2.1 Article selection
The present study uses a systematic literature review approach. An initial search of articles was conducted on Web of Science (WoS) and Scopus databases between 2011 (when the Industry 4.0 concept was created) and 2019. The following combination of keywords was used for searching relevant papers: a) “knowledge and skills” AND “Industry 4.0”; b) “human resources” AND “Industry 4.0”.
22 articles were found in the Web of Science (WoS) database containing the keywords “knowledge and skills” AND “Industry 4.0” included 7 journal papers while 25 articles were found in the Scopus included 8 journal papers. They were published from 2016 and 2019.
51 articles were found in the Web of Science (WoS) database containing the keywords “human resources” AND “Industry 4.0” included 19 journal papers while 139 articles were found in the Scopus database included 47 journal papers. They were published from 2014 and 2019.
This initial search provided in total 73 papers in the WoS and 164 in the Scopus databases. 20 articles were selected for analysis using the criterion of the 5 most cited journals and conference articles in each of the two categories separately at the WoS and Scopus database. The number of citations was checked on October 16, 2020. The results of the selection of articles are presented in Table
1.
Table 1
List of the articles selected for a literature analysis
“knowledge and skills” AND “Industry 4.0” WoS |
1 | Posselt G, Boehme S, et al. | 2016. Intelligent learning management by means of multi-sensory feedback. 6th CIRP Conference on Learning Factories, Norway, JUN 29–30, 2016, Procedia CIRP 54:77–82. | 9 |
2 | Bueth L, Blume S, et al. | 2018. Training concept for and with digitalization in learning factories: An energy efficiency training case. Procedia Manufacturing 23:171–176. | 6 |
3 | Gonzalez I, Calderon A J | 2018. Development of Final Projects in Engineering Degrees around an Industry 4.0-Oriented Flexible Manufacturing System: Preliminary Outcomes and Some Initial Considerations. Education Sciences 8(4):214. | 5 |
4 | Ghislieri C, Molino M, et al. | 2018. Work and Organizational Psychology Looks at the Fourth Industrial Revolution: How to Support Workers and Organizations? Frontiers in Psychology 9:2365. | 5 |
5 | Graczyk-Kucharska M, Szafrański M, et al. | 2018. Model of Competency Management in the Network of Production Enterprises in Industry 4.0-Assumptions. Advances in Manufacturing, Lecture Notes in Mechanical Engineering 195–204. | 3 |
“knowledge and skills” AND “Industry 4.0” Scopus |
6 | Roy R, Stark R, et al. | 2016. Continuous maintenance and the future - Foundations and technological challenges. CIRP Annals - Manufacturing Technology 65(2):667–688. | 114 |
7 | Sackey SM, Bester A | 2016. Industrial engineering curriculum in industry 4.0 in a South African context. South African Journal of Industrial Engineering 27(4):101–114. | 37 |
8 | Ras E, Wild F, et al. | 2017. Bridging the Skills Gap of Workers in Industry 4.0 by Human Performance Augmentation Tools - Challenges and Roadmap. ACM International Conference Proceeding Series Part F128530:428–432. | 26 |
9 | Bueth L, Blume S, et al. | 2018. Training concept for and with digitalization in learning factories: An energy efficiency training case. Procedia Manufacturing 23:171–176. | 12 |
10 | Perez-Perez M.P., Gornez E, et al. | 2018. Delphi prospection on additive manufacturing in 2030: Implications for education and employment in Spain. Materials 11(9):1500. | 11 |
“human resources” AND “Industry 4.0” WoS |
11 | Benesova A, Tupa J | 2017. Requirements for Education and Qualification of People in Industry 4.0. Procedia Manufacturing 11:2195–2202. | 66 |
12 | Schneider P | 2018. Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field. Review of Managerial Science 12(3):803–848. | 30 |
13 | Kazancoglu Y, Ozkan-Ozen YD | 2018. Analyzing Workforce 4.0 in the Fourth Industrial Revolution and proposing a road map from operations management perspective with fuzzy DEMATEL. Journal of Enterprise Information Management 31(6):891–907. | 19 |
14 | Sangil P, Jun-Ho H | 2018. Effect of Cooperation on Manufacturing IT Project Development and Test Bed for Successful Industry 4.0 Project: Safety Management for Security. Processes 6(7):88. | 15 |
15 | Mohelska H, Sokolova M | 2018. Management Approaches for Industry 4.0 - the Organizational Culture Perspective. Technological and Economic Development of Economy 24(6):2225–2240. | 11 |
“human resources” AND “Industry 4.0” Scopus |
16 | Hecklau F, Galeitzke M, et al. | 2016. Holistic Approach for Human Resource Management in Industry 4.0. Procedia CIRP 54:1–6. | 153 |
17 | Benesova A, Tupa J | 2017. Requirements for Education and Qualification of People in Industry 4.0. Procedia Manufacturing 11:2195–2202. | 100 |
18 | Shamim S, Cang S, et al. | 2016. Management approaches for Industry 4.0: A human resource management perspective. IEEE Congress on Evolutionary Computation CEC, 7748365:5309–5316. | 61 |
19 | Schneider P | 2018. Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field. Review of Managerial Science 12(3):803–848. | 43 |
20 | Sivathanu B, Pillai R | 2018. Smart HR 4.0 - how industry 4.0 is disrupting HR. Human Resource Management International Digest 26(4):7–11. | 33 |
2.2 Knowledge and skills of industrial employees and managerial staff indispensable to implement of the industry 4.0 concept
The implementation of the Industry 4.0 concept is possible mainly due to ubiquitous digitization, the development of the Internet, virtual reality and the ability to collect and process huge amount of data in real time. As a result, digitally supported manufacturing technologies, Data Mining, Big Data Analytics and ICT telecommunications are today created. It means a change in the way of production control, including dynamic changeover of machines initiated by information transferred in the manufactured product or its components. There are great organizational and also technological and strategic challenges. This concept requires broadband communication, including at the level of individual device sensors, biosensors and actuators in real time and in extensive network environments [
11,
25]. The advantage of these solutions is that data is available immediately, but also that the intervention protocol can be prepared in advance and the available information reduces the length of the decision-making process and forced downtime [
26]. Besides, a quick response to customer demand is much more easy [
27]. The literature analysis indicated that the implementation of the Industry 4.0 concept is especially important in the automotive and electronics industries to achieve a competitive advantage in the market, but the ripple effect is clearly observed across all sectors in industry [
26].
