CONSOLIDATING INDUSTRY 4.0 INDIVIDUAL SKILLS
an umbrella review
Cristiane Pedron[1]
UNINOVE
cdpedron@uni9.pro.br
Allisson Jonathan[2]
UNINOVE
allisson.jonathan@gmail.com
______________________________
Abstract
The emergence of Industry 4.0 has caused a drastic change in the way professionals work, requiring them to adapt and train themselves to deal with new challenges. These changes require new skills, but they also require existing skills to be updated. Understanding which skills are important for professionals working in the context of Industry 4.0 is extremely important for their proper training. This topic has been studied individually by several authors, addressing specific skills, directly or indirectly citing Industry 4.0. To consolidate the findings of these studies and understand which skills are present in these studies and how they have been developed, this study carried out an Umbrella Review, that is, a Systematic Review of other Systematic Reviews, and presents in its findings the consolidation of the skills present in all these studies and the proper definition.
Keywords: competence acquisition; skill development; industry 4.0; individual skills; systematic literature review.
CONSOLIDANDO AS COMPETÊNCIAS INDIVIDUAIS DA INDÚSTRIA 4.0
uma revisão de revisões sistemáticas
Resumo
O surgimento da Indústria 4.0 causou uma mudança drástica na maneira como os profissionais trabalham, exigindo que eles se adaptem e se capacitem para lidar com novos desafios. Essas mudanças exigem novas competências, mas também a atualização das competências já existentes. Compreender quais competências são importantes para os profissionais que atuam no contexto da Indústria 4.0 é extremamente importante para sua adequada formação. Esse tema tem sido estudado de forma isolada por diversos autores, abordando competências específicas, citando direta ou indiretamente a Indústria 4.0. Para consolidar os achados desses estudos e entender quais competências estão presentes nessas pesquisas e como foram desenvolvidas, este estudo realizou uma Revisão Abrangente, ou seja, uma Revisão Sistemática de Outras Revisões Sistemáticas, e apresenta em seus achados a consolidação das competências presentes em todos esses estudos e a definição adequada.
Palavras-chave: aquisição de competências; desenvolvimento de habilidades; indústria 4.0; competências individuais; revisão sistemática da literatura.
CONSOLIDACIÓN DE LAS COMPETENCIAS INDIVIDUALES DE LA INDUSTRIA 4.0
una revisión de revisiones sistemáticas
Resumen
El surgimiento de la Industria 4.0 ha causado un cambio drástico en la manera en que los profesionales trabajan, exigiendo que se adapten y se capaciten para enfrentar nuevos desafíos. Estos cambios requieren nuevas habilidades, pero también la actualización de habilidades ya existentes. Comprender qué habilidades son importantes para los profesionales que trabajan en el contexto de la Industria 4.0 es extremadamente importante para su adecuada formación. Este tema ha sido estudiado de forma individual por varios autores, abordando habilidades específicas, citando directa o indirectamente la Industria 4.0. Para consolidar los hallazgos de estos estudios y entender qué habilidades están presentes en estos estudios y cómo se han desarrollado, este estudio realizó una Revisión Integral, es decir, una Revisión Sistemática de Otras Revisiones Sistemáticas, y presenta en sus hallazgos la consolidación de las habilidades presentes en todos estos estudios y su definición adecuada.
Palabras clave: adquisición de competencias; desarrollo de habilidades; industria 4.0; competencias individuales; revisión sistemática de la literatura.
1 INTRODUCTION
The development of new technologies and the consequent digital transformation that companies are going through, has driven the need to develop specific skills (Mendes, 2023). Industry 4.0 technologies have great potential to contribute to the development of companies, such as the development of products and services, production management and innovation of business models (Lenart, 2019). These contributions significantly change the way people work, mainly due to the lack of trained professionals to deal with such technologies, so that for their adoption to be successful, companies need to have resources and team engagement (Menendez et al., 2020; Cordeiro et al., 2023). A literature review by Grzybowska e Łupicka (2017) highlights three categories of skills that need to be developed: (1) Techniques, comprising all work-related knowledge and skills, such as knowledge of specific tools; (2) Management Skills, consisting of the skills necessary for problem solving and decision making; (3) Social Skills, defined as social values such as motivation and ability to work in a team.
