Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces
This paper explores the role of digital-based high-tech agriculture as a central driver of innovation and sustainability in the agro-industrial complex. Emphasis is placed on the strategic importance of technology transfer, foresight-based planning and data-driven solutions to improve productivity a...
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Omelyanenko, V.A. 2025-10-25T18:17:59Z 2025 Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces / V.A. Omelyanenko // Економіка промисловості. — 2025. — № 3 (111). — С. 23-39. — Бібліогр.: 28 назв. — англ. 1562-109Х https://nasplib.isofts.kiev.ua/handle/123456789/208361 338.4:631 http://doi.org/10.15407/econindustry2025.03.023 This paper explores the role of digital-based high-tech agriculture as a central driver of innovation and sustainability in the agro-industrial complex. Emphasis is placed on the strategic importance of technology transfer, foresight-based planning and data-driven solutions to improve productivity and enhance resilience. The findings reinforce the notion that high-tech agriculture is not an isolated phenomenon but an integral part of a broader digitalized industrial economy. This paper presents a systems-based digitally supported approach for the transfer and commercialization of agricultural technologies. Агропромисловий комплекс зазнає глибокої трансформації, зумовленої нагальною потребою в подоланні таких глобальних викликів, як продовольча небезпека, зміна клімату, демографічні зрушення та дефіцит ресурсів. Проаналізовано роль високотехнологічного сільського господарства як ключового рушія інновацій і стійкості в агропромисловому комплексі. Наголошено на основних технологічних тенденціях Індустрії 4.0, зокрема інтеграції Інтернету речей (IoT), робототехніки, точного землеробства, біотехнологій і відновлюваної енергетики, які в сукупності переосмислюють сільськогосподарські практики та тісно пов’язують агросектор із сучасними галузями промисловості. Особлива увага приділяється стратегічній важливості трансферу технологій, плануванню на основі форсайту та заснованих на даних рішенням для підвищення продуктивності, зниження екологічного навантаження та посилення стійкості. Взаємозв’язок між сільським господарством та іншими секторами («зелена» енергетика, ІКТ) представлений через системний погляд на інновації, що підкреслює зростаючу конвергенцію галузей. Висновки підтверджують ідею, що високотехнологічне сільське господарство є не ізольованим, а становить складову цифровізованої промислової економіки. Сформовано системно орієнтований підхід до трансферу та комерціалізації аграрних технологій, що передбачає використання цифрових технологій і ґрунтується на принципах паралельної інженерії та теорії інноваційних мереж. Паралельна інженерія широко застосовується в передових галузях промисловості та пропонує трансформаційну альтернативу традиційним послідовним моделям розроблення, забезпечуючи колаборацію та паралельне проєктування. Підкреслено критичну роль етапу проєктування в життєвому циклі агротехнологій. Розглянуто, як паралельна інженерія підвищує ефективність, стійкість та адаптивність в інноваційних мережах сільського господарства. Із використанням цифрових інструментів, інтегрованих систем знань і платформ ІКТ-співпраці такий підхід передбачає узгодження інженерного проєктування з цілями сталого розвитку, забезпечуючи раннє виявлення ризиків, гнучке прототипування та залучення зацікавлених сторін із різних секторів. Запропоновано структуру ІКТ-підтримки інноваційних мереж. Наголошено на необхідності включення міждисциплінарних і географічно розподілених команд у цикл розроблення для забезпечення релевантності, стійкості та створення довгострокової цінності в секторі аграрних технологій. Запропонована організаційна модель трансферу технологій інтегрує підсистеми управління даними, геоінформаційного аналізу та прийняття рішень на основі знань для підтримки життєвого циклу аграрних інновацій. Основну увагу приділено зближенню сільського господарства та Індустрії 4.0 через регіональну смарт-спеціалізацію, публічно-приватне партнерство та формування агротехнологічних кластерів. Висвітлено організаційні та інфраструктурні вимоги до побудови мережі трансферу технологій з фокусом на важливості маркетингових сервісів й інтегрованих високоефективних екосистем трансферу. Завдяки поєднанню інституційного проєктування з цифровою інфраструктурою модель забезпечує ефективніше, масштабоване та стійке впровадження агротехнологічних інновацій у регіонах. en Інститут економіки промисловості НАН України Економіка промисловості Економіко-теоретичні проблеми виробництва Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces Цифрова конвергенція сільського господарства та Індустрії 4.0: можливості та організаційні інтерфейси Article published earlier |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| title |
Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces |
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Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces Omelyanenko, V.A. Економіко-теоретичні проблеми виробництва |
| title_short |
Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces |
| title_full |
Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces |
| title_fullStr |
Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces |
| title_full_unstemmed |
Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces |
| title_sort |
digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces |
| author |
Omelyanenko, V.A. |
| author_facet |
Omelyanenko, V.A. |
| topic |
Економіко-теоретичні проблеми виробництва |
| topic_facet |
Економіко-теоретичні проблеми виробництва |
| publishDate |
2025 |
| language |
English |
| container_title |
Економіка промисловості |
| publisher |
Інститут економіки промисловості НАН України |
| format |
Article |
| title_alt |
Цифрова конвергенція сільського господарства та Індустрії 4.0: можливості та організаційні інтерфейси |
| description |
This paper explores the role of digital-based high-tech agriculture as a central driver of innovation and sustainability in the agro-industrial complex. Emphasis is placed on the strategic importance of technology transfer, foresight-based planning and data-driven solutions to improve productivity and enhance resilience. The findings reinforce the notion that high-tech agriculture is not an isolated phenomenon but an integral part of a broader digitalized industrial economy. This paper presents a systems-based digitally supported approach for the transfer and commercialization of agricultural technologies.
Агропромисловий комплекс зазнає глибокої трансформації, зумовленої нагальною потребою в подоланні таких глобальних викликів, як продовольча небезпека, зміна клімату, демографічні зрушення та дефіцит ресурсів. Проаналізовано роль високотехнологічного сільського господарства як ключового рушія інновацій і стійкості в агропромисловому комплексі. Наголошено на основних технологічних тенденціях Індустрії 4.0, зокрема інтеграції Інтернету речей (IoT), робототехніки, точного землеробства, біотехнологій і відновлюваної енергетики, які в сукупності переосмислюють сільськогосподарські практики та тісно пов’язують агросектор із сучасними галузями промисловості. Особлива увага приділяється стратегічній важливості трансферу технологій, плануванню на основі форсайту та заснованих на даних рішенням для підвищення продуктивності, зниження екологічного навантаження та посилення стійкості. Взаємозв’язок між сільським господарством та іншими секторами («зелена» енергетика, ІКТ) представлений через системний погляд на інновації, що підкреслює зростаючу конвергенцію галузей. Висновки підтверджують ідею, що високотехнологічне сільське господарство є не ізольованим, а становить складову цифровізованої промислової економіки. Сформовано системно орієнтований підхід до трансферу та комерціалізації аграрних технологій, що передбачає використання цифрових технологій і ґрунтується на принципах паралельної інженерії та теорії інноваційних мереж. Паралельна інженерія широко застосовується в передових галузях промисловості та пропонує трансформаційну альтернативу традиційним послідовним моделям розроблення, забезпечуючи колаборацію та паралельне проєктування. Підкреслено критичну роль етапу проєктування в життєвому циклі агротехнологій. Розглянуто, як паралельна інженерія підвищує ефективність, стійкість та адаптивність в інноваційних мережах сільського господарства. Із використанням цифрових інструментів, інтегрованих систем знань і платформ ІКТ-співпраці такий підхід передбачає узгодження інженерного проєктування з цілями сталого розвитку, забезпечуючи раннє виявлення ризиків, гнучке прототипування та залучення зацікавлених сторін із різних секторів. Запропоновано структуру ІКТ-підтримки інноваційних мереж. Наголошено на необхідності включення міждисциплінарних і географічно розподілених команд у цикл розроблення для забезпечення релевантності, стійкості та створення довгострокової цінності в секторі аграрних технологій. Запропонована організаційна модель трансферу технологій інтегрує підсистеми управління даними, геоінформаційного аналізу та прийняття рішень на основі знань для підтримки життєвого циклу аграрних інновацій. Основну увагу приділено зближенню сільського господарства та Індустрії 4.0 через регіональну смарт-спеціалізацію, публічно-приватне партнерство та формування агротехнологічних кластерів. Висвітлено організаційні та інфраструктурні вимоги до побудови мережі трансферу технологій з фокусом на важливості маркетингових сервісів й інтегрованих високоефективних екосистем трансферу. Завдяки поєднанню інституційного проєктування з цифровою інфраструктурою модель забезпечує ефективніше, масштабоване та стійке впровадження агротехнологічних інновацій у регіонах.
