "Kodo E.F. Skorokhodko and semiotics D.A. Pospelov, and for
those Yu.I. Klykov in 1965 formulated a new cybernet
logical concept of control of large systems in the form
method of situational management.
Method Essence
The concept of the situation is taken as the basis for management.
object of description, analysis and decision making. Investigator
but, appropriate means are needed - descriptions, classes
fiction, learning and transformation of situations in accordance with
decisions made.
The classification of situations was justified by the existence,
based on the analysis of the structure of control tasks in large systems
max, at each level of control of the set of situations, the number
which is disproportionately large compared to the multitude of
possible management solutions. Decision problem trak
was formulated as a problem of finding such a partition of the set of situations
divisions into classes, in which each class corresponded to
decision, the most expedient from the point of view of the given criteria
ev functioning. In the presence of such a partition, the search for re
solution in a particular situation was reduced to finding a class and correlating
giving him management decisions. However, such a setting
problem is valid for control systems in which the number of
potential situations (PVS) significantly exceeds
(sometimes by several orders of magnitude) the number of possible solutions
for management. This case corresponds to the context-independent
my way of deriving solutions when the whole set of PVS is split
is divided into classes in such a way that each class, in accordance
The decision was made on management. The case when
sets of situations and decisions were either comparable in power
or sufficiently greater that this fact can be
tanovit, was considered and developed later in the works of L.S. Behind-
gadskaya and her school.
663 The basis of the language for describing the entire set of situations was
the ideas of languages of r-dr-codes and syntagmatic chains are taken. The role of mno
features of domain objects played their sign equivalents
valences in natural language, i.e. words-names, and in the role of relation
words-names corresponding to real connections
between objects or processes. As a grammar of a language
situational management (YaSU) were the rules of generation
new concepts and relations, their transformation and class
fictions (see Language of situational management).
The most important idea of the method is the formation of a semiotic
object models by learning to make decisions. Wherein
Two modes of learning were considered: by an expert, who knows well
researched subject area, or on the basis of the analysis
for the multitudes specific situations and management solutions.
Obviously, the latter case is longer, does not guarantee
completeness of the description, requires the presence of statistics of situations and, when
decisions taken in them, which is far from always possible. That's why
general practice has become mainly the use of the first
approach to learning. Nevertheless, the presence in the YaSU of means of generalization
classification and classification of situations provides a fundamental
the ability to create models that can be improved
decision-making functions in changing conditions
control object bots. In other words, it creates an opportunity
the ability to “grow” the object model for given conditions
functioning.
Development of situational modeling. In 1973 L.S. Riddle-
Skye (Bolotova) developed another, new type of system
topic of situational management, which considered the class of systems
control in which the powers of the sets of possible situations
and management decisions are comparable or unknown. Prev
it was supposed to divide the whole set of situations into classes in such a way
at once, so that each class is assigned a structure
round of a typical solution. At the next stage of the solution, this
the structure was redefined in the process of interpretation and concrete
tization of the solution and taking into account the existing restrictions on the resource
sy. Thus, each typical control solution
and. its structure is put in correspondence with M., and, consequently,
in addition to the set С/ = (t/p U2,...UJ, a set of structures
tour of typical solutions M = (Mp M2,...M^).
664 Then, for each structure, the necessary con
text-layer of knowledge, which has a frame structure and includes
containing rules for interpreting situations within a given structure
tours and many procedures for their transformation and imitation.
A logical-semiotic model of inference was also developed
decisions on a hierarchy of decision making structures.
It is obvious that in the second case it is much more complicated
the problem of constructing a domain model (DOM). Times
the work of MPO is still an art, it requires the application
niya of the highest qualification of system analysts. Required
to answer some questions:
How are the boundaries of the selected subject area set?
area?
How is a consistent opi language formed?
analysis of all sets of situations and processes for MPO with complex
Noah, hierarchical and distributed structure?
How is the system of knowledge about MPO formed, dos
grinding to achieve your goals?
How do the necessary interactions “manifest”
actions between participants in the management and decision-making processes
how are they described?
How are decisions made in the context of
notes, uncertainty and ambiguity?
As a result of research and development of applied systems
situational management, an end-to-end methodology was created
and technology for designing situational control systems
large systems, including the necessary instrumentation
tools and systems based on REFAL and LISP languages.
As follows from the description of the language of situational management (see) and
organization of a situational management model, even then, in the 70s.
XX century, situational control systems (SSU) had everything with
signs of modern expert systems (ES) at least
2nd generation, i.e. dynamic ES. This and the presence of semioti
logical model of the control object and processes of its functional
in the form of a system of production-type rules, and naturally
but-language interface with developers and users, and
the presence of a built-in time logic that ensures the operation
SSU in real time and simulation. This and inst
Rumental software tools for implementing SCS based on
languages LISP and REFAL. Moreover, domestic specialists
665 you created large systems and even put them into practice in
part of industrial automated control systems.
Examples.
Situational management system "Aviaremont", performed
determined by the Odessa branch of the Institute of Economics of the Academy of Sciences of the Ukrainian SSR as
part of ACS "Aviaremont" for TsNIIASU (Riga).
Situational Dispatch Control System
volume and landing of aircraft, developed for VNIIRA (Lenin
hail).
Satellite communication session scheduling system.
A number of special purpose systems, etc. .
In the West, and then in our country, heuristics were developed
some programming (60s of XX century), artificial intelligence
(see) - AI (70s of the XX century), but in our country, behind the curtain, it’s bad
imagined what was going on abroad. Those who had access to ame
rican and western sources, did not understand this direction
leniya and believed that AI is something completely different and no
It has nothing to do with situational management. Change everything
moose in 1975, when the IV International
conference on AI, which was attended by almost all the majors
leading scientists of the world in the field of artificial intelligence. Here is the tog
Yes, it became clear that both our specialists and foreign practitioners
ki are doing the same thing, but from different points of view.
