Beyond Technical Skills: Competency Framework for Engineers in the Digital Transformation Era

Abstract

In the context of digital transformation and Industry 4.0, the engineering profession is evolving rapidly, demanding new skill sets to maintain employability and support career development. This study identifies the competencies engineers must acquire to meet these challenges, addressing concerns from employers who perceive graduates as underprepared for real-world demands. A systematic literature review was conducted in accordance with PRISMA guidelines, using the Methodi Ordinatio technique to select and rank 59 peer-reviewed articles published between 2014 and 2024. The review identified 47 key competencies, which were organized into a comprehensive framework of seven dimensions: 4 transversal, 9 social, 15 personal, 4 cognitive, 7 digital, 3 green, and 5 technical skills. The results highlight that, while technical expertise remains essential, soft skills—such as leadership, teamwork, communication, and adaptability—are increasingly critical for success in the digital era. The proposed framework offers insights for curriculum development, corporate training, and policymaking, contributing to aligning education and professional development with the evolving demands of Industry 4.0. Future research will focus on the empirical validation of the framework to reinforce its applicability across academic and professional settings.

1. Introduction

In recent decades, digital transformation has emerged as a central force reshaping economies, industries, and societies worldwide. Driven by the Fourth Industrial Revolution, or Industry 4.0, this phenomenon is characterized by the integration of advanced digital technologies, automation, connectivity, and large-scale data analytics [1,2]. Although originally associated with data digitization, digital transformation now refers to a broader and more profound reconfiguration of processes, products, and organizational models through the application of emerging technologies [3]. The COVID-19 pandemic has further accelerated this process, highlighting the need for organizations and professionals to rapidly adapt to an increasingly digital environment [4,5].

The dynamic nature of human resources planning and labor economics—constantly influenced by social, technological, and economic changes—has profound implications for engineering. Traditionally recognized for their technical skills, engineers are now required to integrate human skills to thrive in an increasingly competitive and rapidly evolving work environment [6,7].

While numerous studies discuss the evolution of competencies and the impact of technology on employment, few provide a detailed or systematic mapping of the skills specifically required for digital work. This gap is evident in the literature [8,9,10,11,12,13,14,15,16,17].

Several studies have examined the impact of Industry 4.0 on job markets and workforce competencies. However, the literature often lacks a comprehensive, structured, and quantifiable framework specifically tailored to the evolving role of engineers in this new context. Most existing research focuses either on general labor trends or offers fragmented lists of skills without clear categorization, prioritization, or connection to practical implementation in education and professional development [18,19,20,21].

In this sense, a critical problem emerges: the absence of a comprehensive and systematically structured competency framework specifically tailored to the realities of engineers in the digital era.

To address this problem, this study aims to:

1.

Identify the key competencies required for engineers to succeed in the context of digital transformation and Industry 4.0;

2.

Systematically classify these competencies into a comprehensive, practical, and quantifiable framework;

3.

Provide a reference to support the alignment of engineering education, professional development, and workforce planning with the evolving demands of the digital labor market.

To guide this study and provide a structured focus for the systematic literature review and the development of the proposed competency framework, the following research questions were formulated:

RQ1: what are the key competencies required for engineers to thrive in the context of digital transformation and Industry 4.0?

RQ2: how can these competencies be systematically classified into a comprehensive, practical, and quantifiable framework to support education, professional development, and workforce planning?

RQ3: what gaps exist between the competencies currently emphasized in engineering education and the skills demanded by the digital labor market?

In response to these research questions and the identified gap, this study addresses the issue by conducting a systematic literature review to identify, classify, and structure the key competencies required of engineers in the digital age. Applying PRISMA guidelines and the Methodi Ordinatio technique, the research synthesizes findings from 59 peer-reviewed publications and proposes a competency framework consisting of 47 skills organized into seven dimensions: transversal, social, personal, cognitive, digital, green, and technical.

2. Theoretical Framework

2.1. The Future of the Workforce in the Digital Transformation Era

The rapid transformation of labor markets, driven by the digital revolution and emerging technologies, has created a landscape of both uncertainty and opportunity. Digitalization and automation are reshaping employment in profound ways, with varying impacts depending on workers’ skill levels and the nature of their tasks. This technological shift affects industries, occupations, and countries differently, demanding that workers continually adapt to evolving labor market conditions [22,23].

Professions that require manual dexterity or high-level qualifications are expected to remain relevant, whereas routine and administrative roles are more susceptible to automation and displacement [24,25]. Research shows that while technology displaces some jobs, it also generates new ones, enabling human potential to shift toward more complex competencies and collaborative work with intelligent systems [26].

Jobs that are difficult to automate increasingly demand creativity, leadership, adaptability, and collaboration skills [9,26]. Future roles are expected to prioritize advanced cognitive, social, and emotional skills, including ethical behavior, relationship-building, and complex problem-solving [17,27]. Additionally, non-cognitive skills such as dexterity and empathy remain valuable in the modern labor market [25,28].