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Digital convergence of agriculture and industry 4.0: opportunities and organisation interfaces / V.A. Omelyanenko // Економіка промисловості. — 2025. — № 3 (111). — С. 23-39. — Бібліогр.: 28 назв. — англ. |
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23ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
ЕКОНОМІКО-ТЕОРЕТИЧНІ
ПРОБЛЕМИ ВИРОБНИЦТВА
ECONOMIC AND THEORETICAL
PROBLEMS OF PRODUCTION
http://doi.org/10.15407/econindustry2025.03.023
UDC 338.4:631
JEL: O32, O33, Q16
Vitaliy A. OMELYANENKO, Doctor of Economic Science, Senior Researcher, Professor
Е-mail: omvitaliy@gmail.com; https://orcid.org/0000-0003-0713-1444
Institute of Industrial Economics of NAS of Ukraine
2 Maria Kapnist Street, Kyiv, 03057, Ukraine
DIGITAL CONVERGENCE OF AGRICULTURE AND INDUSTRY 4.0:
OPPORTUNITIES AND ORGANISATION INTERFACES
Th is paper explores the role of digital-based high-tech agriculture as a central driver of innovation and sustainability in the
agro-industrial complex. Emphasis is placed on the strategic importance of technology transfer, foresight-based planning and
data-driven solutions to improve productivity and enhance resilience. Th e fi ndings reinforce the notion that high-tech
agriculture is not an isolated phenomenon but an integral part of a broader digitalized industrial economy. Th is paper
presents a systems-based digitally supported approach for the transfer and commercialization of agricultural technologies.
Keywords: agriculture, technology transfer, ICT, innovation network, agri-tech innovations.
Cite : Omelyanenko V. A. Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces.
Econ. promisl. 2025. № 3 (111). P. 23—38. http://doi.org/10.15407/econindustry.2025.03.023
© Publisher PH "Akademperiodyka" of the NAS of Ukraine, 2025. Th is is an open access article under the CC BY-NC-ND
license https://creativecommons.org/licenses/by-nc-nd/4.0/)
Th e agricultural sector plays a key role in ensur-
ing food security, the sustainability of ecosystems
and economic development. According to UN
forecasts, in 30 years humanity will need 1,7 times
more food than is currently produced (FAO, 2023).
Such results cannot be achieved without serious
modernization of agriculture (Alston & Pardey,
2014; FAO, 2017). Th e answer to global challenges
can be technologies: sensors, drones, unmanned
harvesters and robots. Technologies automate har-
vesting and sowing campaigns, optimize the use of
fertilizers and chemical plant protection products
and signifi cantly reduce water consumption. How-
ever, rapid changes in climate, demographics and
technological progress are creating new challenges
for the agricultural sector (Badiane, 2014). For the
growth of these clicks, it is important to promote
new technologies, as well as ensure eff ective tech-
nology transfer between countries and regions.
Th e development of the country’s agricultu re at
the present stage is associated with the transi tion to
new technologies and a strategic factor in strength-
ening the competitiveness of domestic agriculture
is to increase its technological level. According to
scientists and agricultural experts, the weakest link
in the production of agricultural products is tech-
nical equipment and technological modernization,
which leads to incomplete use of resource potential,
decrease in production performance, deterioration
in the quality of work performed and ultimately,
to the unprofi tability of the industry (Shevchenko,
Omelyanenko et al., 2025). At the same time, the
social and economic transformations taking place
recently have revealed a discrepancy between man-
agement methods and the technological capabili-
ties of agriculture. Th is is due to the fact that the
fi eld of process management is less supported by
research and in management theory it is given only
ЕКОНОМІКА
ПРОМИСЛОВОСТІ
ECONOMY
OF INDUSTRY
24 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
a secondary role, in contrast to the study of pro-
duction management as a whole.
Based on the above, technology transfer is an im-
portant process in the agricultural sector and the
economy as a whole. To eff ectively manage tech-
nology transfer from a strategical point of view, it
is important to focus on forecasts for the develop-
ment of innovation. Foresight in the fi eld of agri-
culture can be defi ned as systematic analytics with
the aim of identifying the long-term future of sci-
ence, technology, economy and society in order to
identify areas of strategic research and the emer-
gence of new high technologies that promise to
bring the greatest economic and social benefi ts.
Sustainability of agricultural innovation projects
is infl uenced by a dynamic interplay of community
engagement, technology adoption, and local capa-
city building (Mgendi, Shiping, & Xiang, 2019).
When agricultural technology is transferred, it
can boost productivity, but this may also reduce
the community’s dependence on external support.
As a result, the system seeks a balance between
increased output and long-term project sustain-
ability. A critical factor is how the local commu-
nity perceives the benefi ts of the new technology.
Positive perceptions enhance community partici-
pation, which strengthens stakeholder networks
and builds trust and commitment. Th is collective
engagement improves the project’s operating envi-
ronment, making it more conducive to long-term
success. Local autonomy and the ability to rely on
indigenous resources play a reinforcing role. As
communities become more self-reliant and eff ec-
tively use local assets, their ownership over the pro-
jects deepens. Th is empowerment leads to better
integration of transferred technologies, greater sus-
tainability, and reduced need for continuous exter-
nal input. Stakeholders’ support and eff ective col-
laboration further enhance the benefi ts perceived
by the community, feeding back into stronger par-
ticipation and better outcomes. Over time, these
reinforcing processes create a resilient foundation
for agricultural cooperation that adapts to both ex-
ternal inputs and internal developments.
High-tech agricultural production is produc-
tion with the rational use of technologies that in-
crease productivity and quality of cultivated crops,
using scientifi cally based standards that ensure
increased productivity and economic effi ciency of
agricultural producers. Th e production of high-
tech products requires the formation of large-scale
specialized zones of operation, development and
improvement of interregional connections. Th e
institutional environment for technology transfer
in agricultural production presupposes economic
and geographical unity, similar social and economic
conditions. Th e use of technology responds to the
growth of available data for analysis and decision-
making (Fig. 1).
Scientists in other countries are also exploring
high-tech solutions to combat the severe impacts of
climate change, including drought and loss of arable
land due to desertifi cation and to meet the grow-
ing food needs of a growing global population. As
technology advances, smart farming is transform-
ing agriculture from a practice largely dependent
on grower experience and intuition to a data-driv-
en industry, opening up the possibility of involving
a wider range of participants in the process.
