=Paper=
{{Paper
|id=Vol-3742/paper11
|storemode=property
|title=Information technology to support the digital transformation of small and medium-sized businesses
|pdfUrl=https://ceur-ws.org/Vol-3742/paper11.pdf
|volume=Vol-3742
|authors=Lyubomyr Mosiy,Halyna Kozbur,Iryna Strutynska,Olha Mosiy,Vasyl Yatsyshyn
|dblpUrl=https://dblp.org/rec/conf/citi2/MosiyKSMY24
}}
==Information technology to support the digital transformation of small and medium-sized businesses==
https://ceur-ws.org/Vol-3742/paper11.pdf
Information technology to support the digital
transformation of small and medium-sized businesses
Lyubomyr Mosiy1,∗,†, Halyna Kozbur1,†, Iryna Strutynska1,†, Olha Mosiy1,†, Vasyl
Yatsyshyn1,†
1 Ternopil Ivan Puluj National Technical University, Ruska str., 56, Ternopil, 46001, Ukraine
Abstract
The digital transformation of small and medium-sized enterprises (SMEs) in Ukraine is an
important priority to improve their competitiveness, efficiency and resilience in a dynamic
digital landscape. However, many SMEs face difficulties in determining their current level of
digital maturity and need guidance on how to implement appropriate digital technologies and
strategies. To address these issues, a conceptual model of an online platform for expert
assessment and analysis of the digital transformation of Ukrainian SMEs has been developed.
The proposed platform has the potential to significantly accelerate the digital transformation of
SMEs in Ukraine by providing them with valuable tools and resources to assess digital maturity,
receive personalized guidance and access training materials. This is expected to increase the
competitiveness of Ukrainian SMEs both locally and internationally, fostering innovation and
economic growth in the country.
Keywords
digital transformation, small and medium-sized businesses, online platform, digital maturity,
artificial intelligence, cloud solutions 1
1. Introduction
The digital transformation of small and medium-sized enterprises (SMEs) in Ukraine is
a critical priority, as it contributes to improving the competitiveness, efficiency and
resilience of enterprises in a dynamic digital landscape. However, many SMEs face
difficulties in determining their current level of digital maturity and need guidance on how
to implement appropriate digital technologies and strategies.
To solve these problems, a conceptual model of an online platform for expert
evaluation and analysis of the digital transformation of SMEs in Ukraine was developed.
CITI’2024: 2nd International Workshop on Computer Information Technologies in Industry 4.0, June 12–14, 2024,
Ternopil, Ukraine
∗ Corresponding author.
† These authors contributed equally.
lmosiy@gmail.com (L. Mosiy); kozbur.galina@gmail.com (H. Kozbur); strutynskairy@gmail.com (I.
Strutynska) lmosiy@ukr.net (O. Mosiy); vyatcyshyn@gmail.com (V. Yatsyshyn)
0009-0000-9778-331X (L. Mosiy); 0000-0003-3297-0776 (H. Kozbur); 0000-0001-5667-6569 (I.
Strutynska); 0000-0002-5131-761X (O. Mosiy); 0000-0002-5517-6359 (V. Yatsyshyn)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
This platform consists of seven main blocks: Data Collection Module, Knowledge Base,
Assessment Module, Recommendation Module, Explanation and Visualization Module,
Learning and Onboarding Module, and User Interface.
The platform will be implemented using modern programming languages and
technologies. User interface designs using framework React, JavaScript, HTML and CSS.
Python will be use to implement recommendation engine to ensure high performance,
scalability, and usability. In addition, cloud solutions, including Amazon Web Services
(AWS) or Microsoft Azure, will be used to deploy the platform and manage its
infrastructure. The use of cloud technologies will ensure the reliability, security, and
flexibility of the platform.
The integration of artificial intelligence (AI) technologies, such as machine learning,
collaborative filtering and natural language processing (NLP), will allow the platform to
provide personalized recommendations and insights based on the analysis of large
amounts of data. Expert knowledge in the field of digital transformation will be encoded in
the platform's knowledge base, which will ensure the accuracy and relevance of
assessments and recommendations. As a core of knowledge base now uses relational
database implemented in the MS SQL Server environment.
