Research in Social Sciences and Technology (RESSAT)  

E-ISSN: 2468-6891 
 

Major Features, Benefits, and Prerequisites for Intelligent Enterprise Managing System 

 

Alexander Olefirenko1, Alexey Galuschenko2 

 

Abstract  

The modern conditions of high-tech and highly competitive markets require the evolution of the enterprise 

automation systems. Such new, the post-ERP systems should meet the conditions for the provision of 

customer self-service and other counterparts, the dramatic decrease of the manual labour through 

automation, robotic application, integration with other systems and external contracting systems, multi-

channel marketing and distribution, reduction of supply chains and other current trends. These systems 

should be built on the new IEM Automation Paradigm (Intelligent Enterprise Managing) and therefore 

belong to the IEM system class. As stated in the IEM paradigm article, a modern approach to solving the 

task of automation is based on the application of "best" solutions for each separate entity or business unit. 

In practice, however, this approach has resulted in a series of significant problems. The scale of the 

problems is increasing with the growth in transactional load, competitive pressures on the market, 

requirements for accelerate optimization, and business processes changes. 

 

Key words: Management system, Structure, Solutions, Automation paradigm, Scale, 

Intelligent system, Neuroinformatics. 

 

Introduction 

Research in the area of "Intelligent Systems" has received State support (Order No. 701 of the 

USSR Civil Code on Public Education from 30/08/1989). A research program "Intelligent 

Systems", has been approved, according to which scientists of Research and Development 

Establishments and universities of the country have been tasked to design Intelligent System 

(prototype), based on achievements in the area of systems theory, neurophysiology, computer 

technology and computer science (The USSR State Committee on Public Education, 1991).   

The contribution to the establishment of the «Intelligent systems" scientific area was made in due 

course way back by Pavlov (1949), Russian physiologist, researcher of the higher nervous activity 

                                                 
1 Chief Technical Officer, Ultimate Humanless Enterprises, olefirenko.a@ultimabusinessware.com 
2 Lead researcher, IEM Institute, alex.galush@gmail.com 



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69 

 

mechanisms. Pavlov (1949) concluded that the highest nervous activity is the ability of the body 

to adapt to the environmental conditions. In the "Lectures on the Work of the Cerebral 

Hemispheres" Pavlov (1949) suggested that, in the near future, mathematics would cover many 

areas of expertise by converting natural sciences data into language of mathematics. Intelligent 

systems of the beginning of the 21st century are designed and built on digital technologies with 

digital coding using the characters "0" and "1". Neuroinformatics, based on the neural networks 

theory, is studying ways to describe the cerebration by mathematical tools, and to develop such 

mathematical models that are appropriate to human nature.  

The study under the government program "Intelligent Systems" (1989-1991) clarified the concept 

of the intelligent system. The Intelligent System was defined as a range of means (programs, 

hardware and software) together with a man included into an information process which are able 

to conduct the synthesis of a goal, act, find more effective ways to achieve the goal (Pupkov & 

Konkov, 2001; Tarman, 2010; 2016;2017; Tarman, Baytak & Duman, 2015; Tarman & Baytak, 

2011).  

The concept of intelligent system has many definitions, and it is multidimensional. An intelligent 

system is an information technology system that has the required knowledge data base, an 

operating procedures, intelligent assistance, and thus it solves tasks without the help of an operator 

(the operator is the expert outlining the way of proceeding and responsible for decision-making). 

Intellectual support is understood by us as a multipronged support, including software and tools, 

algorithmic and mathematical support, which can help solve various complexity problems.    

It should be noted that the intelligent system is designed that it can:  

 solve various complexity problems no worse than a human being, and just as he does;  

 learn; systematize, compare, explain, analyze, generalize, and gain the experience of 

solutions and actions.  

An information technology system with the required knowledge data base, operating procedures, 

intelligent assistance, but not capable of solving tasks without the help of the operator, the person 

responsible for decision-making is defined as the intellectualized system. Thus, the difference 

between the intellectual system and the intellectualized one is determined by the fact of presence 

of the person, the professional operator in the decision making process.  



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The intellectualized system, as a system, is capable to work in conditions of self-management, 

indeterminacy, dynamism, scarcity and immensity of information, with different objects, different 

objects life cycles.   

Intelligence, intellectuality, intellectual are the words which are attributed to the qualities, 

characteristics of man, capture a person's ability to think, make decisions that are based not only 

on knowledge and experience, but on a variety of factors (intuition, imagination, etc.), and 

intelligent systems implement this through the system interface with the user in a programming 

language that is close to the natural language.  

In a scientific literature in a narrower sense, an intelligent system is understood as a program 

system developed on the basis of the technology that defines the database, its structure, how to 

obtain and process information, and how to take decisions.  

An intelligent system is also understood as a computer system capable of dealing with tasks that 

only the man could solve.  

 

Materials and Methods 

Ostroukh (2015) relegates the intelligent system to an automated system category with a 

knowledge component. An intelligent system is understood as a set of tools (logical, mathematical, 

linguistic, software, interactive) designed to work with information and to assist a person in 

different types of activity (Anokhin, 1935; 1978; 1998; Kilinc, Tarman & Aydin, 2018; Ritter et.al, 

2011; Tarman & Chigisheva, 2017).   