The concept of skills has changed to adapt to new market scenarios, making companies look for new professionals to meet the need for new skills (Sá; Paixão, 2013). The development of skills for Industry 4.0 differs from the others because these, in addition to technical and cognitive skills, individual skills play a significant role in the use of these technologies (Maisiri et al., 2019). In this way, it becomes important both the development of hard skills, those related to technical skills, and the development of soft skills, which are related to behavioral skills. To meet this demand, educational institutions need to promote innovation in knowledge acquisition methods (Maisiri et al., 2019). These technologies have developed very quickly, making it necessary to develop skills in these professionals more quickly, as knowledge becomes obsolete more quickly (Maisiri et al., 2019; Bianco, 2020). Thinking about this dynamic scenario, it is important to identify which skills should be developed, and if there is an order in their acquisition, for the proper improvement of professionals working in Industry 4.0.
It is important to understand what can be considered competence, this question was raised by Eraut (1998), where through the analysis of several meanings attributed to competence he brought a definition for the term, differentiating competence from ability. Eraut (1998, p. 135) says “... definition of competence as the ability to perform the tasks and roles required to the expected standard should remain; but we need to remember that it refers to performance in a particular job or category of job. Change the job and the definition of competence will also change”. So, the skills of a given individual only become a competence when applied to a given activity, and once the activity changes, the skills used to become competent change as well, as seen in Figure 1 - Eraut (1998).
The same concept of applying skills for the proper performance of activities is used by Mulder (2011), who defines Competence as the Ability to perform effectively. In a systematic review of the definitions of competence, Schneider (2019) concludes that competence is performing specific actions within a given environment, which make you efficient specifically within that environment. Stating that in order to acquire competence it is important to define in which environment these will be used, in the case of this article, the environment is composed of companies that use Industry 4.0 technologies.
Based on the information presented, this article has the following objective: "Understand which Industry 4.0 individual competencies academic literature considers most important.".
2 THEORETICAL FRAMEWORK
The structure was separated into 3 categories: (1) Industry 4.0 Skills, addressing how it has changed the way we work, (2) Skills Development, explaining how these can contribute to the development of companies; (3) Learning Theory, necessary to understand how a consolidate theory defines the distincts Organizational Learning.
2.1 INDUSTRY 4.0 SKILLS
The concept of Industry 4.0 (fourth industrial revolution) is still in the process of improvement, in general it can be said that Industry 4.0 represents the automation of industries from the integration of different technologies from tools such as: (i) IOT: internet of things consists of technologies that make use of the internet to share and acquire knowledge; (ii) Network/Cloud: are computer networks that use the cloud for data management; (iii) Connectivity: internet access, whether via WiFi, 4G or 5G; (iv) Information security: technologies that protect the data collected; (v) Wearable: garments that provide information and help in monitoring; (vi) Blockchain: information sharing networks of a segment; (vii) Artificial intelligence: robots that make decisions based on programming and especially learning; (viii) Machine Learning: systems that use experiments to improve the type of machine response; (ix) Big Data: management of large volume of data; (x) others such as impressoras 3G, virtual reality (Sacomano et al., 2018; Bianco, 2020). The function of these technologies is to automate, streamline, provide more effective solutions and cheapen processes, being essential for various segments to meet the requirements of the actual markets, it is worth noting that the success of these tools depends in most cases on companies and their employees (Simic & Nedelko, 2019). These are not only emerging technologies, but rather disruptive technologies that change the way work is organized, acted and delivered (Bianco, 2020).