E.g. the importance of Internet of Th ings (IoT)
for the further development of agricultural tech-
nologies is approximately the same as the synergy
eff ect for the economy — this is when the combi-
nation of several components gives a greater result
than using them separately. Sometimes synergy is
also called the 2 + 2 = 5 eff ect. Th us, IoT acts as a
tool that combines various technologies and forms
a single ecosystem out of them. Th is symbiosis of
data in one source will allow for better data man-
agement, which increases the effi ciency of using
these technologies. Research service Business Intel-
ligence (BI) predicts that the use of IoT devices will
increase from 30 million in 2015 to 75 million in
2020, an annual growth rate of about 20 %. In 2014,
the average farm generated 190 thousand data
points, then in 2050 there will be 4.1 million. Now
one of the main users of IoT are American farmers
and on average they produce 7.34 tons of grain per
1 hectare, while the global average is 3.85 tons of
grain per hectare. In addition, OnFarm conducted
a study that found that IoT increased the average
American farm’s yield by 1.75 %, reduced energy
costs by $7 to $13 per acre and reduced irrigation
water use by 8 %1. Th is is despite the fact that US
farmers are already quite technologically advanced,
adhere to cultivation technologies and are already
showing high effi ciency. If we talk about Ukraine,
the potential for yield growth due to the introduc-
tion of IoT technologies will be much higher.
1 Pylypchuk A. (2021). Why IoT and Precision Farming
Are the Future of Agriculture. Aggeek. https://app.agro-
online.com/21242/details/ (Access: 07.07.2025).
25ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces
High-tech agriculture is no longer a stand-
alone sector; it is deeply interwoven with various
branches of modern industry, forming a synergis-
tic agri-industrial ecosystem (Table 1). Th is integra-
tion transforms traditional farming into a digitally
enabled, in novation-driven fi eld that draws upon
industrial ad vancements for effi ciency, sustainabi-
lity and scalability.
Precision agriculture, leveraging IoT, Artifi -
cial intelligence (AI) and GIS technologies, mir-
rors the principles of Industry 4.0 by introduc-
ing data-driven decision-making (Vyshnevskyi,
Anufriiev, Bozhyk, Gulchuk, 2024) and automa-
tion into the farming process. Robotics and au-
tonomous machinery, developed in high-tech en-
gineering industries, are now routinely deployed
in fi elds and greenhouses, demonstrating the
crossover between mechatronics and agriculture.
Biotechnological innovations, such as genetically
improved seeds and microbial treatments, show-
case the connection between agriculture and the
biotech and pharmaceutical industries. Similarly,
vertical farming systems depend on industrial en-
vironmental control, lighting systems and green
technologies typically used in advanced manufac-
turing. In the post-harvest phase, agriculture con-
nects to smart food processing industries through
the use of automation, robotics and quality con-
trol systems. Blockchain and digital platforms
support transparency and certifi cation across the
supply chain, linking agriculture to the fi ntech
and digital infrastructure sectors. Cloud comput-
Fig. 1. Estimated volume of data received by one farm per day
Source: Pylypchuk A. (2021). Why IoT and Precision Farming Are the Future of
Agriculture. Aggeek. https://app.agro-online.com/21242/details/ (Access: 07.07.2025).
ing and big data analytics play a key role in man-
aging farm data, highlighting the dependence on
the ICT and soft ware industries. At the same time,
the integration of renewable energy into farming
operations (solar irrigation or biomass reuse) brings
agriculture into the green industrial and environ-
mental engineering space.
Modern agriculture also relies on smart logistics,
cold chain solutions and transportation systems de-
veloped by industrial logistics providers, ensuring
safe and timely delivery of agricultural products.
Finally, the rise of agri-tech startups and platform-
based solutions refl ects the growing involvement
of venture capital and innovation ecosystems in
agricultural transformation. Th ese connections
demonstrate that high-tech agriculture functions
as a core part of the modern industrial economy,
driving both rural development and technological
advancement.
Th e aim of this research is to explore and criti-
cally analyze the convergence between agricul-
ture and Industry 4.0 through the integration of
digital technologies with a focus on identifying
organizational interfaces, innovation potentials
and implementation challenges. In doing so, the
research aims to provide a systematic under-
standing of the opportunities and limitations
associated with the digitalization of agriculture,
assess the readiness of organizational structures
to adapt to these changes and off er strategic rec-
ommendations for policy and technological dif-
fusion in agribusiness sectors.
26 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
Concurrent engineering approach for digital
support of transfer of agri-tech
Th e author`s approach is based on the ideas of us-
ing the concurrent engineering methodology for the
transfer of agricultural technologies. Th e key stage in
the life cycle of agricultural technology is the design
stage (Méndez-Zambrano et al, 2023). Any mistake
at this stage can be costly in terms of design changes
and impact on the production process, delays in the
release of products to the market with the potential
threat of loss of market position and product recall,
signifi cant fi nancial losses and damage to the com-
pany’s reputation. Hence, emphasis must be placed
on design to make sure that the product reaches the
market fl awlessly and as quickly as possible. Doing it
right the fi rst time, which is all the more important in
the global market, is only possible with a good project.
Current trends in the agricultural industry in-
dicate the need for companies throughout the in-
creasingly complex agricultural machinery and tech-
Table 1. High-tech agriculture and its connection to modern industry
Agricultural technology/trend Connected
industrial sector Nature of connection
Precision agriculture (IoT, AI,
GIS)
Industry 4.0 / Smart manufac-
turing
Data-driven control systems, predictive analytics,
automation techniques
Drones, robots, autonomous
machinery
Robotics & Mechatronics in-
dustry
Shared platforms for automation, sensor fusion,
control soft ware
Biotech crops & microbial
inputs
Biopharmaceutical & Biotech
industry
Genetic engineering, lab-to-fi eld translation, mo-
lecular product development
Vertical farming & hydropon-
ics
Green tech / Industrial design Use of controlled environment systems, lighting,
ventilation and nutrient technologies
Agri-food processing automa-
tion
Food industry / industrial Pro-
cessing
Smart factories, robotic sorting, digital quality con-
trol
Blockchain traceability sys-
tems
FinTech / Supply chain manage-
ment
Transparency in production chains, anti-fraud,
certifi cation, digital transactions
Big data & cloud platforms ICT / Soft ware industry Platform-based analytics, farm management sys-
tems, mobile data services
Renewable energy integration Energy & Environmental engi-
neering
Solar irrigation, biomass reuse, integration with
green industrial parks
Smart logistics & cold chains Transportation & Logistics Temperature-controlled supply chains, real-time
GPS tracking, effi ciency optimization
Agri-tech startups and plat-
forms
Innovation ecosystems / Ven-
ture capital
Incubators, accelerators and tech clusters linking
agri-sector with startup ecosystems
Source: author`s generalization based on Alston & Pardey, 2014; FAO, 2017; Dror, 2016; Moussa S. (2002). Technology
Transfer for Agriculture Growth in Africa; African Development Bank: Tunis, Tunisia. https://www.afdb.org/fi leadmin/
uploads/afdb/Documents/Publications/00157678-FR-ERP-72.PDF (Access: 07.07.2025); Pylypchuk A. (2021). Why
IoT and Precision Farming Are the Future of Agriculture. Aggeek. https://app.agro-online.com/21242/details/ (Access:
07.07.2025); Piddubna A. (2024, April 20). Th e Future of AgriTech: Trends and Innovations in Agriculture to Watch in
2023. Intellias. https://intellias.com/innovations-in-agriculture/ (Access: 07.07.2025).
nology supply chain to invest in improving their
development processes2. Th is can be achieved
through the acquisition of collaborative and inter-
operable digital design tools of a multi-dimension-
al parallel engineering approach, as well as through
the integration of universities and engineers who
will practice the design and development of digital
agricultural machines in such environments (dos
Reis, Medeiros, Fernando, et al., 2020).
