1 Introduction

Industry’s evolution has been driven by the changing needs of its customers. Nowadays, they are more informed and more demanding due to the vast amount of information available and the wide range of products offered by various industry players. Customers ask for tailored products, those that will fulfill their specific needs. In response to this, companies have adapted their processes to produce more personalized options, always available when the client needs them. Here is where technology comes into play, allowing companies to be more flexible and supporting their innovation processes.

The term I4, first appeared in 2011, when a group of scientists and industry leaders proposed to the German Government a plan to improve its technological strategy [1]. The concept has evolved, becoming more popular as technology has progressed, and as the production processes have taken advantage of the emerging technologies. I4 is defined as the integration of information and communication technology with industrial advances aimed to develop intelligent factories that will lead to more efficient, green, and custom-made manufacturing processes. In the dynamic environment of innovation, the I4 incorporates new technologies in the labor context, increasing productivity, expanding markets, resulting in the emergence of new professional profiles and, among other things, the satisfaction of the demands of the consumers. As a matter of fact, researchers consider that I4 will make industrial companies’ processes 30% faster and 25% more efficient [2]. Under the I4 context, factories will be completely digital; a central computer will manage and coordinate all the units from the supply chain to distribution and after-sale tasks through an interchange of data among all the subsystems [3]. The final goal of this approach is to produce more personalized products and services by having people, products, and machines constantly interact in all the value chains [4].

There are companies that have already entered in this dynamic with great advances. Siemens, Mitsubishi, and General Electric have, at present, transformed their processes by using I4 solutions [3]. The last one developed a mobile App that, through a mechanism of real-time and proactive feedback, allows the design engineers to validate that the production floor can develop the virtual products that are still in the design phase. As another example, Airbus uses IoT to reduce the cost of having experienced engineers on the floor. Through toolbox information online, the workers can download in their cellphones the instructions when needed. The company gives precise indications related to the tools, tasks, and procedures they must follow to develop a specific job [5]. Other offerors of I4 solutions include DMG Mori, Wittenstein, Bosch, Rockwell, Omron, Schneider, Stäubli, Yaskawa, Krones, PSI, and Software AG [3].

Benefits that I4 can bring to society are numerous, including having access to high-quality and low-cost products, diminishing the environmental impact of the production process, and making businesses more productive and competitive [6]. However, from the side of the manufacturers, there are barriers that must be overcome, such as the high investment needed, not having qualified talent, data safety issues, and the need to shorten the innovation cycles [7]. The countries that are making the greatest progress in adopting the I4 approach are Germany and the United States. According to a survey applied to 600 German and US companies, the main obstacle faced by enterprises in these two countries is the lack of qualified professionals. Data management and security, software development, programming, data science and analytics are the competencies that enterprises of both countries demand from the workforce. Both countries believe that by offering continuing education to their employees, they will solve this problem [8].

As can be seen, the development of I4 is based on the perfect integration/assembly of components such as technology (information) and people. The main goal of this research is to do a review of I4 in this context and determine what universities/organizations are doing to train engineers who will work effectively in I4 systems. The focus is on identifying and describing the workshops, courses, and programs that leading engineering universities offer to students to acquire the knowledge and skills related to I4. The universities selected for this review came from the QS World University Rankings by Subject 2019: Engineering and Technology.

The outline of this work is as follows: Sect. 2 describes the technologies that are enablers of I4. Section 3 analyzes the human talent needed and the qualifications (competencies/skills) required. Section 4 describes the courses and practices that leading universities on engineering and technology are teaching to develop the I4 workforce. Section 5 takes a look at the future by presenting trends related to education in I4. Finally, Sect. 6 concludes the paper.

2 Industry 4.0 technology

There is a wide range of technologies that support I4, including Artificial Intelligence (AI), smart systems, advanced/microsensors, and automation components for microprocessors and microcontrollers [9]. Nine technological advances are considered enablers of the fourth industrial revolution. These will allow industrial companies to get interconnected, both vertically and horizontally, and get a vast flow of information for making faster decisions and achieving more flexible/efficient production processes [2].

2.1 Autonomous robots

Autonomous robots are machines able to perform a task with little or no human aid. They can learn by themselves and make decisions opportunely by collecting and analyzing Big Data quantities. These machines are already used in industrial processes and replace operators, allowing them to perform more valuable/strategic tasks [10]. Robots will be key to implementing I4. They will be able to identify specific and miniature objects, being more precise when manipulating items, having the knowledge and manipulative skills to make decisions and work with complex products [11]. The general benefits that autonomous robots offer to industries include increased productivity, reduced errors and rework, the performance of high-risk tasks, and more efficient production processes [10]. Autonomous robots have been offered by well-established enterprises for years. Kuka Robotics and ABB are examples of some suppliers of these advances in the manufacturing domain. The former produces autonomous robots that interact with each other, and the latter has developed a robot called YuMi, which interacts with humans [2]. Other examples of industrial robots include Baxter (used for packing), BioRob Arm (used in close proximity with humans), and Roberta (a 6-axis robot that can work in tight spaces) [12].