Domestic experts went "from above" and tried to solve
problems that are methodologically and conceptually clear, but not yet
provided with basic means - neither theoretical nor otherwise
instrumental. The conference helped many to realize and op
to determine one's place in the international process of movement towards
artificial mind. At subsequent schools, seminars,
all-Union symposiums on situational management already in
1975, problems were clearly articulated that hindered the development of
development of situational management. This is first of all
botka knowledge representation models and instrumental systems
SSU software support topics.
By 1980, there were dozens of SSUs of varying degrees of development.
tannoy. Most of them are demonstration and research
body samples. There were no commercial samples at all. Before
Few industrial designs were brought for a number of reasons:
lack of instrumental software systems brought
to the stage of commercial samples; lack of culture brought
666 development of their software tools to the commercial stage; missing
the impact of understanding the new paradigm in a wide development environment
kov ACS; underfunding opportunities and benefits from
buildings of commercial instrumental shell systems.
Western scientists went to AI "from below", from games of cubes, chairs
tiki-tac-toe, etc. They were interested in intelligent robots and
planning their behavior. Therefore, these tasks are still
are classical when teaching the theoretical foundations of AI.
It was on them that all the main models of representation were developed.
knowledge generation: production, semantic networks, frames.
Since 1977, stratification began in the ranks of the “situationists”.
School D.A. Pospelova, V.A. Vagina, L.T. Cousin and some
others, closer to theoretical studies by genus
of their position (USSR Academy of Sciences, universities), quickly reorganized
foreign terminology and mastered the achievements of the West. This
was easy to do as the difference was mostly termi
nological.
In the early 80s. expert systems appeared (see), and here
it turned out that in essence they seem to coincide with the SSU,
as we imagined them. And this term seemed more successful
nym, quickly became fashionable. As a result, by the early 1990s
20th century almost all "situationists" were engaged in ES.
Thus, it turned out that situational control
played in our country the role of the basis for a large number of special
alists by artificial intelligence(cm.).
You review the article (abstract): “ SITUATIONAL MODELING, OR SITUATIONAL MANAGEMENT» from disciplines « Systems theory and system analysis in management organizations»
Modeling is the main method for studying production and economic systems. Modeling is understood as such a way of displaying objective reality, in which a specially constructed model is used to study the original, reproducing certain (usually only essential) properties of the object under study. real phenomenon(process).
A model is an object of any nature that can replace the object under study so that its study provides new information about the object under study.
In accordance with these definitions, the concept of modeling includes the construction of a model (quasi-object) and operations on it to obtain new information about the object under study. From the standpoint of use, a model can be understood as a display of a system that is convenient for analysis and synthesis. Between the system and its model there is a correspondence relationship, which allows you to explore the system through the study of the model.
The type of model is determined primarily by the questions that it is desirable to answer with the help of the model. There may be varying degrees of correspondence between the model and the simulated system.
Often the model reflects only the function of the system, and the structure of the model (and its adequacy to the system) does not play a role, it is considered as a black box.
The simulation model already includes a single display of both the functions of the system and the essence of the processes occurring in it.
Modeling as a method of cognition is based on the fact that all models reflect reality in one way or another. Depending on how and by what means, under what conditions, in relation to which objects of cognition this property is realized, a wide variety of models arise. There are a number of principles for classifying models of different nature, of which the most significant are the following:
- according to the way of displaying reality, and, consequently, according to the apparatus of construction (form);
- by the nature of the simulated objects content).
According to the display method or construction apparatus, two types of models are distinguished (Fig. 7.2): material and mental, or ideal.
Rice. 7.2. Model classification
Material models are models that are built or selected by man, exist objectively, being embodied in metal, wood, glass, electrical elements, biological organizations and other material structures.
Material models are divided into three subspecies.
Spatially similar models are structures designed to display the spatial properties or relationships of an object (models of houses, factories, city districts, a transport network, the location of equipment in a workshop, etc.). A prerequisite such models is geometric similarity.
Physically similar models are material models aimed at reproducing various kinds of physical connections and dependencies of the object under study (models of dams of ship and aircraft power plants). The basis for the construction of such models is physical similarity - the sameness of the physical nature and the identity of the laws of motion.
Mathematically similar models - models that have, to one degree or another, the same mathematical formalism that describes the behavior of an object and a model (computer analog, cybernetic functional models). Mathematically similar material models are real or physical shells of some mathematical relations, but not the relations themselves.
Mental (or ideal) models are divided into three subspecies:
- descriptive (conceptual) models in which relationships are expressed in language images;
- visual-figurative models, the images of which in the mind are built from sensory-visual elements;
- sign (including mathematical models in which the elements of the object and their relationships are expressed using signs (including mathematical symbols and formulas).
The classification of models according to the nature of the objects being modeled, due to their extreme diversity, does not seem appropriate here.
The ultimate goal of modeling is to study not the model as such, but some other object of study that is different from it, but reproduced by it.
Obviously, no models can and should not fully reproduce all aspects and details of the phenomena being studied: an enterprise can be characterized from various points of view - a director or chief engineer, an accountant, a supplier or a power engineer.
In accordance with this, both the nature and construction of the model will be different.
Modeling, as a method of scientific knowledge, is based on the ability of a person to abstract the initial features or properties of various phenomena (processes) and establish a certain relationship between them. This creates the opportunity to study phenomena or processes indirectly, namely, by studying models that are analogous to them in some strictly defined respect.
In the general case, the following sequence of system modeling is appropriate: a conceptual description (research) of the system, its formalization, and, finally, if necessary, algorithmization and quantification of the system.