For the proper use, configuration and interpretation of the data provided by these new technologies it is necessary to develop new professional competencies (Alhloul; Kiss, 2022), in addition, social activities such as leadership, are also impacted by these technologies, since they provide a new work environment for professionals (Bianco, 2020). An analysis by Townsend et al. (2022) through 3 rounds of interviews with 63 industry 4.0 professionals pointed out 4 competency groups with 18 specific competencies important for Industry 4.0 professionals (Table 1): (1) Technical (technical skills, systemic understanding of processes, IT security skills, project management trend, analytical skills and business knowledge), (2) personnel (flexibility, learning motivation, decision making, tolerance of ambiguity and ability to work under pressure), (3) methodological (creativity, digital mindset and entrepreneurial thinking) and (4) social skills (communication skills, teamwork skills, being committed and proactive, conflict resolution and leadership skills).
Table 1 – Industry 4.0 Skills.
|
Type of Competencies |
Specific Skills |
|
Technique |
Technical skills Systemic understanding of processes IT Security Skills Project management trend Analytical skills Business knowledge |
|
Staff |
Flexibility Learning motivation Decision-making Tolerance of ambiguity Ability to work under pressure |
|
Methodological |
Creativity Digital mindset Entrepreneurial thinking |
|
Social |
Communication skills Teamwork skills Be committed and proactive Conflict resolution Leadership skills |
Source: Adapted from Townsend et al. (2022).
2.2 SKILLS DEVELOPMENT
The definition of competencies goes beyond the acquisition of knowledge or skills acquired through teaching programs or professional experiences, this is what Bomfim (2012) states that through his bibliographic review showed that the concept of competencies depends on the proper application of these skills in specific situations that can be solved through this knowledge, that is, knowledge without practical applicability in the administrative routine does not fit as competence. This serves as a justification for the statement of Lazzareschi (2016) who points out the need to update skills due to the new market demands that have arisen due to the insertion of new technologies in our routine. This change in the competencies required by companies is not a recent perception, the study of Guglielmino and Carroll (1979) pointed out that from the 70s many companies began to worry about the development of their employees since this development could generate results for the company, John (2009) and Kowal et al. (2022) complement stating that from the end of the 90s the skills valued by companies also began to change, before having a training in the area of work and professional experience was a guarantee of employability, but companies began to seek behavioral skills such as good communication, leadership and creativity, the so-called soft skills, instead of just technical skills such as language mastery and knowledge of systems, the so-called hard skills. The initiatives of companies, and even the use of financial resources and time, to stimulate the development of skills in their employees that contribute to the company's long-term strategies have been the focus of several authors such as Guglielmino and Carroll (1979), Homer (2001), Wallo et al. (2020) and Mamatelashvilli et al. (2020).
Corporate training, also called in-company, has shown potential to assist in this development of mutual interest between companies and employees, can develop both hard and soft skills (Panagiotopoulos et al., 2018). Cosenza and Guerra (2011) explain that the set of skills and capacities necessary to achieve certain objectives are called executive functions, and that because of the change in the world, strategies must be defined that favor this learning: "The modern world is very different from the one in which our brain evolved. Today, there is not always an adequately structured environment for the development of executive functions" (Cosenza; Guerra, 2011, p. 98). The search for efficient ways to develop competencies in collaborators has been the focus of many researches for several decades, we can highlight the articles by Chute (1984) and Huang et al. (1991) that talked about innovative forms of training such as interactive videos, but this has remained present in several studies to this day, as we can see in the studies of Faraz et al. (2009), Chopra (2017), Titko and Bierne (2019) and Allal-Chérif, Lombardo e Jaotombo (2022). One of the reasons for this being a constant theme of studies is the very change in market demands, so that the knowledge needed today will not be the same as in the next decade, making agility and efficiency in teaching even more important (Santos, 2020).