2.2 Simulation

Simulations have been traditionally used in manufacturing, mainly in the design phase, saving time and resources. Due to the many advances (i.e., high fidelity, augmented reality, cloud computing, etc.) that have been developed in the computing field, simulations have been taken to a different level, offering great benefits to the industries [13]. In the context of I4, simulations can be defined as the use of high-fidelity models of real products, services, or processes to simulate their behaviors with the main objective of understanding their reactions when facing specific situations to improve performance. Simulations are referred to as digital twins and, in manufacturing, the main benefits of their use include: (a) doing experiments without taking risks, (b) training operators until they have the needed skills to operate the real machines, and (c) foreseeing problems in the real machines by running the simulations in parallel and identifying the differences in performance [3]. Siemens has developed a simulator that mimics the machining of parts based on data from the physical machine. This lowers the setup time of the real machining process by 80% [2].

There is specialized software widely used in manufacturing. DELMIA is a software developed by Dassault Systèmes that supports digital manufacturing tasks. It helps to plan, simulate, and model global production processes. As a consequence, companies can respond faster to the strategies of competitors, better evaluate market opportunities, and solve problems more efficiently. CenterLine (Windsor) Limited is a Canadian company that used DELMIA to simulate products and processes to optimize operations such as robot movements, plant layout, and material flow. As a result, their organizations have reduced tooling issues and rework by up to 90% and programming time by up to 75% [14].

2.3 Horizontal and vertical system integration

Horizontal integration refers to the combining of various independent production chains; as a result, enterprises with related products collaborate and compete with each other to become efficient as a whole. On the other side, vertical integration consists of the linkage of all the value-added subsystems of a single company. The main benefit of horizontal and vertical integration is that the entire business network works autonomously, taking advantage of all the data generated, making processes optimum, reducing costs, and producing better products [15]. The integration of the systems is a requirement for implementing I4. It allows all the production elements involved, i.e., departments of the organization, suppliers, customers, and vendors, to function by communicating in real-time. This is done in a private workplace set in the cloud in which the business units can collaborate in design and manufacturing tasks, by sharing product and production data [16]. There are technologies that facilitate integration. These include IoT, wireless sensor networks, Big Data, cloud-based services, embedded systems, and mobile internet [17]. For all the technologies to function in an industrial environment, a common/standardized infrastructure is required (e.g., interoperable hardware and software and compatible computers) [6]. Dassault Systems and BoostAeroSpace launched conjointly a collaboration platform named AirDesign for the European aerospace and defense industry. It allows industry players to collaborate in their design and manufacturing processes by exchanging product/production data more effectively [2].

2.4 Industrial internet of things (IIoT)

IIoT is the integration of machine sensors, middleware, software, cloud computing, and storage systems in the industrial processes of companies. This generates a lot of information, which is distributed and then used to develop advanced analytics, discover insights, and make operational decisions [5]. IIoT has a more practical promise to increase operational efficiency through automation, connectivity, and analytics. IIoT’s main features are context, omnipresence, and optimization. Context is related to the possibility of advancing the interaction of an object within a specific environment and responding immediately if a change occurs. Omnipresence refers to the ability to determine the location and conditions of an object. Finally, optimization is related to the possibility of making all the operations in the product life cycle more agile through a network of connections [12]. Basically, IIoT can support manufacturers in monitoring production, improving security and quality control, facilitating maintenance, effectively managing inventory, tracking products, and developing innovative solutions, among others [18]. The benefits that IIoT offers to industrial companies can be summarized in an increase in productivity and efficiency and reduction of costs [5]. Bosch Rexroth, a German manufacturer of drive and control systems, built a production facility for valves with semi-automated and decentralized production processes. Products are identified with radio frequency identification codes and, when each one arrives at a workstation, automatically gets the manufacturing process required [2]. Thames Water, a UK company, uses sensors, remote communication, and Big Data analytics to foresee failures in the machines and respond on time to any emergency [5].

The ABB energy and robotics firm is one of the most visible to adopt the concept of predictive maintenance, using connected sensors to monitor its robots and activate the repair before the faults are present. Airbus has launched the Factory of the Future to optimize operations and strengthen production capacity. The company has integrated sensors in tools and machines in the workshop and has provided workers with wearable technology designed to reduce errors and strengthen safety in the workplace. Wearable devices resulted in a 500% improvement in productivity and almost eliminated errors [19].

2.5 Cybersecurity

I4 generates a constant interchange of data among a network of enterprises. This poses the challenge of making sure that the data is not going to be stolen or systems manipulated. Among the risks organizations face in this regard are attacks against sensors, actuators, information transmitted via a network, the connection between controllers, IIoT gateways, and the Safety Instrument System [20]. The attacks could cause several problems to the organization, such as a decline in productivity due to production disruptions and counterfeit products available in the market due to the theft of design documents [21]. Cybersecurity strategies must be planned in advance, beginning with the design of any I4 initiative, so the potential risks can be managed on time and effectively. Some actions that can be performed to increase the security of industrial activities include the strong use of encryption, AI (e.g., the DARPA platform), and machine learning solutions that result in robust and responsive threat intelligence, intrusion detection, and intrusion prevention solutions. However, it is important to note that all organizations are vulnerable despite the security actions taken. They should have a plan in case they are victims of an attack that includes the role of each person involved, responsibilities, and actions to be taken [22]. There are already in the market companies that offer cybersecurity services to industrial enterprises, including ABB Ltd., Belden Inc., Check Point Software Technologies Ltd., Cisco Systems, and McAfee LLC [23]. Among the products cybersecurity providers offer by using advanced technology are identity and access management, trust management, cyber threats management, network security, system security, cloud security, trusted hardware, endpoint security, and mobile security [24].

