Analysis of the world experience of economic and mathematical modeling of smart enterprises
The paper shows the inevitability of technological mode shift driven by the Industry 4.0, which implies the ubiquitous implementation of information technology, total automation of various processes and creation of cyber-physical systems with artificial intelligence. This requires a complete restruc...
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nasplib_isofts_kiev_ua-123456789-1273752025-02-10T00:40:02Z Analysis of the world experience of economic and mathematical modeling of smart enterprises Аналіз світового досвіду економіко-математичного моделювання смарт-підприємств Анализ мирового опыта экономико-математического моделирования смарт-предприятий Madykh, A.A. Okhten, O.O. Dasiv, A.F. Problems of strategy development, financial and economic regulation in industry The paper shows the inevitability of technological mode shift driven by the Industry 4.0, which implies the ubiquitous implementation of information technology, total automation of various processes and creation of cyber-physical systems with artificial intelligence. This requires a complete restructuring of manufacturing systems and production relations, especially in the economies of those countries that want to take a decent place in the new international division of labour of the digital future. An analysis of the world experience of such changes connected with smart industrialization, digital transformations of the economy, the emergence of the industrial Internet of Things and big data processing made it possible to draw the conclusion that it is necessary to apply economic and mathematical methods to justify the expediency of such transformations: economic validity, as well as physical viability of newly created systems. The use of the apparatus of economic and mathematical modeling allows studying properties of the smart system that is being designed, evaluating its effectiveness and risks, anticipating the emergence of problems and errors - without the risk of incurring significant losses which is inevitable when making direct changes in the object of research.Therefore, the purpose of this paper is to study the world experience in the economic and mathematical modeling of smart enterprises and to substantiate its use in the conditions of Ukraine.The review of publications, reflecting the aspects of economic and mathematical modeling in these areas, allowed to conclude that the methodical and methodological apparatus for modeling these processes is unsystematic and inefficient, as well as to formulate recommendations on the economic and mathematical modeling of smart enterprises in Ukraine. In order to take into account the specific features of Ukraine's technological and institutional development, a number of economic and mathematical modeling tools based on the use of production functions, models of inter-branch balance, network optimization models and simulation models based on stochastic dependencies were offered to support the creation of smart enterprises. Показано неминучість зміни технологічного укладу у зв'язку з промисловою революцією 4.0, що потребує кардинальної перебудови системи виробництва і виробничих відносин. У результаті аналізу зарубіжного досвіду подібних змін, пов'язаних зі смарт-індустріалізацією, цифровими трансформаціями економіки, становленням промислового інтернету речей, обробки великих даних, встановлено необхідність застосування економіко-математичних методів для обґрунтування доцільності подібних трансформацій: як пов'язаної з їх економічною обґрунтованістю, так і з фізичною життєздатністю новостворюваних систем. Огляд публікацій, які відображають аспекти економіко-математичного моделювання в зазначених сферах, дозволив зробити висновок про несистемність і неопрацьованість методичного і методологічного апарату моделювання даних процесів, а також сформулювати рекомендації щодо економіко-математичного моделювання смарт-підприємств в Україні. Для врахування особливостей технологічного та інституційного розвитку України при обґрунтуванні створення смарт-підприємств запропоновано ряд інструментів економіко-математичного моделювання, заснованих на використанні виробничих функцій, моделей міжгалузевого балансу, мережевих оптимізаційних моделей, імітаційних моделей на базі стохастичних залежностей. Показана неизбежность смены технологического уклада в связи с промышленной революцией 4.0, что требует кардинальной перестройки системы производства и производственных отношений. В результате анализа зарубежного опыта подобных изменений, связанных со смарт-индустриализацией, цифровыми трансформациями экономики, становлением промышленного интернета вещей, обработки больших данных установлена необходимость применения экономико-математических методов для обоснования целесообразности подобных трансформаций: как связанной с их экономической обоснованностью, так и с физической жизнеспособностью вновь создаваемых систем. Обзор публикаций, отражающих аспекты экономико-математического моделирования в перечисленных сферах, позволил сделать вывод о несистемности и непроработанности методического и методологического аппарата моделирования данных процессов, а также сформулировать рекомендации по экономико-математическому моделированию смарт-предприятий в Украине. Для учёта особенностей технологического и институционального развития Украины при обосновании создания смарт-предприятий предложен ряд инструментов экономико-математического моделирования, основанных на использовании производственных функций, моделей межотраслевого баланса, сетевых оптимизационных моделей, имитационных моделей на базе стохастических зависимостей. 2017 Article Analysis of the world experience of economic and mathematical modeling of smart enterprises / A.A. Madykh, O.O. Okhten, A.F. Dasiv // Економіка промисловості. — 2017. — № 4 (80). — С. 19–46. — Бібліогр.: 50 назв. — англ. 1562-109Х DOI: doi.org/10.15407/econindustry2017.04.019 JEL codes: С00; С60; С67; С69; О12; О14. https://nasplib.isofts.kiev.ua/handle/123456789/127375 330:47+330:46+330:44 en Економіка промисловості application/pdf Інститут економіки промисловості НАН України |
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Problems of strategy development, financial and economic regulation in industry Problems of strategy development, financial and economic regulation in industry |
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Problems of strategy development, financial and economic regulation in industry Problems of strategy development, financial and economic regulation in industry Madykh, A.A. Okhten, O.O. Dasiv, A.F. Analysis of the world experience of economic and mathematical modeling of smart enterprises Економіка промисловості |
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The paper shows the inevitability of technological mode shift driven by the Industry 4.0, which implies the ubiquitous implementation of information technology, total automation of various processes and creation of cyber-physical systems with artificial intelligence. This requires a complete restructuring of manufacturing systems and production relations, especially in the economies of those countries that want to take a decent place in the new international division of labour of the digital future. An analysis of the world experience of such changes connected with smart industrialization, digital transformations of the economy, the emergence of the industrial Internet of Things and big data processing made it possible to draw the conclusion that it is necessary to apply economic and mathematical methods to justify the expediency of such transformations: economic validity, as well as physical viability of newly created systems. The use of the apparatus of economic and mathematical modeling allows studying properties of the smart system that is being designed, evaluating its effectiveness and risks, anticipating the emergence of problems and errors - without the risk of incurring significant losses which is inevitable when making direct changes in the object of research.Therefore, the purpose of this paper is to study the world experience in the economic and mathematical modeling of smart enterprises and to substantiate its use in the conditions of Ukraine.The review of publications, reflecting the aspects of economic and mathematical modeling in these areas, allowed to conclude that the methodical and methodological apparatus for modeling these processes is unsystematic and inefficient, as well as to formulate recommendations on the economic and mathematical modeling of smart enterprises in Ukraine. In order to take into account the specific features of Ukraine's technological and institutional development, a number of economic and mathematical modeling tools based on the use of production functions, models of inter-branch balance, network optimization models and simulation models based on stochastic dependencies were offered to support the creation of smart enterprises. |
| format |
Article |
| author |
Madykh, A.A. Okhten, O.O. Dasiv, A.F. |
| author_facet |
Madykh, A.A. Okhten, O.O. Dasiv, A.F. |
| author_sort |
Madykh, A.A. |
| title |
Analysis of the world experience of economic and mathematical modeling of smart enterprises |
| title_short |
Analysis of the world experience of economic and mathematical modeling of smart enterprises |
| title_full |
Analysis of the world experience of economic and mathematical modeling of smart enterprises |
| title_fullStr |
Analysis of the world experience of economic and mathematical modeling of smart enterprises |
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Analysis of the world experience of economic and mathematical modeling of smart enterprises |
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analysis of the world experience of economic and mathematical modeling of smart enterprises |
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Інститут економіки промисловості НАН України |
| publishDate |
2017 |
| topic_facet |
Problems of strategy development, financial and economic regulation in industry |
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https://nasplib.isofts.kiev.ua/handle/123456789/127375 |
| citation_txt |
Analysis of the world experience of economic and mathematical modeling of smart enterprises / A.A. Madykh, O.O. Okhten, A.F. Dasiv // Економіка промисловості. — 2017. — № 4 (80). — С. 19–46. — Бібліогр.: 50 назв. — англ. |
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–––––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––
ISSN 1562-109X Econ. promisl. 19
2017, № 4 (80)
UDC: 330:47+330:46+330:44 doi: 10.15407/econindustry2017.04.019
Artem A. Madykh,
candidate of economic sciences
E-mail:artem.madykh@gmail.com;
Oleksiy O. Okhten,
candidate of economic sciences
E-mail:aokhten@gmail.com;
Alla F. Dasiv,
candidate of economic sciences
Institute of Industrial Economics of NAS of Ukraine
03057, Ukraine, Kiev, Zhelyabova str., 2
E-mail: alladasiv@gmail.com
ANALYSIS OF THE WORLD EXPERIENCE OF ECONOMIC
AND MATHEMATICAL MODELING OF SMART ENTERPRISES
The paper shows the inevitability of technological mode shift driven by the Industry 4.0,
which implies the ubiquitous implementation of information technology, total automation of
various processes and creation of cyber-physical systems with artificial intelligence. This re-
quires a complete restructuring of manufacturing systems and production relations, especially
in the economies of those countries that want to take a decent place in the new international
division of labour of the digital future.
An analysis of the world experience of such changes connected with smart industrializa-
tion, digital transformations of the economy, the emergence of the industrial Internet of
Things and big data processing made it possible to draw the conclusion that it is necessary to
apply economic and mathematical methods to justify the expediency of such transformations:
economic validity, as well as physical viability of newly created systems. The use of the appa-
ratus of economic and mathematical modeling allows studying properties of the smart system
that is being designed, evaluating its effectiveness and risks, anticipating the emergence of
problems and errors – without the risk of incurring significant losses which is inevitable when
making direct changes in the object of research.