When modeling production and economic systems, along with formalized, mathematical methods of analysis used for individual subsystems or private processes, it is also necessary to use heuristic methods for analyzing production in those of its elements and relationships that are not amenable to formalization. And when using mathematical methods, due to the multitude of variables, one often has to resort to simplifications, to use methods of decomposition and aggregation of variables. As a result, the solutions acquire an approximate, qualitative character.
Due to the presence in large complex systems of organizational and production management of links and links that are difficult or not formalized at all, for their study it is necessary to use mainly descriptive models, exposing the system to decomposition into separate functional subsystems; then look for those subsystems that lend themselves to mathematical formalization, thus modeling the individual elements of the overall production process.
The ultimate goal of modeling the production and economic system is the preparation and adoption of a managerial decision by the head of the enterprise.
Models of production and economic systems can be distinguished by the following features:
- for the purposes of modeling;
- by tasks (functions) of management;
- by stages (procedures) of management;
– on mathematical modeling methods.
Depending on the goals of modeling, there are models designed for:
– design of control systems;
– performance evaluations;
- analysis of the capabilities of the enterprise in various conditions of its activity;
– development of optimal solutions in various production situations;
– calculation of organizational structures of the management system;
– calculation of information support, etc.
The specificity of the models of this classification subdivision is expressed primarily in the choice of appropriate performance criteria, as well as in the procedure for implementing the simulation results.
Depending on the tasks (functions) of management, there are models of scheduling, enterprise development management, product quality control, etc. The models of this division are focused on specific production and economic tasks and, as a rule, should provide numerical results.
Depending on the stage (procedure) of control automation, models can be informational, mathematical, software. The models of this subdivision are aimed at the corresponding stages of the movement and processing of information.
Depending on the applied mathematical apparatus, the models can be divided into five large groups: extreme, mathematical programming (planning), probabilistic, statistical and game-theoretic.
Extremal models include models that make it possible to find the extremum of a function or functional. This includes models built using graphical methods, Newton's method and its modifications, calculus of variations, Pontryagin's maximum principle, etc. Based on the capabilities of these methods, they are used primarily to solve operational control problems.
Models of mathematical programming (planning) include models of linear programming, non-linear programming, dynamic programming. This also usually includes network planning models.
Mathematical programming combines a number of mathematical methods designed to best allocate the limited resources available - raw materials, fuel, labor, time, as well as to draw up the corresponding best (optimal) action plans.
Probabilistic models include models built using the apparatus of probability theory, models random processes Markov type (Markov chains), queuing theory models, etc.
Probabilistic models describe phenomena and processes of a random nature, for example, those associated with all sorts of non-systematic deviations and errors (manufacturing defects, etc.), the influence of natural disasters, possible equipment malfunctions, etc.
Statistical models include models of sequential analysis, the method of statistical tests (Monte Carlo), etc. This also includes methods of random search.
The method of statistical testing lies in the fact that the course of a particular operation is played, as if copied by a computer, with all the inherent randomness of this operation, for example, when modeling organizational tasks, complex forms of cooperation between various enterprises, etc. The application of this method is called simulation modeling.
Random search methods are used to find extreme values of complex functions that depend on a large number of arguments. These methods are based on the use of a mechanism for random selection of arguments by which minimization is carried out. Random search methods are used, for example, in modeling organizational management structures.
Game-theoretic models are designed to justify decisions under conditions of uncertainty, ambiguity (incompleteness of information) of the situation and the associated risk. Game theory methods include game theory and statistical decision theory.
Game theory is a theory of conflict situations. It is used in cases where the uncertainty of the situation is caused by the possible actions of the conflicting parties.
Game-theoretic models can be used to justify management decisions in conditions of industrial and labor conflicts, when choosing the right line of conduct in relation to customers, suppliers, contractors, etc.
The theory of statistical decisions is applied when the uncertainty of the situation is caused by objective circumstances that are either unknown (for example, some characteristics of new materials, qualities new technology etc.), or are of a random nature (weather conditions, possible time for the failure of individual components of the product, etc.).
Game-theoretic models should be used in preparing, conducting and evaluating the results of business games.
All mathematical models can also be subdivided into efficiency evaluation models and optimization models.
Performance evaluation models are designed to develop the characteristics of production and management. This group includes all probabilistic models. Performance evaluation models are "input" in relation to optimization models.
Optimization models are designed to select the best course of action or line of behavior under given conditions. This group includes extreme and statistical models, models of mathematical programming, as well as game-theoretic models.
Below we will consider some of the most common models used in solving production problems, as well as for the formation of organizational structures for production management.
The main direction of modeling the management of production and economic systems is the creation of production management models.
At present, models of the following production management functions have been developed and are being applied:
– planning of production and economic activities of the enterprise;
– operational regulation;
– management of material and technical supply of production;
- management of sales of finished products;
– management of technical preparation of production.
A system of interrelated models of production and management has also been developed.
Models of planning the production and economic activity of the enterprise. The objective function of the models of this group provides:
- maximization of the criterion of efficiency of the production activity of the enterprise based on the available capacity and resources supplied;
– minimization of resource consumption within the specified efficiency criterion.
Models of planning the production activity of an enterprise are divided into: forecasting models, models of technical and economic planning, models of operational production planning.
Predictive models are models that are either based on mathematical methods (least squares, thresholding, exponential smoothing) or expert judgment methods.
Models of technical and economic planning are based on the methods of mathematical programming (planning). The final results of production, for example, the amount of profit, are usually chosen as the main criterion for efficiency (objective function) when developing an optimal plan. As restrictions, restrictions on the complexity of products, equipment operation time, resources, etc. are taken. Since the value of some of these restrictions is random (for example, the operating time of the equipment), a probabilistic approach is used to solve such optimization problems. Typical optimization models of technical and economic planning are models for calculating the optimal plan, distribution production program by calendar periods, optimal loading of equipment. These models are built using mathematical optimization methods.
Operational production planning models are usually combined with operational management models.