Researches by Anokhin (1935), neurophysiologist of the life forms, adaptive reactions and 

adaptive abilities, mechanisms for achieving goals, functional system theory, form the basis for 

the concept of the intelligent system.   

Anokhin (1935) in the work of "The problems of the centre and the periphery in the physiology of 

neural activity" develops and defines the concept of a functional system. A functional system with 

feedback and an exchange of information about the results of action mechanisms is an enclosed 

physiological formation. The functional system provides an adaptive effect with multiple paths, 

channels of information moving from the periphery to the centre (see fig. 1).  

Adaptation of the organism in accordance with the Anokhin (1935; 1955; 1978; 1998) functional 

system theory is achieved through mechanisms:  

 Afferentation synthesis of data entering to living organisms;  



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71 

 

 The living organism decision-making;   

 Building an afferent model of the expected result;   

 Results action acceptor as a model for the result prevision; reafference model in the form 

of a ring-net of interacting neurons covered by a ring interaction (Anokhin, 1955). 

 Action to implement the decision (Anokhin, 1935). 

 

 

Figure 1. Block scheme of the functional system (Pupkov & Konkov, 2001). 

 

Anokhin (1998) introduced the neurophysiology principle of action acceptor, which is that the 

nervous system continually (in every action) builds a model of expected, presumed reafference 

(passing of the nervous agitation from the periphery to the brain) from the result of the action. 

Reafference, feedback, makes it possible to compare the expected result, the prediction, and the 

results of the action that are received. The operation of comparing the parameters of the action 

acceptor and the result obtained has an emotional component in the sense of 

satisfaction/dissatisfaction.  

Anokhin (1998) found, that the defining characteristic of any result that works towards the goal is 

that achieving the result of any level and complexity is possible on the basis of the principle of 

self-regulation. The mechanisms for achieving results are certain: Afferentation synthesis; 

decision making; Efferent program and action acceptor, reafference, recognizing similarity 

between the result and the prognosis. In a situation when the actions reach the expected result, the 



Research in Social Sciences and Technology (RESSAT)                                       2018: 3 (3),68-91 

 

 

act of adaptation ends. When the model and the real reafference are mismatched (passing of the 

nervous agitation from the periphery to the brain), the living organism reacts differently, he acts 

in an indicative and exploratory manner. In a situation where actions do not achieve the expected 

result, the afferent synthesis and the action program are changed, and this occurs until the result 

has been matched with the action acceptor parameters. After the achievement of the result, the 

living organism feels satisfied.  

Anokhin (1935; 1955; 1978; 1998) research results of the functional system has been put into 

practice only when information and computer technologies have been actively developed. The 

creation of fast-acting, technology-based microprocessors with a large amount of memory, which 

provide the high productivity, the development of networking technologies, the need to obtain and 

process vast amounts of information, and the handling of information and databases to develop 

effective solutions, such as management, has been an objective reason for designing, constructing 

of intelligent systems. The current level of research in the area of intelligent systems involves the 

design of systems that are based on knowledge, actions, algorithms, up-to-date information about 

their state and the state of the environment, and can meet the challenges of different complexity.   

Any system as a whole is a complex of interconnected, interacting elements, parts with uniquely 

defined features and functions, which provides the implementation of the system functions. By 

looking at the structure of the system, you have to assume that the structure is a steady set of 

unchanged, persisting for some time (for example, in the observation time interval) relationship.  

By describing the structure of the intelligent system, it is necessary to rely on the structure 

definition above. Based on this, the structure of the intelligent system consists of elements such as 

the purpose of knowledge, dynamic expert systems and acceptor, actions, subject to management, 

and feedback mechanism (see Figure 2). The invariant nature of the goal should be noted because, 

with regard to the intelligent system, it can only be a synthesis of the goal based on available 

memory, motivation, information on own and the environment state. The objective, as a 

component of the system, is linked to a dynamic expert system, the function of which is in the 

expert assessment, being the basis of the decision on the necessary action to be taken and the 

prognosis of the intended result (see Figure 2). The solution is accepted, and then the object control 

algorithm is developed.  



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73 

 

 

Figure 2. The structure of the intelligent system (Pupkov & Konkov, 2001). 

 

In the "Intelligent Systems" research area, the problem is the classification of intelligent systems, 

the choice of grounds for classification, and the identification of the common and special. The 

common, in our view, for all intelligent systems are:   

 the adaptability (in the conditions of objective changes, the system's ability to develop and 

software configuration);  

 communicative maturity (grasp of ways of user conversational interaction with the system);   

 the ability to solve difficult formalized tasks (tasks with dynamic, undefined data, original 

algorithm);  

 self-learning (ability to build knowledge of acting experience).  

If the characteristics data are taken as a basis for intelligent systems classifying, we can form out:  

 Adaptive Intelligent Systems;  

 Intelligent systems with intelligent interface;   

 Intelligent systems to solve difficult formalized tasks, or expert systems;  

 Self-learning Intelligent Systems.  