To thrive, workers must balance human and technological abilities. STEM-related professions (science, technology, engineering, and mathematics) are gaining prominence, especially when combined with interdisciplinary and innovation-oriented skills [29]. This need for balance also reflects changes in the traditional employer-employee relationship. The rise of remote and hybrid work models has increased the demand for flexibility, digital accessibility, and more human-centered work environments [4,26].

In this dynamic and uncertain 21st century landscape—often characterized as VUCA (volatile, uncertain, complex, and ambiguous)—professionals must develop emotional intelligence, autonomy, and strong collaboration skills. These competencies are critical for building trust, maintaining organizational culture, and succeeding in digitally connected, often remote, work settings [4,26,30,31].

2.2. Worker Competencies in the Digital Era

The concept of competency originates from the Latin term competentia, referring to the capacity to perform a task effectively or the set of knowledge and skills required for a specific job [32]. According to [33], knowledge refers to theoretical understanding; skills involve the practical application of knowledge; and competencies are the demonstrated ability to integrate these elements in work and personal contexts.

In this study, the terms competency and skill are used interchangeably, reflecting common usage in the literature. For instance, ref. [12] defines competencies as a set of knowledge, skills, attitudes, and motivations needed to solve professional challenges, often categorized into soft and hard skills. Soft skills involve interpersonal and behavioral attributes, while hard skills refer to technical expertise acquired through formal education, training, or hands-on experience [9].

Although there is a broad consensus in the literature regarding the importance of competencies in the context of digital transformation, their classification varies significantly across studies [10]. Several authors propose grouping competencies into categories such as technical, social, personal, cognitive, and digital skills, or into broader clusters such as soft and hard skills. Others introduce dimensions such as methodological, emotional, and green competencies, or even domains such as employability and transversal skills.

Despite this diversity, many of these classifications lack detailed descriptions of the competencies themselves, limiting the practical applicability of the proposed frameworks [18,19,20,21]. This generalization often results in abstract categorizations that fail to clearly define the specific knowledge, behaviors, or abilities associated with each group, highlighting a gap that this study seeks to address through a more systematic and structured approach.

3. Materials and Methods

To ensure transparency, reproducibility, and scientific rigor, this study adopted the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [34], which are internationally recognized for structuring systematic literature reviews. PRISMA provides a comprehensive and standardized approach for identifying, screening, and selecting studies, reducing potential bias and enhancing the reliability of the results.

In addition, the Methodi Ordinatio approach proposed by Pagani et al. [35] was employed to select and rank the most relevant articles. This method was chosen over alternative approaches due to its ability to combine three objective indicators—number of citations, year of publication, and the journal’s impact factor (JCR)—into a single metric known as the Index Ordinatio. This index facilitates a structured and transparent prioritization of the articles, particularly relevant in this study given the broad and multidisciplinary nature of the research topic. The Methodi Ordinatio provides greater clarity and robustness to the selection process when PRISMA results in an extensive portfolio by quantitatively identifying high-impact and up-to-date studies, thereby strengthening the validity of the proposed competency framework.

The search strategy involved a combination of keywords distributed across four thematic axes, as outlined in

Table 1. Research axes.

Keyword Combinations	Web of Science	Scopus
TITLE-ABS-KEY: (“Industry 4.0” OR “Industrie 4.0” OR “fourth industrial revolution” OR “4th Industrial Revolution” OR “Industry 5.0” OR “Industrie 5.0” OR “Fifth Industrial Revolution” OR “5th Industrial Revolution” OR “Human-cyber-physical” OR “Society 5.0” OR “digital transformation” OR “Digitalization” OR “Cyber Physical Systems” OR “Advanced Manufacturing”)	79	237
AND
TITLE-ABS-KEY: (“Work market” OR “labor market” OR “employability” OR “Professions of the future” OR “Job market” OR “Employment market” OR “Workforce market” OR “Job sector” OR “Employment sector” OR “Employment opportunities” OR “Job opportunities” OR “Career market” OR “Workforce opportunities” OR “Workforce sector” OR “Labor sector” OR “Employment industry” OR “Labor industry”)
AND
TITLE-ABS-KEY: (“Technologist” OR “engineer” OR “engineering” OR “worker” OR “Production personnel” OR “Production technician” OR “Operator” OR “Collaborator” OR “workforce” OR “employee” OR “Human capital 4.0”)
AND
TITLE-ABS-KEY: (“Skills” OR “Competencies” OR “Knowledge”)

. Before defining the final set of keywords, a preliminary exploratory search was conducted in the selected databases to identify relevant studies and observe the keywords commonly used in this field. This exploratory step helped ensure that the selected search terms were aligned with the terminology most frequently adopted by the scientific community, thereby increasing the sensitivity and specificity of the search. The search terms presented in Table 1 were carefully selected to comprehensively capture the multidisciplinary nature of this research, which lies at the intersection of engineering, workforce development, and digital transformation.