Operational management models. The main tasks of operational management are operational scheduling of production, systematic accounting and control over the implementation of calendar plans, as well as operational regulation of the production process.
Typical models of operational management are models for calculating optimal size batches of products and calculation of the optimal schedule for the launch-release of batches of parts (scheduling).
Models for calculating the optimal size of batches of products can be created in relation to both simple and complete formulation of the problem. In a simple setting, determining the size of production or purchase of a batch of parts, at which annual costs are minimal, reduces to the usual problem of finding the minimum of a function. In the full formulation, such a set of batch sizes is found that corresponds to the minimum total costs for equipment changeover and deductions for work in progress, with restrictions on the duration of changeovers, equipment resources, the interdependence of batch sizes in related operations, and ensuring the employment of the worker. The solution of this problem is achieved with the help of mathematical optimization methods.
Models for scheduling calculations can be:
- statistical with optimization by random search;
– simulation with a set of preference rules;
- heuristic, used in cases where it is impossible to create rigorous algorithms, but there is a need to use information and evaluate facts that do not have a quantitative expression.
Operational regulation models. These models are intended to ensure that the deviation of the results of production activities from planned indicators is kept within the specified limits. In this case, models of two types are used: models of regulation by the criterion of optimality, models of regulation by deviation.
Control models according to the optimality criterion are based on the fact that after a specific measurement of the actual state of the production process, a plan is drawn up that optimally leads the process to a predetermined state at the end of the planning period.
Deviation control models are based on the fact that after a specific measurement, the production process is brought to the originally drawn up schedule as soon as possible.
The construction of both models is carried out using the mathematical optimization apparatus used in the theory of automatic control.
Models of management of material and technical supply of production. The central problem of managing the material and technical supply of production is the task of determining the required volume of stocks of all types of supply. In this case, two fundamentally different models of inventory management can be built - with a fixed order size and with a fixed inventory level. There is also an intermediate model that fixes both the upper inventory level and the lower order level.
The construction of inventory management models is carried out using special mathematical optimization methods, which are called "inventory management theory".
Sales management models for finished products. The main problem of managing the marketing of finished products is the task of calculating the annual plan for the supply of finished products. To solve this problem, using mathematical optimization methods, an optimization model of the annual plan for the supply of finished products is built. The objective function is the cost products sold, as restrictions - the requirement that the total volume of products shipped to all consumers in a certain time interval does not exceed the volume of output for the same time, and the total volume of supplies to the consumer for all time intervals does not exceed the monthly application.
Models of management of technical preparation of production. Technical preparation of production includes the stages of design and technological preparation.
With the help of mathematical modeling, three main tasks of managing the technical preparation of production can be solved:
- determination of the minimum period for the implementation of a set of measures for the technical preparation of production with restrictions on the level of available resources;
- determination of the minimum cost of performing a set of measures for the technical preparation of production, with restrictions on the timing of its implementation and on the level of availability of resources;
- determination of the minimum level of consumption of scarce resources with a restriction on the cost and on the timing of the implementation of measures for the technical preparation of production.
The process of technical preparation of production is most fully and conveniently reproduced by the network model. The network model makes it possible to take into account the probabilistic nature of such basic parameters of technical production preparation operations as the duration of the work and the intensity of resource consumption.
Optimization is achieved by using mathematical programming methods (in particular, the simplex method) and random (statistical) search.
Along with the considered individual models that implement the main functions of managing the production process, there is also a system of interrelated models of production and management. The essence of this system of models, built using the mathematical apparatus of set theory, graph theory and recalculus, is as follows. As sets, we consider a set of products manufactured by an enterprise and a set of resources used in this process. The production process that ensures the release of many products is described by an aggregate graph, and technological process production of an individual product - its design and technological graph. The set of resources that support production consists of subsets of labor resources, equipment, and scarce components and materials. The state of production at any point in time can be described by a vector, which is a set of finished products, semi-finished products and assembly units produced by that moment. Similarly, with the help of a vector, the state of resources at any point in time is also determined. In this case, the planned trajectory of the production process will be described by a vector function.
With this formulation of the problem, the optimal management of the enterprise in the planning period can be found based on the following requirement: on the set of feasible plans defined by the vector function, find a plan that maximizes profit, provided that the probability of its implementation and profit of the established level will be at least a given level, and the resources spent will not exceed those available.
Modeling organizational structures of management is aimed at improving, optimizing the enterprise management system. It is a necessary preliminary step in the automation of the management of production and economic systems, which requires serious preparatory work.
The queuing theory is used as a mathematical apparatus for modeling organizational management structures. At the same time, the elements of the queuing system are taken as elements of the control system, each of which is designed to solve a specific management problem. For all tasks - elements, a system of priorities is provided in the order of solution. For each task, the characteristics of the incoming flows of service requirements are also known - the solution of the corresponding control problems.
An element of a control system that solves a particular problem has one or more information converters, which are either specialists of a certain qualification or technical means.
The effectiveness of the control system is assessed by the quality and duration of service for solving control problems, taking into account their priorities and complexity.
Modeling of queuing systems can be performed by both analytical and statistical methods. The statistical method, the so-called method of statistical tests (Monte Carlo method), has received the greatest application in modeling organizational structures of management. This method is preferred on the grounds that it allows solving problems of great complexity for which there is no analytical (formula) description or the latter is extremely complex.
The statistical model allows you to set up a mathematical experiment, similar to a full-scale one, to simulate the organizational structure of management in the cheapest way and in an acceptable time. At the same time, it is necessary to take into account the specific disadvantages of the statistical test method, of which the main ones are the relatively long simulation time and the particular nature of the solutions obtained, determined by the fixed values of the parameters of the queuing system.
When modeling with the help of the mathematical apparatus of the queuing theory, the structure of the enterprise management system is considered as a set of interconnected functioning elements. Such elements in a real system are the directorate and functional departments of management: production and technical, planning, supply, etc.