For the "methods" criterion, the classification of Intelligent Systems (IS) would be as follows:  

self-organizing IS, communication IS, IS developed using heuristic programming technologies.  

There are intelligent systems of common (solve tasks by algorithm, generate new procedures to 

solve new tasks), and special (solution of defined while designing tasks) purpose   

Another basis for classifying of Intelligent Systems can be the solvable problem, then the 

intelligent system (IS) can be formed out: Game IS, administration IS, IS with informative task 



Research in Social Sciences and Technology (RESSAT)                                       2018: 3 (3),68-91 

 

 

type, computer linguistics IS, IS with identification tasks, IS on solving tasks of intelligent 

information system creation.  

If you take the "used methods" criterion for the classification base, you identify types of intelligent 

systems, such as hybrid, soft, and rigid.  

Let's take a look at some of the designing and engineering approaches of the intelligent systems, 

shown in the work of Chinakal (2008). 

The logical approach comes from the Aristotle logic and the Boolean algebra, it is based on the 

human ability to think logically. The development of science has enabled the introduction of 

subject symbols, the designing of the basic data representation system, creation of knowledge and 

data bases. The structural approach involves modelling of the human brain with its inherent 

structural components- neurons, neural networks. The evolutionary approach is characterized by 

the development of not only the basic model of the intelligent system, but also the rules, following 

them the system can evolve. The simulation approach is linked to the objects like "black box" 

(everything is unknown, high uncertainty), "gray box" (something is known or there are 

hypothetical assumptions), and the "white box" (minimum level of uncertainty). As you can see, 

named objects differ in the level of data uncertainty about their characteristics. The model of such 

objects is designed to take into account the specific behavior in the system of the "impact-

response" and the relationship between response and external influences.   

On the basis of the above, a number of conclusions can be drawn on the relevance and focus of 

research in the area of intelligent systems. Among the problems that are currently being faced and 

addressed in the "Intelligent Systems" research area, which are of a theoretical and innovative 

nature, are:  

 The problem of synthesizing the purpose, the mechanism of target components interaction, 

because the target is a system forming factor;  

 The problem of identifying (capturing) the critical potential of such a set of components of 

a goal when the purpose is synthesized;  

 Development of intellectual management theory, theoretic apprehension of object-

orientated systems;  

 Finding and designing mathematical models that are adequate for wildlife based on 

information process theories and management;   



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75 

 

 Model constructing: The action program model, the action acceptor model, and its practical 

implementation into intelligent systems.  

 

Findings 

The main research areas of in the area of intelligent systems that are currently being developed 

should also be outlined. By defining the research object as a basis for the line of research, priority 

may be given to: A study of the human brain, its structure and operating mechanisms, intelligence 

models; intelligent systems research and computer-based modelling; develop intelligent systems 

with dialogs in the "man-machine" system, interactive characteristics.  

So here are the main issues: 

1. Heterogeneity. Developers of systems that implement one or another functionality unit to 

make their products more attractive have to adapt it to different landscapes. As a rule, this 

means that the product must run on different operating systems, use a large set of database 

management systems and other technologies to store data (including support for various 

application integration mechanisms, including aging or outdated ones) that allow the 

product to be used in a specific environment at the enterprise. Developers face a 

combinatorial explosion of possible configurations, which entails both a significant 

increase in development costs and a reduction in the reliability of the product itself.  

2. Disruption of a single information field. Using some of separate applications (such as 

CRM, ERP, HRM, WMS as a "standard" set for a modern enterprise) breaks the enterprise's 

single information field. Even if there are integration mechanisms between applications, 

each application uses its own dedicated database. As a result of unavoidable 

synchronization errors, the data accumulates discrepancies that distort the real picture of 

the enterprise. In view of the constant data gap, it is extremely difficult to reconcile, and in 

practice it is not done, and errors are suppressed by the periodic introduction of 

amendments to the data based on subjective representations of the employees and/or 

auditors of the enterprise. However, the data remain inconsistent and unreliable. With the 

growth in data volumes, the technical possibilities for their real reconciliation are only 

reducing. During the final (and foreseeable time), the error is not suppressed, and the 

enterprise is losing the picture of reality. 



Research in Social Sciences and Technology (RESSAT)                                       2018: 3 (3),68-91 

 

 

3. Flexibility and full-functionality Any two enterprises cannot operate on identical 

algorithms, they are located in different geographical locations, they work with different 

people, and so on and so forth. In reality, each enterprise is unique and can implement 

process even with the small difference, but with it. Many of these enterprises are "best" in 

their field, and the implementing software product is forced to co-opt a big set of different 

settings, rules, and exceptions. Such way leads only to one result that is the radical 

complication of the product until it would not be possible to modify it further. 

As a result, with an increasing number of implementations, companies are asked to adjust to a 

specific solution, and in future companies face the impossibility of changing the product at the 

time set by the external environment.  

