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TYPE
Original Research
PAGE NO.
159-168
10.37547/tajet/Volume07Issue05-15
OPEN ACCESS
SUBMITED
25 March 2025
ACCEPTED
21 April 2025
PUBLISHED
23 May 2025
VOLUME
Vol.07 Issue 05 2025
CITATION
Dmitrii Pshichenko. (2025). Models for Adapting Business Strategies in
Manufacturing Enterprises Amid Digital Technology Integration. The
American Journal of Engineering and Technology, 7(05), 159
–
168.
https://doi.org/10.37547/tajet/Volume07Issue05-15.
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Models for Adapting
Business Strategies in
Manufacturing Enterprises
Amid Digital Technology
Integration
Dmitrii Pshichenko
Associate Professor, Department of Business Informatics, Higher School of
Business, National Research University Higher School of Economics
Moscow, Russia.
Abstract:
This article explores current approaches to
the digital transformation of business strategies in
manufacturing enterprises, identifying the core
prerequisites and influencing factors for successful
adaptation in the context of Industry 4.0. The study
provides a comprehensive review of discrete maturity
models, platform-based and hybrid approaches,
incorporating BIM frameworks and interregional
partnerships. Six key catalysts of digital transformation
are identified: the predominance of information
exchange, the acceleration of communication
processes, the restructuring of organizational models,
the rise of enabling technologies (IoT, Big Data, AI),
evolving
competency
requirements,
and
the
emergence of digital ecosystems. A unified matrix of
digital tools is presented, including IoT, Big Data, AI,
robotics, ERP/MES/PLM systems, and 3D printing. The
article also outlines organizational and managerial
mechanisms for implementation, covering agile-based
structures, digital functional domains, and project
financing models. The insights presented will be of
interest to researchers in strategic management and
digital transformation, particularly those focused on
the theoretical justification and empirical validation of
adaptive business models within Industry 4.0
manufacturing
environments.
Additionally,
the
approaches discussed may prove valuable to industrial
enterprise executives, digital integration consultants,
and government experts involved in shaping regulatory
frameworks that promote digitization in the
manufacturing sector.
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Keywords:
digital business transformation, digital
maturity, Industry 4.0, business strategy, manufacturing
enterprises, IoT, big data, ERP, agile, ecosystem.
Introduction:
The relevance of this topic lies in the fact
that, in the era of the Fourth Industrial Revolution,
digital change has evolved beyond isolated IT solutions
and now requires a full-scale rethinking of strategic
management
in
manufacturing
enterprises.
Accelerated information exchange, increasing demands
for agile decision-making, and intensified global
competition have significantly elevated the need to
integrate digital technologies at the level of business
models and management processes [1, 2].
Academic studies on the adaptation of business
strategies in manufacturing organizations under digital
integration fall into several major thematic clusters,
each reflecting distinct methodological and subject-
oriented approaches.
First, within the theoretical foundations of digital
transformation, key emphasis is placed on the concept
of dynamic capabilities and the alignment of digital
strategy with the organization’s overall
direction.
Canhoto A. I. et al. [3] emphasize the importance for
small and medium-sized enterprises (SMEs) to develop
flexible digital capabilities that can rapidly respond to
evolving market and technological demands. Ghosh S.
et al. [6] analyze the mechanisms by which enterprises
cultivate “digital dynamic capabilities” that drive
transformation both at the strategic and operational
levels. Shen L., Zhang X., and Liu H. [4], drawing on the
textile sector, show that the impact of digital
technologies on transformation outcomes depends
directly on “digital innovation orientation,” which
moderates the link between digital capabilities and
performance results. Machado C. G. et al. [5] explore
organizational readiness and demonstrate that the
maturity of digital initiatives is heavily influenced by
cultural
and
institutional
conditions
within
manufacturing companies, placing limits on the speed
and depth of transformation.
Second, considerable attention is paid to business
model innovation and its evolution under digital
pressure. Zheng L. J. et al. [1], focusing on SMEs,
propose a step-by-step model for integrating digital
tools, with key elements including environmental risk
assessment, data collection setup, digital supply chain
formation, and continuous refinement of the business
model. Favoretto C. et al. [2] note that the shift from
traditional to digital business models in manufacturing
passes through a stage of rethinking the value
proposition, which imposes new demands on process
architecture and customer relationship systems.
Finally, a third group of studies focuses on the strategic
aspects of digital transformation in international and
logistics contexts. Meyer K. E. et al. [7] analyze
“international business in the digital age,” where digital
platforms and national institutional environments
create a paradox: global strategies must account for
local regulatory and cultural distinctions, complicating
the creation of a unified digital business model.