2. International programs, policies, frameworks and online platforms
for assessing the digital maturity of SMEs
The digital transformation of SMEs is considered at the international and European
levels as a key priority that ensures their competitiveness and sustainable development in
the new economy. In addition, the digitalisation of SMEs is recognized as an important
factor for achieving the UN Sustainable Development Goals and building the EU's digital
single market. With this in mind, a number of international and European programs and
policies have been developed to promote the digital transformation of SMEs (Table 1).
Table 1
Review of International and European Programs, Policies, and Frameworks for Promoting
Digital Transformation of SMEs
Name of the program,
Beginning Key features
policy, framework
A Digital Single Market 6 May The strategy includes three main areas:
Strategy for Europe [1] 2015 improving access to digital goods and services for
EU consumers and businesses, creating favorable
conditions for the development of digital
networks and services, maximizing the growth
potential of the European digital economy
OECD Digital for SMEs 2018 Covers the following topics: digital skills, access to
Global Initiative finance, digital technologies for SMEs, digital
(D4SME) [2] trade
Digital Europe 2021- The program provides comprehensive trans-
Programme [3] 2027 European digital services based on mature
technical and organizational solutions in e-
procurement, cybersecurity, e-health, e-justice,
online dispute resolution, secure internet, open
data
Shaping Europe's Digital 2020- The program consists of 4 key components:
Future [4] 2025 digital infrastructures and support services,
enhanced digital capabilities and interoperability,
cybersecurity and trust, and advanced digital
technologies
An SME strategy for a 10 March The strategy focuses on the following areas:
sustainable and digital 2020 digitalization, innovation and skills development,
Europe [5] green transformation, greening of production and
operations, financing and investment, use of
online platforms, growth in supply chains, and
entry into international markets
The Digital Decade September The program includes 4 main areas: digital skills
policy programme 2030 2021 of the population, secure and sustainable digital
[6] infrastructure, digital transformation of business,
and digitalisation of public administration
«SME digitalisation to December Priority areas: digital infrastructure, digital
«Build Back Better» 2021 technologies, partnerships, innovations and
Digital for SMEs startups, and facilitating SMEs' access to foreign
(D4SME) policy paper markets through digital channels
[7]
Also, a number of online platforms for assessing digital maturity and generators of
personalized recommendations have been developed in European countries. Here are
some of the platforms (Table 2):
Table 2
Existing online platform Digital Maturity
Name of platform Developer Approach to the evaluation
Digital Maturity European The tool uses the following dimensions for
Assessment (DMA) Commission assessment: Overall digital maturity level, Digital
Tool [8] business strategy, Digital readiness, Human-
centric digitalisation, Data management,
Automation & Artificial Intelligence, Green
digitalisation
SME Compass [9] CEVES, Serbia The tool includes 9 pillars: Business sentiment
and performance, Business environment, Human
resources, Business model, Digitalisation and
Industry 4.0, Innovations, Green transition,
Access to finance, Gender equality
IMPULS – Industry IMPULS The online platform consists of 6 maturity levels
4.0 Readiness Foundation of (Outsiders, Beginner, Intermediate, Experienced,
Online Self-Check the VDMA and Expert, Top performers) і 6 dimensions (Strategy
for Businesses [10] Aachen & Organization, Smart Factory, Smart Operations,
University Smart Products, Data-driven Services, and
Employees)
Industry 4.0 i4EU, Co- The Competence Meter performs a
Competence Meter funded by the multidimensional analysis of the users’ digital
[11] Erasmus skills according to four different dimensions:
Programme of Technology, People, Organization, Business. The
the European tool assesses the level of maturity with respect to
Union each of the assessed dimensions and estimates
the distance with respect to the “ideal” level of
maturity needed to successfully implement
Industry 4.0 models
Industry 4.0 Consortium of Industry 4.0 Maturity Index defines 6 successive
Maturity Index [12] research stages: Computerisation, Connectivity, Visibility,
institutions Transparency, Predictive capacity, Adaptability.
together with The Maturity Index has a modular structure and
industrial covers five functional areas: development,
partners production, logistics, services, marketing and
working under sales.
the umbrella of
Acatech
(Deutsche
Akademie der
Technikwissen-
schaften)
These platforms have common features:
Digital Maturity Assessment: They help SMEs understand their current level of
digital development through online diagnostics.
