Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22

Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925

Volumen 8 (4): e418. Octubre-Diciembre, 2025. https://doi.org/10.37636/recit.v8n4e418

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Case studies

Analysis of the main factors involved in the 4.0 industry

implementation process

Análisis de los principales factores que intervienen en el proceso de implementación de

la Industria 4.0

Manuel Baro1, José Manuel Villegas Izaguirre2 Manuel Piña Monarrez1, Cinthia Judith

Valdiviezo1

1Universidad Autónoma de Ciudad Juárez, Manuel Díaz H. No. 518-B Zona Pronaf Condominio, 32315 Juárez,

Chihuahua, México

2Facultad de Ciencias de la Ingeniería y Tecnología, Universidad Autónoma de Baja California, Blvd.

Universitario 1000, Unidad Valle de Las Palmas, 22260 Tijuana, Baja California, México.

Autor de correspondencia: Manuel Baro, Universidad Autónoma de Ciudad Juárez, Manuel Díaz H. No. 518-B Zona

Pronaf Condominio, 32315 Juárez, Chihuahua, México, Correo: mbaro@itsncg.edu.mx, ORCID: 0000-0003-1665-8379.

Received: May 7. 2025 Accepted: September 7, 2025 Published: October 31, 2025

Abstract. - Industry 4.0 is considered the 4th industrial revolution; this term refers to technology and digital

technologies, such as Enterprise Resource Planning, the use of the internet in all kinds of devices, etc. This study

analyses the significant factors driving the successful used of I4.0 in industries, focusing on technological,

organizational, and human aspects. Technological enablers encompass automation, real-time data exchange, and

smart manufacturing systems, while the organizational factors include digital transformation strategies, leadership

commitment, and supply chain integration. Finally, human factors encompass workforce upskilling, change

management, and employee adaptability. The study also examines the right use of I4.0 in factories, including cyber

risks, high costs, and interoperability issues. A review of literature is undertaken, highlighting the critical role of

these factors in optimizing productivity and efficiency in Revolution 4.0. The findings suggest that a holistic

approach combining advanced technologies with strategic planning and workforce development is essential for

successful adoption. This manuscript presents valuable ideas for industries seeking to transition into the fourth

industrial revolution and provides recommendations for overcoming barriers and leveraging opportunities in the

digital era.

Keywords: 4th Industry; Transformation; IoT; Smart manufacturing; Mean factor; Cybersecurity.

Resumen. - La Industria 4.0 se considera la 4ª revolución industrial; este término se refiere a la tecnología y las

tecnologías digitales, como la planificación de recursos empresariales, el uso de Internet en todo tipo de

dispositivos, etc. Este estudio analiza los factores significativos que impulsan el éxito del buen uso de la Industria

4.0 en las industrias, centrándose en los aspectos tecnológicos, organizativos y humanos. Los habilitadores

tecnológicos abarcan la automatización, el intercambio de datos en tiempo real y los sistemas de fabricación

inteligentes, mientras que los factores organizativos incluyen las estrategias de transformación digital, el

compromiso del liderazgo y la integración de la cadena de suministro. Por último, los factores humanos abarcan

la mejora de las cualificaciones de la mano de obra, la gestión del cambio y la adaptabilidad de los empleados. El

estudio también examina el uso correcto de la Industria 4.0 en las fábricas, incluidos los riesgos cibernéticos, los

elevados costes y los problemas de interoperabilidad. Se realiza una revisión de la literatura, destacando el papel

crítico de estos factores en la optimización de la productividad y la eficiencia en la revolución 4.0. Las conclusiones

sugieren que un enfoque holístico que combine las tecnologías avanzadas con la planificación estratégica y el

desarrollo de la mano de obra es esencial para el éxito de la adopción. Este manuscrito presenta ideas valiosas

para las industrias que aspiran a la transición a la industria 4.0 y ofrece recomendaciones para superar los

obstáculos y aprovechar las oportunidades de la era digital.

Palabras clave: Industria 4.0; Transformación; Internet de las cosas; Manufactura inteligente; Factor promedio; Ciber

Seguridad.

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1. Introduction

The Background and Evolution of Industry 4.0

The industrial world has changed over the years,

marked by distinct industrial revolutions that

have redefined production processes and

economic structures [1]. The contemporary era is

characterised by the Fourth Industrial

Revolution, also known as Industry 4.0, which is

driven by the convergence of advanced digital,

physical, and biological systems. In Germany, in

2011, the term Industry 4.0 was first used to

denote the applications of process and operation.