2.6 The cloud

Cloud computing is the use of internet servers that offer an accessible way of using computer resources such as networks, storage, applications, and diverse services. People can have access to resources from anywhere. As a matter of fact, cloud computing enables the performance of other I4 technologies. The cloud is a basic element for the performance of cyber-physical systems [25] as it allows organizations to share data in milliseconds [12]. In manufacturing, the cloud supports the management of a great quantity of data in open systems and allows real-time communication for production systems [26].

A trend that is emerging is the use of cloud platforms to perform Big Data analytics to discover insights (e.g., identify preferences from users). Big data processes, such as deep learning, use a lot of computing resources; this makes such processes physically dependent on the places where data is generated and the knowledge that is required. This is not sustainable in the long term, so new approaches are being sought to decentralize computational processes. The use of cloud technology allows us to collect, process, deliver, and derive value from the data in a more effective way [27]. In the I4 context, the cloud offers great benefits. This technology can improve the efficiency of processes, offer valuable information that can be shared easily with different partners (e.g., reasons for machine failure), reduce costs, and improve systems performance [28]. Numerous enterprises are already using and reaping the benefits of this technology. More than 90% of global enterprises mention using cloud computing in one part of the business. Johnson & Johnson analyses all the data generated in its processes in the cloud to make decisions faster. Pfizer uses the cloud in the supply chain, so all the involved participants have access to the same information in real-time. This has allowed the company to better participate in global markets [29].

2.7 Additive manufacturing

Additive Manufacturing (AM) is a process in which products are built based on digital design and by depositing layers of material [30]. There are different types of AM technologies; the main ones are stereolithography, selective laser melting, selective laser sintering, aerosol printing, fused deposition modeling, layer laminate manufacturing, and 3D-Printing. Each one has its own binding mechanism, and the types and forms of processing of the materials involved are also different [31]. There are AM materials that are drawing the attention of industries due to the benefits they offer in I4. Stainless steel, titanium, shape-memory alloys, piezoelectric, lunar dust, concrete, solid–liquid printing, and multi-material printing are resources that are under research. Expectations are big; smart materials could be used in producing artefacts for extreme environments, and metals offer great mechanical properties. All these characteristics lend support to the main benefit of I4, which is making industries more competitive [32].

In the context of I4, AM technology can produce custom items of great quality. Intermediaries could be eliminated as the customer can go to the printing sites and obtain his product directly; innovation processes can be faster, and the time to market can be shortened, blunting the actions of imitators [32]. This technology offers important benefits to industries, such as the possibility of developing personalized products at low cost, versatility in design (there are no restrictions as a product can be digitally modified and reprint), material savings, and less waste production [33]. AM is already used in industries such as aerospace, biomedical, and manufacturing. In the aircraft industry, Airbus and Boeing have used AM to produce specialized parts and obtain savings. The former designed and built an optimized nacelle hinge bracket for the Airbus A320 aircraft, achieving a 64% weight reduction, which resulted in savings of 10 kg mass per plane. The latter redesigned the air-cooling ducts of the F-18E jet, which allowed a reduction of 50% and 67% of the total and production time, respectively. With AM, manufacturing companies can have numerous benefits, such as the reduction in carbon emissions, the reduction of inventory, and less operational costs, thus, offering more flexibility during the life-cycle of the product [34].

2.8 Augmented reality

Augmented Reality (AR) superimposes virtual objects onto real images that are captured through a mobile device. The idea is to improve the environment [35]. In manufacturing, AR can be used to see replicas of real devices and find a specific problem and compare parts to determine if they are under specification; therefore, technicians can support or solve problems from anywhere [33]. The technology can be utilized in various contexts, e.g., it can be used as an interface to interact with robots on the production floor, or it can be used to train operators and enhance their skills. Generally speaking, this technology increases productivity and efficiency, improves communication when designing and developing products, prevents errors, and saves time and costs [36]. Siemens uses AR to train its employees in emergency scenarios. They have developed a 3D environment in which operators can interact with different machines in order to learn how to handle the difficulties that arise [2].

2.9 Big data and analytics

Big Data is the process of analyzing large quantities of information generated by smart sensors, devices, log files, and video and audio sources [20]. Other providers of information are the design processes, machine operations, staff behavior, logistics, environmental conditions, fault detection, product usage, and customer information [37]. The main goal is to discover knowledge that can be useful for making better, more informed decisions [20].

The main characteristic of Big Data is what some researchers call 5 V, which refers to volume, velocity, variety, veracity, and value. This is why advanced techniques are needed for analyzing this information to get the best out of it. Big Data can help enterprises to improve systems performance, have near-zero downtime, and predict the need for maintenance in the machinery [37].

Infineon Technologies Enterprises is an example of the effective use of Big Data in manufacturing. They made a correlation between single-chip data captured at the end of the process with data collected at a key phase at the beginning of the process. As a result, they were able to analyze patterns that allowed them to eliminate bad chips sooner, improving the quality of their products and reducing failures [2].

There are even technologies ready to support the implementation of I4. Organizations face multiple challenges to make this approach a reality. A couple is that systems have to be autonomous enough, which is not the case today, and high-speed IWN protocols are needed. The current ones cannot provide the right bandwidth for instant communication and the transfer of Big Data quantities. Another challenge is that manufacturing systems still do not produce high-quality data, necessary for making correct analyses and good decisions. Other challenges are that the security of data and systems need special consideration, and high investment is needed because the advanced technologies are still costly [12].