The first axis includes terms such as “Industry 4.0,” “Fifth Industrial Revolution,” “Human-Cyber-Physical,” and “Digital Transformation” to encompass the broad range of technological changes reshaping the labor market and professional requirements in recent years. The second axis focuses on the labor market dimension, incorporating keywords such as “employability,” “job market,” and “professions of the future” to target studies addressing workforce challenges and emerging employment trends. The third axis specifically includes terms related to engineers and technical professionals, such as “engineer,” “technologist,” and “workforce,” ensuring the search was tailored to the professional group under investigation. Finally, the fourth axis targets terms such as “skills,” “competencies,” and “knowledge,” which are central to the research objective of identifying and classifying key competencies required for engineers in the digital era.

This combination of search terms, applied with Boolean operators across titles, abstracts, and keywords, ensures a broad yet precise retrieval of relevant literature, avoiding both excessive generality and narrowness, thereby guaranteeing the methodological robustness of the systematic review.

Among the databases tested, Web of Science and Scopus were selected for this research, as they presented the largest number of publications containing the searched keywords, greater availability of access to the published material, and are considered two of the most relevant databases for engineering, as they incorporate numerous other databases, even from different publishers.

The inclusion criteria were: (i) articles published in peer-reviewed journals between 2014 and 2024; (ii) written in English; and (iii) addressing competencies or skills related to engineers or technical professionals in the context of Industry 4.0 and digital transformation.

The inclusion criteria adopted in this study were defined to ensure the relevance, quality, and alignment of the selected articles with the research objectives. First, only articles published in peer-reviewed journals were considered, as this guarantees a minimum standard of scientific rigor and credibility. Second, the timeframe from 2014 to 2024 was established to ensure the incorporation of recent studies that reflect the current challenges and requirements of engineers in the context of digital transformation and Industry 4.0. The technological evolution in this period, particularly with the acceleration of digitalization driven by events such as the COVID-19 pandemic, has profoundly impacted the competencies demanded in the labor market, making recent research particularly relevant.

Third, publications were restricted to articles written in English, Portuguese, or Spanish to ensure the inclusion of high-quality studies while also capturing relevant research from Latin American and European contexts, where engineering and digital transformation are extensively studied. Finally, the selected articles specifically addressed competencies or skills related to engineers or technical professionals within the scope of Industry 4.0 and digital transformation. This focus is essential to ensure that the results directly contribute to the development of a targeted and practical competency framework for the engineering profession.

In addition to the inclusion criteria previously described, specific exclusion criteria were applied to ensure the relevance and quality of the final portfolio. Studies were excluded if they: (i) did not explicitly focus on competencies or skills required by engineers or technical professionals within the context of Industry 4.0 or digital transformation; (ii) addressed only technological aspects without a clear link to human or skills development; (iii) were not available in full-text or presented significant methodological limitations; and (iv) were duplicates or redundant publications of the same study. This screening process helped ensure that the selected articles were aligned with the research objectives and reinforced the validity of the proposed framework.

Initial searches returned 316 documents. After removing 59 duplicates, 257 unique articles remained. Titles, keywords, and abstracts were screened to ensure alignment with the study’s objectives, resulting in the exclusion of 100 articles.

To ensure transparency and minimize potential bias in the selection and evaluation of studies, a double-screening process was adopted with the support of Mendeley Desktop (version 1.19.8). The relevance of each study was manually assessed using the Methodi Ordinatio approach. Automated tools for risk of bias assessment were not employed, as the selected studies were predominantly conceptual, exploratory, and qualitative in nature, lacking experimental designs or standardized quantitative data that would justify the use of conventional bias assessment instruments.

The remaining 157 articles were then subjected to the Methodi Ordinatio procedure to assess their relevance based on three objective criteria:

• Impact factor of the journal (Journal Citation Reports—JCR);
• Citation count of the article;
• Year of publication.

The number of citations corresponds to the frequency with which the article has been cited by other researchers, as retrieved from Google Scholar. The publication year refers to the year in which the article was published, which, in this case, was obtained directly from the article itself. The impact factor was sourced from the official website of the respective journal. With the data in hand, the InOrdinatio 2.0 formula was applied [35].

Following this process, the top 59 articles—representing 80% of the cumulative Methodi Ordinatio score—were selected for full analysis. These articles formed the final bibliographic portfolio and were examined using both bibliometric and qualitative content analysis techniques.

To support the analysis, the following software tools were employed:

• Microsoft Excel^®—for managing bibliographic data and performing descriptive statistics;
• VOSviewer^®—to analyze co-authorship, keyword clustering, and publication patterns;
• MAXQDA^®—to code and categorize competencies identified in the content of each article.

The entire process is illustrated in Figure 1, following the PRISMA 2020 flow diagram, which outlines the identification, screening, eligibility, and inclusion phases.

4. Results and Discussions

The analysis of the 59 articles in the final portfolio was divided into two stages: bibliometric and content analysis. Microsoft Excel^® was used to examine publication years, journals, and participating countries. VOSviewer^® facilitated the analysis of relationships between keywords, authors, and co-authors. Additionally, MaxQDA^® was employed for systematic reading and content analysis, contributing to the categorization of engineers’ competencies in the digital transformation era.