It employs a range of advanced technologies,

including the Internet of Things (IoT), Artificial

Intelligence (AI), Big Data, Robotics, Cyber-

Physical Systems (CPS), and Cloud Computing,

to facilitate smart, interconnected, and

autonomous production environments. Industry

4.0 represents the union of all these technologies

in all processes, resulting in the concept of smart

factories, predictive maintenance, and decision

making [2].

Implementing Industry 4.0 is crucial for

industrial entities to sustain competitiveness in

an era of accelerating digitalization and

globalization. Key benefits include. Enhanced

Efficiency and Productivity. This paradigm's

automation and real-time data analytics optimise

production processes, thereby reducing

downtime and waste [3].

Customization and Flexibility: Smart

manufacturing facilitates mass customization,

thereby ensuring efficient meeting of diverse

consumer demands [4]. Cost reduction.

Predictive maintenance and energy-efficient

systems contribute to reduced operational costs.

Improved logistics. The Internet (IoT) and

blockchain processes is a very significant role in

this paradigm shift by facilitating the exchange

of data and information seamlessly [5]. The

concept of sustainability is of paramount

importance in this context. The integration of

smart technologies has been demonstrated to

contribute to a reduction in resource

consumption and carbon footprints. However,

the use of industry 4.0's multifaceted process

necessitates the consideration of numerous

factors, including technological, organisational,

and human elements, to ensure its effective

implementation [6]. The significant variables in

the implementation of industry 4.0 are:

Technological factors. In the context of Industry

4.0, the Internet of Things facilitates machine-to-

machine communication, remote monitoring,

and predictive maintenance. Big data and

analytics are essential in data processing to make

better decisions, predict failures, optimize

logistics, and enhance quality control. Machine

learning algorithms facilitate adaptive

manufacturing systems that learn from

operational data [7]. Cyber-physical systems.

The cyber system used processes digital control,

integrates physical processes with digital

controls, making possible autonomous

operations. Examples include smart robots,

digital twins, and automated guided vehicles

(AGVs) [8]. Organizational Factors. Digital

Transformation Strategy: Companies need to

develop a clear roadmap for digital adoption,

aligning technology with business goals [9].

Leadership and Management Commitment.

Successful Industry 4.0 implementation is

contingent on commitment from leadership and

management. Investment in Infrastructure. The

process of upgrading legacy systems to smart

factories necessitates substantial capital

investment in IoT, AI, and automation

technologies.

Interoperability and Standardisation. Ensuring

seamless communication between disparate

systems (machines, software, and platforms) is of

paramount importance. Standardised protocols

(e.g., OPC UA) facilitate integration.

Cybersecurity measures. With increased

connectivity, industries face more cyber threats.

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Robust security frameworks are needed to

protect data [10]. Human factors. Workforce

Upskilling and Reskilling. The advent of

Industry 4.0 has rendered a new skill set

imperative. Employees must be trained in data

analytics, AI, robotics, and IoT to operate in

smart factories [11]. Change Management:

Resistance to digital transformation is common.

Organisations must foster a culture of innovation

and adaptability. Collaboration Between

Humans and Machines. Collaborative robots

work with humans, Productivity, and safety [12].

Adopting Industry 4.0 Despite the many values

of Industry 4.0, there are many things to do

before adopting it. Big Implementation Costs:

The financial demands of the initial investment

may prove challenging for SMEs. Data privacy

increased connectivity raises cybersecurity

threats. The absence of a skilled workforce with

professionals specializing in AI, IoT, and data

science is a significant impediment [13].

Finally, integration with legacy systems remains

problematic, as many industries continue to rely

on outdated machinery, which complicates the

process of digital upgrades [14].

2. Methodology

Industry 4.0 signifies integrating advanced

digital technologies to transform manufacturing

and industrial processes. This study focuses on

identifying and analyzing the primary factors

driving Industry 4.0, including cyber-physical

systems, the Internet of Things, artificial

intelligence, big data analytics, cloud computing,

additive manufacturing, and Augmented

Reality/Virtual Reality (AR/VR). A mixed-

method approach is employed in this study,

combining qualitative and quantitative

techniques to assess the impact of these

technologies on industrial efficiency[15].

Research Design The study adopts an

exploratory and descriptive Research design,

examining key Industry 4.0 components.

The methodology is structured into three phases:

1. Qualitative Research

Literature Review: Analysis from peer-reviewed

journals. Case studies: Examination of effective

Industry 4.0 implementations in leading

companies (e.g., Siemens, Tesla, Foxconn) [16].