These issues have a cumulative effect of reducing the performance of your application. And as a 

result, the implementation of multi-way marketing (especially on the Internet), user self-service 

implementation, integration with a variety of sensors (RFID, Pressure Transmitter, GPS, etc.), 

automatic settlement mechanisms through blockchain or smart contract implementations are 

extremely difficult or impossible to implement. 

As can be seen, many of the decisions that were "good" for the technology of 80-90 years could 

withstand the practice tests in current circumstances. To solve these challenges, it is necessary to 

move to modern automation systems based on the IEM paradigm (see the evolution of enterprise 

automation systems from ERP to the new management and automation paradigm -Intelligent 

Enterprise Managing). 

Thus, the obvious requirements for IEM systems are as follows: 

1. Common information space 

2. Real-time transactions 

3. Reliability and consistency of data at any point in time 

4. High customization level 

5. Uncompromising performance, ability to withstand high transactional load 

To meet the requirements, the IEM systems should be built on the following principles: 

 Centralized data storage (as a private case of the IEM paradigm requirement of a system 

unicity). In this case, it is intended any version of the database management system that 

will ensure data consistency and the transactional atomicity. 



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77 

 

 A closed multifunctional platform, invariant for all enterprises. This platform provides only 

technical tasks for the entire system operating and does not have any business information. 

 The open business logic space. The platform must provide the mechanisms to implement 

an arbitrary configuration of the business logic space without any necessity to make any 

changes to the platform itself.  

 Close integration with featured data management system to achieve maximum 

performance and data consistency. 

 A rich data model. The enterprise is described by high-level abstractions (more high-level 

than the "records" currently in use).  

 Modern, industrial, high-standard language. The lack of integration with a wide range of 

DBMS dramatically reduces the cost of developing and testing of the functionality and the 

functionality itself is tightly divided into the platform and business logic space. Integration 

with favorite DBMS allows you to move a large part of the data processing closer to the 

data itself, dramatically speeding up the transactions. The transporting balance of 

processing mechanisms, however, is limited by the high-level development language use 

requirements. In this way, all DBMS capabilities are used to fetch, aggregate, and filter 

data, but direct processing is done by the defined algorithms of the business logic space.  

In turn, the business logic space implements only that functionality which is needed at the 

particular enterprise and nothing more. This allows you to reduce multiply the code base and 

simplify the application support.  

Agile development techniques using continuous integration provided by the platform and 

performed business logic space over it. 

IEM system is as a process changes driver in the enterprise. Strict regulation of processes in the 

system and a requirement for symmetry (see the IEM paradigm) allow you to directly manage 

processes through the automation system.  

One-time data entry and data reuse as a natural consequence of centralized data storage and the 

general monolithic system 

Endurance to unskilled intervention. A closed platform, according to the principle of integrity, 

controls itself and provides mechanisms for implementing independent verification of the validity 

and consistency of data in the business logic space. This reduces the risk of changes to the software 

code that can cause data degradation. 



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Reducing of owning cost the use of the common development languages (not obsolete/proprietary) 

provides access to a broad marketplace of developers, development tools. In turn, platform tools, 

together with the requirement of a rich data model, significantly (up to 10 times) speed up the 

development and implementation of new enterprise functionality. 

The period of implementation of the system is reduced to months with unacceptable conditions in 

the current year.  

Independence from software manufacturer. Due to the full openness of the business logic space 

for modification, an enterprise can completely abandon its interaction with the manufacturer and 

maintain a completely closed development.  

Any production can be introduced as a sequence of operations which, apart from production, also 

includes organizational processes, planning processes, raw material and financial support process, 

etc. As part of this concept, standard resource planning and distribution tools and multi-faceted 

production and corporations management tools have been developed today and brought about in 

the form of powerful tools, like ICS (Integral Control System). With their help it is possible to 

automate planning, tracking, control and analysis of company business processes. 

In the previous chapter we saw that ICS includes subsystems of information support for 

production, supply, distribution, storage, technical, material, financial support, etc. Having such 

information allows for setting tasks to optimize material and financial flows of the company, 

optimize business process themselves and their structure, and lessen the expenses. 

Certainly, the usage of MRP / ERP systems itself allows for securing more efficient production 

management due to: 

– regulation of resources, elimination of deficiency and finding no sale, sunk financial 

resources and storage expenses, 

– authenticity of order executability assessment based on production assets available, 

– reduction of expenses and time by means of optimizing business processes, 

– reduction of production cycle duration and flexible reaction to demand, 

– monitoring actually produced products, their comparison with target tasks and, as a result, 

correcting production plans, etc. 

New possibilities of an extended ICS application appeared with more active usage of the Internet 

when managing production processes, including when managing deliveries and sales. More 

complete and efficient information influences the efficiency and timeliness of making management 



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79 

 

decisions. The role of efficient usage of databases, DBMS system tools, their logical structure and 

interconnections becomes more important. 

A prominent spot of the agenda of today is given to the tasks of optimal organization of information 

flows in the management system, creating and positioning responsibility centers, need for technical 

problem solution. This also includes the tasks of singling out standard objects, information 

processing tools with the consideration of occurring events and changing situations, software 

standardization. 

That said, tracking and control remain principal management functions of ICS modules. 