4.1. Bibliometric Analysis

The first step was to examine the publication timeline of the selected articles. As shown in Figure 2, the works span from 2017 to 2024, indicating a steady increase in academic interest over time. Rather than prioritizing only the most recent studies, the portfolio captures a broad conceptual development across the years, reflecting both foundational and emerging perspectives.

Analyzing

Table 2. Journals publishing the articles in the final portfolio.

Periodical	Count	Percentage	Publisher
Sustainability	5	8.47%	MDPI
Education and Training	4	6.78%	Emerald Publishing
International Journal of Manpower	3	5.08%	Emerald Publishing
Industry and Higher Education	2	3.39%	Sage Journals
“Others”	45	76.27%	“Others”

, it is possible to observe that approximately 24% of the portfolio (14 articles) comes from 8% of the journals (4 journals), meaning a significant number of publications originate from a small group of journals. Another 45 journals published only one article. It is worth noting that most articles from the journals with the highest number of publications, as identified in Table 2, were maintained in the final portfolio.

Figure 3 shows that the authorships and co-authorships of the 59 articles span 37 different countries. Highlights include Italy with 8 articles, followed by Poland with 6, and Germany and Spain with 5 each. Australia, England, and Slovakia each contributed 4 articles.

Further details regarding the number of articles with authors and/or co-authors affiliated with each country are presented in

Table 3. Countries of affiliation of authors.

Country	Count
Italy	8
Poland	6
Germany	5
Spain	5
Australia	4
United Kingdom	4
Slovakia	4
France	3
Malaysia	3
Russia	3
USA	3
Brazil	2
Croatia	2
India	2
China	2
Romania	2
Sweden	2
Hungary	2
Australia	1
Poland	1
Czech Republic	1
Bangladesh	1
Belgium	1
Canada	1
Chile	1
Greece	1
Indonesia	1
Latvia	1
Malaysia	1
Mexico	1
New Zealand	1
Philippines	1
Portugal	1
Singapore	1
Slovenia	1
Switzerland	1
Turkey	1

.

In the 59 articles comprising the final bibliographic portfolio, a total of 186 authors and co-authors were identified, with 4 contributing to more than one article. Figure 4 presents the relationships between these authors and co-authors.

Given that the topic is emerging and research has evolved over time, it was decided to analyze keywords according to the years of publication, as illustrated in Figure 5. Across the 59 analyzed studies, 316 keywords were identified in total.

In Figure 5, it can be seen that keywords related to competencies appear in green and yellow tones, highlighting emerging topics linked to Industry 4.0 and digital transformation. The keywords with the highest frequency and multiple direct connections to others included Industry 4.0, competencies, skills, future, employment, labor market, management, soft skills, employability, work, employability, digitalization, and digital skills. Figure 6 shows words with a frequency equal to or greater than 3.

To enhance the robustness of the keyword analysis and align with the emerging nature of the topic, a cluster analysis was conducted on the 60 most frequent keywords extracted from the 59 selected articles. The terms were grouped into six thematic clusters based on their co-occurrence and semantic proximity.

Cluster 1 (15 items): this cluster emphasizes educational and managerial aspects of skills development. It includes terms such as communication skills, competencies, employability skills, higher education, management, and soft skills. The presence of innovation, digitalization, and knowledge suggests a strong focus on the integration of human and digital competencies within organizational and academic contexts.

Cluster 2 (11 items): focused on macroeconomic and structural factors, this cluster contains keywords such as labor market, employment, education, technological change, and impact. It reflects the broader implications of digital transformation on workforce dynamics, skill polarization, and the evolution of employment landscapes.

Cluster 3 (11 items): this cluster centers on technological advancements and their relationship with the workforce. Keywords such as artificial intelligence, big data, digital skills, digital transformation, and HRM indicate a strong association with Industry 4.0 technologies and their influence on human resource management and labor markets.

Cluster 4 (10 items): similar to Cluster 1, but more focused on employability readiness, this group includes competencies, graduate work readiness, vocational education and training, and generation. It reinforces the need for educational systems to address the competency gaps brought about by digital transformation and Industry 4.0.

Cluster 5 (8 items): this cluster reflects a technocentric view of the future of work, including keywords such as, skills gap, systems, artificial intelligence, and labor market analysis. It highlights challenges related to automation, system design, and matching labor supply with evolving demands.

Cluster 6 (3 items): composed of digitalization, technologies, and training, this smaller cluster appears to consolidate foundational concepts related to technological adaptation and workforce upskilling.

These clusters provide a comprehensive and structured view of the thematic intersections within the literature, highlighting how technology, skills development, education, and labor dynamics are interconnected in the context of digital transformation.

4.2. Content Analysis: Skills Framework

With the assistance of MAXQDA^®, the competencies mentioned in the articles that make up the portfolio were mapped and categorized into seven dimensions. This process led to the development of a competency framework for engineers in the era of digital transformation, as illustrated in Figure 7.

