Exponential Disruptive Technologies and the Required Skills of Industry 4.0: A Review

The 21st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 was explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done from multidisciplinary databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. Results of the electronic survey showed that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The study identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. The study proposes the need to address integration of industry 4.0 concepts into the current education system.


Industry 4.0 Definitions
The term "Industry 4.0" was coined by German's group of mechanical engineers in the year 2011 to account for the widespread integration and adaptation of ICT in manufacturing industries [18]. The definition of industry 4.0 is ambiguous and no single definition has been conventionally adopted. Institute of Technology Assessment (ITA) [36] defined industry 4.0 as a systemic change bringing about extensive changes in the way works are done. It is further stressed that industry 4.0 is not just about the introduction of a new technology linked with an incremental adaptation of work systems as in the previous three industrial revolutions, but about an assemblage of novel technologies and forms of application, with discrete degrees of technical maturity and systemic effects. Simply put, industry 4.0 is a precipitous transition from the previous industrial revolutions (Table 1).
Schröder [37] defined industry 4.0 as the digital transformation in all areas of industrial processes and production effectuating a new paradigm shift in production systems. In addition, industry 4.0 has also been defined as the massive developmental stage in industrial manufacturing including organisation and the management of the entire value chain [38], and its technologies are the blurring line between the physical, digital and biological sphere of production or manufacturing system [34]. An industry 4.0 definition modified from Cheryl and Helena [34] and Deloitte [38] has been adopted in the present study.  Integration of many engineering disciplines e.g. mechatronic engineering, biomechanical engineering.

Exponential Disruptive Technologies
The industry 4.0 is being powered by exponentially growing disruptive technologies that inaugurate changes rapidly but at a nonlinear pace [1,40]. Besides, these technologies have a potential to cause broader societal transformation by changing the existing economic sectors, tenets of work, production and consumption [41]. In the main, two types of technologies can be appreciated: sustaining and disruptive technologies; the former has a constant or incremental rate of improvement of existing customers whereas the latter creates disruption on the status quo as it produces a unique set of values. The major implication of disruptive technology is the demand for new course content, employment, knowledge and skills [35,38,42].
In this electronic survey, 35 disruptive technologies were identified in 70 publications. Ranking of the technologies was done basing on the number of their appearances in the selected publications as shown in Table 2. From the ranking results, 13 key technologies were selected as illustrated by the pareto chart ( Figure 1). The current and the future development and application areas of these key disruptive technologies are discussed. Internet of Things (IoT) 33 [1,8,15,16,22,25,27,29,40, 2 Big data (data mining, data analytics and advanced algorithms) 30 [8,10,16,29,33,44,46,47,49,50,[51][52][53]55  IoT is not just machine-to-machine (M2M) connectivity but its definition spans beyond by creating an intelligent, invisible network fabric that can be sensed, controlled and programmed through which the physical world objects become intelligent and communicate independently, online [95]. Characteristically, IoT is referred to as internet of everything (IoE) [96]. The 'things' can be electronic sensors, actuators, other digital devices or any other objects (e.g. people, buildings) [59]. The integration of internet to everything was meant to facilitate production systems [50,97]. In industries, IoT has been exploited in automation for lighting, heating, robotic vacuums, remote monitoring and control of machines [50]. Despite it being a dateless technology, IoT has enormous innovative applications of its technologies [98]. For instance, automatic identification technology such as radio frequency identification (RFID) [99] and Beacons [100] are currently used to make any object (such as product) to become smart [101]. The other application domains of IoT include supply chain management, healthcare [102,103], disaster vigilance and recovery [104]. The IoT systems has also found application in predictive maintenance systems and real-time urban microclimate monitoring [105]. Most of IoT applications have been classified as components of smart cities [103,106].
Typical IoT architecture consists of three layers; (i) perception layers (ii) network layers and (iii) application layers [107]. The first layer function as sensors for data acquisition. The second layer operate as a data transmission platform and the last layer is the application layer in which the smart environment is created. Examples of smart environment include smart city, smart home, smart grid and smart government [108]. The great barriers to IoT applications are cyber-attack (i.e. security and forensic challenges) and low connectivity. For these salient reasons, the major development of IoT during the move to industry 4.0 will focus on exploring innovative solutions that will pave way to secure forensically sound deployment of IoT networks [109,110] as well as increase the IoT systems connectivity [111]. The foremost companies behind the IoT inventions and deployment include GE, IBM, CISCO, Google, Amazon, Microsoft, SAP and AG [112]. These companies are respectively responsible for Microsoft Azure IoT, Oracle IoT cloud services, Google cloud IoT core, IBM Watson IoT, AWS IoT and Bosch IoT suite IoT platform markets [113].