Certainly, at the stage of ICS creation and implementation, completely new problems requiring 

their own solution arise. These are, primarily, determining the composition of business processes, 

their analysis and optimization, management of information flows. Problems of management both 

in a separate responsibility center and in a responsibility center as a whole become independent 

problems. 

However, further quality leap in development of MRP / ERP systems lies on the path of their 

intellectual possibilities development. 

That is why IICS (Intellectual Integrated Control System) MMC sets the task to extend intellectual 

possibilities of ICS by means of integrating databases, mechanisms of logical entry, information 

rollup, etc. into their composition, i.e. its task of improving the existent ICSs by means of 

“intellectual add-on”. 

Great significance is given to the problem that there was an attempt to solve when creating IICS 

MMC by the need of efficient management of big arrays of information typical of large industry. 

Total number of indexes characterizing material and financial flows is at least several thousands 

of units which must be taken into account when looking for and making management decision. 

That is why, when creating IICS MMC, great significance is given to analysis of the informational 

structure of a company management system, and such tasks solved with the help of specially 

created databases as diagnostics of management object status, identification of discrepancy 

reasons, formation of management decisions are determined. Functioning of databases as 

subsystems of the highest hierarchical level in the information structure of the company 

management system is based on: 



Research in Social Sciences and Technology (RESSAT)                                       2018: 3 (3),68-91 

 

 

– informational arrays formed by means of automatic registration with the help of automate 

workstations module stubs intended for manual entry of the final data about business 

processes, 

– databases in which data used in the process of developing and making management 

decisions is archived and updated, and data coming from direct information sources is 

combined, 

– elementary images (functions) with the help of which queries are formed, data is 

transformed and analyzed, recording documents on separate business processes and 

company as a while are formed. 

At the same time, already at the level of elementary images (functions), usage of integral 

assessment and logic conclusions is expected. At this point using standard software tools provided 

by system and other shells is virtually impossible. Therefore, there is a need for special methods 

and technologies that have original applied scientific nature. 

This is primarily caused by the necessity to provide real support for the process of developing and 

making management decisions at the company. There emerges a need for expert evaluation usage, 

adequate description of situations where a management decision is to be made, and tracking of 

multi-criteria nature of ratings used in decision making. Management process becomes especially 

complicated when decision making is needed in time crisis or close-to-crisis situations. 

This means that the need to use situational analysis tool in the process of company management 

becomes current. The toolkit applied in the development of methodical system support is 

extending. It includes both elements and technologies of expert analysis, and results of collective 

decision making theory, and “fuzzy” logic, and results of game theory and prediction theory. 

But for us, from the point of view of analysis of management technologies used when creating 

IICS MMC, application of different forms of expert assessment technologies. Starting from 

formation of databases and logic entry mechanisms in a certain subject area and finishing with 

multi-aspect assessment of complex management situations, determining principal tendency of 

their development and preparation of recommendations and alternative options for management 

decisions. 

Constructing a database structure must primarily take into account “horizontal” automation 

principle, i.e. based on consequent interconnection of business processes. 



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81 

 

At the same time, to provide support for management system at all hierarchical levels actually 

existing in a modern company it is also necessary to use “vertical” automation principle allowing 

taking into account management communications actually working in the company. Using 

“vertical” approach implies increasing depth and numeric composition of business processes in 

order to improve company management efficiency. 

When implementing IICS MMC, the administrative functional company management pyramid is 

used (Fig. 3). 

 

 

Figure 3. Administrative functional company management pyramid. 

 

At the base of the pyramid we have information support organization by types of production, data 

control based on the status of separate branches, data formation based on resource base. At the 

second level of the pyramid there is release and shipment control by types and quality control by 

types of assessment of current efficiency by types. I.e. it is not only original data summary but also 



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information about product quality and management efficiency at the level of workshops. At the 

third level we have general control and coordination of product release by types, as well as general 

quality control implemented when managing workshop activity. At the fourth, highest level there 

is industrial complex management; production, commercial and financial activity management of 

the company as a whole. Management at this level of managerial hierarchy is executed by company 

top managers. 

When organizing the company management system, the composition of the principle 

administrative functional pyramid can also include other management pyramids of a lower 

hierarchical level correspondent to separate responsibility centers. Particularly, these can be a 

subsystem of main power engineer’s service control or financial economic service control, etc. 

Composition, specifics, nature of information in such pyramids is defined by both general 

information requirements effective at the company and requirements of certain activity of a 

responsibility center. 

Such structure of information flows organization at the company among which expert information 

plays an important role benefits to the improvement of manageability of its activity. It also allows 

for defining logical interconnection, necessary analysis and information processing, as well as 

priorities in the process of its usage when preparing and making management decisions. 

The mentioned information flows serve as starting point information for the work of intellectual 

subsystems IICS, as well as for creating entry rules, mathematic models, expert procedures used 

in intellectual subsystems of IICS. 

Certainly, the sphere of application for intellectual subsystems is primarily the forth and third 

levels of administrative functional company management pyramid. At the same time it is often 

easier to work out, test and adapt them at the third level of management, since the activity at this 

level of management is less complicated, and the volume of information undergoing analysis is 

less. At the same time the nature of decisions made is essentially kept.  