The division of competencies into different dimensions provides a clear and comprehensive view of the variety of skills required to address current challenges. With the seven dimensions defined for this study, it was necessary to group the mapped competencies based on their affinity with each dimension’s definition, as presented in

Table 4. Mapping of competencies classified by dimension.

Transversal Skills	Description
Continuous and adaptive learning [9,18,19,20,23,25,32,36,37,38,39,40,41,42,43,44,45,46,47]	Refers to the willingness and commitment to seek learning and development opportunities throughout one’s professional life.
Integrated knowledge [9,17,23,31,38,39,40,46,48]	Involves the integration and application of knowledge from different areas to solve complex problems. In other words, it brings together different perspectives, methods, and concepts, allowing for more complex analyses.
Management and coordination [9,18,41,45,46,48,49,50,51]	Ability to effectively manage resources, people, and processes to achieve desired results efficiently and effectively.
Entrepreneurial vision/visionary [7,9,17,18,19,21,23,30,31,32,44,46,48,52,53,54,55,56]	Ability to identify opportunities and have unusual and unconventional ideas.
Social Skills	Description
Interpersonal influence [5,7,9,17,18,23,24,25,32,37,40,41,45,48,50,52,53,55,56,57,58]	Ability to dialogue, convince, negotiate, persuade, manage conflicts, and influence people to reach agreements that are advantageous for all parties involved.
Teamwork [5,7,9,17,18,19,20,21,23,24,32,33,37,38,40,41,42,43,44,45,46,48,51,55,57,59]	Ability to collaborate effectively with others, sharing responsibilities and contributing to a harmonious and productive work environment.
Leadership [5,7,11,17,18,21,23,32,33,40,42,44,45,46,48,50,52,53,57,59,60]	Ability to influence, motivate, and guide teams to achieve common goals.
Mentoring [5,9,23,25,32,53,59]	Ability to guide and support the professional and personal development of others, transmitting knowledge and experiences to help the growth of colleagues or subordinates.
Communication [5,9,17,18,19,20,21,23,24,25,29,31,32,37,38,40,41,42,43,44,45,46,48,49,50,51,53,55,56,57,59,60,61,62]	Ability to convey information clearly and effectively, both verbally and in writing, in different contexts and to different audiences.
Language skills [5,9,17,18,23,37,46,48,50,51,59,63]	Proficiency in communicating effectively in a specific language other than the native language.
Feedback management [21,23,31,50,52,64]	Ability to provide constructive feedback and receive criticism in an open and receptive manner.
Cultural awareness [5,9,18,23,24,31,32,37,40,43,53,56,59]	Ability to understand, respect, and adapt to cultural differences in global and diverse contexts.
Networking [5,9,17,18,23,41,42,46,53,56,58,59,65,66]	Ability to establish, maintain and expand interpersonal connections strategically and effectively.
Personal Skills	Description
Professionalism [7,8,9,17,18,21,23,31,37,40,41,42,45,46,50,52,53,57,61]	Ability to demonstrate appropriate behaviors and attitudes for the work environment, along with the willingness and determination to fulfill responsibilities and obligations with integrity, honesty, and diligence, reflecting ethics, respect, and morality.
Multitasking [9,23,50,63]	Ability to perform multiple activities or tasks simultaneously or in rapid succession without compromising efficiency or quality of performance.
Persistence [5,9,18,21,31,33,37,45,53,59,64]	Skill to inspire and motivate oneself and others to achieve goals and perform at their best.
Curiosity [7,9,18,23,31,50,56]	Willingness and interest in learning and exploring new things, continuously seeking to acquire new knowledge and experiences.
Authenticity [18,23,45,56,57]	Ability to be genuine, true to oneself and one’s values, avoiding imitation or adherence to predetermined standards.
Excellence and growth mindset [7,9,18,23,24,31,45,46,53]	Willingness to learn and grow when facing challenges.
Agreeableness [5,7,9,17,18,23,31,33,40,41,44,45,49,50,53,56,57,58,59,61,63,64]	It involves behaviors that demonstrate consideration and respect for the well-being of others, such as treating people with courtesy and empathy while offering emotional support.
Independence [9,17,18,31,37,42,63]	Ability to work independently, make decisions on one’s own, and manage tasks and responsibilities autonomously.
Optimism [9,17,23,31,41,50]	Ability to maintain an optimistic perspective, focusing on possibilities and solutions even when faced with challenges or difficulties.
Resilience [5,7,9,17,18,20,23,24,29,31,32,33,36,37,38,39,40,41,42,44,45,46,47,48,49,50,51,53,57,59,61,63,67]	Involves the capacity to withstand, adapt to, and remain flexible in the face of adversity.
Organization [9,18,19,21,23,37,41,43,45,46,50,57,62,63]	Skill to plan, organize, and effectively manage tasks, projects, and resources, including strong time management capabilities.
Emotional intelligence [5,7,9,17,18,19,20,21,23,31,32,37,40,41,42,45,46,50,52,53,57,58,59,61,63]	Ability to control emotions, impulses, and behaviors, maintaining calmness and balance in difficult situations.
Intuition [7,18,25,36,44,54]	Capacity to understand and make decisions based on insights and intuitive perceptions.
Proactivity [7,18,19,23,24,29,37,42,43,44,45,55,57,62]	Willingness to act proactively, take initiatives, and seek solutions before problems arise.
Reliability [17,18,21,32,41,49,52]	Ability to be reliable, fulfill promises, and assume responsibilities.
Cognitive Skills	Description
Logical reasoning [23,24,32,33,41,45,50,58,63,66]	Involves analyzing information in a structured and rational manner.
Problem solving and decision Making [3,5,7,9,17,18,19,21,23,25,32,36,37,38,39,42,44,45,46,48,49,50,53,54,55,57,59,61,63,64,66]	Involves the ability to identify, analyze, solve problems, and make decisions efficiently and effectively.
Strategic thinking [3,9,17,18,19,20,23,24,31,32,33,39,41,42,45,46,48,50,51,53,54,55,56,57,61,62,63,64,68]	Capacity to understand the whole and its interconnected parts, as well as to think and make decisions with a long-term perspective.
Creative and innovative thinking [3,5,9,17,18,23,24,25,29,30,31,32,33,36,37,38,41,42,44,45,46,48,50,51,53,54,56,57,58,62,63,64]	Individual ability to generate new and original ideas.
Digital Skills	Description
Programming and coding [5,9,13,17,32,33,37,38,39,44,46,48,51,52,53,54,57,59,61,65,69]	Involves skills in writing, testing, and maintaining code using various programming languages for software and system development.
Systems and networking Competence [5,7,11,29,32,33,38,44,45,51,52,61,69,70]	Knowledge that enables the implementation, administration, and maintenance of computer networks.
Human-machine interfaces [52,53,54]	Expertise that facilitates interaction between humans and digital systems.
IT security and data protection [7,23,32,33,38,45,48,52,53,59,69]	Application of specific knowledge and techniques to protect digital systems and data.
Digital communication and marketing [9,23,33,40,42,45,52,53,55,59,65,69]	Ability to use social networks, understand digital platforms, and develop online communication strategies
Data analysis and management [3,7,9,13,18,29,32,33,37,38,42,44,45,48,51,52,53,57,65,68,69]	Ability to analyze and manage large datasets.
Digital literacy/ computational thinking [3,9,18,23,24,25,40,45,49,50,52,53]	Capacity to effectively use digital technologies for daily tasks.
Green Skills	Description
Sustainable design [9,29,45,46,57,62]	Ability to generate solutions while considering economic, environmental, social, political, ethical, health, and safety constraints.
Sustainable thinking [5,9,29,33,37,40,46,53,57,59,62,63]	Understanding and consideration of environmental impacts in decision-making.
Social responsibility [17,29,42,55]	Awareness and commitment to social responsibility.
Technical Skills	Description
Research skills [5,9,42,48,53]	Ability to plan, conduct, and analyze systematic investigations.
Understanding of processes [13,17,23,25,38,53,59]	Ability to understand and optimize processes.
Knowledge of IT and production technologies [7,9,13,23,32,33,38,39,42,44,48,51,52,65,71]	Ability to implement and manage technologies in the production environment.
Knowledge of science, mathematics, statistics and engineering [9,13,18,24,29,37,38,42,45,49,50,51,54,61,68]	Ability to apply mathematical, quantitative, and statistical principles to solve problems.
Discipline-specific knowledge/know-how [13,20,21,32,37,38,42,46,51,54]	Combination of theoretical knowledge and applied experience in specific contexts.