Shevchenko D. A. et al. [8] illustrate how integrating
intelligent logistics systems accelerates industrial
restructuring by acting as a bridge between digital
ecosystems and physical manufacturing processes.
Kagermann H. [9] examines a cyber-physical systems
integration model that highlights the interplay between
digital services and traditional manufacturing
processes. He argues that moving to a hybrid
infrastructure demands a rethink not only of
operational workflows but also of management
practices, in order to secure a sustainable competitive
edge through the synergy of data and hardware.
Ajayi M. O. and Laseinde O. T. [10] adapt Porter’s value
-
chain framework to pinpoint both opportunities and
shortcomings in digital-technology adoption. They
show how each of the five primary activities
—
and all
four support activities
—
can incorporate digital
solutions, ranging from automated inventory-
management systems to analytics platforms for
demand forecasting.
Finally, several online sources survey digital-
transformation trends and tools at the intersection of
manufac
turing and sustainability. The “Top Digital
Transformation
Trends
Shaping
Sustainable
Manufacturing
in
2024”
report
[11]
on
sustainablemanufacturingexpo focuses on “green”
digital initiatives, particularly digital twins and energy-
management
platforms.
A
Bosch
Software
Technologies post [12] on the company’s official
website explores manufacturers’ environmental
responsibility through the Industry 4.0 lens,
emphasizing cloud services and IoT infrastructure as
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keys to transparency and carbon-footprint reduction.
Despite the complementarity of these approaches, the
literature reveals a number of contradictions. Some
authors place dynamic capabilities at the core of
transformation, while others rely on maturity metrics
—
leading to differing interpretations of what drives
successful change. Furthermore, models describing the
evolution of business models often overlook
institutional and cultural barriers, which are extensively
documented in other works. Several studies focus
heavily on technological and strategic elements but
provide insufficient coverage of human factors, such as
change management and employee resistance, as well
as issues of cybersecurity in large-scale digitization.
There is also a lack of longitudinal empirical research
into the long-term effectiveness of digital initiatives
and their impact on business model resilience.
Moreover, interactions between traditional industrial
processes and new digital platforms and ecosystems at
the supply chain level remain underexplored.
In this context, promising directions for future research
include the integration of dynamic capabilities and
maturity frameworks, along with a more holistic
consideration of institutional, human, and cyber-
physical factors in adapting business strategies during
digital transformation.
The aim of this article is to analyze existing models for
adapting business strategies in manufacturing
enterprises during digital technology integration, taking
into account maturity dimensions such as technological
infrastructure, process transformation, organizational
structure, and financial and human resources.
The scientific contribution lies in the systematic
synthesis of discrete maturity models, platform-based
and hybrid approaches
—
including BIM integration and
interregional
partnerships
—
alongside
the
development of a unified matrix of digital tools and the
description
of
organizational
and
managerial
mechanisms for their implementation in Industry 4.0
manufacturing contexts.
The working hypothesis is that applying a comparative
methodology to existing research on strategic
adaptation under digital integration will reliably
identify their strengths and limitations, synthesize best
practices, and thus support the development of a
unified framework for transforming management
strategies in the context of Industry 4.0.
In this study, the author conducted a qualitative
synthesis of six principal adaptation models based on
criteria such as maturity dimensions (technological
infrastructure, process transformation) and the degree
of platform integration (BIM frameworks, digital
ecosystems). The analytical procedure employed a
rigorous comparative content analysis and thematic
aggregation of model components, systematically
mapping
core
technological
instruments
and
organizational mechanisms to construct an integrated
framework for strategic adaptation in Industry 4.0
manufacturing contexts.
The study’s methodology is based on a comparative
analysis of existing research in the field.
A corpus of twelve sources
—comprising peer‐reviewed
studies and publicly available case reports of modern‐
tool implementations in manufacturing firms
—
served
as the study’s sample. From these works, three
principal adaptation‐strategy models were extracted:
discrete
maturity
frameworks,
platform‐centric
approaches and hybrid schemes.
The models were compared according to:
1.
Maturity
levels
(number
and
descriptive scope)
2.
Core
dimensions
—
technology
infrastructure, process transformation, organizational
structure and financial
–human‐resource assets
3.
Application context (specific industries
and geographic regions)
4.
Degree of BIM and platform‐solution
integration
Analysis
followed
a
systematic
comparative‐
classification method. In the first phase, models were
grouped by type and primary dimensions. Next, their
constituent elements
—
IoT, Big Data, AI, robotics,
ERP/MES/PLM systems and other digital enablers
—
were catalogued alongside organizational mechanisms
such as agile governance structures, ROI/TCO
evaluation
framewo
rks,
workforce‐development
programs and grant‐funding or change‐management
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processes.