3 Industry 4.0 professionals

It is expected that in the upcoming 10 years, there will be 3.5 million jobs available in manufacturing; however, less than half of them will be filled with people with the right abilities [18]. I4 will demand professionals with new profiles; they will need to be more skilled in managing complex manufacturing systems; they will also need to be more creative, strategic, and coordinated [38]. A well-qualified human resource will be now than ever important, and universities will play a key role in training the future workforce. Students of today and tomorrow need to have knowledge and abilities useful for facing a highly technological and interconnected environment. Competencies that need to be developed in 4.0 students include interdisciplinary thinking, decision making, problem-solving, cultural and intercultural skills, and commitment to lifelong learning [39].

3.1 Education 4.0

I4 will force education to be redesigned. Education 4.0 (or Academia 4.0) is a new term that expresses that change is required in the educational system [39]. It consists of combining physical and virtual resources for teaching and learning. It considers that the teaching–learning process is not structured and fixed but takes place anywhere and at any time [40]. Under this approach, education is more personalized. Based on students’ own characteristics, it uses new and innovative devices as well as innovative pedagogical strategies such as project-based, problem-based, challenge-based, experiential, and collaborative learning. Students participate in the design of their own curriculum, and professors adopt the role of mentors [9]. An example of a university that is focusing all its resources and making great efforts to offer to its students the most modernized education is Tecnologico de Monterrey. This institution launched in 2013 a new educational model named Modelo Educativo Tec21 (Tec21 Educative Model), whose main goal is to develop internationally competitive, integral individuals who are trained to meet present and future challenges. The model puts challenge-based learning in the center and commits to having high-quality professors and attracting the best students as the key pillars for its successful implementation. Students acquire not only technical knowledge related to their specific disciplines but also they develop competencies that are going to be useful to function in the future work environment successfully; these include leadership, intellectual curiosity, lifelong learning, communication, and teamwork [41]. Emerging technologies are a key component of the Tec21 model. The institution promotes intensive use of various advanced technologies from 3D printers and virtual/augmented reality to classrooms equipped with Zoom + Room to have synchronous classes on different campuses. Mostla [42] was created as a place in which professors, students, and staff can interact in makerspaces and learn about emerging technologies. The final goal is to offer to the community the emerging technologies that have the greatest potential to create better educational experiences. The institution’s strategy has a clear purpose, which is to improve education to develop professionals who add value to society.

3.1.1 Engineering education 4.0

The training of future engineers needs to be reshaped. They have to be prepared to confront the rapid changes in technology and the decreased product life cycles and adapt to new types of jobs [43]. Future professionals will need to develop new technologies/techniques, collaborate with peers on an international basis, and work on bio and nanotechnology-based solutions to address the main challenges to society, e.g., global environmental issues [44] and even problems that do not exist now. Professional jobs related to I4 transformation that are expected to be in demand in the future include smart environment designer, recycling technologist, ergonomist-designer, domestic robot designer, composite engineer, transport safety engineer, and systems expert in environmental disasters [45]. With this in mind, it is more important than ever to train future engineers with appropriate pedagogical strategies, such as experiment-based learning, research-based learning, and active learning [46]. Besides, professors need to be constantly trained by sending them to companies to get insights or inviting industry experts to the class to share their practical knowledge [47].

Content and courses need to be adapted to the new demands. Examples of courses related to I4 that should be considered in engineering programs include Object-oriented programming, Statistical methods of data analysis, Database systems, Self-organizing maps, IT Security, Materials technology, Production technology, Industrial robotics, Data analytics, Machine learning, Deep learning, Cybersecurity, Artificial intelligence, Cryptology, Speech signal processing, Industrial IT, and Virtual product development [39].

Universities should also train students in various disciplines simultaneously (e.g., mechatronics combining mechanical, electrical, and systems engineering), offer non-technical courses (e.g., risk and safety, emotional intelligence, innovation) and include courses related to arts and creativity. Despite the content that can be offered, it is important to keep in mind that continuous learning is needed when students graduate. They should keep updated through on-the-job training, obtaining micro-credentials (i.e., alternative credentials), and studying individually [47].

Competencies that the future workforce must have include failure analysis, prognosis, adaptation to change, and handling of I4 technologies. They also need to be able to solve problems and implement solutions efficiently through a challenge-based education [7]. According to ABET, successful professionals must have a set of knowledge, abilities, tools, and methods to solve specific problems creatively. They must be able to: (1) apply knowledge of mathematics, science, and engineering; (2) design and develop experiments; (3) analyze and interpret data; (4) design systems or processes considering economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability constraints; (5) identify, formulate, and solve engineering problems; (6) understand the impact of engineering solutions in global, economic, environmental, and societal contexts; and (7) use the techniques, skills, and modern engineering tools necessary for engineering practice. A fundamental competency that has already been mentioned and that engineers must possess is advanced analytics. However, these criteria that ABET now establishes must consider the current and future needs of a continuously changing future with high uncertainty; that is, it will require rapid adaptability to changes. In addition to basic knowledge, skills and competencies in innovation, analysis, and problem-solving play more important roles. These will allow them to analyze large data sets from many sources, optimize production processes, and support real-time decision making [1].