Expert Interviews: These interviews provide

insights from industry experts on the

implementation challenges and opportunities of

Industry 4.0.

2. Quantitative Research Survey

Methodology:

A structured questionnaire was distributed to 150

professionals (engineers, managers, IT

specialists) in smart manufacturing [17]. Data

Analytics: Statistical evaluation of Industry 4.0

adoption rates and performance improvements.

Mixed-Method Integration Triangulation: Cross-

validation of qualitative and quantitative findings

for robust conclusions. SWOT Analysis:

Evaluation of strengths, weaknesses,

opportunities, and threats in Industry 4.0

implementation.

3. Data Collection Methods: Primary Data

Collection

Surveys Target Respondents: Engineers, plant

managers, IT specialists, and Industry 4.0

consultants.

The sample size will be professionals from the

automotive, aerospace, electronics, and

healthcare sectors.

Survey Instrument [18]. The survey instrument is

a structured questionnaire with Likert-scale

questions assessing [19]. Adoption levels of IoT,

AI, and Big Data. Perceived benefits (e.g.,

efficiency, cost reduction, quality improvement.

Challenges (cybersecurity, high costs, workforce

readiness). Semi-Structured Interviews:

Conducted with industry experts [20]. Key Focus

Areas: Technological barriers in Industry 4.0.

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Best practices for successful digital

transformation. Future trends in smart

manufacturing.

Field Observations Smart Factory Visits: The

following three elements were observed on-site:

- Internet of Things (IoT)-enabled production

lines - Artificial intelligence (AI)-driven quality

control - Robotic automation. Data Logging:

Real-time monitoring of Industry 4.0 systems in

operational environments [21].

Secondary Data Collection Academic Sources:

Research papers from IEEE Xplore,

ScienceDirect, Springer, and Scopus. Industry

Reports: Publications from McKinsey, PwC,

Deloitte, and the World Economic Forum [22].

Government and policy documents: The Chinese

"Made in China 2025" strategy is furthermore

included.

In addition, the guidelines established by the U.S.

Smart Manufacturing Leadership Coalition

(SMLC) should be taken into consideration.

Sampling Techniques Stratified Sampling: This

technique is employed to ensure representation

from different industrial sectors [23]. Purposive

Sampling: Selecting respondents with direct

Industry 4.0 experience. Snowball sampling:

Referrals from industry experts to identify

additional participants.

Data Analysis Techniques. Qualitative Data

Analysis: Thematic Analysis: The identification

of recurring themes from interviews and case

studies. Content Analysis: Systematic evaluation

of literature and policy documents. Quantitative.

The following data analysis utilizes descriptive

statistics to address the following research

question: The mean, median, and standard

deviation for survey responses? Correlation

Analysis: The relationship between Industry 4.0

adoption and operational efficiency. Regression

analysis will be employed to examine the impact

of artificial intelligence (AI) and the Internet of

Things (IoT) on production performance.

5.3 Software Tools for Analysis Statistical

Tools: SPSS, R, and Python [24].

Data Visualization: Tableau and Power BI [25].

In addition, the following software tools are

utilized for AI and simulation: MATLAB,

Simulink, and TensorFlow [26].

2. Key Factors for the Successful

Implementation of Industry 4.0 in Businesses

The transformation to Industry 4.0 represents a

radical change in the way companies operate,

produce and compete in the global marketplace.

Its successful implementation depends on

multiple interrelated factors that encompass

technological, organizational, human and

regulatory aspects [27]. The following are the

main elements that industries must consider to

adopt this paradigm effectively [28].

1. Technology Infrastructure and Advanced

Connectivity The core of Industry 4.0 lies in the

ability to connect machines, systems, and people

through digital technologies. For this, it is

essential to have a robust infrastructure that

includes: IoT (Internet of Things) devices,

intelligent sensors, and actuators that collect real-

time data from production processes [29].

High Speed Communication Networks:

Technologies such as 5G, Wi-Fi 6, and fiber

optics enable fast, latency-free data transmission,

essential for critical applications.

Edge Computing: Data processing close to the

source to reduce dependence on the cloud and

improve response speed.

2. Intelligent Automation and Advanced

Robotics. Traditional automation is evolving

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towards more flexible and adaptive systems

thanks to:

Collaborative robots (Cobots): machines that

work alongside humans, improving productivity

and safety [30]. Cyber-Physical Systems (CPS):

Integration of software and hardware to control

physical processes autonomously [31]. Digital

Twins: Virtual replicas of equipment and

processes that allow simulating and optimizing

operations before implementing them in the real

world [32].