According to Dror (2016) the main objectives of
sectoral innovation management include the fol-
lowing three issues:
development of the system of agricultural in-
novations;
studying the evolution of technology-based
approaches to solving problems of agriculture;
2 Piddubna A. (2024, April 20). Th e Future of AgriTech:
Trends and Innovations in Agriculture to Watch in 2023.
Intellias. https://intellias.com/innovations-in-agriculture/
(Access: 07.07.2025).
27ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces
development of various forms of participation
and appropriate tools.
Concurrent engineering methodology involves
the joint work of experts from various functional
areas of technology development (scientists, engi-
neers, users) at the earliest possible stage of develop-
ment in order to achieve high quality, functionality
and manufacturability in the shortest possible time
with minimal costs (Facco et al., 2017). Concurrent
engineering is an expression of the desire to increase
the competitiveness of products by reducing the
product life cycle, as well as improving quality and
reducing prices (Prasad, 2018; Deshpande, 2018).
According to ESA concurrent engineering
methodology provides a “collaborative, co-opera-
tive, collective and simultaneous engineering work-
ing environment” 3. Table 2 outlines the core com-
ponents of concurrent engineering as applied in
innovation networks, highlighting its connections
to industry sectors, digital tools and organizational
models that support agile and synchronized inno-
vation processes.
Th rough the analysis of complex real-world ap-
plications and experiences, the study of Stjepandić,
Wognum & Verhagen (2015) demonstrates that
concurrent engineering is widely used in many in-
dustries and that the same basic design principles
can also be applied to emerging new industries.
Among such industries there is high-tech agricul-
ture, which in modern conditions is developing due
to digitalization and the development of complex
technologies. Such case of IoT device placements
for agriculture based on concurrent engineering is
presented in study Tirupathi & Niranjan (2022).
From the point of view of agriculture, it is im-
portant that new products and technologies meet
the goals of sustainable development. Th e sustain-
ability of a product or technology can be most in-
fl uenced early in product development through the
Quality Function Deployment (QFD) method and
the ‘sustainability house’, which translates sustain-
ability requirements into technical solutions for the
product (Rihar & Kušar, 2021). From this research
we can outline such tasks relevant for agriculture:
establishment of criteria for continuous im-
provement of products and processes by creating
added value of products;
3 ESA (2024). What is concurrent engineering? https://
www.esa.int/Enabling_Support/Space_Engineering_
Technology/CDF/What_is_concurrent_engineering
(Access: 07.07.2025).
increasing the potential for innovation and so-
lutions to create environmentally friendly products;
identifi cation of opportunities and risks in
sustainable product development;
strengthening competitive advantage through
innovative solutions for the development of prod-
ucts that have a low impact on the environment;
designing and planning the introduction of
sustainable products by choosing cleaner technol-
ogies (eco-technologies);
creation of requirements for product process-
ing and production of new products from renew-
able raw materials;
sustainable procurement of materials with less
impact on the environment;
bringing together all stakeholders in the procure-
ment-production-supply chain to coordinate shared
responsibility and risks related to environmental re-
quirements and reduce environmental fi nes.
In the table 3 we present the key technologies
that defi ne high-tech agriculture and outline their
major benefi ts. It also refl ects how these technolo-
gies link agriculture to broader innovation systems
and industrial value chains, supporting the transi-
tion toward sustainable, resilient and future-ori-
ented food systems.
Сoncurrent engineering is positioned as a trans-
formative methodology that shift s traditional, se-
quential development models toward integrated
real-time collaboration. Expectations from this ap-
proach span from incremental effi ciency gains to
comprehensive process automation across innova-
tion networks. By replacing the conventional linear
sequence of design, testing and implementation with
a parallelized framework, concurrent engineering
accelerates development cycles, reduces costs and
enhances responsiveness to change. It ensures that
all stages (design, production, operational manage-
ment, support services, end-of-life disposal) are con-
sidered simultaneously from the project’s inception.
Th is holistic perspective, supported by advanced
ICT tools, enables synchronized actions across di-
verse innovation actors and strengthens the agility,
resilience and performance of the entire network.
In the era of interconnected innovation eco-
systems ICT play a foundational role in enabling
collaboration, knowledge sharing and system-level
integration across diverse actors. Innovation net-
works comprise research institutions, industries,
startups, public agencies and users and require real-
time coordination, data transparency and intelligent
28 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
Table 2. Concurrent engineering in innovation networks
Component Description Role in innovation networks Example tools / platforms
Integrated multi-
stakeholder collabo-
ration
Promotes real-time, cross-disci-
plinary collaboration among all
partners
Enables distributed actors to
co-develop solutions
Microsoft Teams, Miro,
Confl uence
Parallel development
processes
Multiple development tasks pro-
ceed simultaneously rather than
sequentially
Reduces overall project time,
increases adaptability in com-
plex ecosystems
Trello, Asana
Early involvement of
partners
All relevant actors (design, manu-
facturing, etc.) are involved from
the start
Ensures alignment and avoids
late-stage friction or failures
Stakeholder mapping
tools, CRM systems
Shared digital infra-
structure
Centralized access to designs,
data and prototypes across loca-
tions
Enhances transparency and
trust among network partici-
pants
Cloud PLM, shared Git
repositories, CAD plat-
forms
Continuous feedback
loops
Constant testing and refi nement
based on live inputs from various
actors
Improves innovation quality
and user-centric design
Digital twins, A/B testing
tools, simulation apps
Agile knowledge
sharing
Knowledge is exchanged itera-
tively across the network
Accelerates learning and
knowledge-based decision-
making
Knowledge hubs, open
innovation portals
Rapid prototyping &
scaling
Designs can be prototyped and
scaled simultaneously in diff erent
nodes of the network
Enhances speed of innovation
and responsiveness to market
needs
3D printing hubs, living
labs, fab labs
Design for network
resilience
Solutions are designed consider-
ing interoperability, sustainability
and adaptability
Supports long-term network vi-
ability and modular innovation
Modular design soft -
ware, sustainability scor-
ing
Source: author`s generalization based on Alston & Pardey, 2014; FAO, 2017; Dror, 2016; Moussa S. (2002). Technology
Transfer for Agriculture Growth in Africa; African Development Bank: Tunis, Tunisia. https://www.afdb.org/fi leadmin/
uploads/afdb/Documents/Publications/00157678-FR-ERP-72.PDF (Access: 07.07.2025); Pylypchuk A. (2021). Why
IoT and Precision Farming Are the Future of Agriculture. Aggeek. https://app.agro-online.com/21242/details/ (Access:
07.07.2025); Piddubna A. (2024, April 20) Th e Future of AgriTech: Trends and Innovations in Agriculture to Watch in
2023. Intellias. https://intellias.com/innovations-in-agriculture/ (Access: 07.07.2025).
infrastructure to co-create and scale solutions ef-
fi ciently. ICT tools support not only communica-
tion and data management but also strategic func-
tions such as forecasting, modeling, simulation
and lifecycle monitoring.