2.3 INDIVIDUAL LEARNING
A learner is an individual who actively engages in acquiring new knowledge and skills, adapting continuously to challenges, and staying competitive in their field (Kinyua, 2015). This concept is supported by Individual Learning Theory (ILT), which shares similarities with psychology and cognitive research, as learning begins at the individual level (Mumford, 1991; Brandi; Elkjaer, 2012). For individual learning to translate into actionable knowledge, the information must be internalized, enabling the person to apply it effectively to achieve personal and professional goals (Cha et al., 2008). Anyone looking to remain competitive needs to learn from their own mistakes and successes, constantly seeking new ideas by observing their environment, acquiring new knowledge, and dedicating resources to their personal development (Kinicki; Kreitner, 2009). The development of individual learning capabilities shapes one’s beliefs and behaviors, fostering innovation and growth as new learning is integrated into one’s skill set (Tortorella et al., 2020).
According to ILT, for learning to occur, individuals must go through three stages: data acquisition, interpretation, and adaptation/action (Adhikari et al., 2021). Data acquisition is the process where an individual creates a "memory" of actions and outcomes, which is continually updated (Hult et al., 2000; Kinyua, 2015). The interpretation stage involves continuously comparing actual results with expected ones, where unexpected outcomes are critically evaluated to add new information that explains their causes (Hult et al., 2000; Kinyua, 2015). The adaptation/action stage is when the individual uses the knowledge acquired in the previous steps to make informed decisions about their future actions, based on the memory of past actions and results. This adaptation process should be ongoing, adjusting to both internal and external environmental conditions (Hult et al., 2000; Kinyua, 2015). Once this adaptation process is complete, the individual’s knowledge base must be updated with new action-result records (Adhikari et al., 2021).
According to Zahller (2012), learning is based on 12 independent constructs, representing the relationship between actual results and expected outcomes: (1) Levels of Learning (individual or organizational); (2) Learning Processes (cognitive, vicarious, deductive, experiential, digitalization, or grafting); (3) Type of Learning (action-result, probability); (4) Complexity of the Environment; (5) Degree of Personal Change; (6) Environmental Dynamism (rapid change, slow change); (7) Stress (functional or dysfunctional); (8) Strategic Approach (prospective, analytical, defensive); (9) Stage of Personal Development; (10) Personal Structure (centralized or decentralized); (11) Sociocultural Environment (endogenous factors, technology, administrative processes); and (12) External Environment.
The constructs presented by the Individual Learning theoretical lens show significant alignment with the objectives of this discussion, particularly concerning levels, processes, and types of learning, degree of personal change (which accelerates with the advent of new challenges), stress level, and environmental dynamism.
3 METHODOLOGICAL PROCEDURE
In order to find in the literature the articles that can meet the objective of this research: "Understand which Industry 4.0 competencies the literature considers most important.", a systematic literature review (SLR) was carried out, based on the Prisma model proposed by Liberati et al. (2009).
Before starting the Prisma protocol, a bibliographic survey was carried out in order to provide a prior understanding of the research context, as well as the relevant keywords and databases that best correspond to the research. This first survey was made in the Scopus database with the following search string: TITLE-ABS-KEY ("Administration" OR "Business") AND ("Skill development" OR "Competence development")). The research was limited to articles written in Portuguese, English or Spanish since the year of 2015, the reason to choose this range of date is because this is the rise of the Industry 4.0. These search criteria brought 515 articles, which after reading the titles and abstracts, 27 were selected for full reading. The content of these articles brought to light new keywords and clarified how this field of research has been explored, not necessarily a new SLR about Industry 4.0 skills, despite this, there is no standard for the studies carried out, causing each one to create their own way of presenting and naming such skills. For this reason this SLR is going to analyze the results of previews SLR, allowing it to reach a larger amount of studies and create a standard for those results (Shi; Wallach, 2022).
After this previous study, the stages of the Prisma Protocol were initiated. In the first step of the process, the databases that the documents will be collected are selected. To reach a representative number of articles, a search was performed in two databases: Scopus and Web of Science. The choice was due to their scope, having articles in several areas of knowledge, and also due to their international recognition.