Therefore, the purpose of this paper is to study the world experience in the economic and
mathematical modeling of smart enterprises and to substantiate its use in the conditions of Ukraine.
The review of publications, reflecting the aspects of economic and mathematical model-
ing in these areas, allowed to conclude that the methodical and methodological apparatus for
modeling these processes is unsystematic and inefficient, as well as to formulate recommen-
dations on the economic and mathematical modeling of smart enterprises in Ukraine. In order
to take into account the specific features of Ukraine's technological and institutional develop-
ment, a number of economic and mathematical modeling tools based on the use of production
functions, models of inter-branch balance, network optimization models and simulation mod-
els based on stochastic dependencies were offered to support the creation of smart enterprises.
Keywords: Industry 4.0, digital technologies, smart enterprises, big data, economic and
mathematical modeling.
JEL codes: С00; С60; С67; С69; О12; О14.
In 2011, at the Hanover Fair, a group
of German researchers, businessmen and
public figures from the Industry-Science
Research Alliance for the development of
strategic principles of high-tech production,
offered the term "Industry 4.0" and its prin-
ciples [23].That event marked the compre-
hension and the beginning of the transition
© A.A. Madykh, O.O. Okhten, A.F. Dasiv, 2017
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry –––––––––––––––––––––––––––––––
20 ISSN 1562-109X Econ. promisl.
2017, № 4 (80)
to a new industrial revolution, based on the
ubiquitous application of "smart" technolo-
gies that can completely exclude humans
from the process of making routine deci-
sions in the area of manufacturing. For sev-
eral years, these ideas have spread so much
in the scientific and business environment
that the Fourth Industrial Revolution be-
came the main and dominant topic at the
46
th
World Economic Forum in Davos [6].
Fig. 1 shows the evolution of world
industry development and its relationship
with the change of technological modes.
The Third and the Fourth Industrial Revolu-
tions are considered by many researchers as
two different ones: the Third revolution is
the digital revolution associated with the
digitization of all processes and the Fourth
one is a revolution of cyber-physical sys-
tems, associated with the emergence of ma-
chines with artificial intelligence. At the
same time, there are publications, in which
these revolutions are not separated [19], and
introduction of technologies of the 5
th
and
6
th
technological modes is considered to be
the Third industrial revolution, and the key
factor in that revolution is the significant
change in the role of information and in-
formatization of production processes.
1770 1790 1830 1850 1880 1900 1930 1950 1970 1990 2010 2030 2060
Pre-industrial society:
- manual labor;
- trade shops, manufactories;
- agrarian society, subsistence
economy.
The first technological mode:
- energy of water;
- development of the textile
industry;
- mechanization of industrial
production.
The second industrial revolution:
- conveyor;
- line production;
- energy of electricity and hydrocarbons.
T
h
e
th
ir
d
in
d
u
st
ri
al
r
ev
o
lu
ti
o
n
:
-
C
N
C
m
ac
hi
ne
s,
r
ob
ot
ic
s;
-
fle
xi
bl
e
au
to
m
at
ed
p
ro
du
ct
io
n.
T
h
e
f
o
u
rt
h
i
n
d
u
s
tr
ia
l
re
v
o
lu
ti
o
n
:
-
cy
b
e
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h
ys
ic
a
l s
ys
te
m
s;
-
in
d
iv
id
u
a
liz
e
d
p
ro
d
u
ct
io
n
;
-
In
te
rn
e
t
o
f
th
in
g
s.
The second technological
mode:
- energy of steam, coal;
- development of transport,
ferrous metallurgy;
- steam engine;
- replacement of muscular
force by mechanical.
The third technological
mode:
- electricity;
- development of heavy
engineering, electrical
engineering;
- the electric motor;
- standardization of
production.
The fourth technological
mode
- energy of hydrocarbons;
- development of automobile
industry, oil refining, non-
ferrous metallurgy, polymer
industry;
- internal combustion engine;
- mass and serial production.
The fifth technological
mode:
- nuclear power;
- development of
microelectronics, information
technologies;
- microelectronic components;
- individualization of
production;
- globalization.
The sixth technological
mode:
- renewable energy sources;
- development of
nanotechnology,
bioengineering;
- artificial intelligence,
nanomaterials;
- complete individualization of
production.
The first industrial revolution:
- machines, machine tools;
- factories;
- energy of water and steam.
Source: compiled from [5, 22].
Fig. 1. Evolution of the world industry development and its relationship
with the change of technological modes
There’s certain logic behind that as,
firstly, the term the "Third industrial revolu-
tion" appeared just 5 years before the
"Fourth" one and the principles of manufac-
turing organization, associated with it, are
only beginning to spread across the Western
countries (and are still in their infancy in
Ukraine). Secondly, it’s obvious that the
emergence of cyber-physical systems is an
evolutionary development of the digitaliza-
tion process. Therefore, we believe that the
concepts of the Third and Fourth revolutions
most probably will eventually be combined
into a single one, "digital revolution".
In any case, the spread of information
technology, comprehensive automation of a
wide variety of processes, the discovery of
fundamentally new materials and non-waste
ways of using them, success in the creation
of cyber-physical systems that have artificial
intelligence – all that has revolutionized the
opportunities in the organization of industri-
al manufacturing. Ukraine, whose industry
uses technologies of the 3
rd
and 4
th
techno-
logical modes [3], is far behind the Western
–––––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––
ISSN 1562-109X Econ. promisl. 21
2017, № 4 (80)
countries in its development, and the chanc-
es of catching up with them in an evolution-
ary manner seem doubtful. Nevertheless,
while being on the periphery of the world
economic processes, Ukraine has no right to
remain aloof from these major transfor-
mations. The creation of new enterprises,
operating the technology of the 6
th
mode,
might allow Ukraine to occupy a worthy
niche in the new international division of
labour of the digital future.
However, any project, even a local
one, requires careful justification of its suit-
ability. First of all, from the point of view of
the physical viability of the designed system
in the environment in which it will exist.
The creation of the most modern smart en-
terprise in the conditions of a corrupt sys-
tem, undeveloped institutions, contractors
working according to old principles, unde-
veloped culture of using information tech-
nologies, might actually lead to non-
viability of such an enterprise. Another as-
pect is the economic feasibility: the costs of
creating such enterprises should be justified,
and the efficiency of their operation has to
exceed the efficiency of the current ones.
However, in the above mentioned condi-
tions, such efficiency is not always
achieved. Therefore, the transition to a new
smart production system and measures to
transform production relations should be
carefully justified, and economic and math-
ematical modeling is the most effective tool
for describing the systems and processes
being designed. The use of the apparatus of
economic and mathematical modeling
makes it possible to conduct any experi-
ments with the system being designed, study
its properties, evaluate efficiency and antici-
pate the occurrence of problems and errors
without the risk of incurring colossal losses
that are unavoidable in the case of direct ex-
periments.
The apparatus of economic and math-
ematical modeling is currently developed
enough to describe any, even the most com-
plex processes and systems, however, the
novelty of the tasks to be solved when creat-
ing smart enterprises does not allow making
an unequivocal choice in favor of using cer-
tain specific tools. In order to choose the
most effective and expedient tools of eco-
nomic and mathematical modeling, it makes
sense to study the foreign experience of ap-
plying these methods in the creation of
smart enterprises, since the developed coun-
tries are way ahead of Ukrainian reformers
and already have certain empirical
knowledge in this field.
Therefore, the purpose of this article
is to study the foreign experience of eco-
nomic and mathematical modeling of smart
enterprises and the rationale for its use in
Ukrainian conditions.
The following concepts, associated
with the digital revolution can be distin-
guished, which have a certain synonymous
character:
– «the Fourth industrial revolution»
[35], practical manifestations of which are
the intensification of information exchange
in production, the Internet of Things, cyber-
physical systems and cloud computing [38];
– «Industry 4.0» (German"Industrie
4.0"), which is used in Germany to describe
the Fourth industrial revolution [33];
– «smart factory» or «smart enter-
prise» – modular, structured factories, in
which cyber-physical systems control physi-
cal processes, create a virtual copy of the
physical world, and make decentralized de-
cisions [34];
– «cyber-physical systems» (CPS) –
hardware and software systems, being a
close interlacing of the physical and virtual
world. Such systems are formed by network
of embedded systems that are connected to
the outside world using sensors and drives,
receiving data streams from the physical
world and creating and constantly updating
the virtual copy of the physical world [29;
44];
– «Internet of Things» [24] (IoT) – in-
formation networks of physical objects (ob-
jects, goods, machines, cars, buildings and
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry –––––––––––––––––––––––––––––––
22 ISSN 1562-109X Econ. promisl.
2017, № 4 (80)
other objects) that ensure the interaction and
cooperation of these objects for achieving
common goals;
– «Industrial Internet of Things» [36]
(IIoT) – an information network that, among
other things, connects transport and indus-
trial production (digital product views,
cyber-physical systems of smart factories,
etc.).
Before analyzing the economic and
mathematical models of smart enterprises, it
is necessary to classify all the variety of
publications, concentrated on the problems
of their implementation and functioning, and
to highlight the areas that make sense study-
ing within the scope of the scientific re-
search on identifying the smart industry de-
velopment directions in Ukraine.
According to the objects of research,
the following directions of such publications
can be highlighted:
1) technical and technological direc-
tion, associated with the design and imple-
mentation of high-tech physical systems;
2) information direction, associated
with the accumulation, processing and
transmission of information;
3) economic direction, associated
with changes in the provision of benefits
and economic interests of individuals and
social groups.