Personalized recommendations: Based on the results of the assessment, the
platforms generate customized roadmaps and recommendations to improve the
digital capabilities of SMEs.
Learning Resources: They provide access to training materials, webinars, and
courses to develop digital skills and knowledge.
Expert support: Some platforms offer access to a network of experts and mentors
to provide advice on digital transformation.
The study of problems related to the digital transformation of small and medium-sized
enterprises (SMEs), the determination of the level of digital maturity, and the formulation
of recommendations regarding the implementation of relevant digital technologies and
strategies have received attention from international and Ukrainian researchers [13-18],
in particular, in terms of practical application in various industries [19-21].
But despite this, it should be noted that the direct application of these tools to support
the digital transformation of SMEs in Ukraine requires adaptation to the specifics of
Ukrainian SMEs due to the difference in business conditions, levels of technological
readiness and legal environment. After all, in Ukraine, SMEs have different levels of digital
readiness, there are differences in legislation and regulatory environment, language
adaptation is needed, industry specifics and the impact of the war must be taken into
account.
3. Proposed methodology/model/technique
Based on the analysis of international programs, policies, frameworks, and online
Digital Maturity platforms, the authors developed a model for the development of SMEs in
Ukraine (Figure 1) and a conceptual model of an online platform for expert evaluation and
analysis of digital transformation of SMEs in Ukraine (Figure 2).
Figure 1: Model SME development of Ukraine
Figure 2: Conceptual model of the online platform for expert evaluation and analysis of
digital transformation of SMEs in Ukraine
The proposed conceptual model of the online platform for expert evaluation and
analysis of digital transformation of SMEs in Ukraine consists of 7 blocks that perform the
following functions (Table 3):
Table 3
User-friendly and intuitive interface for interacting with the system
Platform blocks Features
1. Data collection module Interface for entering data about an SME company
Integration with external data sources (financial
reports, operational data, etc.)
Data pre-processing and normalization
2. Knowledge base Storing expert rules and criteria for evaluating digital
transformation
SME digital transformation ontology (key concepts,
relationships)
Methodologies and best practices for digital
transformation
3. Evaluation module Assessment of the current level of digital maturity of
SMEs based on collected data
Application of expert rules and criteria from the
knowledge base
Use of AI algorithms (e.g., machine learning) for data
analysis and forecasting
4. Recommendation module Generation of personalized recommendations for
digital transformation based on assessment results
Proposals for the introduction of new technologies,
process optimization, digital skills development, etc.
Use of AI to prioritize and adapt recommendations to
the specifics of the company
5. Explanation and visualization Providing clear explanations of assessment results and
module recommendations
Visualization of key indicators, trends and progress of
digital transformation
Interactive interface for research and analysis of
results
6. Training and adaptation Continuous training and improvement of AI models
module based on feedback and new data
Adaptation of expert rules and criteria in accordance
with changes in the industry and new knowledge
Ability to add new rules and knowledge by experts
7. User interface Convenient and intuitive interface for interacting with
the system
Ability to enter data, view results and receive
recommendations
Access to educational materials and resources on
digital transformation
The core of knowledge base is implemented relational database, which consists with 30
tables. In the Figure 3 showed main entities of domain which were displayed into the
tables on the physics level.
Company
Methodology
Infrastructure
Company
Experts
Expert
Assessments
Human resource
Expert
recommendations
Figure 3: Main entities of domain
Entity “Company” includes general information about the field in which concrete
company work, structure of the company and information about worker’s space.
Entity “Company Infrastructure” describes the set of data about characteristics of
workspaces digitalization, which include information about hardware, software and
communication.
Entity “Methodology” includes information about methodologies, which can be used in
the process of company’s level digitalisation defining. Each methodology designed on
expert’s questionnaires.
Entity “Human Resource” describes a set of data about workers and their workspaces’
digitalization.