To synthesize findings, content‐analysis techniques and
narrative summaries were employed, allowing the
identification of both common patterns and
substantive divergences
across the adaptation‐strategy
landscape.
1. Preconditions and Drivers of Digital Transformation
in Business Strategy
Digital transformation in manufacturing began with the
implementation of standalone IT solutions and the
automation of narrow operational tasks. However, with
the rise of the Fourth Industrial Revolution (Industry
4.0), the very nature of business models has shifted.
Industry 4.0 is understood as the integration of cyber-
physical systems, the Internet of Things (IoT), and
artificial intelligence into production environments
—
enhancing the flexibility and adaptability of enterprises
[1, 7].
The table 1 below outlines the key factors that
stimulate companies to revise their strategies under
the influence of digital transformation.
Table 1: Factors stimulating enterprises to revise strategies under the influence of digital transformation [1, 7,
9]
Factor
Description
Dominance
of
information
exchange
Intellectual capital and data as core strategic resources
Speed
and
volume
of
communication
Real-time transfer of large data volumes via digital networks
Organizational
structure
transformation
Shift toward agile, networked structures and integrated platform
solutions
Emergence of new technologies
Big Data, AI, blockchain, virtualization, digital twins
Changing
competency
requirements
Demand for digital literacy, analytical thinking, and engineering
skills
Formation
of
digital
social
ecosystems
Partner networks and platforms linking all actors in the value
chain
As shown in Table 1, the primary factors driving
companies to overhaul their strategies in the wake of
digital transformation are, first, the ascendancy of
information exchange
—
where intellectual capital and
data have become the organization’s chief strategic
assets, and communications over digital networks
occur in real time
—
and second, the shift in
organizational structures toward agile, networked
teams and integrated platform-based solutions.
In parallel with these digital transformation "catalysts,"
enterprises face a number of constraints, including:
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●
Slow adaptation of internal processes.
Many companies struggle to rethink entrenched
business models and encounter resistance to change at
both management and operational levels [2].
●
Financial limitations and investment
risks. Implementing IT platforms and upgrading
equipment requires significant capital; underfunding
can hinder digital transformation, especially for SMEs
[6].
●
Technological and infrastructure risks.
The unreliability or incompatibility of emerging
technologies and challenges in ensuring cybersecurity
pose substantial threats to operational continuity.
●
Human
resource
constraints.
A
shortage of qualified specialists and the difficulty of
workforce reskilling delay the deployment of integrated
digital solutions [5].
●
Regulatory and institutional barriers.
Issues such as standardization, legal frameworks, and
platform interoperability may limit access to
international markets [7].
Thus, successful digital transformation depends
on achieving a balance between technological
advancement and comprehensive risk management
—
an essential condition for revisiting and adapting
business strategies in manufacturing enterprises.
2. Review and Classification of Strategy Adaptation
Models
Three principal classes of models are commonly
distinguished in the context of digital strategy
adaptation: discrete maturity models, platform
(ecosystem-based) approaches, and hybrid or industry-
specific frameworks.
Discrete maturity models describe the stages of digital
maturity an organization passes through and are
primarily used to assess readiness and identify strategic
development directions.
Platform models, the second category, emphasize the
creation or participation in digital platforms as a core
driver of strategic transformation.
Finally, hybrid models combine maturity-based and
platform-based approaches while incorporating sector-
specific characteristics and requirements.
The table 2 below provides a classification of business
strategy
adaptation
models
applicable
to
manufacturing enterprises.
Table 2: Classification of Business Strategy Adaptation Models for Use in Manufacturing Enterprises [3, 7, 8]
Model
Maturity
Levels
Key Dimensions
Application
Domain
Digital Maturity
3
(low,
medium,
high)
Organizational readiness, competencies,
infrastructure, data
Manufacturing
enterprises
Adoption Maturity
Model
3 groups / 8
indicators
Strategy,
integration,
infrastructure,
analytics, adoption
Industrial firms
(Italy, Canada)
Industry
4.0
Readiness Model
5
Culture, technology, processes, strategy,
governance
Manufacturing
sector
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Model
Maturity
Levels
Key Dimensions
Application
Domain
Four-Stage
BIM-
Integrated Model
4
ICT, communications, IoT, data, business
model (incl. BIM)
Construction
industry
Digital Ecosystem
–
Omnichannel architecture, modularity,
partnerships, data governance
General
business
Digital
Transformation
Playbook
–
Customer centricity, agile frameworks,
innovative product/service design
General
business
As Table 2 shows, all business-strategy adaptation
models aim to incrementally build digital capabilities
across key dimensions
—
from organizational readiness
and infrastructure to processes, governance and
analytics
—
while targeting specific sectors (machinery,
construction and general business). Some frameworks
define a precise numerical maturity scale (three to five
levels or eight indicators), whereas others present
conceptual guidelines without fixed stages, highlighting
the flexibility of digital-transformation approaches.