The second step of the process corresponds to the selection of keywords for the searches in the bases. In addition to the word "development", the synonyms "enhance”, “foster”, “improve” “build” and “acquire” were adopted, it was required in the search that the title, the abstract or the keywords had at least one of these variations and also one of the four terms related to “skill”, “ability”, “competence” and “capability”. Those key words represent the “Skill Development” terms but also was required that those studies were a Systematic Literature Review about industry 4.0 skill, so the terms “Systematic Literature Review” and “Industry 4.0” were added. The combination of those words create the following search string: TITLE-ABS-KEY ((develop* OR enhanc* OR foster* OR improv* OR build* OR acquir*) AND (skill* OR abilit* OR competence* OR capabilit) AND "Industry 4.0") AND "Systematic Review"). It was added the symbol “*” after the radical of the words to allow variations as “acquire” and “acquiring” to arise in the searches made.
The third stage of the protocol consists of searching on the selected databases. The searches were performed individually in the databases, considering the select search string, always filtering by peer-reviewed articles, to ensure the credibility of the study, and written in Portuguese, English or Spanish, allowing the complete understanding of the articles. As for the period of analysis, the deadline for the search of the articles was not specified, in order to map the emergence and growth of the theme in literature. The databases (Table 2) brought the following results: 48 articles in Scopus and 3 in the Web of Science, during the exploratory research, 4 extra articles were added on this research, since his content has a high affinity with the research, but they were not available on those databases. All articles were exported and inserted into an Excel file, adding up to a total of 67 articles after the deletion of duplicates.
Table 2 – List of Articles by Base.
|
Research String |
Articles in each Base |
|||
|
TITLE-ABS-KEY (((develop* OR enhance* OR foster* OR improve* OR build* OR acquire*) AND (skill* OR ability* OR competence* OR capability*) AND "Industry 4.0") AND "Systematic Review") |
Scopus |
WoS |
Extras Articles |
Total Without duplication |
|
48 |
16 |
4 |
67 |
|
Source: Developed by the Author.
In the fourth stage, the analysis of keywords and reading of the abstracts of the articles were performed, with the objective of performing a pre-selection of which ones would have their complete content analyzed. The following premises were used as inclusion criteria (I) and exclusion (E):
(I) articles about industry 4.0 skill;
(E) articles that do not talk about the development of Management competencies, even if they talk about learning in other areas;
(E) articles aimed at developing skills in specific audiences, such as children, elderly or people with disabilities;
(E) articles focus on developing organization skills;
(E) articles that are not a Systematic Literature Review.
After reading the abstracts and keywords, 47 articles were identified that did not fit the objective of this analysis. The conclusion of this first analysis brought a total of 17 articles kept for the next stage.
The fifth and final stage of the review was reading the complete content of the articles for a better understanding if they fit the theme, respecting the same rules presented in the fourth stage. After the completion of this last stage, 11 articles were selected, adding more 4 extra articles, getting the total of 16 articles for cross-referencing of the content.
Figure 1 shows the complete path followed to attend the Prisma Protocol, highlighting the amount of articles selected on each step of the SLR.
 |
Figure 1 – Article Selection Scheme.
Source: Developed by the Author.
The 15 selected articles had their content analyzed and compared, but also, were analyzed the content of the articles used on those SLR, considering the amount of articles included on each SLR selected we are able to cross the content of 1.263 articles.
4 RESULTS
For the proper analysis of systematic reviews, it is necessary not only to analyze their content and findings, but also the content of the articles that were the focus of these reviews, unfolding their conclusions, their keywords and especially which competencies they addressed. This in-depth analysis is necessary to meet the objective of this study, defining a standard in the various studies on Industry 4.0 competencies, allowing a consolidated view of the literature on which individual competencies are most present in Industry 4.0 research, considering findings present in 1.263 articles, probably there is a duplicity of articles within all those mentioned in the 15 systematic reviews, unfortunately, only 2 authors provided the list of articles sought, making it impossible to remove duplicates, the SLR that has the most articles has 419 articles analyzed without duplicates, thus this way, this can be considered the minimum number of articles explored.