Entities “Experts”, “Expert Assessments” and “Expert Recommendations” describes
evaluation process of company’s level digitalization.
In the physical level of database management system was implemented tables and
relationships, which display all the entities in the Figure 3.
In the Figure 4 we showed the part of relational database that is responsible for the
entity “Company” implementation.
Hardware
ID_Hardware
Software
ID_Software
This part of the database oriented for saving data
ID_HardwareCategory
about company name and location,
ID_SoftwareCategory
HardwareTitle SoftwareTitle
fields, in which company Description works, departments,Description
workers and workspaces. Table
“CompanyFieldsDetails” was created to implement relationships many-to-many between
tables “Company” and “CompanyFields”.
In the Figure 5 showed the part of database, which is responsible for the description of
company digital infrastructure.
Company
ID_Company
CompanyName
CompanyLocation
Desccription
Department
ID_Department
ID_Company CompanyFieldDetails
ID_CompanyField
DepartmentName
ID_Company
DepartmentResponsibility
CompanyField
WorkSpace ID_CompanyField
ID_WorkSpace
CompanyField
ID_Deprtment
Description
WorkSpaceNumber
Worker
ID_Worker
ID_Workspace
Surnameinitials
WorkerPosition
Figure 4: A part of the relational database that describes entity “Company”
SoftwareDetails
ID_Software
ID_HardwareDetails
Description
Software
ID_Software
ID_SoftwareCategory
SoftwareTitle
Description HardwareDetails
ID_HardwareDetails HardwareCharacteristic
ID_HWCharacteristic
ID_Hardware
ID_Hardware
ID_HWCharacteristic
HWTitle
HWCharacteristicValue
Description
Description
SoftwareCategory
ID_SoftwareCategory
CategoryTitle
CommunicationHW
ID_Communication
ID_Hardware
Hardware
ID_Hardware
ID_HardwareCategory
HardwareTitle
Description
Communication
ID_Communication
ID_CommunicationType
CommunicationTitle
HardwareCategory
ID_HardwareCategory
CommunicationType
CategoryTitle
ID_CommunicationType
Title
Description
Figure 5: A part of database to describe company’s digital infrastructure
Tables “Software”, “Hardware” and “Communication” are main tables, which stored
data about characteristics of digital infrastructure of the concrete company. Others tables
was used for the detailed description some aspects of company’s infrastructure and
normalization scheme of the database.
In the Figure 6 displayed the part of database that shows digitalisation of workspaces.
As showed in the Figure 6, in this part of database was build relationships between
software, hardware, communication interfaces and worker’s spaces. Each workspace will
be evaluate by experts in future.
In the Figure 7 showed the part of database that displays data to support the process of
expert assessment.
Communication
WorkSpaceCommunication ID_Communication
ID_WorkSpace
ID_CommunicationType
ID_Communication
CommunicationTitle
WorkSpaceSW
ID_WorkSpace
ID_Software
WorkSpace
ID_WorkSpace
ID_Deprtment
WorkSpaceNumber
Software
ID_Software
ID_SoftwareCategory
SoftwareTitle
Description
WorkSpaceHW
ID_WorkSpace
ID_Hardware
Hardware
ID_Hardware
ID_HardwareCategory
HardwareTitle
Description
Figure 6: A part of the relational database that describes digitalisation of workspaces
The main tables of this part of the database are:
“Methodology” – a table for storing data about description of some methodology
for assessing the level of company digitization;
“Questionary” – a table, which display information about questioner metadata in
some methodology;
“Questions” – a table for storing questions related to some questioners;
“Expert”, “ExpertDetails”, “ExpertSkills” – tables, which describe information about
experts and their skills;
“Answer”, “AnswerScale” and “Assessments” – tables for storing data about the
answers and experts’ assessments of the company level digitalization;
“Recommendation” – expert recommendations for improving of company
digitalisation level.