While discrete models help structure the stages of
digital progression, they are often generalized and
require industry-specific calibration. Platform-based
approaches focus on ecosystems and partnerships,
which are essential for global competitiveness but less
effective for managing operational processes on the
shop floor. Hybrid and industry-specific models
—
such
as those that integrate BIM
—
offer a synthesis of both
perspectives and account for the practical realities of
industrial sectors.
For manufacturing enterprises, an optimal approach
involves combining diagnostic maturity models with
platform elements and tailoring them to the specific
industry context. This integrated methodology forms
the foundation for the author’s model presented in the
following section.
3. Tools and Practical Mechanisms for Implementing
Digital Technologies in Manufacturing Enterprises
3.1 Technological instruments
This section presents an original modular framework
for adapting business strategies in the context of
manufacturing transformation. The model is based on
a synthesis of contemporary research findings [1, 3; 4
–
6] and integrates a comprehensive set of technological,
organizational,
financial,
and
human-capital
instruments.
Effective digital transformation in manufacturing
enterprises requires not only a robust technological
foundation but also well-structured organizational
mechanisms and sustainable financial and talent
support. The digital toolkit consists of a range of
complementary technologies, each addressing specific
tasks related to production and management:
●
Internet of Things (IoT) and Cyber-
Physical Systems (CPS): These technologies enable real-
time data collection and transmission via smart sensors
and devices, supporting predictive maintenance and
dynamic resource management. For example, General
Electric deployed IoT sensors and AI-driven analytics
across its manufacturing plants, reducing unplanned
downtime by 20 % and boosting overall equipment
effectiveness by 5 % [11].
●
Big Data Analytics and Data Lake
Platforms: Provide the ability to store and process large
volumes of structured and unstructured data, helping
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uncover hidden patterns and optimize business
processes.
●
Cloud Computing (IaaS/PaaS/SaaS):
Offers scalable computing resources, reduces capital
expenditures, and enables instant access to services
from any location [3].
●
Artificial Intelligence and Machine
Learning: Support intelligent automation of quality
control, failure prediction, and managerial decision-
making. Bosch provides a notable example: by
deploying an Industry 4.0 framework that integrates AI,
IoT and analytics platforms, the company achieved a 10
% uplift in process efficiency and a 10 % increase in
throughput, while saving up to €0.5 million per year on
a single production line. At the same time, eleven
factories
—
covering some 5,000 machines
—
were
interconnected into a unified network, enabling
seamless connectivity and real-time data exchange
[12].
●
Robotics and Smart Automation:
Incorporates industrial and collaborative robots to
increase productivity and workplace safety [6].
●
Digital Management Platforms (ERP,
MES, SCM, CRM, PLM): Integrate key business
processes
—
from production planning to customer
interaction and full product lifecycle management.
●
Additive Manufacturing (3D Printing):
Enables direct production of complex components
from digital models, reducing time and cost of
prototyping [1, 4].
3.2 Organizational mechanisms
Organizational mechanisms in the context of
digital transformation at manufacturing enterprises
form an integrated system of formal and informal
structures, processes and cultural practices that
synchronize strategic objectives with agile operational
execution. These mechanisms encompass the
establishment of Centres of Excellence and digital
laboratories for piloting innovations, the formalization
of roles such as Chief Digital Officer and Change Agents
to steer transformation, the creation of end-to-end
Scrum and Kanban teams that foster cross-functional
collaboration, and the launch of corporate accelerators
and hackathons to stimulate bottom-up innovation.
Process-oriented tools include a digital PMO employing
hybrid Water-Scrum-Fall methodologies, a continuous-
learning environment delivered through LMS platforms
and gamified training, and adaptive KPIs within an
Extended Balanced Scorecard (Balanced Scorecard 4.0)
that track not only financial outcomes but also digital
maturity and innovation activity. Together, these
elements comprise a change-management architecture
that continuously recalibrates internal resources,
accelerates decision-making and secures sustainable
competitive advantage in the Industry 4.0 era.
Figure 1 outlines the organizational and managerial mechanisms for strategic adaptation in manufacturing
enterprises.