As a result of the joint functioning of these elements in the control system, the state information is converted into command information, which is the basis of enterprise management.
The mentioned elements - divisions of the enterprise management system constitute a chain, the analysis of the functioning of which can be sufficiently formalized in order to optimize the management process. The simplest chain that gives a good approximation to reality is a strictly sequential chain of elements. When modeling such a chain, two approaches are possible: quasi-regular and random representation. In a quasi-regular model, modeling is carried out for each element separately according to averaged indicators.
In a random model, statistical estimates are calculated for each service request that passes not through individual elements, but through the system as a whole.
Along with the modeling of organizational structures of control using chains of elements, there is a method for mathematical description of the organizational structure of a control system using linear stochastic networks, which are one of the classes of multi-phase queuing systems. In this model, information also passes sequentially through a number of elements of the control system, each of which is described using the mathematical apparatus of the queuing theory. With the sequential passage of information through the elements of the network, transitions of the Markov type take place. The structure of such a network with the corresponding transitions is represented by a certain graph. A stochastic transition matrix is compiled.
Since the objective function (efficiency criterion) in mathematical modeling of organizational management structures, as a rule, can only be described statistically, optimization is carried out mainly by numerical methods, of which the methods of dynamic programming and statistical search are most widely used.
The solution of the optimization problem by the dynamic programming method is implemented by compiling a functional recurrent equation (Bellman equation) for each step of the control process.
Optimization of organizational structures of management using the method of statistical search, despite the less stringent restrictions imposed on the efficiency criteria and assumptions describing the physics of the phenomenon with this method, has not yet received, in relation to the problem under consideration, a fairly wide distribution.
Game modeling occupies a special place among the methods used to automate the management of production and economic systems. A distinctive feature of this method is the involvement of people involved in the development and conduct of a business game to model the management process. In this case, a business game is understood as an imitation by a group of persons of solving certain tasks of an economic or organizational activities enterprise, performed on a model of an object in an environment as close as possible to the real one.
Introduction to the model of a person as an element of the organization of management makes it possible to take into account his behavior in cases where it cannot be adequately described using mathematical models known today; allows you to solve such management tasks that do not fit into the framework of existing formalized methods.
The business game introduces psychological and emotional moments into the process of preparing and making managerial decisions, encouraging the use of past experience of managers, their intuition in this process, developing the ability to make heuristic decisions. The business game is carried out in relation to a specific managerial task according to a carefully developed scenario in advance. The general game model is formed as a set of private models created by participants - persons preparing and making managerial decisions.
The business game model includes both formalized and non-formalized parts. The participants of the game act according to certain rules. They are guided by specially developed instructions for playing the game, as well as by the data of the situation at their disposal.
In accordance with the scenario of the game, participants periodically receive introductory information about changes in the situation. When preparing their decisions, the participants in the business game assess the situation and make necessary calculations manually or by computer. At the same time, formalized, pre-prepared elements of the game model are used, which correspond to modern methods of operations research.
By managing the course of a business game, its leader evaluates the decisions of the participants, establishes the results of their actions and brings the latter to the players. If necessary, the head of the game can change the setting, bringing these changes to the participants in the form of input. The assessment of the actions of the participants in the game is carried out by calculations, expert methods, as well as based on the experience of the leader, his intuition and common sense.
The main type of game simulation carried out at enterprises is a production business game. Its goal is to improve existing and develop new forms of organization of production management, development of guidance documents, restructuring of production, etc.
As models for conducting business games, methods of network planning and management (SPM), built on the basis of network graphs, are widely used. When solving planning problems, dynamic programming methods are used, and when solving problems of resource allocation - linear programming.
To train management personnel, a production business game can be held in educational version, i.e. educational business game. Its main task is to train employees and improve their management skills. If necessary, an educational business game is also used to certify the executives of enterprises in the performance of their official duties, as well as when they are promoted to the highest position.
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1. Methodsituational modeling in managerial decision-making
Situational modeling is based on the model theory of thinking, within which it is possible to describe the main mechanisms for regulating decision-making processes. At the center of the model theory of thinking lies the idea of the formation of an information model of an object and the external world in the structures of the brain. This information is perceived by a person on the basis of the knowledge and experience he already has. Expedient human behavior is built by forming the target situation and mentally transforming the initial situation into the target one. The basis for constructing the model is the description of the object in the form of a set of elements interconnected by certain relationships that reflect the semantics of the subject area.
Modeling complex economic, political, social situations with feedback and a large number of control parameters requires specialized tool packages, including an internal model description language, numerical integration tools, optimizers, and a developed interface.
Today one of effective ways analysis of critical situations, as well as the functioning of complex organizational and technical complexes are systems of situational modeling.
The implementation of the formation of an enterprise strategy based on the use of situational modeling methods involves the implementation of a number of stages:
Justification of the formation of a strategy based on a strategic analysis of the real conditions for the functioning of an industrial enterprise;
Development and use of a certain type of models or their combination for a formalized description of situations;
Modeling the development of situations under various scenarios of changes in the external and internal environment of the enterprise;
Involvement in the process of modeling situations as many managers, specialists and performers as possible.
Traditionally, it is believed that the situational approach should be used only when solving current organizational problems. But the formation of a development strategy involves taking into account the current circumstances, the internal and external environment of the enterprise and other factors. This allows us to make a conclusion about the appropriateness of applying the situational approach in the case under study.
Situational modeling allows solving such tasks as monitoring data, analyzing trends in the development of a situation, predicting and modeling behavior at the strategic and operational levels. Situational modeling systems are a universal tool for management and decision support in the largest organizations, public authorities and various other companies. The most important component here is the means of dynamic (imitation) modeling, which allow to calculate the possible consequences of different scenarios. In the process of situational modeling, optimization methods are used to find the best solution, assess risks, predict and conduct business games.