Assesments * AnswerScale
ID_Answer ID_AnswerScale
ID_AnswerScale ScaleTitle
ID_Expert ScaleType
EvaluationDate MinValue
AssesmentValue MaxValue
ExpertDetails
ID_Expert
Questions ID_Skill
ID_Question
QuestionText Expert
Description ID_Expert
ExpertName
ExpertLevel
ExpertSkills
ID_Skill
SkillTitle
SkillDescription
Recommendation
ID_Recommendation
ID_Company
ID_Question
ID_Expert
RecommendationRext
Company
ID_Company
CompanyName
Question_Questionary_Methodology CompanyLocation
ID_Methodology Desccription
ID_Questionary
ID_Question
Questionary
ID_Questionary
QuestionaryTitle
Answers * Methodology
AmountOfQuestions ID_Answer
ID_Methodology
ID_Company
MethodologyTitle
ID_Methodology
Description
ID_Questionary
ID_Question
AnswerText
Figure 7: A part of database for supporting the process of expert assessment
Created database is using for the preparation data, which will be send to the
recommendation service or recommender engine. Recommendation service works using
technology of collaborative filtering. In the Figure 8 showed others approaches that can be
used to implement recommendation system.
Figure 8: Approaches for building recommendation systems
The main idea of using collaborative filtering technology is preparation of cross-
tabulation matrix, where columns interpret for the questions and rows display company
name. In the cells of such matrix will be expert assessments, as quantitative values of
answers for each questions in the interval from 1 to 5. In the Figure 9 showed the
structure of the cross-tabulation matrix, and in the Table 4 – the scale, which using in the
expert evaluation process.
Figure 9: Cross-tabulation matrix
Table 4 The scale for expert evaluation
Unformal term Assessments Value
Need complex digitalization 1–2
Average level of 3–4
digitalisation which can be
improved
Perfect level of 5
digitalization
The main idea of using collaborative filtering in the process of making
recommendations is to improve the level of company digitalization. In case when company
has no digitalization, the system can make recommendations based on the most popular
digital solutions implemented in the companies of the same working area. It is a cold start
of digitalisation that can be describes with formula (1):
1
𝑟𝑥𝑖 = (1)
𝑘 ∑𝑦∈𝑁 𝑟𝑦𝑖
where 𝑟𝑥𝑖 – a vector, which will store predicted values of expert assessments for each
characteristics (Q1…Qn) of used evaluation methodology;
𝑘 – a total amount of companies in the same working area;
𝑟𝑦𝑖 – a vector, which stored values of expert assessments for each characteristics
(Q1…Qn);
N – a set of 𝑘 companies most similar to company 𝑥, which also rated characteristics 𝑖;
In case, when company has expert assessments for some or all characteristics and
wants to improve its level of digitalisation it is possible to use formula (2):
∑𝑦∈𝑁 𝑠𝑖𝑚(𝑥, 𝑦) ∙ 𝑟𝑦𝑖
𝑟𝑥𝑖 = (2)
𝑘 ∑𝑦∈𝑁 𝑠𝑖𝑚(𝑥, 𝑦)
where 𝑠𝑖𝑚(𝑥, 𝑦) – a similarity of digitalisation level for company x and y.
Similarity measure of level digitalisation uses different evaluation metrics. The two
most popular metrics are: cosine metric and Pearson’s correlation metric. Cosine’s metric
calculated by formula (3):
𝑟⃗⃗⃗⃗𝑥𝑖 ∙ ⃗⃗⃗⃗
𝑟𝑥𝑗
𝑐𝑜𝑠𝛼 = (3)
|⃗⃗⃗⃗
𝑟𝑥𝑖 | ∙ |𝑟⃗⃗⃗⃗ 𝑥𝑗 |
𝑟⃗⃗⃗⃗𝑥𝑖 – a vector of digitalisation level for company which wants to improve it;
𝑟𝑥𝑗 – a vector, which interpret level of digitalisation for other company.
⃗⃗⃗⃗
∑𝑞∈𝑄(𝑐,𝑐′)(𝑟𝑐𝑞 − 𝑟̅)
𝑐 ∙ (𝑟𝑐′𝑞 − ̅̅̅)
𝑟𝑐′
𝑠𝑖𝑚(𝑐, 𝑐′) = (4)
2∙∑ 𝑟𝑐′ 2
√∑𝑞∈𝑄(𝑐,𝑐′)(𝑟𝑐𝑞 − 𝑟̅)
𝑐 𝑞∈𝑄(𝑐,𝑐′)(𝑟𝑐′𝑞 − ̅̅̅)
where 𝑠𝑖𝑚(𝑐, 𝑐′) – similarity of company 𝑐 and 𝑐′.