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Figure 1. Organizational and managerial mechanisms for adapting business strategies in manufacturing
enterprises [1, 3, 4, 6]
The success of digital transformation also depends
heavily on ensuring stable financial support and
developing relevant competencies. Key elements
include:
●
Investment Evaluation and ROI:
Calculating total cost of ownership (TCO) and net
present value (NPV) for each digital initiative at the
business case stage is essential for substantiating
expenses.
●
Grant and Incentive Mechanisms:
Public programs and dedicated funds (e.g., the "Digital
Economy" fund) can partially cover CAPEX and
encourage the adoption of domestic technological
solutions.
●
Development
of
Digital
Skills:
Continuous training systems, retraining programs, and
partnerships with universities help ensure a qualified
workforce capable of managing and advancing digital
tools.
●
Change Management: Encompasses
internal
communication,
training,
KPIs,
and
motivational frameworks designed to reduce resistance
and accelerate the adoption of new technologies [5,
10].
The strategic adaptation model includes five
interrelated blocks: 1) Assessment Module
–
Focuses
on digital maturity and strategic diagnostics, identifying
bottlenecks and prioritizing the implementation of
IoT/CPS, Big Data/Data Lakes, and cloud services. 2)
Organizational and
managerial mechanisms,
adaptation of business
strategies in
manufacturing enterprises.
Digital functional areas
Digital human resources
management: platforms
for e-learning and
competence management
Digital marketing and
CRM: automated
segmentation, chatbots
and predictive analytics
systems
Digital finance: e-
invoicing systems,
algorithmic cash-flow
forecasting
Digital logistics:
intelligent warehouses and
real-time tracking of
shipments
Flexible organizational
structures and agile
management
The transition from rigid
hierarchies to cross-
functional agile teams
accelerates innovation and
increases business
adaptability.
Platform-based
knowledge management
The introduction of
internal digital
"laboratories" and
knowledge-sharing
platforms accelerates the
dissemination of best
Partner integration and
ecosystem interaction
Collaboration with
suppliers and customers
within a single platform
increases the response
rate and product quality.
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Technology Module
–
Covers the integration of AI/ML,
robotics, and additive technologies to enable intelligent
quality control, predictive maintenance, and agile
prototyping. 3) Platform Module
–
Implements digital
platforms (ERP, MES, SCM, CRM, PLM) to form cohesive
product lifecycle management chains. 4) Organizational
and
Managerial
Module
–
Includes
change
management
frameworks,
communication
and
motivation systems, and educational partnerships
aimed at developing digital competencies [5]. 5)
Resource Module
–
Ensures financial and investment
support through ROI, TCO, and NPV calculations,
alignment of grant and incentive mechanisms, and KPI
monitoring for implementation.
Inter-module connections and feedback loops allow for
real-time adjustment of strategic priorities, ensuring
resilient and adaptive digital transformation within the
enterprise.
In sum, the combination of advanced technologies,
flexible organizational mechanisms, and carefully
structured resource support forms a solid foundation
for the effective digital transformation of business
strategies in the manufacturing sector.
CONCLUSION
In today’s environment of gl
obal competition and rapid
technological advancement, digital transformation has
become an essential component of strategic
development for manufacturing enterprises. The
review conducted confirms that while traditional
strategy models offer clear frameworks for assessing
digital maturity, they often require industry-specific
adaptation. In contrast, platform- and ecosystem-
based approaches enable companies to integrate
partners and customers into a shared digital
environment. Hybrid models that incorporate BIM
integration
and
interregional
partnerships
demonstrate higher practical relevance for the
manufacturing sector.
The analysis of digital transformation drivers identified
six key catalysts: the dominance of information flows
over physical ones, accelerated communication, the
shift toward flexible organizational structures, the
active implementation of IoT, Big Data, and AI, evolving
competency profiles, and the emergence of digital
ecosystems. The study shows that successful
deployment of digital tools (IoT platforms, Big Data
analytics, cloud services, robotics, ERP/MES/PLM
systems) requires not only technical infrastructure but
also new managerial mechanisms, including agile
teams, digital functional zones, knowledge-sharing
platforms, and balanced project financing models.
Building on these findings, the study proposes an
integrated methodology for adapting business
strategies
—
combining a discrete assessment of
maturity across four dimensions (technology,
processes, structure, resources) with a roadmap for
implementing digital tools and organizational practices.
This framework enables manufacturers to structure
their transformation processes in stages, mitigate risks,
and maximize the synergistic impact of digitalization.
The practical value of the research lies in the
applicability of the proposed model for strategic
planning and phased implementation of digital
innovation by manufacturing managers. It also provides
a foundation for justifying investment decisions and
evaluating the effectiveness of transformation
initiatives. Future research should focus on empirically
validating the model and expanding it to account for
sector-specific characteristics and regulatory contexts.
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