To begin the presentation of the results, it is important to demonstrate the profile of the selected articles, all of which deal with industry 4.0, as seen, this topic is very recent, where most articles on the subject were published from 2020, thus, As the survey focuses on systematic reviews of these articles, the date range becomes even smaller, as a result, only articles published from 2019 onwards were found, with 13% published in 2019, 20% in 2020, 33% in 2021 , 27% in 2022 and 7% in 2023. Since the articles are so recent, there was little time interval for them to be cited, with an average of 31 citations per article. Of these articles, 3 have not yet been cited, 2 of the articles raised stand out and have 147 and 134 citations, the latter being one of those added to the systematic review, even though it is not available in the two databases that were the focus of this study. Here we see how current this topic is, but even in systematic reviews it has a very wide range in the literature, making it difficult for researchers to decide which approach to use. Mainly due to the sample size of this SLR and the date range, there was no author who was repeated among the selected articles.
As for the scope of the selected studies, we have a wide range of keywords used in the strings of these systematic reviews to refer to the skills of the users of these technologies, terms derived from “Skill” appeared in 46% of the articles and those derived from “Skill” in 27% of them, the other terms were variations of the words “Knowledge”, “Ability”, “Training”, “Education”, “Learn”, “Capability”, “Study” and “Qualification”, but which appeared in less than 20% of the articles raised. It is worth noting that despite clearly speaking about competencies, article “A15” “Basic competencies for human work in industry 4.0” did not use any of these words in its search string, focusing its search on variations of terms that refer to Industry 4.0 workers.
In the theoretical scope, through the theoretical lens of organizational learning, we can relate the constructs presented by Zahller (2011) with the articles analyzed in this systematic review. As for the Level of Learning, articles A3, A4, A5, A10, A13 and A14 talk about the difference between the skills developed at the individual and organizational level, highlighting how each process takes place and the difference and complexity of each one, when talking about of individual skills, aspects such as motivation and the importance of knowing why that is being learned stand out. The constructs Learning process and Type of learning were seen in articles A2, A9, A12, highlighting the learning approaches used, such as active methodologies and the use of technologies for better use and absorption of knowledge. The Stress construct (functional or dysfunctional) was worked on in articles A4 and A12, speaking only of functional stress, as a form of motivation for learning. Dysfunctional Stress was not addressed in the articles surveyed, but it is present in the business environment.
The constructs Complexity of the Environment, Level of Organizational Change, Dynamism of the Environment, Stage of Organization Development, Organization Structure, Sociocultural Environment, and External Environment, were not addressed by the articles explored in this systematic review, since these focus on the development of individual competencies, which end up having less or no impact from the Organizational and External Environment.
The systematic reviews addressed in this study brought a total of 29 important skills for Industry 4.0 professionals, it is worth noting that some articles addressed these skills more generically, such as A10, which only mentions the need for technical skills, adaptation and readiness, not detailing which are the skills within those mentioned, unlike what was done in A15, where 10 types of Industry 4.0 professionals were presented and the necessary skills for each of them, mentioning 18 skills that were repeated among the mentioned professionals, in addition to specifications of technical skills that unfolded in skills such as Systems Tester, Knowledge of Components, Graphic Skills, Management of Productive Resources and Machine Operation, all of these by definition enter into technical skills, not being necessary for this research to unfold each one from them. It is worth noting that even with the grouping of technical skills, these were not the most cited, the survey highlighted Communication skills, which was cited in 92% of the articles, followed by Problem Solving and Flexibility, which were cited in 69% of the articles, so Teamwork, being cited in 54% of the articles. Even with the grouping of the various Technical Skills, these appeared in only 46% of the articles. The most common grouping of skills found was the differentiation between Soft Skill and Hard Skill, these skills were organized according to the definition given by the theoretical lens of Individual Learning”. The list of skills found, their group, their definition and their representativeness in the studies, can be seen in table 3.