In recent years, knowledge discovery in databases (KDD) technologies have been actively developed. On the basis of KDD technology, a large number of software products have been developed that are suitable for solving information and cognitive problems. Elements of automatic data processing and analysis are becoming an integral part of the concept of "data warehouses" (data mining). Text-analytical systems (TAS), which allow extracting and analyzing the knowledge necessary for decision-making from large information arrays, can be of the greatest importance.
Document management and knowledge extraction software tools, as well as powerful report builders, allow you to aggregate elements of various descriptions in a single workspace and provide a look at the problem from different points of view at the same time. A special section of the situational center organizes monitoring and visualization of key parameters, extraction of implicit knowledge from texts and data, as well as generation and publication of reports. Thanks to the implementation of the above functions, it becomes possible to organize computer data processing not from application to application, but from one problem to another, which allows you to build single system making collective decisions.
To give a definition of a situational system, it is necessary to first understand the concept situations. The word itself is used daily in various aspects and is sometimes inseparable from such concepts as state, event, process, position, etc. The founders of situational management, Klykov [Klykov, 1974a] and Pospelov, clearly identify the situation with the state in their early works. A situation (a discrete set) is understood as a set of transactions (operational elements) located at certain points of a static system [Pospelov, 1972]. Later, the authors expand the concept by adding information about the relationships between objects: "the current situation is the totality of all information about the structure of the object and its functioning at a given moment in time"[Pospelov, 1986]. All information also implies causal relationships, which can be expressed by many successive events or processes. In this sense, the situation is fundamentally different from the state and event, which can correspond to only one moment in time.
Rice. 1 - Classification of situations.
Some authors, trying to separate the situation from the state, consider it as a synonym for the word relationship. Other researchers of this issue present the situation as a kind of generalizing concept. In Fig.1. the classification of situations is given.
This approach is rather controversial and controversial, but, nevertheless, indicates the main elements that can be used to determine the situation. Based on this, two important properties of the situation can be distinguished: multiplicity and heterogeneity of initial data. It is important to note that the situation is always a kind of assessment (analysis, generalization) of a set of data. Moreover, this assessment is subjective, because it depends on the means and methods of generalization of a particular person (man-machine system).
Summarizing all the above formulations, the situation can be defined as follows: The situation of the system is an assessment (analysis, generalization) of the totality of the characteristics of objects and the relationships between them, which consist of constant and cause-and-effect relationships that depend on the events that have occurred and the ongoing processes.
A generalized description (display) of a system using situations is called situational model(CM). In this regard, all situational systems can be called situational modeling systems (SSM). The abbreviated name for this class of systems is more euphonious than "SS" and differs from commonly used abbreviations for terms such as semiotic system, semantic network, and situational network.
Quite often, SM is mistakenly called simulation, thus equating situational modeling with simulation. If the system only displays information, and the understanding of the situation is formed exclusively by the subject, then it (the system) does not differ from tracking systems. Any program where a model is created, or a device that broadcasts real objects, can be called CCM, SC or situational room.
To narrow the class of systems under consideration, we introduce the following definition: SMS refers to a set of software and hardware tools that allow you to store, display, simulate (simulate) or analyze information based on the SMS.
It is quite difficult to give a clear definition of the term "situational center" (SC). In the most general way situational center (room or hall) can be called a room where the current situation is observed or a possible situation is analyzed. However, with this approach, any room in which there is an observer and a television broadcasting news about the situation in the country can be considered a situation room. If, in the room, there is also a radio, telephone, fax, computer and a geographical map, then the room can be called a personal SC.
SC can be divided into external And internal. External SCs serve as the technical or informational environment necessary for operational personnel to assess the situation. Internal SCs operate with the concept of a situation at the level of display, modeling, analysis or control. In fact, internal SCs automate the processing of the situation itself, while external ones automate the initial data necessary for its identification and analysis. For further consideration, we will accept the following definition of SP (internal):
SC is a set of software and hardware tools, scientific and mathematical methods and engineering solutions for automating the processes of display, modeling, situation analysis and control.
SC is a set of various SMS, scientific and mathematical methods and engineering solutions for automating control processes.
The structure of the SC, like any automated control system, includes different kinds software (software, technical, linguistic, etc.). SC has 4 main levels: scientific and mathematical, engineering, software and technical. The scientific and mathematical level is a set of scientific theories, methods, algorithms, research and development necessary for the implementation of other levels. It allows to substantiate the expediency of creating a SC, to determine the effectiveness of its functioning, to integrate heterogeneous components, to carry out the correct and timely correction of errors.
The engineering level represents specific decisions in the selection and development of hardware and software. It includes the necessary technological and design calculations, models of technical devices and premises, program specifications, operation algorithms, etc.
The software and technical levels contain the appropriate support necessary for the implementation of the tasks and functions set at the upper levels. The levels include the following mandatory components:
--measuring (sensor environment);
--information (situational or simulation) model of the environment;
--information support environment;
--hardware support environment;
--visualization environment;
--operational team.
Under measuring (or sensory) SC environment is understood as a set of hardware and software tools that serve to obtain information about the state of the problem environment. These can be antenna systems, communication channels, video and audio transmissions, sensors, etc. The main task of the measuring environment is to ensure the adequacy of the SC information model to some selected fragment of the real world.
Information (situational or simulation) model of the environment is a set of at least the following components [Gasov, 1990]: a thematic component that determines the set of modeled concepts of the problem environment; a spatial component that defines spatial relationships between model objects; a graphic component that specifies the mapping of model objects into a set of graphic symbols (graphic primitives). resolution managerial decision vault
Information Support Environment -- this is a set of programs and information flows that ensure the functioning of the information model and visualization environment of the SC. First of all, this includes CCM, expert systems and simulation systems. A characteristic feature of any SC is the binding of the situational model to the terrain, so geographic information systems may be included. For example, the report [Friedman, 1999] considers a decision support system using GIS-based situational modeling. To assess the development of situations, forecasting systems based on neural networks and genetic algorithms can be used. The efficiency of graphic and text representation can be achieved through the use of fractal and cognitive graphics.
