Industry 4.0 Key Enabling Technologies

Industry 4.0 has been able to expand as rapidly as it has in the last few years thanks to the introduction of the Internet and a host of other new technologies. This chapter takes a look at these technologies, evaluating the different ways in which it is aiding the progress made in Industry 4.0.

Industrial Internet of Things (IIoT)

The Industrial Internet of Things entails using IoT technologies so as to facilitate the smooth operation of manufacturing and industrial processes (Wikipedia). In order to ensure that this is achieved, IIoT integrates Big Data and Machine Learning technologies to gather sensor data and automate technologies that have been used in the industrial setting for years. IIoT is driven by the ideology that smart machines are able to perform tasks more accurately and consistently compared to humans. Data is captured and communicated in real-time whereby all the stakeholders use it in determining inefficiencies and problems long before they are manifested.

Industrial IoT is one of the most important technologies aiding the growth of Industry 4.0. It is based on a 3-component architecture including:

  • Data acquisition: Entails collecting data from different devices, systems, sensors, and equipment. With regard to OEM monitoring, the sensor is typically connected to an intelligent device featuring memory, a processor, and software.
  • Networking and security: Devices are networked in such a way that they can communicate freely. Information is transferred in a hierarchical manner, from the sensor to control system and the cloud. Technologies are also working on the possibility of peer to peer communication between machines. That is yet to be realized but holds immense possibilities once it becomes a reality.
  • Cloud: As at the moment, Cloud data analytics paves the way for prediction so that failures can be determined in advance and repairs scheduled. There are also Cloud applications that help get operating issues which need attention. Under relevant circumstances, an alert is triggered at the maintenance or operations section.

One of the major benefits of IIoT has to do with elimination of manual labor and mitigation of human errors as well as overall increase in efficiency. The Industrial IoT is a part of the Internet of Things which is rich in big Data that is aggregated and shared in a manner that makes sense. The overall goal here is to ensure that automation level at commercial and domestic levels is increased. That is the same strategy which is used in the Industrial Internet of Things. The IIoT does not have the goal of replacing human work. Rather, it is focused on complementing, enhancing or optimizing it, thereby creating unexplored revenue streams and business models (GE Digital, 2018).

Machines have an intelligent loop that makes it possible for immediate response to maintenance issues. This eliminates risks so that safety level is boosted. Researchers are moving the IIoT towards a point where manufacturing processes would operate in an almost independent manner.

Cyber-Physical Systems (CPS)

The future looks brighter. Technologies are working towards an industry where automated guided vehicles, robots, controllers, products, raw materials, and databases freely communicate with each other. Other researchers are even more optimistic, seeking to make such an industry automatically orchestrated via a central intelligence system aided by controls and monitors. That is the essence of Cyber Physical Space (Wikipedia).

CPS is the integration of networking, computation, and physical processes with the overall goal of controlling a physical process through feedback and ability to adapt in real-time. Thanks to CPS, humans have begun to view engineering systems from a whole new angle. You could compare this to the manner in which the Internet transformed people’s interaction with information. This is a scenario that requires humans because we are the most flexible entity in the CPS. We take the upper position in the whole system, monitoring highly-automated processes.

The Cyber Physical Space is made up of numerous heterogeneous elements. Therefore, it needs complex models for proper defining of its behavior and the sub-systems involved. There is an over-arching model for orchestrating dynamic interactions in the sub-systems. There is a need to upgrade the current design tools so that various sub-systems can interact with each other. The interaction may also be further improved between interfaces and abstractions (Ptolemy Project).

Any interaction taking place between sub-systems is influenced by communication performance which is defined on the basis of reliability, bandwidth, and latency. In the case of a wireless network, elements such as propagation conditions, device location, and traffic load are dynamic. Therefore, it is important for the communication network to be integrated as a model of model in the CPS.

As CPS advances, it is bound to be a part of all industry sectors in the near future, taking a central role in the Industry 4.0 paradigm. Cyber Physical Space has the potential of opening new production methodologies and may even be listed as the point of reference for tomorrow’s industry. The production environment will have been moved to a state where it is self-adjusting, self-configuring, and self-optimizing, bringing about greater flexibility, agility, and cost- effectiveness (Wikipedia).