Big Data
The Big Data are rather distinct from the traditional data due to their large growing dataset [114]. Lately, Big data have been defined in terms of huge datasets that consists of six main characteristics namely: volume, variety, velocity, veracity, value and complexity [115]. The intricacy of Big data brought its own problem as it demand for new skills and knowledge [114,116]. In addition, it has a pronounced impact on board level or stakeholders decision making [117]. With the move to industry 4.0, the main application areas of Big data include smart grid [118], smart meter [119], internet of things [120], E-health (notably pharmaceutical data, lab and clinical data), public utilities (such as water supply and sewage system), transportation and logistics (for example, the number of passengers using buses, number of accident occurring per year), agricultural remote sensing (data obtained from soil moisture, temperature changes) [121]. Another application area of Big data is the digital finance, where it is used as big data credit investment which fully utilizes modern digital information techniques [122]. Finally, Big data have recently been adopted for massive open online course (MOOC) and its application in this area is expected to grow explosively in the era of industry 4.0 [123].
The major techniques commonly used in Big data are relational and non-relational data stores, computations, and MapReduce while the software frameworks in Big data include Hadoop, Spark, Hive, and Google's BigQuery [124]. However, with the continuous advancement required to shape industry 4.0 movements, more advanced Big data software frameworks that can handle extensively huge amounts of data are expected to emerge.

3D Printing
3D printing (additive manufacturing) unlike subtractive manufacturing is the technology that build up physical objects based on 3D-CAD file by consecutive addition of liquid, sheet or powered materials [125]. The populously employed materials by 3D printers are plastics (such as polylactic acid, acrylonitrile butadiene styrene and hydrogel composites) [126,127], and metallic materials notably steel, stainless-steel, titanium, gold and silver [128]. The recently developed materials used by 3D printers are liquid crystal elastomers and jammed microgel ink [129]. The technologies behind 3D printing advancement include nanoparticles jetting, laser engineering net shape, wire and arc additive manufacturing, electron beam melting, selective laser sintering/melting, atomic diffusion additive manufacturing, single pass jetting, fused deposition modeling, direct ink printing and filament extrusion method [130].
Universally, 3D printing has been applied to produce nearly everything, ranging from buildings to human organs (such as the kidney and the heart) and tissues (bones, muscles, teeth) [127,129,131,132]. Though its application for printing body parts (3D bioprinting) is in its infancy, it is anticipated to rise astronomically with industry 4.0 movements [133]. Beyond, the growth of 3D printing will strategically explore innovations for bioproduction of living responsive materials (such as shape-memory polymers, aqueous droplet) and devices such as soft robots [134].

Cloud Computing
Cloud computing is a service model where computing services that are available remotely permit users to access applications, data and physical computation resources over a network, on demand or pay-per-use fashion [135,136]. The application domains of cloud computing technology in education include e-learning (such as curriculum content management, virtual lab environment, office productivity suite, library management, collaborative learning), communication (email and notifications) and administration (such as students registration management, human resources management) [137]. Cloud computing has not only been used in the education sector but also in other sectors such as healthcare [138], manufacturing, entertainment, transportation and energy [139].
Over the years, cloud computing has been used for some enterprise and analytics applications, but in the era of industry 4.0, the performance of cloud technologies is expected to improve particularly following the security in both network, application and host level [135]. The main companies behind cloud computing development and deployment are Amazon, Microsoft, Google and IBM. These cloud providers often implement inflexible pricing schemes for cloud users based on the duration [139].