Educational technology needs to be included as an integral part of the educational process. In engineering education, technologies that are being used include remote laboratories and virtual laboratories. These variants of laboratories offer the advantage of linking theory and practice in a convenient and modern way [46]. There are other technological advances that have the potential of accelerating the changes needed in Engineering 4.0 education: (1) 3D printing provides students a deeper understanding of the subject under study; (2) AR is an interactive technology that captures the attention of students and lets them have better sensorial stimuli; (3) VR enhances the ability of students to make connections between concepts learned and information being analyzed; (4) cloud computing gives students access to academic work from anywhere; (5) holograms permit students to learn in real-time in an interactive way [48]; (6) AI allows students to have a better understanding of the subjects and, thereby, improve their learning outcomes; (7) robots better capture and maintain the attention of students and are useful devices for personalized instruction [49], and (8) IoT helps students become active learners by letting them work and learn independently or in collaboration, developing their own understanding of the topics [50].

4 University programs in Industry 4.0

We went to the official websites of universities included in the QS World University Rankings by Subject 2019 and analyzed those offering Engineering and Technology courses [51]. The goal was to determine the programs, courses, or workshops they offer to prepare I4 engineers, but most importantly, to identify the technological advances and proposals they use to teach them and the competencies they declare to be developed. Although there is a significant number of universities with academic programs focused on Industry 4.0, we only present three of them in this report.

4.1 Massachusetts Institute of Technology (QS Rank #1)

MIT’s mission is to develop advanced knowledge in science and technology to contribute to make the world a better place. Researchers are pioneers in investigating areas such as AI, climate adaptation, HIV, cancer, and poverty alleviation. Students are taught by doing; mind and hand is the catchphrase of this institution.

4.1.1 Makerlodge

This is a program for first-year undergraduate students in which they learn to handle a variety of technologies and machinery such as 3D printers, circuit board manufacturing, laser cutters, band saws, drill presses, and hand tools. After completing the program, students can continue developing their practical skills in more than 10 makerspace communities distributed all over the campus. The program offers various resources such as 3D printing services that students can use if available apparatuses are busy and a variety of software for using the machines; these include SolidWorks, HSMWork, Cura, and VCarve Pro. The end goal of the program is that students acquire technical abilities and also become innovators who can offer to society new solutions to solve problems [52].

4.1.2 MIT leaders for global operations (MITLGO)

This is a graduate program that offers students the possibility of acquiring technical, analytical, and business competencies necessary to propose and develop strategic initiatives in high-tech, operations, and manufacturing companies. The main competency developed is leadership. Students do a lot of practical work in the form of research internships performed in partner companies such as Amazon, Boeing, Boston Scientific, Caterpillar, and Johnson and Johnson. Among the subjects taught are Programming in Python, System Optimization and Analysis for Manufacturing, Machine Learning, and Data Mining. They can design a robotic system and microsystems; also, they use analytics, machine learning, and related digital technologies to solve manufacturing problems [53].

4.1.3 Smart manufacturing program

This is an online technical program aimed at training individuals interested in implementing I4 in their organizations. Some of the topics taught include modeling, sensors, control of manufacturing processes, machine vision, and advanced data analytics. The program uses a smart machine (Fiber Extrusion Device) to demonstrate concepts to students, solve problems, and generate new ideas to improve the performance of the apparatus. Students learn through live webinars, peer discussions, and practical assignments (e.g., they analyze data generated by the smart machine). They develop mainly problem-solving skills and are benefitted with knowledge and skills that they can instantly apply in their respective work fields [54].

4.2 ETH Zurich—Swiss Federal Institute of Technology (QS Rank #3)

ETH Zurich is a leading university in science and technology. It is well known for its cutting-edge research and innovation. Research is focused on a wide range of disciplinary areas, from engineering and architecture to chemistry and physics. Education is based on the transfer of theory to practical application.

4.2.1 Institute of virtual manufacturing

The Institute’s main objective is to perform research related to virtual process planning and optimization, mathematical material modeling, failure prediction, and virtual modeling of manufacturing behavior. Students participate in applying engineering methods learned in industry projects. Courses taught are related to the basics of forming technologies, introduction to FEM simulation of non-linear processes, numerical optimization, and virtual process control in the designing and implementation of manufacturing systems. Depending on the course, students mostly acquire technical competencies. Among the technologies offered are tensile testers, torsion testers, non-destructive material testers, texture x-ray goniometers, and diverse software, such as the Abaqus/CAE [55].

4.2.2 Workshop on science, technology, and policy: the future of work

The Singapore-ETH center offers this course to teach topics related to AI, robotics, cybersecurity, and the management of disruptive changes; all are aimed to enable the students to perform future work. The topics taught are related to the impact of AI on decision making, the impact of robotics in the future of work, cybersecurity, and ethics. The pedagogical strategy includes presentations, dialogues, field visits, debates, and group discussions. The main competencies developed are analysis and problem-solving. The visits to strategic enterprises such as AI Singapore, Desay Automotive SV, and One North-JTC Corporation allow students to have an understanding of the technologies involve in the theme [56].

4.3 RWTH Aachen University (QS Rank #44)

RWTH Aachen University is the largest university of technology in Germany and one of the most prestigious in Europe. It is a leading scientific and research institution mainly associated with the natural sciences and medical fields. In this university, an international atmosphere is enjoyed as numerous foreign students and scientists come to take the high-quality courses it offers and use its advanced infrastructure.