Table 3 – List of Skills
|
Category |
Skills |
% |
Articles |
Definition |
|
Soft Skills |
Communication |
92% |
1, 2, 3, 5, 6, 9, 10, 11, 12, 13, 14, 15 |
The act of giving, receiving, and sharing information with clarity. |
|
Problem Solving |
69% |
1, 2, 3, 5, 8, 12, 13, 14, 15 |
The act of defining a problem, determining the cause, identifying, prioritizing and selecting a solution. |
|
|
Flexibility |
69% |
1, 3, 6, 8, 10, 13, 14, 15 |
Being able to quickly adapt to new circumstances as they arise. |
|
|
Teamwork |
54% |
1, 3, 4, 12, 13, 14, 15 |
Been able to work together toward a common goal. |
|
|
Leadership |
46% |
3, 6, 7, 12, 13, 15 |
The action of leading a group of people or an organization. |
|
|
Time Management |
38% |
1, 4, 6, 12, 14 |
The coordination of tasks and activities to maximize the effectiveness of an individual's efforts. |
|
|
Anticipating problems |
38% |
1, 6, 12, 14, 15 |
The ability to plan ahead considering distincts possibilities. |
|
|
Motivation |
38% |
3, 4, 13, 14, 15 |
Enthusiasm for doing something. |
|
|
Creativity |
38% |
3, 5, 13, 14, 15 |
The ability to make or otherwise bring into existence something new, whether a new solution to a problem. |
|
|
Proactivity |
31% |
13, 14, 15 |
The act or habit of thinking and acting so as to prepare for, intervene in, or control expected events. |
|
|
Innovation |
31% |
6, 13, 14, 15 |
The practical implementation of ideas that result in the introduction of new goods, services or improvements. |
|
|
Autonomy |
23% |
1, 3, 4, 6, 8, 10, 13, 14, 15 |
The ability to make your own decisions about what to do rather than being influenced by someone else. |
|
|
Feedback |
23% |
1, 6, 14 |
The transmission of evaluative or corrective information about an action, event, or process. |
|
|
Tolerance |
23% |
3, 13, 14 |
The ability to deal with something unpleasant, or to continue producing despite bad or difficult conditions. |
Table 3 – Continued
|
Category |
Skills |
% |
Articles |
Definition |
|
Soft Skills |
Sustainable |
23% |
3, 14, 15 |
The ability to utilize resources without causing depletion (not only natural resources). |
|
Conflict Solving |
23% |
3, 14, 15 |
The ability to negotiate with different teams to find a solution and keep the good relationship. |
|
|
Decision Making |
23% |
3, 14, 15 |
The ability to decide about something important, especially in a group of people or in an organization. |
|
|
Work under Pressure |
15% |
3, 14 |
The ability to keep the good quality of the work even under pressure from different sources. |
|
|
Intercultural |
15% |
3, 14 |
The ability to deal with different cultures. |
|
|
Network |
15% |
3, 14 |
Ability to build useful connections to the activities to be carried out. |
|
|
Empathy |
15% |
6, 14 |
The ability to share someone else's feelings or experiences by imagining what it would be like. |
|
|
Hard Skil |
Technical |
46% |
3, 4, 7, 10, 13, 15 |
Specifics skills to deal with systems, machines and technologies. |
|
Coding |
38% |
3, 5, 13, 14, 15 |
The knowledge to read and write codes. |
|
|
Mathematical Knowledge |
38% |
4, 6, 8, 14, 15 |
The knowledge to use numbers, measurements and mathematical logic. |
|
|
Data Analytics |
38% |
4, 6, 8, 10, 15 |
The knowledge to analyze distincts amounts of data, transforming those into information and insights. |
|
|
Language |
15% |
3, 14 |
Knowledge of languages other than your native. |
|
|
Media |
15% |
3, 4 |
Understand the use of different digital file formats. |
|
|
Research |
15% |
3, 14 |
The ability to discover new information on distincts sources. |
Source: Developed by the Author.