In a closer look, the following objects
of study can be identified in these areas (fig.
2).
Technical and technological
direction
Informational direction Socio-economic direction
Sensors, meters
Additive non-waste 3D
technologies
Robotics and cyber-physical
systems (CPS)
Technologies of product
identification and traceability
Geoinformation systems (GIS)
Information and communication
equipment, broadband Internet
Digital product design and modeling
(CAD)
Cyber security technologies, etc.
Enterprise information systems
Technologies for working with big
data
Standardization in smart enterprise
design processes
Resource planning (ERP)
Management of production processes (MES)
Automated control of technological processes (SCADA)
Product lifecycle management (PLM)
Cloud technologies
Data mining and knowledge modeling technologies
Grid-technologies for big data processing
IoT-platforms, Internet of things, industrial Internet of
things (IIoT)
Process specification language (PSL)
General purpose information modeling languages for
engineering systems (SysML, Modelica)
General standards of information business modeling (BPMN,
BPDM, XPDL, WSDL, SCOR, B2MML, OAGIS, etc.)
Standards for presenting programmable logic control
(PLC), etc.
The connection with increasing the
production efficiency (labor productivity,
added value, competitiveness, etc.)
Substantiation of the processes of smart
enterprise formation
End-to-end planning and management in the
industry based on a single digital industry space
Effective routes of зкщвгсе movement in the
Internet of things
Increase in demand in conditions of introduction
of digital business models and expansion of
digital interaction with customers
Implementation of specific technologies for
solving applied problems
The impact of smart industrialization on
socio-economic processes
The connection with unemployment
The need for specialists in certain categories
The need for changes in the education system,
etc.
Source: compiled by the authors
Fig. 2. The main focus objects in the publications on smart industry
Also, all the variety of publications,
concerned smart enterprises, depending on
the goals that are pursued in a publication,
can be divided into following tasks to be
solved:
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1. Descriptive and introductory – the
purpose of which is to familiarize the reader
with certain objects or phenomena in the
smart industrialization. Such publications
are not relevant to economic and mathemat-
ical modeling, but their subject enables
identifying problems that can be solved us-
ing such methodological tools.
2. Engineering – the purpose of which
is to describe the processes. Such publica-
tions present numerous descriptions of vari-
ous models, including mathematical ones,
but these models are of engineering nature
and solve no economic problems. However,
as in the previous case, their analysis ena-
bles identifying the occurrence of accompa-
nying economic problems.
3. Economically reasoning publica-
tions, which confirm the economic feasibil-
ity of implementing certain processes, asso-
ciated with smart industrialization, or sub-
stantiate the emergence of new tasks, repre-
senting economic and social problems. Such
publications directly relate to the subject of
this study, but are scarce in number, often
inconsistent and are unable to adequately
solve the problems.
The publications of the second and
third groups usually also include a descrip-
tive-introductory part and thus may overlap
with the publications of the first group.
However, we were unable to find publica-
tions that would represent an intersection
of the first and the third group, which
would have considered the problem of the
engineering design of smart enterprises
through the prism of solving economic prob-
lems.
Fig. 3 shows the relationship of the
nature of publications according to the tasks
they solve with their directions according to
the objects of research.
Technical and technological
direction
Informational direction Socio-economic direction
Descriptive and
introductory
publications
Engineering
publications
Economically
reasoning
publications
Source: compiled by the authors
Fig. 3. Relationship of the nature of publications according to the tasks they solve
with their directions according to the objects of research
Let’s begin the overview of publica-
tions on smart enterprises with descriptive
and introductory studies related to the iden-
tification of objects and their functioning,
since these publications form a basic repre-
sentation of the way such enterprises func-
tion.
According to [31], 4 principles of "In-
dustry 4.0" design are distinguished:
– interaction: the ability of machines,
devices, sensors and humans to connect and
interact with each other via the Internet of
Things or the Internet of People;
– information transparency: the ability
of information systems to create a virtual
copy of the physical world by filling digital
models of enterprises with sensor data. This
requires aggregation of raw sensor data into
context information with a higher level of
usefulness;
– technical assistance: firstly, the abil-
ity of support systems to help humans by
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aggregating and visualizing information to
make informed decisions and quickly solve
urgent problems in a short time; secondly,
the ability of cyber-physical systems to
physically support humans by performing a
number of tasks that are unpleasant, physi-
cally exhausting or dangerous to humans;
– decentralized decision-making: the
ability of cyber-physical systems to inde-
pendently make decisions and perform their
tasks as autonomously as possible. These
tasks are transferred to a higher level only in
case of abnormal situations, interference or
conflicting goals.
Authors of [34] mention three areas of
"smart enterprises" that distinguish them
from traditional ones:
1. Monitoring and control– monitor-
ing and control systems implemented at
smart enterprises in real time collect and
transmit a wide range of data on the status
of enterprise facilities, their operation, the
use of resources, and the state of their envi-
ronment, which allows them to react quickly
to changes.
2. Information exchange and interac-
tion– the modern information infrastructure
allows exchanging large volumes of infor-
mation between humans and humans, hu-
mans and physical objects, as well as be-
tween physical objects without human inter-
vention. Often the components of infor-
mation exchange and cooperation are com-
bined with monitoring and management
components, initiating the exchange of in-
formation or certain actions in the event of a
certain situation detected by sensors. Such
capabilities allow production management
automation, when human intervention will
be necessary only in cases of certain events’
occurrence, and rest of the time the ex-
change of information is limited to physical
objects.
3. Big data and data analysis– collect-
ing large amounts of data on the status of
objects, processes and the environment, and
increasing the capacity of data processing
systems make it possible to expand the use
of analytical tools to improve business pro-
cesses at all stages, including the develop-
ment, production and sales.
Given this, the following criteria can
be used to classify an enterprise as a smart
enterprise: the use of intelligent sensors for
monitoring and processes control; automa-
tion of information exchange processes and
interaction of workers with each other,
workers with physical objects (mainly with
machines and computer systems), as well as
physical objects with each other; use of big
data for continuous analysis and process im-
provement.
Thus, the key factor in the modeling
of smart enterprises is the work with big da-
ta, the research of which is the topic of a
large number of publications, related to
smart industrialization.
The use of big data, which, along with
the software of cyber-physical systems,
forms the basis of information support for
the smart industry, is associated with signif-
icant difficulties in their processing using
traditional methods. Such a complexity is
explained not only by the large amount of
data, but also by their unstructured nature
(the collected data is not generated initially
in accordance with the rules for database
design), the lack of centralization of collec-
tion and processing (data from a variety of
different sources can be used), and the weak
relationship within the data itself (data from
different fields of activity). In [40], big data
is defined as data sets with sizes beyond the
capabilities of typical database management
software to collect, store, manage and ana-
lyze data.
In a review article [32], the lifecycle
of big data consisting of four stages (genera-
tion, collection, storage and analysis) was
analyzed, and the main approaches and tools
that can be used at each stage are consid-
ered. Similar to other publications on this
topic, the main problem of analyzing big
data is defined as their initial absence of pat-
tern (which not only makes it difficult to
collect and store such data, but also makes it
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impossible to use traditional structured da-
tabases), and modeling, visualization, opti-
mization and forecasting are mentioned as
the main areas of using big data in practice.
Analysis of big data to obtain practical
conclusions is directly related to data mining
technologies. Data mining is a collective
name, served to denote a set of methods
used for detecting previously unknown, non-
trivial, practically useful and accessible in-
terpretations of knowledge that is required
for decision-making in various areas of hu-
man activity [15].
In [48], the following main areas of
development of big data and the scope of
big data use in the industry are offered:
– new and improved methods for ana-
lyzing big data and data mining;
– cloud solutions, related to the stor-
age and transmission of big data;
– use of big data in control and moni-
toring;
– data-driven optimization and fore-
casting within manufacturing systems;
– data-driven solutions for supply
chain development and risk management;
– the use of the theory of big data in
modern industrial applications;
– big data-based solutions for intelli-
gent power transmission networks and clean
energy systems.
As a part of implementing data mining
in manufacturing management, the authors
of [30] propose a platform for advanced
manufacturing analytics to eliminate such
shortcomings in existing approaches, as iso-
lated consideration of individual data sets,
limited tools, insufficiency of reporting and
visualization tools, the lack of mechanisms
for obtaining specific recommendations,
based on results of analysis. Such platform
includes three levels:
1. Process optimization – involves the
use of analytical findings, obtained at level 2
to improve manufacturing processes.
2. Process analysis – includes various
ways of processing data, collected at level 3,
including data mining. The results are stored
in the manufacturing analytics repository.
3. Data integration – includes a manu-
facturing data warehouse, which reflects all
the data, obtained during the manufacturing
process (all aspects of the manufacturing
process).
The authors offer two approaches to
improving manufacturing processes using
big data: optimization of manufacturing
processes based on indicators (involves
changing the parameters of processes, taking
into account the conclusions derived from
the analysis) and the optimization of manu-
facturing processes on the basis of templates
(represents the development of the approach
to the optimization of manufacturing pro-
cesses on the basis of indicators by using
templates that include sets of indicators for a
particular application in the context of time
and elements of the manufacturing process).
As a tool for data analysis, it is pro-
posed to use standard models and methods,
such as neural networks, reference vectors,
decision trees, Bayesian classifications and
the creation of decision making rules. The
main advantage of the abovementioned ap-
proach is the accentuation of levels of big
data use in the improvement of manufactur-
ing processes and the emphasis on the need
to create repositories of manufacturing ana-
lytics. The shortcomings include the absence
of specific models or authorial ways of deci-
sion-making support.