Since the main task of any management system is making efficient management decisions based 

on the upcoming information about activity of the company and its subdivision, it becomes an 

important task to create and implement into usage intellectual subsystems IICS providing adequate 

analysis of management situations. Based on this analysis, preparation of alternative options of 

management decisions with assessment of expected efficiency of their implementation is 

performed.  



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83 

 

At this stage of management process, usage of expert information in interaction with the feedback 

mechanism functioning when making management decisions is expected. At the same time, 

management decisions made might also concern reorganization of the existent management system 

at the company. With the usage of intellectual subsystems IICS, tactical and strategic goals of the 

company might be modified if external or internal situation demands it. 

One of the features of IICS MMC is the possibility to work with large volumes of information and 

integrated account of multiple factors, indexes, criteria, complex classification and diagnostics 

tasks, necessity to develop decisions in complex management situations. This makes it worthwhile 

to use technologies of situational analysis to provide efficient work of company management 

system. 

Because of diversity and multi-faceted indexes, factors, criteria based on which management 

decisions are prepared and made, the role of expert assessment, group decision preparation and 

making technologies, multi-criteria choice increases. This brings special significance to the 

necessity track composition, interconnection and combination of different indexes and factors, 

their correspondence to the goals set and the tasks planned. 

The foreground is given to the so-called problem of observability of indexes used in the 

management process. It can be direct, indirect, or expert depending on its nature and application 

in the process of decision preparation and making. 

As we know, it is only possible to manage what is measurable. In our case one of the main company 

management tasks is to determine the composition of indexes to be measured, to determine or 

assess their current values, to provide their planned values. 

For every responsibility center, its own composition of indexes values of are to be controlled in 

the management process is determined. The task of determining the composition of indexes to be 

controlled in the management process is one of the primary tasks of management system 

formation. 

Next task of no less importance is the task of measuring their values for appropriate decision 

preparation and making. In the management process, it is not sufficient to determine the value of 

issue of a certain type of product in the current moment of time. It is necessary to be able to assess 

how much the current index value corresponds to the management goals, and if the correction of 

planned tasks or taking other measures are necessary in case of discrepancies in the planned and 



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actual values. No to mention the fact that not all characteristics to be managed in the course of 

complex production activity can be directly measured. 

Besides, when assessing product quality or solving marketing problems, numeric data is not always 

enough. If, say, maximum speed of the vehicle is 130 km/hr, is it good or bad? If it is a car, it is 

clearly not enough today to be able to meet sales market competition. But if it is a truck with great 

weight-bearing capacity, it might absolutely satisfy the buyer. 

When managing complex diverse company activity, we must also be able to assess management 

quality which sometimes could be a rather difficult task. This is necessary in order to introduce 

timely corrections into the management system which, according to recommendations of leading 

specialists in the quality sphere, must be implemented at least once a year. For example, it might 

be worthwhile to single out new responsibility centers or correct an existing system of such centers. 

When changing goals, it might be even the composition of the measured indexes that could be 

subject to revision, not to mention weight coefficients characterizing their comparable importance 

in achieving goals. 

A considerable amount of indexes taken into account in the management process makes the 

assessment of the current status and the expected result of alternative option implementation more 

complicated. Form the point of view of efficient functioning of the management system, it might 

be more worthwhile to choose a relatively small amount of key indexes and monitor their values 

in the course of achieving of the set goals. But the choice of the index system and determination 

of current status assessment mechanism, as well as the overall facility work results assessment 

mechanism, and even assessing their values can be performed only with the help of certain methods 

of expert assessment. Development of correct integral assessment indexes is also only possible 

using modern methods of expert assessment. 

One more type of problems which must be solved when managing a company are problems of 

optimal resource distribution, especially in case of deficit types of resources. If we take into 

account that the resources in the management process today are thought to include virtually all of 

them, necessary or beneficial for achievement of the set goals, it becomes clear how complicated 

this managerial task is, especially when managing a large-scale company. Because today, it is even 

information that is included in main management resources. This problem can also be solved to a 

considerable extent only using expert management technologies, i.e. intellectual systems. They are 

especially important in case of high sensitivity of the situation towards the management decision 



  Olefirenko and Galuschenko 

85 

 

made when even minor changes in the chosen resources can cause considerable changes in the 

result, including quality characteristics of the output product. 

In order to prepare information about management situations introduced by a set of correspondent 

indexes, as part of situational analysis, we can break down statuses into normal, standard and 

abnormal with different degrees of deviation from the expected or the planned status. In standard 

situations, results of the management decisions made are predictable. Abnormal situations, as well 

as situations with a high degree of uncertainty require a bigger volume of analytical work and 

imply using expert assessment methods to a greater extent. In this case, the significance of quality 

preparation of alternative options of management solutions rises. 