Autonomous Robots
Autonomous robots as the name suggests, performs autonomous production more precisely and can work alongside humans or even in human restricted places. They have the faculty to complete assigned tasks accurately and perspicaciously in time, focusing on safety, flexibility, versatility and collaboratively [140], contrary to the olden days when robots were designed primarily to tackle complex assignments in manufacturing industries. The autonomous robots are also being utilized in logistics such as warehouses and container terminals [141]. The development of autonomous robots has been continuously advancing to meet the need of industry 4.0 [142,143]. The major companies behind the autonomous robots' innovations and development are Kuka, Rethink Robotics, Bionic robotics, Roberta Gomtec, Honda, ABB and Fanuc. The autonomous robot architecture entails functional and decisional components [144] which with the upsurge of industry 4.0 will have to be developed as the exploration of new areas of applications of autonomous robots increases.

Virtual and Augmented Reality
Virtual reality (VR) and augmented reality (AR) are complementary technologies of industry 4.0. With VR, the users are transported, usually via a headset, into a virtual world while with AR, applications present an illusion of layers of graphic information superimposed on some portion of the user's field of view [80]. In most cases, the two technologies are combined (also known as Mixed reality) to yield gigantic applications by transcending the distance, time and scale and increasing comprehension, teamwork, communications and decision making. Although AR is regarded as a developing technology with some of its technical manuals missing [145], it remains emblematic of industry 4.0 as it bring together the physical and digital worlds, and indeed, the public and the private sphere [66].
The foremost application domain of VR and AR has been in education since the 1990s to teach subjects like mathematics, geometry, physics, chemistry and anatomy [146]. In the past few years, VR has been applied in virtual training. For instance, a virtual plant-operator training module is being used to train plant personnel to handle emergencies [147]. In maintenance, AR has been used for repairing and servicing complex systems such as hydraulic breakers [80,148]. Other application areas of VR and AR include tourism, retail and fashion, business, marketing, storytelling, healthcare, defense, design and development. The main companies behind the development of VR and AR are Google, Microsoft, Apple and Espon. Examples of currently used AR smart glasses include Google Glasses, Microsoft HoloLens, Apple Headset and Espon Moverio Pro BT-2000 [145].

Cyber Physical System (CPS)
CPS is referred to as a networked system in which the cyber or computational part is tightly integrated with the physical components. CPS uses multiple sensors such as touch, light and force sensors to achieve distinct purposes. This makes CPS exceedingly discrete from just an embedded system [149]. Lately, CPS frameworks have been congruously utilized in various fields including manufacturing [150], laboratory and teaching factory [151,152]. The latest development of CPS is the Mobile CPS that extend CPS application domains [153]. further development of CPS will focus on the protection of critical industrial systems, manufacturing lines and other CPS application frameworks from cyber security threats. Consequently, secure, reliable communications as well as sophisticated identity and access management of machines and users are very essential [153,154]. Hence, CPS will be integrated with other technologies including IoT, cloud computing and smart sensors to form the new smart CPS that will link the virtual, physical and digital worlds. This will enable intelligent or smart objects to properly and rapidly communicate and interact with each other [154].

Artificial Intelligence (AI)
AI is the knowledge-based and thinking program coded and designed in machines to imitate human or animal reasoning ability [155]. For the past few years, AI has been applied in complex operations such as drilling fluid, underground mining [156,157] and maintenance as well as monitoring of sophisticated manufacturing systems [158]. The emerging AI applications that are currently shaping industry 4.0 journey include self-driving cars, human speech and face recognition, interpreting of complex data and medicines (for example cardiovascular medicine) [159]. As we move to industry 4.0, AI advancement gear towards integration of AI technology with other technologies such as Big data, Cloud computing to perform gigantic tasks and to widen their application in all fields. For example, a recent finding indicate that AI can be properly applied to handle infectious disease big data analytics in healthcare sectors [160]. Notable companies behind AI development include Google, SpaceX, Apple, GE and Microsoft [159,161].