As innovation processes become increasingly digi-
tal and distributed, ICT enables the implementation
of advanced methodologies such as concurrent engi-
neering, digital twins and smart project management
(Prokopenko, Järvis, Omelyanenko, Maslov, Lopes,
2025). Th ese tools help align processes across geo-
graphically dispersed teams, synchronize workfl ows
and reduce development cycles. In the table 4 we out-
line how ICT contributes to the functionality and ef-
fectiveness of innovation networks, highlighting core
technological enablers, their roles and their impact
on innovation performance and sustainability.
Th e concept of ICT support of innovation net-
work is based on several basic principles (Brookes,
& Blackhouse, 1996; Brookes & Blackhouse, 1998)
that can be solved with model ICT applications
(Th orat, Patle, & Kashyap, 2023):
1) early detection of the problem; the later the
problem is discovered, more eff ort and time and
consequently, money, is spent on eliminating it;
2) early decision making; in the early stages there
are much more opportunities to make changes to the
project, the “window of opportunity” is much wider;
3) structuring of work; the entire process must be
divided into jobs in such a way that each job can be
performed independently of the others so that it can
be handled by a person, a computer or a machine;
4) close teamwork; will achieve optimal results
in terms of combined knowledge and insights. A
strong team is more than the sum of its people;
5) use of knowledge for decision making; mod-
ern products are so complex that it is impossible to
create expert systems and decision support systems
29ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces
Table 3. High-tech agriculture: key technologies and impacts
Technology Function Benefi ts
Precision farming
(Smart Farming)
Uses GPS, sensors and data analytics to man-
age variability in fi elds
Optimized inputs, increased yields, reduced
waste
Drones and UAVs Aerial surveillance, crop monitoring, spraying Real-time data, effi cient spraying, reduced labor
IoT sensors (Soil &
Climate)
Monitor soil moisture, nutrients, weather and
crop health
Better irrigation, timely intervention, data-
driven decisions
AI Analyzes big data for forecasting, decision-
making and automation
Predictive analytics, disease detection, opti-
mized operations
Robotics and automa-
tion
Automates harvesting, weeding, planting and
sorting
Labor savings, precision, 24/7 operation
Vertical farming Indoor, stacked cultivation using controlled
environments
Urban farming, resource effi ciency, year-
round production
Hydroponics & aqua-
ponics
Soil-free systems using water/nutrient circu-
lation or fi sh integration
Space saving, effi cient water usage, pesti-
cide-free production
Blockchain Tracks agricultural supply chains and ensures
product traceability
Food safety, fraud prevention, transparency
Genetically modifi ed
crops (GMO)
Engineered for traits like drought resistance
or pest control
Higher productivity, reduced chemical use
Satellite imagery &
remote sensing
Monitors large-scale changes and crop health
remotely
Early warning systems, regional planning,
crop insurance
Big Data analytics Aggregates and analyzes diverse farm-related
datasets
Smarter resource allocation, profi tability
tracking
Source: author`s idea.
for all occasions. However, human knowledge and
experience always remains the most important tool;
6) mutual understanding; If each member of
a work group knows what the other is doing, the
whole group works better. For example, the design-
er knows what diffi culties the technologist will en-
counter when changing some design parameters;
7) possession; groups work with enthusiasm
when they have the freedom to make decisions
and when they are given “ownership” and respon-
sibility for the product they produce;
8) constancy of purpose; you need to change
your thinking away from the indicators of each
specifi c department to the indicators of the entire
company as a whole. Focusing on the goals of the
entire company will allow everyone to contribute
to the common good.
Th e principle of concurrent engineering involves
performing the processes of developing and design-
ing a technology simultaneously with modeling the
processes of its practical operation. Th is also includes
the simultaneous design of various components of a
complex product. With concurrent engineering, ma-
ny problems that may arise at later stages of the life
cycle of agricultural technology are identifi ed and sol-
ved at the design stage. Th is approach allows to im-
prove quality, reduce time to market and reduce costs.
Th e diff erences between concurrent engineer-
ing and the traditional approach to organizing sci-
entifi c and engineering processes are:
eliminating traditional barriers between the
functions of individual specialists and organiza-
tions by creating and if necessary, subsequent
transformation, multidisciplinary working groups,
including geographically distributed ones, which
is important for agricultural technologies in order
to most eff ectively localize application;
iterative process of approaching the required
result.
Multidisciplinary working groups in modern ag-
riculture include specialists from diff erent fi elds and
are created to solve specifi c problems. For example,
representatives of the operator, general developer
and component supplier, i.e. specialists from diff er-
ent organizations can be brought together into one
group to solve problems that arise during operation.
Th e main patterns of agricultural development,
which require the practice of organizing communi-
30 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
Table 4. ICT support of innovation network
Component Function in innovation network Expected benefi ts
Knowledge management Store, organize and share innovation-related
knowledge
Enhanced knowledge continuity and
onboarding
Collaboration platforms Facilitate cross-organizational teamwork and
virtual meetings
Real-time cooperation and reduced
communication delays
Innovation portals Centralized access to open calls, R&D initia-
tives, idea submissions
Better visibility and participation in
innovation initiatives
Data analytics & business
intelligence
Extract insights from data to guide strategy Evidence-based decision-making
CRM & stakeholder man-
agement
Map and manage relationships with ecosys-
tem actors
Stronger stakeholder engagement and
coordination
Project & portfolio man-
agement
Track innovation project status, risks and ROI Transparency, prioritization and re-
source optimization
Cloud infrastructure &
storage
Provide scalable, shared access to computing
and data resources
Agility, scalability and cost-effi ciency
Cybersecurity & identity
management
Protect innovation data, IP and communica-
tions
Security, compliance and trust in digi-
tal environments
Digital twin / simulation
tools
Model systems, test innovations virtually Lower prototyping costs, faster itera-
tion
Blockchain & smart con-
tracts
Ensure trust and traceability in collaborative
innovation settings
Transparency, trust, automated execu-
tion
Open data platforms Enable access to datasets for co-creation and
experimentation
Data-driven innovation and civic tech
engagement
Artifi cial intelligence &
ML
Predict trends, personalize solutions, auto-
mate processes
Acceleration of complex problem-
solving
IoT platforms & sensors Collect real-world data for smart infrastruc-
ture and urban innovation
Real-time monitoring and adaptive
systems
Incubation & mentoring
platforms
Support early-stage innovators and connect
them to mentors and investors
Nurturing of startups and access to
capital
Community & networking
tools
Build relationships and shared identity among
ecosystem actors
Sustained engagement and knowledge
exchange
Source: author`s idea.
cations within the framework of multidisciplinary
working groups, include the need to adapt agri-
cultural technologies to local conditions; develop-
ment of agricultural technologies with maximum
environmental safety; formation of specialized in-
dustry; digitalization of agricultural production;
broad integration of agricultural science and edu-
cation into agricultural production systems.
Concurrent engineering involves replacing the
traditional sequential approach with a set of oper-
ations overlapping in time, aimed at systematically
improving the solution being developed until the
desired result is achieved.
Th e initial understanding of the problem leads
to the fi rst version of documented requirements,
from which the initial design solution is developed.
It gives rise to new questions and allows us to clar-
ify the formulation of the problem.
Eff ective implementation of this approach is
impossible outside of the information system.
Th e possibility of applying the principles of con-
current engineering arises due to the fact that in
the information system all work results are pre-
sented in electronic form, are up-to-date, acces-
sible to all participants and can be easily adjusted
(Jha, Ranjan, & Gaur, 2020).