As part of the situational approach, expert systems to use when managing facilities of different 

purpose are developed (30 Ip). The type of management decisions requiring a higher quality level 

from the company management system also include situations where a transition from one 

management situation to another is provided for, since the degree of uncertainty is higher in them 

than in a usual standard situation. It is worthwhile here to use intellectual subsystems especially 

designed when managing a company (42 Ip). 

 

 

Figure 4.  EA as a management system object. 

 



Research in Social Sciences and Technology (RESSAT)                                       2018: 3 (3),68-91 

 

 

In IICS MMC the management system feedback loop includes subsystem of status diagnostics of 

economic activity (EA) of the management object (Fig. 4), its production, commercial and 

financial component. 

Subsystem contains a database in which, apart from other data, the information about decision 

maker’s goals and their corrections, and the information about their priorities when making 

management decisions is entered. The information about priority goals can be entered in an 

interactive mode. 

When using situational analysis technologies in IICS MMC in the process of company 

management, the assessment of management situations with the help integral ratings introduced 

by integral indexes (Chapter 2.6). This also implies active usage of expert assessment methods, 

i.e. intellectual subsystems of types AEAS (Automated Expert Assessment System) or DSS 

(Decision Support System). Particularly, the possibility of analysis of nature of changes in separate 

indexes, their influence on the course of decisions made earlier gains special significance. The 

analysis results are used in preparation of recommendations on achieving the index values that 

meet the set goals. 

IICS MMC implies managing three types of resources: universal, unique and non-renewable, as 

well as mutually substitutable with the possibility of their recount with the help of specially entered 

non-dimensional scales taking into account their maximum consumption and minimum acceptable 

value. The author introduces an interesting term of production potential characterized by the 

smallest value of the most deficit resource. The group of management rules determined when 

developing the system is intended for preparation of management decisions when distributing 

resources of different types. Essentially, the problem of optimal resource distribution is viewed in 

the system as the main company management problem which is solved based on application of 

intellectual subsystems being a part of IICS MMC. 

Playing a definitive role when managing modern company, responsibility centers in which, based 

on using intellectual subsystems IICS MMC, major management decisions are made, also use 

expert information to a considerable extent. It is on the basis of expert information, being an 

integral part of informational flows, that decisions are made by manager of different hierarchical 

levels of company management. 

Databases determined based on expert information allow performing diagnostics of EA status of 

both company as a whole and its responsibility centers. On their basis recommendation on 



  Olefirenko and Galuschenko 

87 

 

preparation of management decisions are formed necessary for prevention of emerging deviations 

in reaching index values characterizing EA and correspondent to the goals of company activity. 

They use coefficients of confidence identifying the degree of influence of emerging deviations of 

the results of EA calculating the accuracy of emerging deviations and assessing changes in EA 

status. 

It is important for successful functioning of IICS MMC to organize the process of information 

processing and rollup for assessment of company EA as a whole or a separate responsibility center. 

Main components of this process are introduced on Fig. 5. 

 

 

Figure 5. Structure of Intellectual System of Management Accounting of Company Economic Activity. 

Particularly, it implies functioning of management decision formulation database updating which 

must be performed constantly when new information comes. Management influences are formed 



Research in Social Sciences and Technology (RESSAT)                                       2018: 3 (3),68-91 

 

 

in the occurrence of deviations in the results of production, commercial and financial activities, of 

unstable or transitional EA status, as well as standard EA status. 

Thus, IICS MMC created by Y.V. Ipatov includes intellectual subsystems of IS types we have 

looked at earlier, and, above all, expert systems (ES) and decision support systems (DSS). 

We believe that the area of intellectual integrated control systems (IICS) creation and development 

is a valid way to improve efficiency of the ICS generation used today when managing the largest 

companies and organizations. When preparing and making management decisions having, to a 

considerable extent, multi-criteria and collective nature, the possibilities of automated expert 

assessment systems (AEAS) must be applied to a bigger extent. This is determined by the fact that 

AEAS are intended for improvement of efficiency in the usage of experience and knowledge of 

professionals of high qualification in the management process. 

The future of the next generation of integrated control systems belongs to efficient combination of 

the possibilities of expert management technologies with the possibilities of modern informational 

systems and computer technologies. 

 

Conclusion and Implications 

It can be seen that almost all giants, working in highly competitive markets such as Amazon, Ebey, 

Walmart and similar, work on their own development systems, which are very similar to IEM 

systems except for some details. Successful solutions to the classical scheme in modern markets, 

the author is not known.  

Correspondingly, it will not be a significant deviation from the truth to say that the IEM systems 

are implementing all the benefits of self-development systems, sparing operators from the risks of 

developing such a system ab initio. You can even estimate the risks of such a project (developing 

your own automation system) by estimating the number of success stories in several dozen 

companies and the number of forgottens on this path, which are many hundred thousand. Just 

statistical probability of success is the share of the percentage.  

On the other hand, following the classical approach will statistically result in a 100% probability 

defeat.  

To sum up, it can be seen that the use of the IEM approach makes it possible to make significant 

progress in the development of enterprise automation systems. In the marketplace, are not only the 



  Olefirenko and Galuschenko 

89 

 

transitional-level systems from ERP to IEM (not meeting the 1-2 requirements), and fully 

compliant with all requirements.  