With regard to the processing of big
data, the main approach, currently used for
the distributed processing of large amounts
of data and promoted by such major compa-
nies as Google and IBM, is the MapReduce
architecture [18]. Within the framework of
this architecture, the array of input data is
processed using the user-defined "map"
function (that assigns a value to each attrib-
ute called a "key", for example, the frequen-
cy of attribute’s occurrence in a specific
document) and "reduce" function (folds the
"key-value" pairs by summarizing the key
values for each characteristic from an array
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of intermediate data). The user has to speci-
fy the data sources, specify the required at-
tributes (keys), the rules for assigning values
to the keys (the map function), and folding
rules (the reduce function). In turn, the data
processing systems form data packets and
distribute the execution of these functions
on the data packets among the hardware.
This approach allows processing data arrays,
which even theoretically cannot fit in the
RAM or hard drives of individual comput-
ers, creating a basis for distributed pro-
cessing and analysis of big data.
In [41], the use of big data in manu-
facturing is analyzed, and the conclusion is
made that data has become an important
production factor along with tangible assets
and human capital, and big data allows
companies to create new and improve exist-
ing products and services, and invent com-
pletely new business models.
This conclusion is backed by empiri-
cal studies of the McKinsey Global Institute,
which provides the following facts about big
data as of 2011 [40]:
– the volume of data created increases
by 40% each year, while the IT infrastruc-
ture spending increases by only 5%;
– the additional need for advanced da-
ta analytics professionals in the US alone is
about 200 thousand people, and the need for
senior specialists with data processing skills
is about 1.5 million people;
– big data allows increasing the profit-
ability of retail enterprises by 60%;
– the potential economic effect of the
comprehensive use of big data in the US
healthcare system is USD 300 billion.
The same paper distinguishes the fol-
lowing mechanisms, by means of which big
data creates economic value [40, p. 5]:
– ensuring transparency – the very
fact of relevant stakeholders being able to
access big data in a timely manner makes it
possible to obtain a significant economic
effect;
– the ability to conduct experiments
to identify needs, analyze variability and
increase productivity – by digitally collect-
ing and storing large amounts of data about
their activities, organizations can collect
more accurate and detailed data in real or
near real time about all areas: from invento-
ry to staff sick leave days, which creates the
conditions for modeling and forecasting the
relevant aspects;
– customer segmentation and indi-
vidual solutions – big data allows organiza-
tions to segment and adapt their products
and services with high degree of precision to
meet the needs of specific customers;
– replacement / support of human
decision making using automated algorithms
– in-depth analytics can significantly im-
prove the decision-making process, mini-
mize risks and discover valuable ideas that
are hidden from the attention of a research-
er, who is not armed with big data;
– development of new business mod-
els, products and services– manufacturers
can employ data on the use of existing prod-
ucts to improve and develop the next gener-
ation of products and create innovative of-
fers in the field of after-sale services.
The general conclusion is that in the
near future the use of big data will be a key
factor of competitiveness in all sectors of
the economy, including industry.
Analysts of the McKinsey consulting
company [25] indicate that industries with
the maximum potential for the introduction
of analytics, based on big data, are pharma-
ceutical, chemical and mining. In these in-
dustries, in the opinion of the authors, minor
changes in the characteristics of the process
can significantly affect the result, which
creates the conditions for the application of
"advanced analytics" – the processing of
economic data with the help of statistical
and other mathematical tools for evaluating
and improving various areas of activity.
A number of publications, which will
be discussed below, are of engineering na-
ture and consider the models of the func-
tioning of smart enterprises or certain as-
pects of their functioning, the mechanisms
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of transforming regular enterprises into
smart ones, and the methods of economic
and mathematical modeling associated with
these processes. We will omit the analytical
part of the publications, which is devoted to
purely technical aspects, associated with the
introduction of cyber-physical systems (see
the technical and technological block in the
Fig. 2), and will review the most informa-
tive publications of that group.
Traditional approaches to centralized
control and rigid management are unable to
cope with the vast ecosystem of networked
systems that are becoming increasingly
widespread in the economy as a whole and
in the manufacturing sector in particular,
which requires the use of modeling tools to
predict the behavior of such systems in cer-
tain situations and develop optimal control
inputs. However, since simulation and mod-
eling tools are usually created for applica-
tion in a particular field, it’s difficult to de-
velop such models, since both physical and
cybernetic aspects of such systems need to
be modeled [28]. The modeling of cyber-
physical systems uses simulation tools such
as hybrid Petri nets, hybrid automata and
hybrid processes, aggregated modeling
techniques (including such tools as Dymola
and gPROMS) [44].
It should be emphasized that in this
case we are talking about the modeling of
cyber-physical systems, and not about mod-
eling the economic aspects of the function-
ing of enterprises, which use such cyber-
physical systems in their manufacturing
processes.
One of the main modeling trends in
the last few years has been the use of the
advantages, provided by the modern pro-
gramming languages and development tools
[4]: object orientation, class libraries and
visual design environments. Modelica,
which is one of the most popular tools at the
moment, is a visual modeling environment
that includes the Modelica universal object-
oriented language for modeling complex
physical systems and such tools as Dymola
or MathModelica. The Dymola (Dynamic
Modeling Laboratory) package supporting
Modelica modeling language is a complex
tool for modeling and research of compli-
cated systems in such areas as mechatronics,
automatics, aerospace research, etc. [27].
The ability to combine components of a dif-
ferent physical nature in one model makes it
possible to build models of complex systems
that better mirror the reality and to obtain
more accurate and transparent results.
The critical importance of the devel-
opment of cyber-physical systems was noted
in [13] from the point of view of national
interests and, first of all, for the creation of
new digital products with unprecedented
economic efficiency. However, calculations
of the consequences of the influence of digi-
tal technologies on the economy are carried
out on the basis of individual, practical ex-
perience of functioning of existing digital
manufacturing systems, without using the
tools of economic and mathematical model-
ing. The paper emphasizes that the model,
used in the management system, is the key
one in cyber-physical systems, the viability
and functionality of cyber-physical system
depends on how that model relates to reality.
The reality of the world is embodied in the
form of models and data populated in them,
so in order to create systems that can work
in the real world, a new discipline is re-
quired – model engineering. With the pur-
pose to understand the new ideology of
product lifecycle management (PLM), it’s
necessary to combine the building infor-
mation model (BIM) with the manufacturing
information model (PLC), which forms a
completely new quality. As we can see, the
authors of [13] pay considerable attention to
the modeling of cyber-physical systems, but
mainly to engineering modeling.
The Chinese authors in [50] argue that
the modeling of digital manufacturing
(which in the context of the work in ques-
tion is equal to smart manufacturing or
manufacturing at smart enterprises) doesn’t
require any specific approaches to model-
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ing – it uses standard modeling methods.
The lifecycle of the digital manufacturing
model includes data collection, data pro-
cessing, data transmission, monitoring, in-
teraction management and decision support.
It consists of an ordered series of models,
which typically includes a product devel-
opment model, a resource model, an infor-
mation model, a control and management
model, an organizational model, a decision-
making model, etc. "Ordered" means that
these models are built at different stages of
the lifecycle of the digital manufacturing
system [50, p. 24]. Objects of modeling are
products, resources, information, organiza-
tional aspects, decision making, production
process and network environment (interac-
tion models). Thus, the authors suggest us-
ing standard modeling tools and models,
including process models, object models,
structural models, Petri net models [49],
optimization models, etc.
Petri nets are used to model asynchro-
nous systems that function as a set of paral-
lel interacting processes. Analysis of Petri
networks allows obtaining information on
the structure and dynamic behaviour of the
simulated system. However, the prospects
for the practical application of Petri nets in
the modeling of smart enterprises belong to
the technical rather than the economic field,
in particular, in the field of modeling manu-
facturing processes, as well as the processes
of data collection and processing.
Optimization modeling [12] has a sig-
nificant potential for practical application
both in substantiating general directions of
introducing smart technologies, and in se-
lecting and planning specific measures. Its
application enables designing mathematical
models for solving a wide range of both
technical and economic problems, involving
the allocation of limited resources to alterna-
tive uses, choosing from a list of alternative
options, scheduling certain measures in
time, etc. The optimization model consists
of an objective function capable of taking
values within an area, limited by the task
conditions (areas of admissible solutions),
and constraints, characterizing these condi-
tions. The objective function consists of
three elements: controlled variables, param-
eters (that can’t be controlled, for example,
those depending on the external environ-
ment), and the shape of the relationship be-
tween them (the shape of the function). In
general, an optimization model is represent-
ed as follows:
{
𝑈 = 𝑓(𝑥𝑖, 𝑦𝑗) → 𝑚𝑎𝑥 𝑜𝑟 𝑚𝑖𝑛 ;
𝑥𝑖 = 𝐴, 𝑥𝑖 > 𝐴 𝑜𝑟𝑥𝑖 < 𝐴 .
where
U – the objective function, for which a
maximum or a minimum is sought, depend-
ing on which indicator is chosen as the crite-
rion;
xi – controlled variables, for which
there are optimal values at which the objec-
tive function would reach the desired extre-
mum, іх Х – the set of controlled varia-
bles;
іy – parameters, used in calculations
in the form of fixed values (constants),
іy Y – the set of constants.
When modeling smart enterprises, op-
timization models can be used to select
technologies for implementation, to deter-
mine the optimal parameters of technologi-
cal processes or investment projects, and to
solve other problems related to the choice of
available alternatives.