Applied decision for digital support
of agri-tech transfer
Th e information & analytical system is designed to
support and manage the process of development
and transfer of agricultural technologies. Th e system
31ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces
is based on a сoncurrent engineering model and
allows to create an environment for communica-
tion between science and business for the joint de-
velopment of technology. Th e proposed model in-
volves the simultaneous implementation of various
stages of development and testing of new technol-
ogy, which signifi cantly reduces the costs of adapt-
ing technology to new conditions that are signifi -
cant for agricultural production.
Th e scheme of the system’s operation is based
on the analysis of existing solutions to the prob-
lem of сoncurrent engineering and the stages of
process planning in the development of a new
product, as well as an object-oriented informa-
tion model of the process (Th ankachan, Bhasi, &
Madhu, 2010), which includes classes of necessary
information. Th e environment formed through
the use of concurrent engineering unites project
participants through an information system that
allows for the exchange of knowledge, establishes
eff ective mechanisms for coordinating project ac-
tivities and reduces development time (Aguilar-
Virgen et al, 2021).
Taking into account the above requirements,
we have developed a formalized conceptual model
of an agent-oriented environment for the develop-
ment of innovation, which is the basis for repre-
senting the structure and algorithms of opera-
tion of the developed information and analytical
knowledge management system about the innova-
tive development of the economy, its functionality
and component soft ware modules.
Th e main goal of creating an information &
analytical system is to prepare and maintain mul-
tidimensional information that displays a related
set of data that serves as the basis for prompt and
informed decision-making on the development
and transfer of agricultural technologies.
Th e information & analytical system is based on
the concept of integrating information from vari-
ous subject areas, the ability to quickly access it,
keep it up to date, use eff ective tools for analyz-
ing and displaying aggregated and interconnected
subsets of information, its retrospective analysis
and providing access to it for users of various lev-
els in in accordance with their authority.
Th e basis of the information & analytical system
is a unifi ed information base in which relation-
ships are established between the main informa-
tion components, which include:
register of territorial units;
register of users with powers according to the
user matrix;
register of information events;
register of documents and regulatory informa-
tion.
Th e information & analytical system consist of
three subsystems:
data management subsystem;
information and cartographic subsystem;
information and analytical subsystem.
Main ideas of information & analytical system:
1. A system for constructing a unifi ed informa-
tion space, containing a communication interface
module connected to a number of information
sources (inputs) representing technical and soft -
ware information processing tools, an automatic
design system for entering data and queries, an
electronic archive module and an information
model of an agricultural technology (Figure 2).
2. A system for constructing a unifi ed informa-
tion space, containing a communication interface
module connected to a number of information
sources (inputs) representing technical and soft -
ware information processing tools, an automatic
design system for entering data and queries, an
electronic archive module and an information
model of an agricultural technology or product.
3. Th e system is characterized by adaptive digital
environment depending on the technology or prod-
uct, it is possible to create a set of target indicators.
4. Th e system contains an updating function de-
signed to collect and update measurement infor-
mation regarding a set of target indicators, formal-
ize initial data and knowledge about the state and
monitor these states.
5. Th e system contains a metadata conversion
block and a virtual data integration block connect-
ed to each other and to the specifi ed information
environment, a unit of services for a single infor-
mation space and a data exchange module.
6. Th e system contains of control centers for
data and knowledge processing are designed to
store data and knowledge, various product life cy-
cles and the system also includes tools for process-
ing analytical reporting and tools for generating
recommendations for decision-making.
7. Th e information system contains a geoinfor-
mation module for storing and processing geo-
physical information of target territories (territo-
ries where the technology will be implemented),
including cartographic and reference information,
32 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
Fig. 2. Unifi ed information space for innovation tech development
Source: author`s idea.
a means of constantly entering and replenishing
geophysical information and a system for admin-
istering user accounts.
8. Th e module contains the ability to connect
digital video data of a territory with the ability
to link a three-dimensional image to the current
time and transfer it to a server, which is designed
with the ability to permanently store retrospec-
tive and prospective maps. For this purpose, it is
possible to enter into the database an image of the
area, its coordinates and the time the video image
was recorded and with the ability to enter time and
coordinates.
9. Th e geoinformation module is distinguished
by the fact that it is designed to transmit informa-
tion online.
10. Th e geoinformation module is distinguished
by the fact that it is designed with the ability to
transmit retrospective information to the user (re-
searcher, technician worker).
11. Th e geoinformation module is distinguished
by the fact that it is designed with the ability to
predict events in the time of future information.
12. Th e geoinformation module is distinguished
by the fact that it is designed to determine the co-
ordinates of an object on the plan, taking into ac-
count the specifi ed time.
Organizational interfaces of digital
convergence of agriculture and Industry 4.0
Emphasizing the signifi cance of innovation revital-
izing of agricultural businesses’ business models, it
is vital to draw attention to the primary factors that
need to be considered when formulating frame-
work for digital convergence of agriculture and
Industry 4.0:
focusing on regional development priorities,
especially the agriculture sector; establishing pub-
lic-private partnerships and encouraging private
investment in the agricultural sector (Omelianen-
ko О., Omelyanenko V., Pidorycheva, 2025; Pido-
rycheva, Bash, 2024);
allocation of funds from local and regional
budgets to initiatives that are strategically impor-
tant for the area, guaranteeing food and environ-
mental security;
activation of communication and informa-
tional eff orts, with the aim of presenting a favora-
ble picture of the area to a prospective investor and
giving him the most comprehensive information
regarding investment off ers and the benefi ts of
funding the agriculture industry.
Unlike traditional ones, the mechanism for
managing the agro-industrial complex’s sustain-
able development based on the concept of a busi-
ness model proposed in the study is regarded as a
system of network business interaction of enter-
prises in the agro-industrial complex. It reveals the
key components of the value chain of enterprises in
the agro-industrial complex, implies that an agro-
industrial complex enterprise chooses the best
kind of innovative business model and enables the
determination of strategic directions for achieving
the region’s sustainable development goals.
33ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces
Th e following justifi es the speed with which a
business model concept can be selected for the es-
tablishment of an integration mechanism for the
sustainable growth of agricultural enterprises:
considering the growing popularity of the
sharing economy (economy of joint use) theories,
business networks of agricultural fi rms function
as organizational innovations. Long-term meg-
atrends, primarily brought about by technological
advancements, resource scarcity and social chang-
es, come together to form the economy of shared
consumption;
business models explain how enterprises in-
teract with one another in networks and they can
also be a source of competition between players
(the interaction of stakeholders in the network oc-
curs on a competitive market basis);
agro-industrial complex’s business networks
are based on both formal and for the most part,
informal groups of businesses that interact with
one another. Th is calls for knowledge integration
and exchange. By addressing social and cognitive
issues, business modeling seeks to improve enter-
prise group interaction and foster the creative po-
tential of each employee in the business network.
Within the framework of smart specialization
and organization of interaction between technol-
ogy transfer participants, it is proposed to create
a technology transfer center, the purpose of which
will be the development and implementation of
a system of commercialization and technology
transfer in the region’s agriculture, the transfer of
scientifi c results, new technologies from their de-
veloper to a new owner.
Th e tasks of this center can include: preparation
and organization of events to identify promising
scientifi c developments aimed at further commer-
cialization and technology transfer; search for part-
ners for the commercialization of developments;
organization and holding of advertising activities,
exhibitions, seminars, symposia and conferences
focused on the commercialization of R&D results;
information support for the process of commer-
cialization and transfer of developments; assess-
ment of costs associated with the acquisition of
technologies; conclusion of an agreement and
transfer of technology.