𝑞 ∈ 𝑄 – characteristics of level digitalization;
𝑟𝑐𝑞 – vector of digitalisation level for company 𝑐;
𝑟𝑐′𝑞 – vector of digitalisation level for company 𝑐′.
Formula (4) is the same that metric like centered cosine and gives more accurate
results than classical cosine metric. It takes into account not only positive value and zero,
but negative values in the cross-tabulation matrix.
This expert system structure ensures the collection and analysis of data on the digital
transformation of SMEs, the application of expert knowledge and AI to assess and provide
recommendations, as well as continuous training and adaptation of the system. The
modular architecture allows for flexible expansion and improvement of the system in the
future.
4. Results/Discussions
The proposed conceptual model of the online platform for expert assessment and
analysis of the digital transformation of SMEs in Ukraine provides a comprehensive
framework for supporting SMEs in their digital transformation journey. The key results
and points for discussion based on this study are as follows:
1. Adaptation to the Ukrainian context: The developed model takes into account the
specific challenges and needs of Ukrainian SMEs, such as varying levels of digital
readiness, legislative differences, and the impact of the ongoing war. This localized
approach is crucial for the effective implementation and adoption of the platform by
Ukrainian businesses.
2. Integration of expert knowledge and AI: The platform leverages both expert knowledge
and artificial intelligence techniques to provide accurate assessments and personalized
recommendations. The combination of expert rules and machine learning algorithms
enables the system to continuously learn and adapt to the evolving digital landscape
and the unique requirements of individual SMEs.
3. Modular architecture: The modular structure of the platform allows for flexibility and
scalability, facilitating future expansions and improvements. This is particularly
important given the rapid advancements in digital technologies and the changing needs
of SMEs. The ability to easily integrate new modules and features ensures the
platform's long-term relevance and value.
4. User-centric design: The platform emphasizes a user-friendly interface and intuitive
interaction, making it accessible to SMEs with varying levels of digital literacy. The
provision of educational materials and resources further supports SMEs in their digital
transformation efforts and helps bridge the knowledge gap.
Conclusion
The proposed conceptual model of the online platform for expert evaluation and
analysis of the digital transformation of SMEs in Ukraine has significant potential for
accelerating the digital transformation of Ukrainian SMEs. The platform offers a
comprehensive approach that takes into account Ukrainian-specific context, integrates
expert knowledge and AI technologies, has a modular architecture, and focuses on
usability.
Adapting to Ukrainian realities, including different levels of digital readiness of SMEs,
legislative differences, and the impact of the current war, is key to the effective
implementation and use of the platform by Ukrainian businesses. The combination of
expert rules and machine learning algorithms allows the system to continuously improve
and adapt to the changing digital landscape and the unique needs of individual SMEs.
The platform's modular structure allows for flexibility and scalability, facilitating future
expansion and improvement. This is especially important given the rapid development of
digital technologies and the changing needs of SMEs. The ability to easily integrate new
modules and features guarantees the long-term relevance and value of the platform.
The implementation of the proposed online platform can become a powerful catalyst
for the digital transformation of SMEs in Ukraine, providing them with the necessary tools,
recommendations and support for successful adaptation to the digital economy. This, in
turn, will contribute to increasing the competitiveness, efficiency and resilience of
Ukrainian SMEs both locally and internationally.
References
[1] Communication from the Commission to the European Parliament, the Council, the
European economic and social committee and the Committee of the regions. A Digital
Single Market Strategy for Europe, 2015. URL: https://eur-lex.europa.eu/legal-
content/EN/TXT/?uri=celex:52015DC0192.
[2] The OECD D4SME Initiative. URL: https://www.oecd.org/digital/sme/.
[3] The Digital Europe Programme. URL: https://digital-
strategy.ec.europa.eu/en/activities/digital-programme.
[4] Shaping Europe’s digital future. URL: https://digital-strategy.ec.europa.eu/en.