In [1], models of digital transfor-
mation of the industry at the macro-level are
presented in the framework of process, sec-
toral and technological approaches. The
model of the process approach is based on
viewing the industry as an industrial chain –
from the development of industrial products
to their sale and service. The elements of
digital transformation of the industry in-
clude: digital R&D center, digital factory,
digital storage and transportation, electronic
commerce and digital services. It’s noted,
that the creation of the Eurasian technology
transfer network and the Eurasian network
of industrial cooperation and subcontracting
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can become effective tools for digital trans-
formation of industry.
The industry-specific approach to
digital transformation of industry is based
on the industry's connection with other sec-
tors of the economy and includes the follow-
ing digital industrial markets: food and wa-
ter production and delivery systems, intelli-
gent resource extraction systems, digital
(smart) factories, distributed power systems,
unmanned automobile systems, unmanned
aerial vehicles, digital railway, telemedicine,
personal medicine, smart houses, smart
roads, digital financial technologies, safety
systems, e-commerce, e-education, digital
culture and the media.
The model of the technological ap-
proach to the digital transformation of the
industry includes a set of technologies that
form a digital agenda in the industry: IoT
and industrial Internet, digital design and
modeling, quantum technologies, big data,
element base (processors), robotics, sensors,
meters, additive 3D technologies, cloud
technologies, supercomputer technologies.
This set of technologies is open and
can be expanded. At the heart of virtually all
technologies are software and hardware, the
core of which is software and microelectron-
ics. Broadband Internet access is of key val-
ue for the development of digital transfor-
mation of the industry. According to Swe-
dish scientists, doubling the average speed
of the broadband Internet access in a coun-
try increases its GDP by 0.3%. According to
the authors of the study [1], an increase in
GDP by 0.3% in OECD countries will lead
to an increase in the world economy by
USD 126 billion. Historically, this is about
1/7 of the average annual growth rate in
OECD countries over the past ten years.
It should be noted, that in paper [1] the
models of digital transformation of industry
within the framework of process, sectoral
and technological approaches are presented
only in an object form. It lacks economic-
mathematical models of digital transfor-
mation of industry, but is devoted to the
ways of supporting such initiatives in the
field of modeling:
– through the introduction of infor-
mation modeling in the field of industrial
and civil construction (BIM-systems). In
such way authors offer to encourage pro-
jects, aimed at creating and implementing
automated process control systems (ACS) in
the industrial sectors, including supervisory
control and data collection systems
(SCADA);
– through the development of mathe-
matical modeling and design of mathemati-
cal models for use in industry and engineer-
ing.
As for micro-level models, the so-
called "S-Model" of digital manufacturing is
proposed in [43], where "S" symbolizes sta-
tistical processing and simulation (model-
ing). Within the framework of this model, a
closed cycle digital manufacturing system
with an autonomous statistical analysis
module and an autonomous modeling mod-
ule for discrete events is offered to create a
flexible and efficient value chain. To inter-
act with personnel in this model, it was sug-
gested to use the forecast panel and an inter-
active production planning interface. That
model is not an economic-mathematical
model, but rather the author's vision of the
use of econometric models in manufacturing
control: for example, based on the analysis
of statistical information it’s proposed to
predict crises (equipment failure), demand,
and other factors, and make manufacturing
planning interactive and adjust it in real
time, using the appropriate interface.
Pharaos Navigator [46] is one of ex-
amples of practical implementation of the
smart enterprise concept, intended for enter-
prises of various areas of activity (manufac-
turing, services, etc.). It allows visualizing
the operation of a smart enterprise, display-
ing in a visual form the results of data col-
lection from smart sensors on all equipment
and thus allowing the management to re-
ceive real-time information about the opera-
tion of the enterprise.
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In the paper [14], the main focus is on
standardizing the processes of digital trans-
formation of industry, as well as the matters
of information modeling of manufacturing
systems. And the standards are considered
as a link between information models and
manufacturing at a factory through design
systems. Two sets of international standards,
specific for modeling manufacturing sys-
tems and data exchange are described: the
standards of manufacturing resources and
processes and the standards of construc-
tion/facility modeling. The paper describes
the characteristics of standards and their
purpose for information modeling. The
analysis of paper [14] showed that the main
focus is on standardization of information
and engineering modeling of manufacturing
systems for the purpose of digital transfor-
mation, but the matters of standardizing
economic and mathematical modeling of
smart enterprises and economic problems
solved at the stage of their formation are not
sufficiently investigated.
The paper [21] investigates the conse-
quences of introducing new technologies
and creating of smart enterprises, such as
technogenic catastrophes, serious manufac-
turing problems, caused by stealing of con-
fidential data, as well as complete collapse
of manufacturing process. The authors of
the paper note that new cyber defense tools
can’t be tested in real manufacturing condi-
tions, since this may entail a slowdown and
even stop manufacturing processes, which is
completely unacceptable for business. For
this reason, this kind of work includes a
stage of applied research, at which engineers
use special equipment that simulates real
manufacturing processes to the best possible
extent. Research [21] deals not only with the
issues of ensuring information security, but
also assessing the impact of cyber defense
tools on the productivity of industrial enter-
prises, which also needs to be taken into ac-
count in the economic and mathematical
modeling of smart enterprises.
The most interesting from the point of
view of economic and mathematical model-
ing are the papers, devoted to the economic
justification of the efficiency of the intro-
duction of the smart industry and its impact
on the economy of the country and socio-
economic processes.
Let’s begin analyzing this direction
with paper [45], in which, based on a survey
of a number of Dutch companies operating
in various areas, it’s concluded that compa-
nies are actively engaged in the implementa-
tion of elements of the smart industry, and
the larger the company, the more actively it
works in this area. It focuses on the fact that
the introduction of digital technologies af-
fects all aspects of a company’s operation:
products, manufacturing processes, etc.
Nevertheless, the work doesn’t provide any
calculations or even assessments made by
the interviewed companies regarding the
qualitative or quantitative indicators of the
introduction of smart technologies or the
economic effect of their implementation.
This work is indicative, as it illustrates a
whole layer of works on this topic, in which
it is possible to distinguish several elements:
a short or a more extensive listing of smart
industry definitions, such as digital technol-
ogies, big data, etc.; a set of statements de-
claring that it is very important and promis-
es various advantages; if a model or system
is declared, in most cases, it’s represented
by a rather abstract drawing. At the same
time, there are no calculations, economic
and mathematical models or analysis of sta-
tistical data. Thus, the overwhelming num-
ber of works on smart industry, in the same
manner as the abovementioned paper [45],
are devoted to convincing the reader in the
importance of this direction, but lack any
scientific or practical novelty.
The following few works are a rare
exception to the indicated trend.
For example, in a Korean study on the
impact of the smart industry on urban de-
velopment and the country's economy in
general [37], the following approach was
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used: the main industries are picked which
are suppliers and consumers of smart prod-
ucts (primarily computer equipment, micro-
circuits, industrial automation, communica-
tion equipment, etc.) and on the basis of in-
put-output tables, the impact of demand for
such products on the production volumes in
the city, employment, added value, etc. was
analyzed. An unconditional advantage of
this work is an attempt to give a numerical
assessments of smart manufacturing (as op-
posed to the abstract approach seen in many
other works), as well as the fact that a spe-
cific list of smart products was compiled.
On the example of the implementation of
the smart cities development program in Ko-
rea, the corresponding economic effect is
shown – an investment of USD 10 million in
such a program allowed increasing output
by USD 19 million due to an increase in
demand in related industries. The drawback
of this work is that there has been no com-
parison of investments in the smart industry
with investments in other industries, as a
result of which there was no answer to the
question of whether a USD 1 investment in
the smart industry gives a greater or lesser
effect than a USD 1 investment in tradition-
al industries.
Based on the statistical data on the US
industrial enterprises, paper [26] analyzes
the impact of data-driven decision-making
on the value-added created by an enterprise
and concludes that the introduction of data-
driven decision-making increases the value
added by 3% on average. Such an estimation
is made using regression analysis on the
basis of a production function (similar to the
Cobb-Douglas function) with the added val-
ue as the dependent variable and labour
productivity, capital, labour resources, ener-
gy consumption, IT-capital (in the form of
cost of hardware and software), measure of
structured management (the degree of au-
tonomy of mid-level staff in decision-
making) and data-driven decision-making as
factors.
Authors propose the following model:
𝑌𝑖𝑡 = 𝐴𝑖𝑡𝐾𝑖𝑡
∝𝐿𝑖𝑡
𝛽
𝐸𝑖𝑡
𝛾
𝐼𝑇𝑖𝑡
𝜆𝑒𝜇𝑆𝑀𝑖𝑡𝑒𝜂𝑋𝑖𝑡𝑒𝛿𝐷𝐷𝑇𝑖𝑡,
where
𝑌𝑖𝑡 – actual value added (output – ma-
terial costs);
𝐴𝑖𝑡 – productivity;
𝐾𝑖𝑡 – capital value at the beginning of
the period;
𝐿𝑖𝑡 – labour (number of employees);
𝐸𝑖𝑡 – consumption of energy re-
sources;
𝐼𝑇𝑖𝑡 –value of IT assets (hardware and
software) at the beginning of the period;
𝑆𝑀𝑖𝑡 – measure of structured man-
agement;
𝑋𝑖𝑡 – additional factors, such as the
industry and the level of education;
𝐷𝐷𝐷𝑖𝑡 – measure of data-driven deci-
sion-making.