Table Note: The competencies in bold indicate the main skills mapped within each of the seven dimensions. Each dimension is presented with a background color that matches the corresponding section in Figure 7, facilitating visual alignment and comprehension of the competency framework.

.

Table 4 brings together the competencies identified as essential for the era of digital transformation. To avoid redundancies, similar competencies were grouped based on their similarity and complementarity, consolidating skills with common purposes and applications. This approach seeks a clearer and more practical organization, facilitating the understanding of professional requirements in the context studied.

The competency “Continuous and adaptive learning” was used to unify several competencies related to constant development and adaptation. “Integrated Knowledge” covered multidisciplinary and interdisciplinary knowledge competencies, seeking solutions that go beyond the traditional boundaries of the areas of study. “Interpersonal Influence” combined negotiation, persuasion, conflict management, and influence, focusing on strategic interactions.

The “networking” competency encompassed sociability and network building, while “Professionalism” brought together composure, ethics, morality, commitment, responsibility, and integrity. These were united because they are interconnected by fundamental values and behaviors that regulate how individuals act in professional and social environments.

The following competencies were portrayed as “Persistence” because they all involve the ability to influence and maintain motivation, both in oneself and in others.

“Excellence and Growth Mindset” reflected continuous improvement, assertiveness, and self-efficacy. “Multitasking” incorporated agility, and “Agreeableness” encompassed competencies such as empathy, compassion, sensitivity, respect, and tolerance, with a common focus on interpersonal relationships and the way we deal with others.