The level of applied manufacturing technologies (intelligent machines and devices, autonomous means of transport, etc.) requires a highly qualified staff employed, the ability to knowledge transfer, teamwork and openness for changes. The importance of lifelong learning increases. Manufacturing companies need to promote the climate of innovation and learning and to change the learning culture, which means a change in values and expectations [
7,
28]. They should build the learning management system and procedures to evaluate and control a learning progress and knowledge transfer results [
29,
30]. The exchange of knowledge and skills within the network of enterprises can be also a crucial for their quickly updating [
31]. The Industry 4.0 technologies will automate many processes in enterprises, what allows more efficient and leaner work teams to be built but still requires a completely new approach to talent development staff [
32].
Ahrens and Spottl point out that employees need a specific knowledge and a new skill paradigm [
33], because the number of workspaces with high level of complexity increases significantly [
34]. The demand for new skills results from: 1) a growing need for comprehensive integration and information transparency; 2) increasing automation of production systems, 3) self-management and decision-making by objects, 4) digital communication, 5) interactive management functions, 6) staff flexibilization [
33].
In the last years one of the most important challenge for future human resources development is digitalization. The Internet of Things and Cloud Computing play a major role within the Industry 4.0 context [
35‐
37]. The digitalization is the most promising enabler for increasing the overall performance of production systems [
35,
36]. The digitalization should increase effectiveness of operative management, the efficiency of manufacturing and supporting processes [
38]. Such effects can achieve by reducing operational costs for manual data acquisition, their recurring analysis and evaluation [
2]. The quickly advancing digitalization requires using a training as an object and should consist of three basic modules including a theory and application part: 1) technological basics; 2) systematical approaches; 3) digitalization, which introduces Industry 4.0 hardware and software step by step. The biggest mistake of teaching digitalization is that employees only learn how to operate the software interface, but they do not acquire competencies of method-based acting and the ability to assess the effectiveness and accuracy of the applied measurement and analytical activities. Changes in boundary conditions can lead to the uselessness of this knowledge [
2].
Nowadays educators and policy makers play a key role in preventing competence obsolescence. They are responsible for the continuous updating and development of knowledge and skills required by the current and future labor market [
39]. Gonzalez I. and Calderon A. J. underline a need of learning students as well as teachers. They propose to develop final projects in engineering degrees as a form of learning containing the following main scope of knowledge and skills: advanced automation, supervision, robotics and industrial network communications included system integration, sensors, actuators, etc. [
40].
Many researchers highlight the role of employees’ knowledge on IT and production technologies, awareness of IT security and data protection in the Industry 4.0 environment [
41‐
43]. The significant IT job profiles are as follow: Informatics Specialist, PLC Programmer, Robot Programmer, Software Engineer, Data Analyst, Cyber Security while production job profiles include: Electronics Technician, Automation Technician, Production Technician and Manufacturing Engineer [
42]. The curriculum enhancement initiatives for the Industry 4.0 should include aspects connected with data, its automated gathering, processing and communication such as: data science, big data analytics, data communication, advanced simulation, virtual plant modeling, networks and system automation, novel human-machine interfaces, digital-to-physical transfer technologies (e.g. 3D printing), real-time inventory, closed-loop integrated product and process quality control and management systems; and logistics optimization systems [
44‐
47]. There is also a need to train next generation leaders and young talent for Performance Augmentation for Industry 4.0 [
48] and for Additive Manufacturing, which allows shorter production runs, the capacity to manufacture parts with geometries impossible using current methods, generating unique items and flexibility with respect to design changes to be obtained [
49].
Nevertheless, the implementation of the Industry 4.0 concept is also a challenge for management sciences and requires research in the following areas: strategy and analysis, planning and implementation, cooperation and networks, business models, human resources, change and leadership [
50]. Employees need also knowledge and skills in a decision making and process management [
41,
51]. In the future will be needed a stronger partnership between manufacturer, customer and the supply chain supported by an internal organizational culture. The novel business models should be developed which share the risks of guaranteeing the through-life performance [
37].