Among the advantages of the ap-
proach is the attempt to analyze the impact
on production efficiency, based not just on
investments in the IT infrastructure, but spe-
cifically on the use of data analysis results in
decision-making. Among the shortcomings
of the offered approach is the abstractness of
the very concept of "data-driven decision-
making", as well the fact of using data-
driven decision-making as a factor in the
model (for each particular enterprise this
parameter can be estimated as 0 or 1) that is
established according to the results of the
survey carried out at enterprises, therefore
the question of the intensity and directions
of using such an approach remains unan-
swered. In addition, one of the disad-
vantages is the inclusion in the function of
such poorly assessable factors as structured
management and data-driven decision-
making, as well as the use of the number of
employees as the indicator of labour re-
sources.
From the point of view of the pro-
spects for the introduction of smart technol-
ogies in specific industries, worthy of inter-
est is the vision of such perspectives by
management of the metallurgical industry,
as reflected in the results of a survey, con-
ducted by the PwC consulting agency
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among more than 2,000 respondents from
the nine main industrial sectors and 26
countries [17]. According to the manage-
ment of metallurgical enterprises, the intro-
duction of digital technologies increases the
maneuverability of supply chains, promotes
a deeper understanding of processes and in-
creases the level of capacity utilization. Au-
tomation combined with data analysis is
used to ensure flexibility and manufacturing
efficiency. To improve productivity, algo-
rithms are used to trace the relationship be-
tween the physical properties of raw materi-
als, used for manufacturing and manufactur-
ing costs, as well as factors that limit the
production activity of enterprises. Then, in-
tegration of previously specified processes
is performed, which allows reducing heat
losses, energy consumption, manufacturing
time, stock level, and optimizing prices. In
general, the management of metallurgical
enterprises expects that in 2016-2021 the
introduction of digital technologies would
be increasing revenue by an average of 2.7%
per year and reducing costs by an average of
3.2% per year. All that confirms that the in-
troduction of digital technologies is in de-
mand in industry in general and in metallur-
gy – in particular, and the management of
enterprises places high hopes in it.
In conclusion, the study proposes the
following sequence of steps to turn the en-
terprise into a smart one:
1. Development of an individual strat-
egy for implementing the concept of "Indus-
try 4.0".
2. Development of the first pilot pro-
jects.
3. Assessment of the necessary re-
sources.
4. Implementation of data analysis.
5. Transformation of the company into
a digital enterprise (comprehensive imple-
mentation of digital technologies).
6. Active planning of the ecosystem
approach (cooperation with the market envi-
ronment – suppliers and consumers).
In paper [7] attention is paid to the
impact of digital transformation (digital
technologies, the Internet) on the labour
market and labour productivity. It is noted
that some of the perceived benefits of digital
technology are compromised by the risks
that arise. Many economically developed
countries are facing increasing polarization
of labour markets and growing inequality –
in part because new technologies comple-
ment more skilled work and, at the same
time, replace standard labour operations,
requiring many workers to compete with
each other for low-paid jobs. In the absence
of accountable institutions, public invest-
ment in the development of digital technol-
ogies strengthens the influence of the elites,
which can lead to subordination of policy to
the interests of the establishment and to in-
creased state control. The digital revolution
can generate new, profitable business mod-
els for consumers – but not where the estab-
lished companies control the entry to the
market. Technology can increase the
productivity of workers – but not where they
do not have the skills and knowledge, neces-
sary for its application. Digital technology
can help control the presence of tutors in the
workplaces and improve academic perfor-
mance – but not where education system is
not accountable.
Despite the fact that a fairly modest
number of jobs are created directly in the
area of digital technologies, these technolo-
gies contribute to the creation of a consider-
able number of jobs in other areas. This
way, in Kenya, the digital payment system
M-Pesa provides additional income for more
than 80,000 of its agents. And according to
the China State Information Center, the re-
cent rapid growth in the e- commerce sector
in the country has led to the creation of 10
million jobs in online stores and related ser-
vices, which is about 1.3% of total jobs in
the country.
If digital technologies promote eco-
nomic growth, how are these benefits allo-
cated on the labour market? Although digital
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technologies increase productivity and over-
all well-being, labour market turmoil can
turn out to be unhealthy and lead to in-
creased inequality. Thus, another area for
the use of economic and mathematical mod-
eling of digital and smart enterprises is to
assess the impact of digital technology on
the labour market, employment structure
and labour productivity.
In general, the nature of publications
on the modeling of the smart industry and
the processes of its implementation, is un-
systematic, fragmented and incomplete.
That is a consequence of the fact that this
scientific direction is still on the early stages
of its development, there are no established
concepts for the introduction of the smart
industry and its modeling, and existing ex-
amples of practical implementation of smart
enterprises are based more on heuristic
methods, rather than on accurate mathemati-
cal justifications. As can be seen from the
above analysis, most of the publications,
devoted to the development of the smart in-
dustry, are either descriptive and introducto-
ry, or view this process from the engineering
point of view, which mainly covers tech-
nical, technological and informational direc-
tions (Fig. 2). A few mathematical models,
which are mentioned in them (but are not
given explicitly) are strictly of applied na-
ture and solve technical problems.
Publications, which consider the eco-
nomic aspects of Industry 4.0, are generally
scarce. At the same time, even if certain
mathematical justifications for some conclu-
sions are present, most often they’re of em-
pirical descriptive nature, based on existing
observations, and the methodological varie-
ty of economic and mathematical models
used at best covers correlation-regression
analysis.
However, it should also be noted that
the conditions for smart industrialization in
Ukraine are significantly different from
those in the countries of the West. This not
only includes technological lagging, but also
the weakness of state institutions, insecurity
of capital and investment, unpredictability
of state policy (in such areas as taxes, fi-
nance, trade, international relations, etc.),
the virtual lacking of financial support from
the government, corruption in all areas of
potential stakeholders’ activity. More details
about the peculiarities of technological and
institutional development of Ukraine can be
found in [2; 8-11; 16; 20; 42; 47].Thus, the
peculiarities of the functioning of the
Ukrainian economy, the specifics and the
level of development of its institutions make
it senseless to directly use the Western expe-
rience of smart industrialization in Ukraine
and requires a more thorough scientific jus-
tification for the feasibility and cost-
effectiveness of implementing measures for
the development of the smart industry in
Ukraine.
As noted in the study of the Com-
monwealth of Independent States (CIS) Ex-
ecutive Committee on the status, problems
and prospects for the development of the
information society, it’s necessary to devel-
op new methods to ensure the efficiency of
informatization processes in the Common-
wealth states that will allow a person to cor-
rectly understand and explore the new high-
ly dynamic information picture of the world
that opens before them [20].Undoubtedly,
methods of economic and mathematical
modeling, which allow obtaining objective
and unbiased quantitative justification,
should play a prominent role among such
methods.
Based on the carried above analysis of
the current trends in the study of smart in-
dustry development in the West and taking
into account the peculiarities of the Ukraini-
an economy, the following promising areas
of economic and mathematical modeling of
smart enterprises can be highlighted.
1. First of all, we are interested in the
development of the macroeconomic produc-
tion function in connection with the transi-
tion to the neo-industrial smart economy.
The use of methods of economic and math-
ematical modeling makes it possible to theo-
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retically substantiate the qualitative changes
of this function in connection with the
emergence of new technological combina-
tions of classical factors of production, and
the possible emergence of a new production
factor in the form of informatization or arti-
ficial intelligence.
It is possible to propose several speci-
fications of the enterprise production func-
tion with the account for the effect of this
new factor (denoted by I below) using:
– multiplicative function (similar to
the Cobb-Douglas function):
y=0K
1
L
2
I
3
,
where production factors are presented
in the natural form;
– additive-multiplicative shape:
y=a1K+a2L+a3I+a4KL+a5KI+a6LI+a7KLI,
where the factors of production are
presented in a standardized form.
The second version might be more in-
formative for static models, since it’s able to
reflect the various multiplicative effects, ob-
tained from different combinations of fac-
tors. If we consider the development of the
production function in the dynamics, the
first variant might be more informative,
since there are reasons to believe that the
parameter 3 is described by a time-
dependent S-shaped curve, for example, the
Gompertz curve or the logistic curve:
1
3
1 atbe
.
The choice of the S-shaped curve is
due to the avalanche-like character of in-
formatization processes, and, possibly, the
development of artificial intelligence, when
increments depend on the level reached, and
in the beginning they increase with the ac-
celeration of development, and then, upon
saturation, they decelerate.
Parametrization of models in the first
and in the second cases is possible using the
standard methods of regression analysis,
namely using the method of least squares (in
the first case, the equation must be trans-
formed by logarithm).
Another direction in the use of eco-
nomic and mathematical models of smart
enterprises has a more practical focus. They
make it possible to do the following.
2. Different variations of the Leontief
input-output model and the inter-branch bal-
ance which can be used to solve at least
three problems:
- end-to-end planning and manage-
ment of the industry, based on a common
digital industry environment;
- selecting enterprises that require pri-
ority digital integration, estimating losses
from retaining "unsmartized" participants in
the value chains, etc.;
- increasing demand in the context of
introducing digital business models and ex-
panding digital interaction with customers
by reducing transaction costs.
Both natural and monetary values can
be used as the coefficients of the technolog-
ical matrix of the input-output model. When
using monetary terms of the cost factor, it’s
possible to distinguish certain cost compo-
nents, for example labour costs (lij),
transport costs (trij), transaction costs, asso-
ciated with intermediate and final consump-
tion of products (zij). In the same way, the
time factor (tij) can be considered to be a
cost element, associated with the value
chain.
That opens a whole block of optimiz-
ing tasks that allow identifying the intercon-
nected industries and consumers that are
most in need of integration on the basis of a
common digital industry environment.
Let’s consider one of the versions of
the general mathematical formulation of
such problems.