3. Big Data and Advanced Analytics. Industry

4.0 generates huge volumes of data that must be

processed to extract value [33]. This requires:

Data management platforms (such as Hadoop or

Spark). Predictive and prescriptive analytics: Use

of algorithms to anticipate failures and

recommend actions [34]. Real-time dashboards:

Visualization of KPIs for agile decision making

[35].

4. Artificial Intelligence (AI) and Machine

Learning. AI allows machines to learn from data

and make autonomous decisions, optimizing:

Predictive maintenance [36]: Early detection of

machinery failures [37]. Supply chain

optimization: Intelligent inventory and logistics

management. Mass customization: Production

adaptable to specific customer demands [38].

5. Cloud Computing and Scalable Systems. The

cloud provides flexibility and global access to

data, facilitating [39]. Unlimited and secure

storage and software-as-a-service deployment

for industrial applications. Remote collaboration

between multidisciplinary teams [40].

6. Industrial Cybersecurity. With increased

connectivity, the risks of cyberattacks increase,

requiring Firewalls and an intrusion detection

system [41].

7. Interoperability and Open Standards. For

different systems to work in harmony, protocols

such as: OPC UA (Open Platform

Communications Unified Architecture) [42].

MQTT for machine-to-machine (M2M)

communication [43]. Open APIs that allow

integration between ERP, MES and SCADA

[44].

8. Human Talent and Continuous Training. The

adoption of new technologies requires:

Upskilling and reskilling programs for workers

[45]. Training in digital skills (programming,

data analysis, AI management). Culture of

continuous learning and adaptability [46].

9. Leadership and Organizational Culture. The

success of Industry 4.0 depends on: Top

management commitment to digital

transformation [47]. Encouragement of

innovation and tolerance for controlled failure

[48]. Agile organizational structures that allow

for rapid change [49].

10. Collaboration between Business,

Government and Academia. Cooperation

between sectors drives [50]: Public policies that

encourage digitalization [51]. Alliances with

universities to develop specialized talent.

11. Sustainability and Energy Efficiency.

Industry 4.0 contributes to the circular economy

by: Optimization of energy consumption with

smart grids [52]. Reduction of waste through lean

production. Use of recyclable materials and eco-

efficient processes [53].

12. Financial Investment and Return on

Investment. The transition requires significant

resources, so companies must: Evaluate costs vs.

benefits in the medium and long term [54]. Seek

public-private financing. Start with scalable

pilots before migrating the entire operation [55].

13. Legal and Regulatory Framework.

Regulations must evolve to cover aspects such

as: Industrial data protection. Standardization of

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security protocols [56]. Legal liability in

autonomous systems [55].

As can be seen, the implementation of Industry

4.0 is not a single process, but a strategic journey

that requires planning, investment, and

continuous adaptation [57]. Companies that

manage to integrate these factors in a balanced

way will be able to achieve higher levels of

efficiency, competitiveness, and innovation,

positioning themselves as leaders in the era of

industrial digitalization [58]. However, the

biggest challenge is not technological, but

cultural: it involves a change of mindset at all

organizational levels to embrace the

transformation as an opportunity for sustainable

growth [59].

3. Case Study

3.1 Siemens' Digital Factory Methodology: The

utilization of the Internet of Things (IoT) for

predictive maintenance purposes and the

employment of AI for the detection of defects.

Findings: The results of this study indicate that

there has been a 30% productivity increase and a

20% reduction in machine downtime [60].

3.2 Tesla’s Gigafactories Methodology:

Robotics, AI-powered automation, real-time Big

Data analytics. Findings: The production of high-

speed electric vehicles is achieved with near-zero

defects.

3.3 Foxconn’s Lights-Out Manufacturing

(China)Methodology: The production process is

fully automated with no human intervention.

Findings: A 50% cost reduction and 24/7

operational efficiency.

Ethical Considerations Informed Consent: The

survey and interview participants were briefed on

the research objectives [61]. Data Anonymity:

Confidentiality was maintained for all

respondents. Bias Mitigation: Triangulation of

data sources was employed to ensure the validity

of the results [62]. The limitations of the study

are as follows: Geographical constraints: The

study is limited to companies in Europe, the

USA, and Asia. The sample size was limited to

the execution of more extensive surveys could

enhance the generalizability of the findings [63].