Smart Sensors
Several types of smart sensors have been developed in the recent past to furnish the need of industry 4.0. These sensors are engineered in the manufacture of smart devices or objects such as smart dust, smart cameras, smart phones and smart homes [162]. Smart sensors have been largely used for monitoring purposes [163]. Monitoring systems for water and flood levels, gas, environmental, structural health, remote and equipment fault diagnostic systems, as well as advanced medical applications employ smart sensors [162,164,165]. In industry 4.0 era, these smart sensors will be integrated with the IoT system and the advancement of smart sensors will continue to grow tremendously [166]. Smart home, smart cities and smart grids are now available because of various installed temperature, proximity, optical, pressure and ultrasonic smart sensors [167,168].

Simulation
Simulation is a routine method of analyzing the behavior of complex systems. Simulation is a classical antiquity technology which dates far back to the era of analog computers [169]. Nonetheless, its application have proliferated in different fields because of its demonstrated ability to improve components of manufacturing systems (products, materials and ergonomic design, energy consumption, production processes and efficiency) [170], education and other industrial sectors [171]. For instance, simulation has been utilized in complex automobile manufacturing, ceramic production and chemical processes [172], medical operation training in pediatric urology and surgery [173]. Additionally, it has also been applied to study complex systems such as cloud based system [174] and group safety especially for underground miners [175].
Simulation is forecasted to advance swiftly due to the need to understand the behavior of complex systems with the latest technological innovations in fields such as transportation, communication, medicine and metrology. Evidently, simulation software developers are continuously advancing (updating) their software to meet the needs of industry 4.0 for example Siemens, Rockwell automation, MathWorks and Festo which are respectively developers of Solid edge, Arena, Simulink and FluidSim software have released updated (latest) versions of these software [169].

Nanotechnology
Nanomaterials are the smallest materials with singular unit within nanoscale (1-100 nm) [176]. They evolved following obsessive research in the field of materials science [177]. The ideology of nanotechnology is "science small" as it is the technology applied to produce nanomaterials. Though an aged technology, its novel and numerous innovative applications have paved the way for nanotechnology to industry 4.0 movements. It has been applied in making vital components in aerospace, automobile, construction, manufacturing, food processing and packaging fields [178,179], medicine [180] and forensic science [181]. An emerging application of nanotechnology is in the production of the biofuels [182]. In industry 4.0 age, the applications of nanotechnology in energy storage, lighting and photovoltaics are extensively needed to support the popularly growing application areas of industry 4.0. Furthermore, medical and high-tech applications of nanotechnology will also continue to advance [183], especially in areas such as new materials for batteries, 3D printing [184] and DNA nanotechnology [185].

Drones
Drones, frequently called Unmanned Aerial Vehicle (UAV), Remotely Piloted Aircraft (RPA) and Unmanned Aircraft Systems (UASs) [186] are aircrafts without pilots on board (flying robots) [187,188]. There are three main types of drones: rotary wing, fixed wing and lighter-than-air. The most common drone configuration is multirotor with four, six or eight propellers and made with very small, powerful and affordable electronic components that are also used in smartphones. Some of the manufacturers of drones include Kuleuven, Delair, Vives, Vito, AltiGator, Flying-Cam and Drone Matrix [189].
Initially, drones were considered as toys for children before they later got adopted as gadgets of leisure that are sent to the skies to shoot impressive photographs and high-definition videos. In general, drones have been majorly used for entertainment and media [186]. However, with the move to industry 4.0, drones are being equipped with smart devices (sensors, cameras) combined with other technologies like Big data analytics and machine learning. Significantly, this has widened its field of applications to agriculture [190,191], energy and utility, entertainment and media, infrastructure [192], insurance, security, telecom, transport, logistics, space exploration [193] and wildlife monitoring [194].

Biotechnology
Biotechnology encompasses many fields such as synthetic biology, molecular biology, genetic biology, gene editing, proteomics, biomimicry and genomes [195]. In the era of industry 4.0, synthetic biology will be more explored than any other fields of biotechnology. Synthetic biology is an emerging field where biology and engineering disciplines are in unison [196]. It has been cited as a lucrative technology in industry 4.0 movements. The main function of synthetic biology is to create different artificial biological pathways, devices or organisms, that can imitate the naturally-made biological systems [197,198]. The main application domains of synthetic biology is in agriculture [197] and healthcare where it has been used in the treatment of complex diseases such as cancer. In industry 4.0 epoch, synthetic biology will be extensively utilized in the field of renewable and clean energy with improved efficiency for power supply to many systems such as robots and self-driving cars [55,143].