4.3.1 Master’s program in automation technology

This is an interdisciplinary program whose main objective is to teach students how to make technical systems and processes work autonomously, i.e., without any human intervention. Each student creates a personalized curriculum based on his interests. Among the courses available are Advanced Control Systems, Data Mining in the Context of Technical Processes, Data Communication and Security, Process Control Technology, Plant Automation, and Rapid Control Prototyping. Students have access to various technologies, including MATLAB/Simulink software, Virtual Control Lab, and the Netlab program [57]. Competencies promoted include leadership, problem-solving, team working, management and the tackling of complex tasks [58].

4.3.2 Master’s program in data analytics and decision science

The goal of the program is to develop professionals who can analyze the vast amount of information generated as a result of the wide range of data sources now available. The final objective is to teach them how to develop state-of-the-art predictive models and make better decisions. Among the courses offered to students are Algorithms and Data Structure, Machine Learning, Heuristic Optimization, Predictive Modeling, Principles of Data Mining, and Design and Analysis of Algorithms. The main competencies developed are technical ones; however, other competencies identified include decision making, effective presentation of results, managerial competencies, and problem-solving. Students work at international enterprises such as Deutsche Post, DHL, and the PTV Group, where they have the opportunity to use advanced technology and develop hands-on activities. In this context, they are able to apply the skills and knowledge acquired during the program [59].

4.3.3 Master’s program in robotic systems engineering

This master’s program has two main educational objectives: (1) teach students how to use robots optimally, and (2) train them in the development and construction of new robot systems. This is done through a mix of various pedagogical strategies such as lectures, exercises, and practical activities (internship in industry companies). Among the courses taught are Computer Vision, Multi-Body Dynamics, Machine Learning, Advanced Robot Kinematics and Dynamics, Simulation, Robotic Sensor Systems, and Automation Technology for Production Systems. Students have access to specialized facilities in which technologies such as industrial robots, mobile robots, assistance robots, and intralogistics robotic systems are available. Among the competencies developed are technological, analytical, and problem-solving skills [60, 61].

5 Education in Industry 4.0 Trends

There are nine trends in Education 4.0:

  1. (a)

    Learning will take place at any place and time.

  2. (b)

    Students will learn with a mix of tools/programs/techniques that are most preferred by them.

  3. (c)

    Study tools will be adapted to the learning style of each student.

  4. (d)

    Learners will train themselves, as professors will take the role of mentors.

  5. (e)

    Students will learn with practical activities such as internships and collaboration projects.

  6. (f)

    Students will need to be more organized and better manage their time to work in a shorter time to complete their tasks.

  7. (g)

    Students will design their own curricula, based on their interests.

  8. (h)

    Assessments will be based on results and not on examinations.

  9. (i)

    Data interpretation will be a key competency that must be taught in all curricula [62].

The latter general trend is a key aspect that this research has found will have a big impact on I4 education.

An approximated forecast of which content, competencies, and technologies related to I4 are going to be required for the successful training of future professionals is needed. However, I4 is still evolving, and now, only the tip of the iceberg is presented in this report for what is required based on the current state-of-the-art. Continuous review of the I4 environment is necessary to determine opportunely the paths and trends that it will follow and, consequently, the needs that will emerge in all the areas mentioned above in this article.

5.1 New programs

Universities must teach in a more interdisciplinary way, as I4 is composed of different elements that require a person to have various fields of expertise. This new vision demands that study programs be reevaluated to match I4 relevant topics [63]. We note that countries and organizations are taking measures globally to include knowledge related to I4 in the education of their future professionals. In Spain, the Autonomous Community of Madrid officially included the subject Technology, Programming and Robotics in their Compulsory Secondary Education for the 2015–2016 academic year. Additionally, the United States, Canada, New Zealand, and Romania have announced investments to implement robotics in education [64].

The teaching of Cyber-Physical Systems (CPS) content is gathering momentum. Although CPS degree curricula are in its infancy [65], there are universities that are already incorporating the topic, taking three approaches:

  1. (1)

    Offer continuous education in which general training in CPS is given to people in different industrial fields.

  2. (2)

    Provide CPS training at the doctoral level in specific industrial fields.

  3. (3)

    Incorporate the CPS topic as a course in Engineering curricula.

Some masters and doctoral CPS programs already being offered worldwide include Master in CPS (Vanderbilt School of Engineering, USA), Master in CPS and Social Systems (University of Lyon, France), Master in Embedded Systems (Nanyang Technology University, Singapore), Doctorate in CPS (Delft University of Technology, Netherlands), Doctorate in Design and Analysis CPS (Australian National University, Australia) and Doctorate in Intelligent CPS (Japanese National University, Asia) [66]. New CPS programs have to be designed with caution and consider the departmental structures, the professors’ expertise, and available resources. A robust CPS curriculum should include the following areas/topics:

  1. (1)

    Basic computing concepts (e.g., embedded hardware, models of computation, data structures, and algorithms).

  2. (2)

    Computing for the physical world (e.g., properties of sensors and analysis of their signals, programming with sensors and actuators in open environments).

  3. (3)

    Discrete and continuous mathematics (e.g., graph theory and combinatorics, probability, statistics, and stochastic processes).

  4. (4)

    Cross-cutting applications of sensing, actuation, control, communication, and computing.