“Independence” combined autonomy and self-discipline, while “Resilience” combined adaptability and overcoming difficulties. Lastly, “Emotional Intelligence” combined self-control, self-management, and the ability to work under pressure, focusing on recognizing and managing emotions. The “Strategic Thinking” competency encompassed terms such as holistic vision, systemic thinking, critical thinking, and analytical thinking, as they all share the idea of understanding and dealing with complexity and interdependent relationships within a larger system. In addition, the Creative, Innovative, and Abstract Thinking competencies were grouped together as “Creative and Innovative Thinking” due to their interdependence in generating new ideas and solutions, especially in unconventional contexts.

4.3. Discussions

The growing body of research on competencies required for engineers in the context of Industry 4.0 and digital transformation reflects a broad consensus regarding the importance of developing both technical and human-centered skills. However, despite this progress, the literature still presents significant limitations that hinder its practical application. Many existing studies tend to offer generic or fragmented lists of skills, often disconnected from the specific realities faced by engineers in the manufacturing sector [17,42]. Furthermore, frameworks proposed in the literature frequently lack a structured and comprehensive categorization capable of guiding educational practices, professional development, and human resources strategies in an integrated manner [2,3,13,18,29,37,38,44,51].

In response to these limitations, the competency framework proposed in this study offers a significant contribution by systematically organizing 47 competencies into seven dimensions that encompass transversal, social, personal, cognitive, digital, green, and technical skills. This approach addresses critical gaps identified in the literature, particularly the need to articulate soft skills—such as leadership, teamwork, communication, and emotional intelligence—with technical and digital skills in an integrated and contextualized manner [2,3,13]. By doing so, the framework moves beyond the reductionist separation between “hard” and “soft” skills, reflecting the complex and interdisciplinary nature of engineering practice in the digital era [18,21,29,32,36,44].

A considerable portion of the studies analyzed focuses on sector-specific or technology-specific competencies, such as those related to artificial intelligence [50], additive manufacturing [27], or the maritime industry [60]. While these contributions are undoubtedly relevant, they present limitations in terms of applicability to broader engineering contexts. Moreover, these initiatives often fail to address the interrelationships between different dimensions of competencies or how such competencies evolve throughout engineers’ professional careers.

Beyond this, there is a clear consensus in the literature regarding the need to integrate technical, digital, and socio-emotional competencies [45]. However, few studies offer a clear conceptual structure capable of systematically and interdependently organizing these competencies. In this regard, [45] and [72] emphasize the relevance of skills such as communication, teamwork, critical thinking, problem-solving, and entrepreneurship, often complementing or even surpassing the importance of traditional technical proficiency. This convergence represents a fundamental pillar of the framework proposed in this study, which devotes significant attention to social and personal skills.

The importance of non-technical skills is also reinforced by study [72], while study [18] reveals a notable lack of consensus regarding a common skills taxonomy. Furthermore, policymakers and academics tend to focus on specific domains, whereas mobility programs emphasize management and career-related skills. This fragmentation in the conceptualization and categorization of skills represents a significant gap that the present research seeks to address by proposing a more unified and comprehensive framework.

Importantly, many of the 47 competencies identified in this study require continuous development throughout both engineering education and professional practice. For example, the study by [10] underscores the critical relevance of digital competencies yet reveals generally low to moderate proficiency levels among students. These findings highlight the need for substantial improvement in digital skills before students enter the labor market, particularly regarding digital literacy and problem-solving. In contrast, competencies such as communication and teamwork exhibit comparatively stronger development, with a higher proportion of students performing at intermediate to advanced levels.

While hard skills remain essential for engineering professionals in the digital age, soft skills play an equally critical role. According to [6], competencies such as leadership and management, teamwork, communication, problem solving and decision making, interpersonal influence, and professionalism are indispensable to the profession. These soft skills—encompassing transversal, personal, social, and cognitive dimensions—not only enable engineers to collaborate effectively with others but also facilitate the clear communication of ideas, proposals, and innovations, thereby contributing to both individual and organizational success.

This argument is further supported by [73], who highlight management, communication, and teamwork as essential to good leadership and technical mastery. Their study also reveals that approximately one-third of executive leaders do not mentor their teams to develop future leaders, underscoring the strategic importance of talent development within organizations themselves.

Thus, while engineers bear responsibility for continuous learning and skill enhancement, organizations and society at large must also play an active role in cultivating a skilled and adaptable workforce. Awareness of this need, coupled with a solid understanding of the practices current leaders can adopt to develop competencies within their teams, is essential to fostering a cultural shift towards the systematic development of talented leaders [1,73].

This broad spectrum of competencies aligns with the demands of Industry 4.0, where organizations are required to provide not only technological infrastructure but also a highly skilled workforce. Nevertheless, many employees still require upskilling to meet the challenges posed by digital transformation [74].