As a result of the study, the network and clus-
ter form of organization of agricultural produc-
tion was identifi ed as the most acceptable for the
implementation of the relationship between sci-
ence and production. In world practice, a cluster
approach is used in the implementation of inno-
vation policy, according to which a cluster is un-
derstood as a network of independent production
and service fi rms, including suppliers, creators of
technologies and know-how, connecting market
institutions and consumers interacting with each
other within a single value chain.
A feature of a cluster is the emphasis on the links
between industries, enterprises and organizations
that contribute to: the development of production
and competition; simplifi ed access to the latest
technologies; risk distribution in various types of
joint activities; organization of joint scientifi c re-
search and the process of training and retraining
of specialists; reduction of transaction costs, etc.
Th e creation of an agro-technological cluster in
terms of technological development will allow:
to identify problems and determine the cur-
rent state of the technical and technological sphere
of the region;
to assess the resource potential;
to determine the mechanisms for regulating
technological development;
to assess the expected socio-economic results
from the activities carried out.
An agro-technological cluster is understood as a
system of interconnected organizations integrated
with the aim of simultaneous and interconnect-
ed solution of agricultural production problems
based on eff ective technologies.
Th e fundamental diff erence of the proposed
cluster model is its construction based on tech-
nology transfer. Th e cluster boundaries will cover
several industries, as it develops, the depth of sec-
ondary raw material processing will increase and
the supplier and consumer bases will expand. Re-
source-saving technologies will form the basis of
the agro-technological cluster.
In the cluster structure, a large role is given to
scientifi c institutions, since these institutions act
as a system for promoting knowledge and technol-
ogy, in addition, inventions are transformed into
innovations in them.
Th anks to information and consulting services,
participants in the cluster formation receive com-
petitive advantages from innovations.
Th e implementation of the cluster strategy will
require attracting large investment resources. Th e
enterprises themselves should act as the main in-
vestors, which is unlikely at present. Th erefore,
34 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
additional scientifi c research is required, signifi -
cant amendments to a number of laws and regula-
tions on agro-industrial formations of the holding
and cluster type.
Th e productivity of organizations, supported by
the technological connectivity of the cluster value
chain, provides its critical mass and the basis for
further innovations. Based on this, it should be
noted that the interaction of participants in the
sphere of the innovation process has a number of
characteristic features expressed in the specifi cs of
the industry and the innovation cycle itself. Anal-
ysis of experience in the creation of innovation
structures provides broad opportunities in choos-
ing a model of innovative development, the main
result of innovation activity is the transfer and
commercialization of technologies. Technological
development in the institutional sphere provides
for developments related to the creation of forms
and mechanisms for connecting science and pro-
duction — this is the central link in the implemen-
tation of integration processes, intensifi cation of
science and production, acceleration of the pace of
scientifi c and technological progress. To solve this
problem in agriculture, research, creation and op-
eration of multi-level scientifi c organizations with-
in the framework of cluster policy are necessary.
Following the example of developed countries,
it is advisable to involve a third party in the trans-
fer of material technologies and innovative servic-
es, whose task is to provide information support
for this process. Such mediation will be carried
out through the information provider system. Th e
function of providers is also to mediate between
manufacturers and the fi nancial sector.
Th e development of an information provider
network is possible only in the structure of an ef-
fective technology transfer system, functioning in
the form of a branched network and providing for
the coordination of all transfer participants from
a single center. Examples of such networks are
European Entrepreneurship Network, American
Technology Transfer Center, which have a complex
multi-level hierarchical structure of regional cent-
ers and representative offi ces. At the same time, the
commercialization of technology, as well as ensur-
ing the purchase of innovation on the most favora-
ble terms, are usually carried out with the media-
tion of their participants.
Th ere are two large networks in Ukraine (Ukrain-
ian Technology Transfer Network with a coordina-
tion center in Academy of Technical Sciences and
National Technology Transfer Network), as well
as several transfer centers, business incubators,
technology parks, operating independently of
each other. Unlike foreign practice, the domestic
technology transfer sector is characterized by sig-
nifi cant fragmentation. Th is is due to the fact that
the specifi ed non-integrated technology transfer
networks perform almost the same functions. In
addition, none of the domestic networks is a par-
ticipant in foreign technology transfer platforms,
which not only signifi cantly complicates access to
foreign databases, but also makes it impossible for
providers and manufacturers from other countries
to access data on domestic achievements in science
and technology. From the above, we can conclude
that it is necessary to create a single centralized do-
mestic technology transfer network.
Technology transfer services within the frame-
work of agricultural production should cover:
1) legal support for the development commer-
cialization process;
2) patenting and licensing;
3) creation and maintenance of an information
support platform for the innovation process;
4) economic assessment of the prospects of in-
novations, their eff ectiveness;
5) marketing services for technology transfer, in
particular, promotion of technologies, search for
the necessary innovations for specifi c buyers.
It is advisable to integrate personnel for servic-
ing technology transfer in agriculture on the ba-
sis of agricultural and natural science universities.
Th is is explained by the availability of specialists
with the necessary qualifi cations, in particular,
practicing legal scholars, economists who are
specialists in the fi elds of investment, innovation
management, intellectual property, fi nance, mar-
keting, etc., IT specialists who are capable of not
only servicing but also creating information sys-
tems. Scientifi c institutions in such a system will
play, fi rst of all, the role of technology developers,
the commercialization of which will be carried out
by transfer specialists
It is advisable to create the following divisions
in the structure of Center for Technology Transfer
in Agriculture:
1) technology department (technological audit
of technology and the enterprise — the customer
of the innovation; development of accompanying
technical documentation, etc.);
35ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces
2) marketing department (research of the mar-
ket situation for innovations in agriculture, in par-
ticular, supply and demand; organization of par-
ticipation in conferences, exhibitions);
3) fi nancial and economic department (assess-
ment of socio-economic and environmental ef-
fi ciency of an idea, development; preparation of
business plans and innovation and investment
projects; establishment of contacts with budget fi -
nancing bodies, optimization of fi nancing sources
and innovation portfolio);
4) patent service (preparation and support of pat-
ent applications, protection of intellectual property);
5) legal service (legal support for registration of
license agreements, loan agreements, agreements
with contractors, consulting in case of violation of
intellectual property rights);
6) information support department (creation
and maintenance of information support of the
technology transfer center; ensuring two-way
communication of the center’s information sys-
tem with the information platform of the national
technology transfer network).
Conclusions
High-tech agriculture represents a paradigm shift
in how food is produced, managed and delivered in
the 21st century. It merges traditional farming with
cutting-edge technologies from across the industrial
spectrum, transforming agriculture into a knowl-
edge-intensive, data-driven and innovation-orient-
ed sector. Th e integration of IoT, AI, robotics, bio-
technology and renewable energy into agricultural
systems fosters not only higher productivity and re-
source effi ciency but also greater sustainability and
adaptability to climate and market uncertainties. As
shown by international experiences, especially in
technologically advanced countries, the implemen-
tation of smart agricultural technologies results in
measurable improvements in yield, energy use and
water effi ciency. However, realizing similar out-
comes in developing and transitioning economies,
such as Ukraine, requires a focused eff ort in strategic
planning, capacity building and technology transfer.
Th e institutional environment, research support and
international cooperation play decisive roles in this
transformation. By positioning high-tech agricul-
ture within the broader context of Industry 4.0 and
innovation ecosystems, this study confi rms that the
sector can act as a catalyst for inclusive economic
growth and regional development.