Hardware Support Environment-- this is a set of technical computing tools that ensure the functioning of the information support environment of the SC: computers, office equipment, network equipment, etc.
Visualization environment-- this is a set of screens for collective and individual use, providing an information and command interface between a human operator and the hardware and software environment of the SC.
Operational staff -- is a team of specialists with its own internal organizational structure. The purpose of the operational staff is to provide a solution to the set of regular tasks of the SC based on the analysis of the information model of the real world situation, formed by the hardware and software environment of the system.
2. Ethical foundations for the preparation of managerial decisions. moral decision
The process of making a managerial decision is inextricably linked with its information support. In a market economy, independent, independent producers of goods and services, as well as all those who ensure the continuity of the cycle "science - technology - production - marketing - consumption" will not be able to successfully operate in the market without information. An entrepreneur needs information about other manufacturers, about potential consumers, about suppliers of raw materials, components and technology, about prices, about the situation on commodity markets and capital markets, about the situation in business life, about the general economic and political situation not only in one's own country, but throughout the world, about long-term trends in the development of the economy, prospects for the development of science and technology and possible results, about the legal conditions for managing, etc. P.
The reasons for the existence of different approaches to the problem of choosing a solution and its rationality can be found only when it is clear that since decision-making in management is a systematized process, then the systemic nature of this process should be a function of the influence of some factor that acts as the basis or the foundation on which all these approaches are built - economic, social, behavioral, etc. Assuming that the decision-making process consists of four phases: motivation for developing a decision; technology and solution development mechanism; choice of decision (volitional act - motivation); the results of the decision (the consequence of the choice are pre-motivated), then the starting point is motivation. However, motivation in the psychological sense is nothing more than the conditioning of volitional actions by certain motivating causes.
The issue of responsibility for making managerial decisions places a heavy material, moral and political burden on decision makers. However, responsibility in this context is intertwined with the concept of ownership, or more precisely with the ownership of property rights, whether direct (as in a private organization) or indirect (as in the case of collective or public organizations). And power and its hierarchies in the economic organization are inseparable, as is known, from the right of ownership.
Ethical issues often arise in management. They go far beyond the commonly discussed issues of bribery, collusion, and theft, penetrating into areas such as corporate borrowing, politics, marketing, and capital investment.
“Correct”, “true” and “fair” are ethical concepts. They express a judgment regarding the behavior of people, which is considered fair. We believe that there are right and wrong ways to behave towards other people, right and wrong actions, fair and unfair decisions. These beliefs are our moral standards. Moral norms are different for different individuals, because the values on which these norms are based are also different; and no one can say with certainty that a given moral norm is right or wrong, provided that the given norm really reflects our obligations to other members of the community, and not only to our advantage. The problem is that it is quite difficult, even in the simplest situation, to distinguish between "us" and "others" and between "benefits" and "obligations", and it is especially difficult to make this distinction in management. Why? Different groups of people are always involved in business - managers at various levels and with various functions, workers with various skills and degrees of training, suppliers of various materials, distributors of various products, lenders of various types, shareholders of various holdings, and citizens of various communities, states, and countries - and the benefit to one may serve as a negation of obligations towards other specific groups of people.
Ethical dilemmas are actually managerial dilemmas as well, because they are a conflict between economic activity organization (measured by revenues, costs, and profits) and the social reflection of its activities (manifested in obligations to people both inside and outside the organization). The nature of these obligations can, of course, be interpreted in many ways, but most often they include measures to protect loyal employees, create competitive markets, and produce products and services that are useful and safe for members of the community.
In a detailed examination of the relatively minor problem that the anxious manager faces in trying to understand the nature of the above ethical dilemma, five conclusions can be drawn regarding the complexity of managerial ethics:
1. Most ethical decisions have the widest possible implications.
The results of managerial decisions and actions are not limited to the consequences of the first level. On the contrary, their results extend to the whole of society, and this extension is the essence of the ethical dilemma: the decisions of managers have an impact on other people - both within the organization and within society - who are beyond their control, but nevertheless must be taken into account. during decision making. Bribes change government procedures. Pollution environment impact on the health of community members. The use of hazardous materials can ruin an individual's life. There is a dilemma here because most people recognize the broad implications of managerial action. The dilemma stems from the existence of multiple alternatives, mixed outcomes, questionable cases, and personal involvement that complicate the decision making process leading to the above actions.
2. Most ethical decisions have multiple alternatives.
It is commonly believed that ethical issues in management are fundamentally dichotomous - a choice between "yes" and "no" and no other alternatives. Should the manager pay a bribe or not? Should the factory pollute the air or not? Should the company produce hazardous products or not? Although the dichotomous structure presents ethical issues in sharp contrast, it does not accurately reflect the managerial dilemma. As numerous examples show, multiple alternatives must be considered in making an ethical choice.
3. Most ethical decisions have mixed results.
It is generally assumed that ethical issues in management are largely the antithesis of financial gains and social costs. Pay an indirect bribe, but keep the commercial volume of imported goods through instant delivery. Cause some harm to the air or water environment, but avoid unnecessary costs for the installation and operation of treatment facilities. Develop some product that is dangerous to humans, but reduce material and labor costs. Like the dichotomous framework, the antithetical outcome model sharply presents ethical issues but does not accurately portray the managerial dilemma. Social benefits and costs as well as financial gains and expenses are associated with almost all alternatives in ethical choice.