We’ll assume that the costs zij in the
inter-branch balance model can be lowered
by virtue of smartization of the manufactur-
ing in branches I and j:
zij = zij (1 – SiSj),
where Si, Sj – a certain level of enterprise
smartization, measured by a value in the
range (0; 1).
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(Moreover, we’ll note that if one of
the interacting parties is not a smart enter-
prise, the effect of reducing costs will not be
observed). The level of enterprise smartiza-
tion is presented using an S-curve function
of investment spending K, connected with
the transformation of a traditional enterprise
into a smart one:
1 1
, .
1 1 j ji i
i j m Km K
i j
S S
b e b e
There‘s a reason to assume that within
one industry the relationship between in-
vestment costs and the level of smartization
is described by the same function (parameter
b is the same), and differs only in the pro-
duction scale parameter (mi, mj), since it’s
obvious that the larger the enterprise, the
more smart equipment has to be installed in
order to achieve the same level of produc-
tion smartization.
Thus, the task of reducing production
costs through the introduction of smart in-
dustrialization within the framework of lim-
ited investment resources can be presented
in the following way:
mini ij
i j
X z
X = (E – А)
–1
Y,
zij = zij (1 –
1
1 i im K
ibe
1
1 j jm K
jb e
),
limi
i
K K
.
Where A=(aij)nn – a technological ma-
trix, the elements of which aij = xij/Xj show
how many units of i industry products
should be spend for the production of one
unit of industry j products, Yn1–column vec-
tor of the final product.
Work on the creation of digital busi-
ness-to-business (B2B) platforms is already
being carried out not only in the countries of
the West, but also in Eurasian Economic
Union (EAEU) countries [1]. The interac-
tion of smart enterprises within these digital
platforms significantly reduces transaction
costs, creates conditions for the develop-
ment of an end-to-end planning and man-
agement system in industry, frees resources
that increase national income (quadrant 2)
and, accordingly, the volume of the final
consumption, which can also be estimated
by balance models.
3. The third direction of economic and
mathematical modeling of smart enterprises
is represented by variations of network
models, transport tasks, assignment tasks,
etc. Building a network graph of interactions
between consumers, manufacturers and oth-
er counterparties, for example, in a particu-
lar industry will help finding solutions to the
following problems:
– substantiation of network effects in
the creation of smart enterprises in the in-
dustry and assessing the minimum necessary
level of digitalization of the network, in
which the costs from the further introduction
of smart technologies will be compensated
for by the increase in the efficiency of the
network as a whole;
– within the limits of the amount of
available investment resources, selecting
enterprises that require the digitization of
their production the most, so that the path
from order placement to order receipt would
be associated with minimal possible costs;
– optimization of the movement of
products (from their design to consumption
by end customers) in the conditions of the
IoT and smart infrastructure.
The standard objective function in
such problems is aimed at minimizing the
costs of moving from the initial to the final
vertex:
min,ij ij
i j
Z c x
where xij – the volume of products, moved
from vertex i to vertex j; сij– cost of moving
them (for different arcs can be either con-
stant or dependent on the volume of the
products being moved).
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Standard constraints: the demand of
all consumers must be satisfied, the total
production is equal to the total consumption:
, ,ij i
j
x a i
, ,ij j
i
x b j
.i j
i j
a b
An obvious extension of this task is to
determine the effective path in the condi-
tions of the possibility of smartization of
individual enterprises that form part of this
network. The following restrictions will be
added:
сij = сij (1 –
1
1 i im K
ibe
1
1 j jm K
jb e
),
limi
i
K K
.
Digitalization and the IoT can virtual-
ly eliminate the cost of traffic through some
intermediate vertexes, associated with trans-
actional and organizational costs. In addi-
tion, they expand the number of vertices,
available for analysis, by increasing the di-
mension of the graph, and accordingly, mak-
ing the choice more valid and effective. The
accessibility of some vertices mathematical-
ly in this task can be regulated by the re-
striction on the throughput of the vertex. For
some vertices that determine the known
trunk path, it will be a constant value, for
others – a value, proportional to the degree
of integration of the enterprise into the IoT,
that is, proportional to the value
1
1 i
i mK
S
be
(0;1):
, ,ij j j
i
x P S j
, ,ij i i
j
x PS i
where Pi – nominal (basic, potential) vertex
throughput.
4. Another topical area of economic
and mathematical modeling is the evaluation
of social effects, associated with the impact
of digitalization of the economy on the em-
ployment. The replacement of human labour
by cyber-physical systems has the potential
risk of massive job losses in manufacturing,
which is the area of primary income distri-
bution. In this case, the effects of lowering
transaction costs in the conditions of the IoT
may turn out to be lower than the negative
effects of a decrease in effective demand,
associated with a decrease in the primary
incomes of the employed in manufacturing
population. This problem becomes especial-
ly urgent in the conditions of Ukraine, when
the potential superprofits from smart manu-
facturing will not be redistributed into the
economy and stimulate domestic demand,
but will accumulate in the pockets of oli-
garchs and then moved to offshore.
Stochastic modeling, in particular cor-
relation-regression models for estimating
stochastic dependencies, as well as simula-
tion models for assessing the consequences
of various scenarios of smart industrializa-
tion consequences for employment, income
of the population and the economy as a
whole, can be instrumental in assessing such
effects.
Here are some dependencies that re-
quire evaluation, specification and para-
metrization within this research area:
1) labour costs (Li) in industry i, de-
pending on the smartization of that indus-
try(Si) (assessment of job losses);
2) demand for labour (L) in the regi-
on, depending on the degree of smartization
of various industries in this region (assess-
ment of the emergence of new vacancies);
3) production volumes (Q) in the re-
gion, depending on the degree of smartiza-
tion of various industries in the region (as-
sessment of changes);
4) taxable incomes of the population,
depending on the possible growth of produc-
tion volumes and changes in labour costs
(assessment of changes);
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5) deductions from income of the
population (assessment of changes in rele-
vant funds j);
6) volumes of consumption of house-
holds depending on the income of the popu-
lation (assessment of changes);
7) burden of social security funds, de-
pending on the number of population (N)
and employment level.
These (and, probably, many more) de-
pendencies can be combined into a single
simulation model, the analysis of which will
allow assessing the balance of the develop-
ment of the smart economy, at least along
two contours: the balance of household in-
comes and expenditures for expanded con-
sumption; balance of revenues, raised to
budgets and social funds, and the need to
spend money from them.
The use of the apparatus of economic
and mathematical modeling in substantiating
the programs of smart industrialization of
Ukrainian economy will make it possible to
obtain scientific explanations for solving
problems of the formation of smart enter-
prises and to increase the efficiency of these
processes.
Conclusions
1. The Fourth Industrial Revolution
(which, in the opinion of some scientists, is
the stage of development of the Third one,
the digital revolution) is based on the
achievements of the 6
th
technological mode,
characterized by the massive introduction of
additive production technologies, nanotech-
nology and bioengineering, full digitaliza-
tion of manufacturing, implementation of
cyber-physical systems that have artificial
intelligence, creation of a global information
network of products, transport, buildings
and industries, capable of interacting with
each other independently without human
intervention. Ukraine, whose industry uses
technologies of the 3
rd
and 4
th
technological
modes, is lagging far behind in its develop-
ment from Western countries, and chances
of catching up with them in an evolutionary
manner seem doubtful. At the same time,
the creation of new enterprises that operate
the technologies of the 6
th
mode can enable
occupying certain niches in the world’s digi-
tal production.
2. The most effective way of justifying
the economic feasibility of creating smart
enterprises and their viability in Ukraine is
the use of tools of economic and mathemati-
cal modeling that allow conducting experi-
ments with the system being designed, stud-
ying its properties, evaluating efficiency and
anticipating the occurrence of problems and
errors. Despite the rather good development
of the modern economic and mathematical
modeling apparatus, the novelty of the tasks
to be solved when creating smart enterprises
prevents from making an unequivocal
choice in favor of the use of certain specific
tools. To justify such a choice, it seems use-
ful to study the foreign experience of apply-
ing economic and mathematical methods in
the creation of smart enterprises, since cer-
tain empirical knowledge has already been
accumulated in that area.
3. The objects of research, which are
given attention in the publications devoted
to the Fourth industrial revolution and the
functioning of smart enterprises, can be
classified in three directions. The first one,
technical and technological direction de-
scribes the operation of sensors, meters, ro-
botics and cyber-physical systems, technol-
ogy of product identification, cyber defense,
data transmission, etc. The second direction
is the informational one, describing the op-
eration of information systems of various
levels at enterprises, technologies for work-
ing with big data, and approaches to stand-
ardizing the development processes of smart
enterprises and their elements. Finally, the
third one– the economic direction–is con-
nected with the rationale for the economic
expediency of digitization of certain seg-
ments of the economy, or with its impact on
socio-economic processes.
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4. Most of the publications devoted to
the development of the smart industry are
either descriptive or introductory, or view
this process from the engineering point of
view, which mainly covers technical, tech-
nological and information directions. A few
mathematical models mentioned in them
(but not shown in explicit form) are of strict-
ly applied nature and solve technical prob-
lems. Publications, which affect the eco-
nomic aspects of Industry 4.0, are generally
scarce. At the same time, even if certain
mathematical justifications for some conclu-
sions are present, they are, in most cases,
empirically descriptive, based on existing
observations, and the methodological varie-
ty of economic and mathematical models
used at best covers correlation-regression
analysis.
5. A separate large segment of publi-
cations, devoted to smart enterprises, is con-
nected with the big data, which along with
the software of cyber-physical systems form
the basis of information support for the
smart industry.