Required Skills
In industry 4.0 revolution, all skills are required. This is fundamentally because all the previously disconnected technologies and applications have come into convergence. However, the opus of the existing workforce will need to change to match the skills required to support the success of industry 4.0. Further, the development of novel technologies such as smart sensors, intelligent assistant, robots and automation will continue to demand change in the types of skills as well as the labour landscape [199]. Eventually, there will be great transition for job demand from lower-skilled to highly-skilled jobs [200,201]. In order to clearly describe the skill requirements for industry 4.0, the present study categorized the required skills in two major groups: technical and personal (soft) skills. The technical skills are required for highly technical tasks while soft skills are mainly needed for teamwork on the shop floor level and communication in the daily business. Technical skills are subcategorized into theory and expertise skills, hardware skills, and software and algorithm skills (digital skills) as summarized in Table 3.

Building Skills into the Workforce of Industry 4.0
There is a dire need to identify and develop the disciplines, and the required missing abilities in order to build suitable skills into the workforce of industry 4.0 [202]. The following measures therefore need to be taken seriously to prepare the workforce of the future.
Higher education (universities and technical colleges) play a critical role in shaping the societal transitions necessary for industry 4.0 movements. However, today's higher education was developed in context of the previous three industrial revolutions which do not provide the necessary skills for shaping industry 4.0 movements [203]. In addition, most manufacturing and service industries will no longer demand for specialist personnel but the generalists. Therefore, higher education especially the Universities ought to properly and extensively educate and develop capacity for knowledge retention among the graduates to prepare them for a productive life necessary for the ever-changing labour landscape [204,205].
Another crucial issue in building the skills of industry 4.0 is the need for diversifying education and credentialing systems. This can be achieved by empowering and encouraging the education market places especially online learning platforms (MOOC) to continuously put much effort to accommodate the widespread needs of those willing to learn [123]. In addition, employers are required to develop attitude towards training and retraining their workers. Also, self-teaching efforts by jobholders themselves should be encouraged [26]. More importantly, to survive in the job market of industry 4.0, there is need to nurture human skills such that the AI is unable to replicate [206].
Furthermore, skills of industry 4.0 can be built by developing new curriculum especially in the old field of studies such as industrial and mechanical engineering to incorporate industry 4.0 infrastructures. The development of these curriculum can only be achieved through extensive research along this line. In the recent years, few researches have been conducted in the area of curriculum development with industry 4.0 context. For instance Sackey and Bester [204] examined the impact of industry 4.0 on existing industrial engineering curriculum. More research is on-going to ensure curriculum development reach all the technology and engineering fields of study [205]. However, the curriculum development in context to industry 4.0 should not only target the technical fields of study but also cut across other fields such as business, economics and management studies [207,208].

Conclusion
The present study identified differences in the view of previous researchers on the key technologies of industry 4.0. These differences were due to the different scopes of the case studies undertaken by the researchers. This is because industry 4.0 technologies are being adopted among countries or industries at different paces. Most literature focused their case studies on countries like China, USA, Germany, UK, South Africa, Korea, Russia, Philippines and Malaysia. This accounts for the differences because these countries have different capabilities in terms of resources, knowledge and finances to implement industry 4.0 technologies. Thus, 35 disruptive technologies were explored and 13 key disruptive technologies were identified. This implies that the rate of industry 4.0 adaptation has been increasing among countries and industries over the years. The race among countries and companies toward industry 4.0 will further increase the rate of adaptation of these technologies. However, the more the industry 4.0 implementations, the more the skills required to support its growth. It is therefore proposed that further research should investigate the capability and readiness of developing countries in adapting industry 4.0 in terms of changes in education systems and industrial manufacturing settings.
Funding: This research received no external funding