Th e transition to concurrent engineering in ag-
riculture marks a paradigm shift in how modern
agri-technologies are developed, tested and de-
ployed. By replacing linear development models
with synchronized, collaborative processes, this
methodology addresses the pressing need for fast-
er, more sustainable and context-adapted innova-
tions in a rapidly evolving sector. Th e integration
of ICT tools and digital infrastructures facilitates
the coordination of multidisciplinary teams, sup-
ports knowledge-driven decision-making and en-
sures early problem detection. Moreover, the inclu-
sion of sustainability indicators during the design
phase, supported by methods such as QFD and
the «sustainability house», strengthens alignment
with environmental and socio-economic goals. As
high-tech agriculture continues to evolve within
complex innovation ecosystems, the adoption of
concurrent engineering provides a robust founda-
tion for systemic, scalable and inclusive techno-
logical advancement. Th e proposed approach not
only enhances product and process innovation but
also supports the resilience and competitiveness of
agriculture in the face of global challenges.
Th e eff ective transfer of agricultural technolo-
gies requires not only scientifi c excellence but
also well-structured, digitally enabled systems
that can bridge the gap between research and
practice. Th is study proposes a comprehensive
model of a concurrent engineering-based infor-
mation and analytical system that facilitates the
co-creation, adaptation and dissemination of
innovations within the agri-food sector. By in-
tegrating data-driven modules, geospatial tools
and decision support functionalities into a uni-
fi ed platform, the system empowers stakehold-
ers to reduce development time, manage risk and
increase the effi ciency of technology adoption in
diverse territorial contexts.
Th e formation of agro-technological clusters and
technology transfer centers is central to operation-
alizing this model, fostering collaboration among
universities, startups, producers and policymakers.
Th ese clusters provide a functional environment
for the coordinated development of sustainable
and locally adapted technologies, while the digital
architecture ensures transparency, knowledge con-
tinuity and stakeholder accountability. Further-
more, the institutionalization of transfer services
is essential for scaling innovations and creating ro-
bust, investor-ready ecosystems.
36 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
Given the fragmentation of the current technol-
ogy transfer landscape in Ukraine, the paper un-
derscores the need for a centralized, integrated na-
tional network that connects domestic innovation
actors with international platforms. Such an ap-
proach not only enhances global competitiveness
but also aligns agricultural innovation with nation-
al goals for food security, environmental sustain-
ability and regional development. Th e proposed
model demonstrates that digital infrastructure,
organizational integration and smart specialization
can collectively transform technology transfer into
a dynamic engine of agricultural modernization.
Prospects for further studies
Future research will focus on expanding the func-
tionality of the proposed system through the in-
tegration of real-time data analytics, which can
further automate scenario generation and deci-
sion-making processes. Another important direc-
tion includes testing the tool in specifi c sectors,
such as green infrastructure, high-tech agriculture
and urban mobility, to tailor the models to sector-
specifi c needs. Th ere is also signifi cant potential
for international collaboration, particularly within
the framework of EU digital and innovation eco-
systems, to adapt the tool for cross-border infra-
structure projects. Furthermore, researchers aim
to investigate the role of local innovation commu-
nities in optimizing the interaction between tech-
nological systems and human decision-makers.
Th ese steps will contribute to refi ning the system’s
responsiveness and scalability across various envi-
ronments and use cases.
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38 ISSN 1562-109X Econ. promisl. 2025. № 3 (111)
V. A. Omelyanenko
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Received: 11.07.2025
Accepted: 29.07.2025
39ISSN 1562-109X. Економіка промисловості. 2025. № 3 (111)
Digital convergence of agriculture and Industry 4.0: opportunities and organisation interfaces
Віталій Анатолійович Омельяненко, д-р екон. наук, старший дослідник, професор
Е-mail: omvitaliy@gmail.com; https://orcid.org/0000-0003-0713-1444
Інститут економіки промисловості НАН України
вул. Марії Капніст, 2, м. Київ, 03057, Україна
ЦИФРОВА КОНВЕРГЕНЦІЯ СІЛЬСЬКОГО ГОСПОДАРСТВА ТА ІНДУСТРІЇ 4.0:
МОЖЛИВОСТІ ТА ОРГАНІЗАЦІЙНІ ІНТЕРФЕЙСИ
Агропромисловий комплекс зазнає глибокої трансформації, зумовленої нагальною потребою в подоланні
таких глобальних викликів, як продовольча небезпека, зміна клімату, демографічні зрушення та дефіцит
ресурсів. Проаналізовано роль високотехнологічного сільського господарства як ключового рушія іннова-
цій і стійкості в агропромисловому комплексі. Наголошено на основних технологічних тенденціях Індустрії
4.0, зокрема інтеграції Інтернету речей (IoT), робототехніки, точного землеробства, біотехнологій і віднов-
люваної енергетики, які в сукупності переосмислюють сільськогосподарські практики та тісно пов’язують
агросектор із сучасними галузями промисловості. Особлива увага приділяється стратегічній важливості
трансферу технологій, плануванню на основі форсайту та заснованих на даних рішенням для підвищен-
ня продуктивності, зниження екологічного навантаження та посилення стійкості. Взаємозв’язок між сіль-
ським господарством та іншими секторами («зелена» енергетика, ІКТ) представлений через системний
погляд на інновації, що підкреслює зростаючу конвергенцію галузей. Висновки підтверджують ідею, що ви-
сокотехнологічне сільське господарство є не ізольованим, а становить складову цифровізованої промисло-
вої економіки. Сформовано системно орієнтований підхід до трансферу та комерціалізації аграрних техно-
логій, що передбачає використання цифрових технологій і ґрунтується на принципах паралельної інженерії
та теорії інноваційних мереж. Паралельна інженерія широко застосовується в передових галузях промис-
ловості та пропонує трансформаційну альтернативу традиційним послідовним моделям розроблення, за-
безпечуючи колаборацію та паралельне проєктування. Підкреслено критичну роль етапу проєктування в
життєвому циклі агротехнологій. Розглянуто, як паралельна інженерія підвищує ефективність, стійкість та
адаптивність в інноваційних мережах сільського господарства. Із використанням цифрових інструментів,
інтегрованих систем знань і платформ ІКТ-співпраці такий підхід передбачає узгодження інженерного про-
єктування з цілями сталого розвитку, забезпечуючи раннє виявлення ризиків, гнучке прототипування та
залучення зацікавлених сторін із різних секторів. Запропоновано структуру ІКТ-підтримки інноваційних
мереж. Наголошено на необхідності включення міждисциплінарних і географічно розподілених команд у
цикл розроблення для забезпечення релевантності, стійкості та створення довгострокової цінності в сек-
торі аграрних технологій. Запропонована організаційна модель трансферу технологій інтегрує підсистеми
управління даними, геоінформаційного аналізу та прийняття рішень на основі знань для підтримки життє-
вого циклу аграрних інновацій. Основну увагу приділено зближенню сільського господарства та Індустрії
4.0 через регіональну смарт-спеціалізацію, публічно-приватне партнерство та формування агротехнологіч-
них кластерів. Висвітлено організаційні та інфраструктурні вимоги до побудови мережі трансферу техно-
логій з фокусом на важливості маркетингових сервісів й інтегрованих високоефективних екосистем транс-
феру. Завдяки поєднанню інституційного проєктування з цифровою інфраструктурою модель забезпечує
ефектив ніше, масштабоване та стійке впровадження агротехнологічних інновацій у регіонах.
Ключові слова: сільське господарство, трансфер технологій, інформаційно-комунікаційні технології, іннова-
ційна мережа, агротехнологічні інновації.
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