The analysis of the skills presented in the systematic reviews that analyzed 1.236 articles showed that even with the transformations that technology has brought to the job market, behavioral skills, called soft skills, are still the most relevant for work. The literature has shown that even though it is important to have technical knowledge such as data analytics, knowledge of tools and use of different media formats, knowing how to deal with the environment around you remains the most important. Highlighted skills such as flexibility, creativity and innovation, have shown potential for professionals to follow the transformations provided by technology.
This organization of skills related to industry 4.0 professionals will shed light so that companies can direct the training of their teams, making it not only focus on how to use certain systems or interpret data, but also on how to use these new possibilities for the optimization of the work performed.
5 DISCUSSION
The discussion of the results in light of the theoretical framework highlights the close alignment between the identified competencies and the concepts explored in the literature. The skills mapped by Townsend et al. (2022), categorized into technical, personnel, methodological, and social competencies, are consistent with the challenges presented by Industry 4.0, as identified in the theoretical framework. For instance, the need for a "systemic understanding of processes" and "IT security skills" aligns with the technical demands of Industry 4.0, which require a deep understanding of interconnected systems and cybersecurity, as discussed by Sacomano et al. (2018) and Bianco (2020). The results support the idea that competencies in Industry 4.0 go beyond traditional technical skills, encompassing a broader set of capabilities.
Furthermore, the results resonate with the theoretical framework's emphasis on the evolving nature of competencies in the context of Industry 4.0. The theoretical discussions by Bomfim (2012) and Lazzareschi (2016) on the need for continuous updating of skills are reflected in the results, which highlight the dynamic and rapidly changing requirements of Industry 4.0. The identification of competencies such as "learning motivation," "creativity," and "entrepreneurial thinking" in the results underscores the importance of adaptability and continuous learning in this new industrial paradigm. This aligns with the argument presented by Guglielmino and Carroll (1979) and Santos (2020) that the competencies required by companies are constantly evolving, necessitating a proactive approach to skill development.
Finally, the discussion also illustrates how the results contribute to understanding the interplay between individual learning and organizational needs in Industry 4.0. The results reflect the stages of individual learning, such as data acquisition, interpretation, and adaptation/action, as outlined by Kinyua (2015) and Hult et al. (2000). The identified competencies align with these stages, indicating that effective skill development in Industry 4.0 requires not only technical knowledge but also the ability to interpret and adapt to new information continuously. This supports the theoretical perspective that individual learning is a critical component of competency development in the context of rapidly advancing technologies.
6 FINAL CONSIDERATIONS
The articles found in this systematic review pointed out that this is a topic with great potential, both for the number of researchers and companies that have been interested in the subject in recent years, and for the promising results that research has pointed out, bringing direct relations with the improvement in learning. In addition, the articles seek to answer current and important questions for the future, for example, how and what competencies should be developed in twenty-first century professionals.
The article mapped, standardized and grouped the main competencies highlighted in the literature, namely Communication, Problem Solving, Flexibility, Teamwork, Leadership, Time Management, Anticipating problems, Motivation, Creativity, Proactivity, Innovation, Autonomy, Feedback, Tolerance, Sustainable, Conflict Solving, Decision Making, Work under Pressure, Intercultural, Network, Empathy, Technical, Coding, Mathematical Knowledge, Data Analytics, Language, Media, Research, Transfer Knowledge. Although some of these competencies appear more frequently than others, it was not possible to measure their importance, that is, some competencies may appear more frequently than others more for a matter of ease of study or coverage of areas than because they are truly more relevant to the market. Assessing the relevance of one of these competencies is a proposal for future studies.
This study explored 2 large databases, Scopus and Web of Science, without date cut, but wasn't able to bring a significant volume of articles for analysis, the application of the same protocol in other databases may be relevant but should bring a smaller number of articles. It is pointed out as a suggestion for future studies that the comparison of these elements found in the literature with reports of professionals who need to develop management skills in their times and with the gray literature, in order to validate and bring a practical look of the market to the issues found. In addition, studies that seek to bring light to the development of competencies from other areas as Project Management, Management and Product Development, can contribute to the creation of new solutions, since this review did not find articles that spoke in depth about these areas.
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