Rapid Technological Changes: The industry is

undergoing a transformation with the advent of

Industry 4.0, necessitating continuous updates

[57]. The methodology delineated herein

provides a method to analyze the main factors of

Industry 4.0, combining qualitative insights with

quantitative data. The findings will assist

industries in comprehending best practices,

challenges, and future trends in smart

manufacturing. Future research endeavors should

explore the domains of standardization,

cybersecurity, and workforce upskilling in

Industry 4.0. [64].

4. Discussion

The efficacy of Industry 4.0 implementation is

contingent upon several pivotal factors, including

but not limited to advanced technological

infrastructure, workforce upskilling, robust

cybersecurity, organizational adaptability, and

supportive government policies [65]. A robust

technological foundation is imperative,

encompassing the Internet of Things (IoT) for

real-time machine communication [66], big data

analytics for predictive maintenance and

efficiency [67], cyber-physical systems (CPS) for

autonomous operations [68], and cloud

computing for scalable data storage and remote

collaboration [69]. It is imperative to

acknowledge the significance of workforce

readiness in this context. This entails the

cultivation of digital literacy in AI and robotics

[70], the implementation of continuous learning

programs to maintain congruence with the

accelerating pace of innovation [71], [72], and the

facilitation of training in human-machine

collaboration [73]. Considering the increasing

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interconnectedness, it is imperative to accord

cybersecurity the highest priority by leveraging

secure networks [60], adhering to stringent

regulatory compliance standards (e.g., GDPR)

[74], and employing AI-driven threat detection

methodologies [23]. In addition, organizations

must embrace change management, with

leadership commitment driving digital

transformation [24], agile ensuring adaptability

and a cultural shift toward innovation [72].

Finally, government policies play a pivotal role,

with financial incentives [73] and international

standards fostering interoperability and security

[60]. In summary, the successful adoption of

Industry 4.0 necessitates a comprehensive

strategy that integrates advanced technology,

skilled labor, security measures, organizational

flexibility, and policy support to ensure

competitiveness in the digital industrial era [74].

5. Conclusion

Industry 4.0 signifies a radical transformation

The advent of sophisticated digital technologies

has precipitated a paradigm shift in

manufacturing and industrial processes. This

study has examined the key factors that define

Industry 4.0, including Cyber-Physical Systems,

the Internet of Things, Artificial Intelligence, Big

Data Analytics, Cloud Computing, Additive

Manufacturing, and Augmented Reality/Virtual

Reality. The synergy of these technologies

fosters the emergence of intelligent factories. In

the contemporary industrial landscape, there is an

increasing convergence of machines, systems,

and human operators working in real-time

collaboration to achieve production optimization,

cost reduction, and enhanced efficiency. The

subsequent summary outlines the principal

findings. The foundational technologies of

Industry 4.0 are constituted by Cyber-Physical

Systems (CPS) and the Internet of Things (IoT),

which facilitate uninterrupted communication

between machinery and centralized control

systems. IoT sensors collect real-time data, while

CPS integrates physical processes with digital

models (digital twins) for predictive maintenance

and automation. The integration of Artificial

Intelligence (AI) and Machine Learning (ML)

further enhances decision-making capabilities by

analyzing extensive datasets to predict

equipment failures, optimize supply chains, and

improve product quality. The integration of AI-

driven robotics further automates repetitive

tasks, thereby enhancing precision and

productivity. Big Data Analytics plays a crucial

role in transforming raw industrial data into

actionable insights. Leveraging tools such as

Hadoop and Apache Spark enable manufacturers

to detect inefficiencies, forecast demand, and

reduce waste. Cloud Computing provides

scalable storage and computing power,

facilitating real-time collaboration across global

supply chains. Cloud-based Enterprise Resource

Planning (ERP) systems facilitate seamless data

sharing between departments, enhancing

operational agility. Additive Manufacturing,

otherwise referred to as 3D Printing, facilitates

rapid prototyping, customized production, and

reduced material wastage. Furthermore,

industries such as aerospace and healthcare

benefit from lightweight, complex components

that are not possible to produce using traditional

manufacturing methods.

6. Authorship acknowledgements

Manuel R. Piña Monarrez: Original draft;

Conceptualization; Ideas; Writing. Alberto Jesús

Barraza Contreras: Data analysis; Writing;

Original draft; Review and editing. Cinthia

Judith Valdiviezo: Conceptualization; Ideas;

Methodology. Manuel Baro Tijerina:

Conceptualization; Ideas; Methodology; Formal

analysis; Research. José Manuel Villegas

Izaguirre: Review and editing.

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