6. The analysts of the McKinsey con-
sulting company indicate that the industries
with the maximum potential for the intro-
duction of analytics, based on big data, are
pharmaceutical, chemical and mining. In
those industries, in the opinion of the au-
thors, minor changes in the characteristics of
the process can significantly affect the re-
sult, which creates the conditions for the ap-
plication of "advanced analytics" – pro-
cessing of economic and technical data us-
ing statistical and other mathematical tools
for assessing and improving various fields
of activity. PwC consulting agency empha-
sizes the positive prospects for the introduc-
tion of smart technologies in the metallurgi-
cal industry: the introduction of digital tech-
nologies increases the maneuverability of
supply chains, promotes a deeper under-
standing of processes and increases the level
of capacity utilization. All these industries
are well developed in Ukraine, and these
conclusions would be useful to take into ac-
count, when forming smart industry here.
7. A large number of analyzed publi-
cations are of engineering nature, and reflect
the design features of certain objects of the
smart industry and cyber-physical systems.
Modeling objects include products, re-
sources, information, organizational aspects,
decision making, manufacturing process and
network environment (interaction models).
In general, they use standard modeling tools
including process models, object models,
structural models, Petri net models, optimi-
zation models, hybrid automata, queuing
systems, balance input-output models, ag-
gregated modeling techniques, including
tools such as Dymola and gPROMS, etc.
Nevertheless, the actual models, pre-
sented in these publications, are usually lim-
ited to object representation in the form of
diagrams and graphs, which makes it impos-
sible for them to be directly used in practice,
which gives a wide range of possible inter-
pretations about how these models can be
specified for solving specific problems.
8. The few publications that reason the
economic consequences of the introduction
of smart enterprises are devoted to the eco-
nomic feasibility of such measures. In par-
ticular, in a Korean example of investing in
the development of the smart industry, it has
significantly increased output due to in-
creased demand in related industries. At the
US enterprises, the use of big data in deci-
sion making was accompanied by a 3% av-
erage growth in value added. Despite the
fact that a fairly modest number of jobs are
created in the area of digital technologies,
many publications confirm that these tech-
nologies help create jobs in related areas,
which helps alleviate the reduction of jobs,
caused by the automation of manufacturing
processes.
9. In general, the nature of publica-
tions on the modeling of the smart industry
and the processes of its implementation is
unsystematic, fragmented and incomplete.
–––––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––
ISSN 1562-109X Econ. promisl. 39
2017, № 4 (80)
That is a consequence of the fact that this
scientific direction is still quite new, there
are no established concepts for the introduc-
tion of the smart industry and its modeling,
and existing examples of practical imple-
mentation of smart enterprises are based
more on heuristic methods, rather than on
accurate mathematical justifications. The
overwhelming number of papers on the top-
ic of the smart industry focuses on convinc-
ing the reader of the importance of this di-
rection, but lacks any scientific or practical
novelty.
10. The peculiarities of the function-
ing of the Ukrainian economy, the specifics
and level of development of its institutions
make it senseless to directly apply the West-
ern experience of conducting smart industri-
alization to Ukraine and require a more
thorough scientific justification for the fea-
sibility and cost-effectiveness of implement-
ing measures to develop the smart industry
in Ukraine. However, based on a review of
foreign experience, the economic and math-
ematical modeling of smart enterprises in
Ukraine does not require creating any fun-
damentally new types of models. It can be
performed through the evolution of well-
known models, with additional parametriza-
tion of specific conditions, specific to
Ukraine's institutional features, the level of
development of its industry and the infor-
mation technologies used.
In particular, among the promising ar-
eas of economic and mathematical modeling
of smart enterprises in Ukraine are the fol-
lowing:
– the use of modifications of produc-
tion functions– to justify the qualitative
changes in the factors of production, the
emergence of new factors of production,
their new technological combinations;
– the use of modifications of Leontief
input-output models and optimization mod-
els for end-to-end planning and management
of the industry, justification of enterprises
requiring priority digital integration, reduc-
tion of transaction costs in the context of
introducing digital business models and ex-
pansion of digital interaction with custom-
ers;
– the use of modifications of network
models and optimization models– to opti-
mize the movement of goods (from their
design to consumption by end customers) in
the conditions of the IoT and smart infra-
structure, and also to justify the primary
candidates for digitalization in conditions of
restrictions on the amount of available in-
vestment resources;
– the design of correlation-regres-
sion models– for assessing economic sto-
chastic dependencies, as well as simulation
models for assessing the consequences of
certain scenarios of smart industrialization,
that allows assessing the consequences of
these scenarios for employment, incomes of
the population and the economy as a whole.
The concretization of the formulation
of these models and approaches to their im-
plementation requires an in-depth study of
the specifics of the tasks being solved and
the formalization of specific institutional
factors. All of that is the subject of further
research.
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ISSN 1562-109X Econ. promisl. 45
2017, № 4 (80)
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Артем Анатолійович Мадих,
канд. екон. наук
E-mail: artem.madykh@gmail.com;
Олексій Олександрович Охтень,
канд. екон. наук
E-mail: aokhten@gmail.com;
Алла Федорівна Дасів,
канд. екон. наук
Інститут економіки промисловості НАН України
03057, Україна, Київ, вул. Желябова, 2
E-mail: alladasiv@gmail.com
АНАЛІЗ СВІТОВОГО ДОСВІДУ ЕКОНОМІКО-МАТЕМАТИЧНОГО
МОДЕЛЮВАННЯ СМАРТ-ПІДПРИЄМСТВ
Показано неминучість зміни технологічного укладу у зв'язку з промисловою ре-
волюцією 4.0, що потребує кардинальної перебудови системи виробництва і виробни-
чих відносин. У результаті аналізу зарубіжного досвіду подібних змін, пов'язаних зі
смарт-індустріалізацією, цифровими трансформаціями економіки, становленням про-
мислового інтернету речей, обробки великих даних, встановлено необхідність застосу-
вання економіко-математичних методів для обґрунтування доцільності подібних тран-
сформацій: як пов'язаної з їх економічною обґрунтованістю, так і з фізичною життєзда-
тністю новостворюваних систем. Огляд публікацій, які відображають аспекти економі-
ко-математичного моделювання в зазначених сферах, дозволив зробити висновок про
несистемність і неопрацьованість методичного і методологічного апарату моделювання
даних процесів, а також сформулювати рекомендації щодо економіко-математичного
моделювання смарт-підприємств в Україні. Для врахування особливостей технологіч-
ного та інституційного розвитку України при обґрунтуванні створення смарт-
підприємств запропоновано ряд інструментів економіко-математичного моделювання,
заснованих на використанні виробничих функцій, моделей міжгалузевого балансу, ме-
режевих оптимізаційних моделей, імітаційних моделей на базі стохастичних залежнос-
тей.
Ключові слова: промисловість 4.0, цифрові технології, смарт-підприємства, великі
дані, економіко-математичне моделювання.
JEL codes: С00; С60; С67; С69; О12; О14.
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry –––––––––––––––––––––––––––––––
46 ISSN 1562-109X Econ. promisl.
2017, № 4 (80)
Артем Анатольевич Мадых,
канд. экон. наук
E-mail: artem.madykh@gmail.com;
Алексей Александрович Охтень,
канд. экон. наук
E-mail: aokhten@gmail.com;
Алла Федоровна Дасив,
канд. экон. наук
Институт экономики промышленности НАН Украины
03057, Украина, Киев, ул. Желябова, 2
E-mail: alladasiv@gmail.com
АНАЛИЗ МИРОВОГО ОПЫТА ЭКОНОМИКО-МАТЕМАТИЧЕСКОГО
МОДЕЛИРОВАНИЯ СМАРТ-ПРЕДПРИЯТИЙ
Показана неизбежность смены технологического уклада в связи с промышленной
революцией 4.0, что требует кардинальной перестройки системы производства и про-
изводственных отношений. В результате анализа зарубежного опыта подобных изме-
нений, связанных со смарт-индустриализацией, цифровыми трансформациями эконо-
мики, становлением промышленного интернета вещей, обработки больших данных
установлена необходимость применения экономико-математических методов для обос-
нования целесообразности подобных трансформаций: как связанной с их экономиче-
ской обоснованностью, так и с физической жизнеспособностью вновь создаваемых си-
стем. Обзор публикаций, отражающих аспекты экономико-математического моделиро-
вания в перечисленных сферах, позволил сделать вывод о несистемности и непрорабо-
танности методического и методологического аппарата моделирования данных процес-
сов, а также сформулировать рекомендации по экономико-математическому моделиро-
ванию смарт-предприятий в Украине. Для учёта особенностей технологического и ин-
ституционального развития Украины при обосновании создания смарт-предприятий
предложен ряд инструментов экономико-математического моделирования, основанных
на использовании производственных функций, моделей межотраслевого баланса, сете-
вых оптимизационных моделей, имитационных моделей на базе стохастических зави-
симостей.
Ключевые слова: промышленность 4.0, цифровые технологии, смарт-предприятия,
большие данные, экономико-математическое моделирование.
JEL codes: С00; С60; С67; С69; О12; О14.
Cite this publication:
Madykh A.A., Okhten O.O., Dasiv A.F. Analysis of the world experience of economic
and mathematical modeling of smart enterprises. Economy of Industry. 2017. № 4 (80).
pp. 19-46. doi: 10.15407/econindustry2017.04.019
Madykh, A.A., Okhten, O.O., Dasiv, A.F. (2017). Analysis of the world experience of
economic and mathematical modeling of smart enterprises. Econ. promisl., 4 (80), pp. 19-46.
doi: 10.15407/econindustry2017.04.019
Recieved 30.09.2017
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