  5. (5)

    Modeling heterogeneous and dynamic systems and integrating control, computing, and communication.

  6. (6)

    CPS system development [65].

These are some initiatives adopted by those educational organizations that are aware of the future requirements they will need to fulfill. In the end, all educational organizations will have to follow the trend to succeed in the educational arena.

5.2 Competencies

Future professionals must be promoters of change. With this in perspective, there are competencies that universities should foster in learners to make them creators of these transformations. This will allow professionals to have a greater impact when integrating into future economies [67]. Researchers are devoting their resources to determine precisely the competencies needed. They use different methodologies to get the most accurate and reliable results. By performing a literature review, a group of researchers determined a set of competencies that the new workforce will need for the implementation of a full I4. These were categorized as technical, methodological, social, and personal (see Table 1) [38].

Table 1 Competencies required by the I4 workforce
Full size table

Based on a literature review and interviews with industry representatives and experts, another research group determined competencies needed that are associated with different elements of I4 (see Table 2) [67].

Table 2 Competencies required associated with elements of I4
Full size table

Interest in determining the competencies that will be required by the future professionals is also of concern in specific regions. A group of consultants conducted research to define the skill levels of the BRICS (Brazil, Russia, India, China, and South Africa) nations and offer them suggestions about the ones they need to develop to have a prepared I4 workforce (see Table 3) [68].

Table 3 Competencies required by BRICS nations
Full size table

The required competencies presented by the different research projects are varied. However, there are some in common, mainly related to the ability to use and interact with I4 technologies (e.g., robots and AI), data analysis, technical knowledge, and the need for personal skills.

5.3 Educational technology

The correct and relevant use of technology in education has the potential to enhance learning by maximizing the academic experience of the students. Educational technology allows imparting education over distances, improving assessments of learning objectives by adapting evaluations to the learning style and facilitating the access to and updating of learning materials. Students are able to have access to experts in a given field easier [62]. Under the I4 context, related technologies are by themselves sources of learning [69]. Some educational technologies that will have a great impact on training I4 students are described below.

5.3.1 Remote and virtual laboratories

Laboratories are the places where theory is linked to practice. Various laboratory formats are emerging, such as virtual, decentralized, real-time, immersive [39], and remote. Regarding the virtual laboratory concept, these labs are imitations of real experiments, and the infrastructure needed is simulated in computers. In the case of remote laboratories, these have real equipment; however, they are handled from a distance, through special software [70]. There are already remote and virtual laboratories focusing on addressing I4 topics, and these will evolve and multiply in future years. As an example, the ELLI project is an initiative in which RWTH Aachen University, Ruhr-Universität Bochum, and TU Dortmund University collaborate. The main goal is to develop innovative projects to improve the transfer of engineering knowledge. Various remote and virtual laboratory designs have been proposed with this endeavor. The Tele-operative Tube Bending Cell offers students the possibility of managing an Incremental Tube bending process (ITF) with stress superposition machines remotely. Another example of a project is the interface-with-data management system, which was developed using LabVIEW software; a great number of high definition cameras are installed around the machine, so students have feedback about the process from multiple angles. Students learn how to operate a machine remotely, which is a key aspect of I4.

Regarding virtual laboratories, a testing cell was recreated with a high degree of realism. It was done by developing a CAD model of the real machine, which was then transformed into virtual images by using special software such a Maya. Only the parts of interest of the machines are able to be seen, and there is the possibility of selecting details to increase realism [46]. ARI is a remote laboratory focusing on training students in automation systems. It enables users to control devices such as motors, motion drives, and pneumatic actuators by Programmable Logic Controllers (PLC) through standard Field Networks. Its design is modular and includes a PLC, a motion drive, and remote I/O distributed on a standard field network such as Modbus/RTU, Modbus/TCP, CanOpen, Profibus, Ethernet TCP/IP, and ASi) [71].

In the last years, new technologies, such as VR, have been in the spotlight. Researchers have used VR to provide more realism and improve the experience obtained in virtual and remote laboratories. Nowadays, it is possible to manage these systems without hands by exploiting human motion tracking capability and haptic interfaces. This is a concept proposed by a group of researchers who developed a measurement instrument interface able to add novel, touchless, bi-directional, interactive modes in virtual instruments and remote laboratories. [72]. In the future, and with the advance of research and development of new technologies, remote and virtual laboratories will be more realistic than ever, making education more convenient and increasing student satisfaction and learning results.

5.3.2 Learning factories

In the ‘90 s, learning factories began to be created with the main objective of training engineering students in a practical, secure, and economical way. These were factories’ physical replicas in which experiments, research, and education could be performed. Today, there is a wide range of formats ranging from the original physical spaces to digital environments or simulators [40]. In this practical learning setting, an integral education is achieved for technical (e.g., operate IT devices), transformational (e.g., solve challenges related to the production system), and miscellaneous (e.g., teamwork and knowledge transfer) acquisition of knowledge [73].

Nowadays, there are learning factories that intensively use technology to develop in students the future demanded competencies regarding I4. For example, the Smart Automation Lab from RWTH Aachen University is a replica of future automation systems. It has a central production control system, advanced industrial robots, RFID, and cloud technologies, to mention a few [74]. At this site, Aachen University engineering students can work as assistants. They work in international and national industry research projects that allow them to get involved in on-the-job tasks before graduating. They can also develop their theses and, based on the experiences had, decide to continue with doctorate studies in their fields [75].