4. Most ethical decisions have questionable consequences.
It is generally assumed that ethical issues in management are free from risk or doubt, having a known outcome for each alternative. Pay the bribe and get your imported goods quickly. Invest in a wastewater treatment plant and the emission will be reduced by X percent at Y cost of operation. Produce a completely safe product at an additional cost of Z dollars per unit. A deterministic model - that is, one without probabilities - simplifies the process of analysis, but does not accurately describe the managerial dilemma. Because it is not at all clear what consequences any of the considered alternatives will lead to, and it is not at all clear what consequences most of the accepted ethical decisions will lead to.
5. Most ethical decisions are self-interested.
It is generally believed that ethical issues in management are largely impersonal, separated from the lives and careers of managers. In fact, every character trait of an individual manager is unmistakably present in the decisions he makes, and often the desire to move up his own career outweighs the manager's obvious obligations to other members of the organization or community, although in his own eyes his actions may be quite motivated from the point of view of his own career. morals.
Ethical decisions are not a simple choice between right and wrong; they are complex judgments about the balance between economic and social behavior organizations. Should there be a balance between economic and social behavior? How to achieve this balance? Three methods of analysis are relevant here: economic, legal, and ethical.
1. Economic analysis -- the ability to consider many of the problems of management as having a certain ethical content from the point of view of microeconomic theory, relying on impersonal market forces in choosing a solution between economic and social behavior.
2. Legal Analysis-- the ability to consider each of the problems that have ethical content, on the basis of legal theory, relying on impersonal social forces in the choice between "right" and "wrong". The underlying belief here is that a democratic society can make its own rules and that if people and organizations follow those rules, then the members of that society will be treated as fairly as possible.
3. Ethical Analysis- the ability to consider each of the problems that have a moral content, using the structure of normative philosophy, relying on the basic principles in the choice between "right" and "wrong". The belief underlying normative philosophy is that if all rationally thinking individuals in a society operate on the same principles of utility and logic, then the members of that society will also be treated as fairly as possible.
In summary, there are three forms of analysis that can help achieve a relatively correct balance between economic and social behavior. These forms of analysis are: economic, based on impersonal market forces; legal, based on impersonal social forces; and philosophical, based on personal principles and values.
But neither economic, nor legal, nor philosophical analysis in isolation is completely satisfactory as a means of resolving ethical dilemmas. When we are trying to find a balance between the economic and social behavior of an organization, none of the forms gives us a method of deciding on a course of action that we can say with confidence - "right", "correct" and "fair".
Economic analysis. The pursuit of Pareto Optimality through impersonal market forces is quite attractive - all we have to do is maximize income and minimize costs, and market relations, coupled with political decisions, will eliminate or negate the harm and loss that we inflict on others. However, there are both practical and theoretical problems with microeconomic theory. We have to admit that markets are not that efficient and voters are not that generous.
Legal Analysis. The concept of impersonal social processes is also attractive - all we have to do is obey the law, and then we can feel that we are following the collective moral standards of the majority of the population. However, this concept falls apart when we are faced with a process where individual norms, beliefs, and values are institutionalized into a legal structure. It must be admitted that there are too many differences, too many compromises between individual moral values and standards, and national legal laws.
Philosophical analysis. The concept of personal rational analysis is also attractive - all we have to do is base our decisions on a single moral principle (preference or consistency) or on a single value (justice or freedom) - but rational analysis has an inherent flaw. When attempting to use any of the principles or any of the values in moral resonation, we find that we must add a second principle or second value (often in direct conflict with the first) to the causal chain in order to reach a logical conclusion. We must recognize that a combination of conflicting principles or values cannot be rational.
If one of the decisions or actions generates an adequate financial return, complies with current legislation, provides significant benefits for the majority of members of the community, when it is possible to wish that everyone faced the same set of alternatives and basic factors that it is “fair” in in the sense of increasing the potential for social cooperation, and "impartially" in the sense of exercising the ability of others to make their choice - then we can say that a decision or action is "correct", "correct" and "fair".
List of used literature
1. Mardas A. N., Mardas O. A. Organizational management. St. Petersburg: "Peter", 2003 - 336 p.
2. Pereverzev M.P., Shaidenko N.A., Basovitsky L.E. Management M.: INFRA-M, 2003 - 288 p.
3. Khomutskaya L. P. Ethical problems of management. // Ethical and aesthetic: 40 years later. Materials of scientific conference. September 26-27, 2000 Abstracts of reports and speeches. St. Petersburg: St. Petersburg Philosophical Society, 2000. P. 160-164
4. Emerson G. Modern management. M.: "NORMA", 2005 - 434 p.
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The concept of management decision. Characteristics of problem situation modeling. The process of developing solutions in difficult situations. Basic and alternative concepts, classical and retrospective model of the managerial decision-making process.
term paper, added 12/20/2010
The essence of the analysis of the situation of making managerial decisions. Expertise organization methods: case method, two-round questioning, factor analysis and multidimensional scaling. Analysis of the situation in state authorities when making managerial decisions.
term paper, added 07/26/2010
Essence and classification of risks. Techniques for the development and selection of management decisions under risk. The main characteristic of a travel company. Risk in making managerial decisions in travel company LLC "Travel Company of Olga Romanova"
term paper, added 01/21/2014
The concept and types of methods for developing management decisions. The history of the development of the Soviet school of development of managerial decisions. The essence and features of the application of economic-mathematical and expert methods for the development of management decisions in the enterprise.
term paper, added 12/20/2009
Essence and functions of managerial decisions, their classification and stages of development. Management decision-making methods based on mathematical modeling and creative thinking. Features of the "brainstorming", its advantages and disadvantages.
term paper, added 03/06/2014
Essence and characteristics solutions. Classification of management decisions. Description of the distribution of decision-making powers. Study of the management structure and methods of making managerial decisions in the organization LLC "Leader".
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