[5] Communication from the Commission to the European Parliament, the Council, the
European economic and social committee and the Committee of the regions. An SME
Strategy for a sustainable and digital Europe, 2020. URL: https://eur-
lex.europa.eu/legal-content/EN/TXT/?uri=COM:2020:103:FIN.
[6] Europe’s Digital Decade: digital targets for 2030. URL:
https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-
digital-age/europes-digital-decade-digital-targets-2030_en.
[7] SME digitalisation to “Build Back Better” – Digital for SMEs (D4SME) policy paper,
2021. URL: https://www.oecd.org/cfe/sme-digitalisation-to-build-back-better-
50193089-en.htm.
[8] European Digital Innovation Hubs Network. DMA Tool. URL: https://european-
digital-innovation-hubs.ec.europa.eu/dma-tool.
[9] About SME Compass, 2023. URL: https://mspkompas.rs/about-us/.
[10] Industry 4.0 Readiness. Online Self-Check for Businesses. URL:
https://www.industrie40-readiness.de/?lang=en.
[11] Industry 4.0 Competence meter. URL: https://www.i4eu-pro.eu/competence-meter/.
[12] The Industry 4.0 Maturity Index: 6 Levels Toward Digital Transformation. URL:
https://matics.live/blog/industry-4-0-maturity-index/.
[13] Schumacher A., Nemeth T., Sihn W., Roadmapping towards industrial digitalisation
based on an Industry 4.0 maturity model for manufacturing enterprises, Procedia
CIRP, 2019, Vol. 79, P. 409–414. URL: https://doi.org/10.1016/j.procir.2019.02.110.
[14] V. Aulin, O. Lyashuk, O. Pavlenko, D. Velykodnyi, A. Hrynkiv, S. Lysenko, et al.,
"Realization of the Logistic Approach in the International Cargo Delivery
System", COMMUNICATIONS, vol. 21, no. 2, pp. 3-12, 2019.
[15] Bierhold T., For a better understanding of Industry 4.0 – An Industry 4.0 maturity
model, 11 th IBA Bachelor Thesis Conference, Enschede, The Netherlands, 2018, P. 1–
22. URL: https://www.semanticscholar.org/paper/For-a-better-understanding-of-
Industry-4.0-An-4.0-Bierhold/2fb6a29a25c5f5f424d401cdcb85c5299822c8fd.
[16] Kljajić Borštnar M., Pucihar A., Multi-Attribute Assessment of Digital Maturity of SMEs,
Electronics, 2021, Vol. 10, no. 8, P. 885. doi:10.3390/electronics10080885
[17] Moroz M., The Level of Development of the Digital Economy in Poland and Selected
European Countries: A Comparative Analysis, Foundations of Management, 2017,
Vol. 9, no. 1, P. 175–190. doi:10.1515/fman-2017-0014.
[18] Zinchenko I.G., Lavdanska O.V., Modern technologies for assessing the effectiveness of
digitalization, Bulletin of Cherkasy State Technological University, 2022, No. 2, P. 34-
42. doi:10.24025/2306-4412.2.2022.263563.
[19] R. Brozzi, R. D. D’Amico, G. Pasetti Monizza, C. Marcher, M. Riedl & D. Matt, Design of
Self-assessment Tools to Measure Industry 4.0 Readiness. A Methodological Approach
for Craftsmanship SMEs, Product Lifecycle Management to Support Industry 4.0,
2018, P. 566–578. URL: https://link.springer.com/chapter/10.1007/978-3-030-
01614-2_52.
[20] Yavorskyi, A.V.; Karpash, M.O.; Zhovtulia, L.Y.; Poberezhny, L.Y.; Maruschak, P.O. Safe
operation of engineering structures in the oil and gas industry. J. Nat. Gas Sci.
Eng. 2017, 46, 289–295.
[21] Buketov, A.; Maruschak, P.; Sapronov, O.; Zinchenko, D.; Yatsyuk, V.; Panin, S.
Enhancing Performance Characteristics of Equipment of Sea and River Transport by
Using Epoxy Composites. Transport 2016, 31, 333–342.