In the next 10-20 years, the market for IEM systems will rapidly develop along with large-scale 

migration to the IEM system, the transition to automated and robotic production, the shortest and 

most integrated supply chains from the producer to the final consumer. 

IEM systems allow you to step directly to an autonomous (Unmanned, Humanless Enterprise) 

enterprises and, in turn, they are as a link to the new technological revolution. 

You can guess how the "Internet of Enterprises (IoE)" has been created. Each of the companies 

under IEM systems associated with other transaction chains can make excessive more than 90% 

of the global economy inventory together with their infrastructure processing and shifting. 

The IOE nodes, unlike the modern Internet, interact solely with structured information nullifying 

the mistakes and the probability of the attack, including virus one. 

So the Internet of Enterprises is an integral meta-IEM: 

– Self-organizing and self-balancing economic environment of planetary scale, the global 

homeostatic self-regulation mechanisms of which are extremely resistant to the imbalance 

of interference of any nature. 

– Completely unmanned processes of wealth creation, operated by the world-wide network 

of IEM systems, in the next few years will realize the centenary dream of mankind: Getting 

rid of the routine work. 

A similar approach has been shown with a "value network" concept developed by a number of 

authors from the middle of 90th. According to value network ideology IEM Enterprise is the 

analogy of internal value network while Internet of Enterprises is the analog of external value 

network. Again, IEM System is the intelligent infrastructure basis of value networking. Despite of 

mathematical derivation of IEM Paradigm, value network is based on practical business analysis 

and currently more like a set of empirical techniques and approaches than a science theory with a 

elaborated mathematical basis. Whoever it is important to mention the equality of the results of 

two fundamentally different approaches: a fabulous evidence of high reliability and practical effect 

of conclusions. 

 

 

 



Research in Social Sciences and Technology (RESSAT)                                       2018: 3 (3),68-91 

 

 

References 

Anokhin, P.K. (1935). The problems of the centre and the periphery in the physiology of neural 

activity. Nizhny Novgorod: Nizhny Novgorod State Medical Academy. 

Anokhin, P.K. (1955). Features of the afferent apparatus of the conditioned reflex and their 

importance for psychology, Psychology issues, 6, 16-38.  

Anokhin, P.K. (1978). Philosophical aspects of functional system theory: selected works. Moscow: 

Science.  

Anokhin, P.K. (1998). Selected Works. Cybernetics of functional systems. Moscow: Medicine.  

Chinakal, V.O. (2008). Intelligent systems and technologies. Moscow: The Peoples' Friendship 

University of Russia.  

Kılınç, E., Tarman, B., & Aydın, H. (2018). Examining Turkish social studies teachers’ beliefs 

about barriers for technology integration.Techtrends, 62(3), 221-223. 

Ostroukh, A.V. (2015). Intelligent Systems. Krasnoyarsk: Science and Innovation Facility. 

Pavlov, I.P. (1949). Lectures on the Work of the Cerebral Hemispheres. Selected works. Moscow: 

The USSR Academy of Sciences Publishing House. 

Pupkov, K.A. & Konkov, V.G. (2001). Intelligent Systems (Research and creation). Moscow: 

Bauman Moscow State Technical University. 

 Ritter, N., Kılınç, E., Navruz, B., &Bae, Y. (2011). Test review: Test of nonverbal intelligence-4 

(TONI-4). Journal of Psychoeducational Assessment, 29(5), 484-488. 

Tarman, B. (2017). Editorial: The future of social sciences. Research in Social Sciences and 

Technology, 2(2), 1-6. Retrieved from http://ressat.org/index.php/ressat/article/view/329   

Tarman, B. (2016). Innovation and education. Research in Social Sciences and Technology, 1(1), 

77-97. Retrieved from http://ressat.org/index.php/ressat/article/view/3  

Tarman, B., Baytak, A. & Duman, H. (2015). Teachers’ views on an ICT reform in education for 

social justice. Eurasia Journal of Mathematics Science and Technology Education, 11(4), 

487-496. 

Tarman, B., Baytak, A. (2011). The new role of technology in education: Social studies teacher 

candidates’ perceptions, Gaziantep University Journal of Social Sciences, 10(2),891-908. 

Tarman, B. & Chigisheva, O . (2017). Editorial for Special Issue: Transformation of Educational 

Policy, Theory and Practice in Post-Soviet Social Studies Education. Journal of Social 

http://ressat.org/index.php/ressat/article/view/329
http://ressat.org/index.php/ressat/article/view/3


  Olefirenko and Galuschenko 

91 

 

Studies Education Research, 8 (2), i-iv. Retrieved from 

http://dergipark.gov.tr/jsser/issue/32450/360860 

Tarman, B. (2010) Global Perspectives and Challenges on Teacher Education in Turkey, 

International Journal of Arts & Sciences (IJAS), 3(17): 78-96, United States. 

The USSR State Committee on Public Education "Comprehensive scientific, research-and-

technology and educational programs and projects of the Public education of the USSR 

1989-1994 years". (1991). Moscow: Public education of the USSR. 

 

 

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