The Bochum University of Applied Sciences, Velbert/Heiligenhaus campus, offers Mechatronics and Information Technology curricula. The institution built an I4 Learning Factory, in which real-time supply chain information is provided, the monitoring process is sufficiently efficient, real-time reactions to problems are possible, and the product genealogy can be collected. To perform the above tasks, various technologies are installed, including (a) SAP ERP system, (b) SCADA, MES and Energy Monitoring IT-Systems from Schneider Electric, (c) PLC, HMI, and engineering tools, (d) SIEMENS CNC, (e) Mitsubishi robot, (f) simulation and offline programming tool from Festo and (g) industrial communication standards such as Modbus, ProfiBus and CANopen as field busses, and Modbus-TCP, ProfiNet and OPC (DA and UA) as Industrial Ethernet systems [76].

The SEPT Learning Factory from McMaster University is a place in which the latest technology in I4 has been established. There are great enthusiasm and support from the industrial sector. It was created to develop the future workforce, one that will have abilities and skills that will allow them to use smart systems and perform advanced manufacturing tasks. It has an IoT learning station, design and system management tools (CAD, Thin Client Manager, EOS software), a 3D printer, Sensors-IR camera, Delta Robot, micrometer, CMM, smart cameras, and RFID technology, among others [9].

5.3.3 Educational robots

Robots are a foundational element of I4; special attention is paid to those that are able to collaborate with human beings. Educational robots can be used as learning tools and educational supporters. In the former, students are able to analyze their mechanics, sensors, and actuators. In the latter, robots accompany the process of learning by giving instructions and aiding in solving problems, etc. Their main contribution to education is the ability to motivate and engage students, which improves educational results indirectly.

Future industrial environments will require professionals with knowledge in robotics and who are able to develop new robotic concepts. It is argued that, in higher education, robots and related kits allow students to practice with technology that will aid them in performing well in real working environments [77]. Some of the platforms that have been used to teach students about robotics include Arduino processors, Thymio robots, and VEX robots [64]. Nowadays, there are more advanced options, such as the one offered by the Chinese enterprise Shanghai Comau. The organization launched in 2018 the e.Do educational robot [78]. It was designed to help students from a wide range of ages to learn STEM and humanities subjects, but also to engage them in the world of robotics. The device is modular and flexible, which enables users to build it and use it easily. Users are able also to program it by using the different resources the product brings attached (e.g., software, app, lessons, etc.).

Another robot, which was developed for specifically performing educational tasks, is the JD Humanoid [79]. It was developed to teach students about technology. One advantage that it has is its scalability, i.e., it offers learning opportunities according to the knowledge level of the student. Learners are able to program it and make it interact, as its eyes can display a variety of expressions.

Technology is advancing, and, in the future, robots will assist people in daily activities, including educational tasks. Educational robots will have the ability to interpret students’ reactions and emotions; they will be able to offer appropriate responses and adapt a learning strategy to each individual [80]. In the I4 context, it is expected that by 2021, most of the factories, including the smaller ones, will use intelligent robots [81]. This urgently demands that students be trained in both developing and handling of robot technology. It must be considered that in order for robots to be effective in education, the abilities and expertise of the professors in robotics must be ideal. Skillful teachers are needed, those who will motivate, stimulate, and influence students to learn about robots [82].

6 Conclusions

To maintain their market share, industrial companies work to satisfy the changing needs and desires of their customers, who demand more personalized products. This has resulted in efforts by the enterprises to adapt their production processes to make them more efficient and flexible. In the future, factories will be completely digital; the different units of production will be integrated and managed by a central computer. This approach is known now as Industry 4.0, which is evolving and influencing the worldwide production of goods.

The coming of Industry 4.0 means that universities must take responsibility to provide students with the skills needed to perform in the industries of the future effectively. More than ever, universities need to update their existing curricula and develop programs that support the training of the future workforce. Content and competencies to need to be developed and balanced perfectly with technology in relevant educational programs that seek to train future professionals.

New curricula need to be designed, taking into consideration the technologies and topics that involve the Industry 4.0 environment. Technologies are varied and are interrelated; the main ones, those that are considered the pillars of Industry 4.0 include: autonomous robots, simulation, horizontal and vertical integration, industrial internet of things, additive manufacturing, augmented reality, cybersecurity, the cloud, and big data and analytics. Based on these, we can determine which topics, subjects, and abilities are needed to develop future professionals.

Nowadays, there are universities already offering programs related to Industry 4.0, e.g., MIT, ETH Zurich, and RWTH Aachen University. These range from those aimed at transferring knowledge about Industry 4.0 to those in which students learn how to handle innovative technologies and do real work in global enterprises. However, educational organizations have to be aware of the possible directions of the path that Industry 4.0 will follow as it makes twists and turns; the educational organizations need to be able to design and adapt their strategies accordingly. It is important to consider that in addition to the technical knowledge and technical abilities students must possess, there are transversal competencies that will allow them to perform competitively regardless of the industry in which they participate. These are called soft skills and include lifelong learning, leadership, communication, flexibility, adaptability, teamwork, and decision making. Regarding technology, there are various options, all bringing important benefits to education for Industry 4.0. Remote and virtual labs, learning factories, and robots are now state-of-the-art in these technologies and have the potential of disrupting Industry 4.0 education.