Finally, the role of education emerges as both a point of convergence in the literature. There is general agreement on the need for universities to adapt their curricula. In this regard, study [72] suggests exposing students to interdisciplinary teaching, research, innovation, and industrial training. Complementarily, study [18] argues that education systems must evolve to meet the changing demands of the labor market by redefining roles to balance technological proficiency with human intellect while fostering a new collective and ethical awareness. The persistent gap between the skills taught and those required by the labor market is a recurring theme, reinforcing the urgency of curricular reforms that integrate the new competencies demanded by the digital era.

5. Conclusions

This study highlights the increasing importance of updating engineering competencies in the face of digital transformation. In an era marked by rapid technological change, engineers must combine technical proficiency with interpersonal, cognitive, and digital skills to effectively meet evolving demands.

By conducting a systematic literature review based on the PRISMA methodology and applying the Methodi Ordinatio technique, this study identified and classified 47 key competencies across seven dimensions: 4 transversal, 9 social, 15 personal, 4 cognitive, 7 digital, 3 green, and 5 technical skills. This multidimensional classification offers a comprehensive and practical framework to guide professional development, organizational strategy, and curriculum design in the context of Industry 4.0 and 5.0.

While the existing literature extensively discusses the relevance of soft skills for engineers, there remains limited emphasis on the intersection and integration between hard and soft skills. The findings of this study suggest that engineers must develop transferable competencies applicable across diverse scenarios, as exemplified by the transversal skills identified. Moreover, social skills are crucial to building strong relationships and promoting harmonious work environments. Self-knowledge and emotional intelligence strengthen the ability to make responsible and balanced decisions, which is why a class called personal skills and the class of cognitive skills represent the mental capacities linked to learning.

In addition, in the digital age, engineers need to master technological tools and have sustainable engineering as a priority, which is why a dimension for digital skills and green skills is included. Finally, technical skills continue to be the basis of engineering, being indispensable for success in the field and a class for them.

Beyond its implications for professional development and organizational competitiveness, the competency framework proposed in this study also contributes to addressing the broader social impacts of digital transformation. As technological advances reshape not only industries but also labor markets, social structures, and access to opportunities, the development of a workforce equipped with both technical and socio-emotional competencies becomes essential for reducing inequalities and promoting social inclusion. The emphasis on transversal, personal, and social skills within the framework reflects the need to foster professionals capable of engaging in diverse, collaborative, and ethical environments, which is critical for building resilient, cohesive, and equitable societies in the digital era.

The findings presented in this research hold particular relevance for engineering professionals, human resource managers, educators, policymakers, and organizations seeking to adapt to digital transformation. However, the transition to Industry 4.0 requires not only individual effort but also structural changes that foster continuous learning, upskilling, and resilience in an increasingly technological and dynamic work environment.

5.1. Implications and Contributions of the Study

The findings of this study carry significant implications for various stakeholders, including the education sector, industry, and policymakers. For education, the proposed competency framework highlights the urgent need for more flexible and interdisciplinary curricula that integrate not only cognitive and technical skills but also transversal, social, personal, and digital competencies. The emphasis on continuous upskilling and reskilling for professionals suggests that higher education institutions and lifelong learning platforms should develop programs that address these growing demands, thereby reducing the gap between the skills taught and those required by the labor market.

For industry, this study reinforces the importance of companies investing proactively in training and development to ensure their workforce is equipped to adapt to new technologies. The identification and mapping of employee competencies become critical strategies for closing existing skill gaps and maintaining competitiveness. The proposed framework serves as a practical guide for human resources professionals and managers in identifying, assessing, and developing talent within organizations.

For policymakers, this work highlights the urgency of designing policies that support workforce adaptation and educational alignment with the demands of Industry 4.0 and 5.0. The need for a more consensual skills taxonomy, such as the one proposed in this study, can facilitate the measurement, development, and alignment of market needs with educational offerings, providing a coherent structure for strategic planning in education and labor policies.

5.2. Study Limitations

Despite its contributions, this study presents limitations that must be acknowledged to ensure scientific transparency. First, the framework is based exclusively on a systematic literature review; no primary empirical data were collected to validate the identified competencies. Consequently, while the framework offers theoretical robustness and practical potential, its applicability to specific industries, regions, and organizational contexts requires further verification.

5.3. Directions for Future Research

Future research should also focus on empirically validating and refining the proposed competency framework through direct engagement with both engineers and employers. A large-scale survey is planned to capture the perceptions of approximately 384 practicing engineers across different career levels (junior, mid-level, and senior) to identify the most relevant competencies for each stage of professional development. In addition, a second phase will involve qualitative research with employers to refine the framework based on industry expectations and labor market demands.

It is expected that this empirical validation will find differences in competency requirements across hierarchical levels. The study will also explore how specific competency dimensions, such as transversal, cognitive, personal, social, technical, green, and digital skills, evolve throughout an engineer’s career trajectory.

The refinement and validation of the proposed framework through these studies will provide a practical tool to support engineers in assessing their career readiness, assist companies in strengthening their talent management strategies, and guide educational institutions in aligning curricula with the evolving demands of the labor market and technological advancements.