Every sign points to the likelihood of the future factory being wholly CPS or a set of interacting CPSs. In this system, highly-skilled workers will have access to the operation processes both from the controlling perspective and also decision-making view.

Additive Manufacturing (3D Printing)

Commonly referred to as 3D printing, Additive Manufacturing is an emerging technology which has endless applications in the manufacturing processes, from pre-production to the release of the final products. Whereas a process would take weeks to be finalized on a small scale setting, Additive Manufacturing can reconstruct it on a large scale and accomplish the task within a day (Dean, 2017).

In simpler terms, 3D printing techniques are used in creating 3 dimensional objects with the aid of digital models in which case materials are placed in successive layers. The process uses different items including custom plastics, organic compounds, and many other resources. The main goal here is to create harmony between product and nature.

The success of 3D printing is based on a number of technologies such as Direct Digital Manufacturing (DDM), Rapid Prototyping (RP), additive fabrication, and layered manufacturing. It is predicted that the 3D market size will grow as high as $7.7 billion by 2025 (EOS, 2015).

Industry 4.0 is being actualized as a result of environmental-friendly manufacturing capabilities which take place closer to the consumer. With this regard, additive manufacturing plays the important role of reducing waste and making customization a possibility such that only what is required is printed. On the other hand, manufacturers only proceed to print an item when it is needed. This has the effect of cutting down shipping costs and mitigating the time it takes a product to reach the market.

One of the major barriers to the success of Industry 4.0 exists in printed technologies. Whereas built-in intelligence plays a central role in this industry, it is mind-boggling that up to now, our largely connected world still has few options for using 3D printing to print much-needed intelligent elements (Dean, 2017). The future industry can only prosper once we have integrated intelligence into the additive manufacturing process.

There are billions of data lying freely in our connected world. Much of this data can be found in the Cloud and has a direct impact on physical manufacturing. As an example, shoe manufacturers may include sensors into their boots for logging steps as well as an antenna for sending data to a fitness app. This data undergoes intensive analysis, enabling the creation of footwear that best works for an individual’s foot shape. However, the current state of 3D printers is not advanced enough to build such solutions, thus thwarting the progress of Industry 4.0. Good news is that the future is closing in, and soon all the possibilities of 3D printing shall be realized (Spilasers, 2017).

Self Driving Vehicle (SDV) and Automatic Guided Vehicle (AGV)

Every stakeholder can clearly see that self-driven vehicles and automatic guided vehicles are set to change the manufacturing industry. As the Internet of Things paves the way for a connected manufacturing sector, SDV and AGV are riding on this hype to have a fair share of their own impact (Shea, 2016).

Self-driving vehicles have an autonomous approach. Users are not required to interfere with the automobile. Rather, they have the liberty of autonomously navigating through locations as they wish even though there is the option of setting the speed of such vehicles and determining the trajectory they have to follow.

Similar to SDVs, AGVs are electric-powered vehicles that do not need human interference. They can be programmed so that they drive along a given pathway in the industrial facility following pre-marked paths. The markers may be visible sites or wires built inside the facility’s floor. The operation of AGVs is more of a stop or go forward mechanism. What this means is that there is very minimal room for decision-making. Their programming is in such a way that they have a better understanding of the environment and reaction to it is in the real-time paradigm. The environment sends information to these vehicles as gathered by sensors and processed by software. When the AGV solves a problem, it also learns from it, coming up with new ways to better handle the issue if it were to occur again in the near future. Essentially, the operation of an AGV relies on the efficiency of the programmer who came up with the algorithm (Shea, 2016).

As much as SDVs and AGVs jointly facilitate the growth of Industry 4.0, the former is more performant compared to the later. As a matter of fact, any discussions about self-driven vehicles are mostly made from the perspective of SDV technology as opposed to AGV. That happens because SDVs have a higher flexibility compared to their counterparts. Whereas AGVs need beacons, embedded wires, or other markings for them to be operational, SDV have a sensing capability that makes them fit to be placed in literally any environment. These get rid of the time and costs involved in installation.

Robots and self-driven cars are some of the most futuristic technological innovations taking place in our world currently. SDVs and AGVs in the industry are a classic example of how impactful these technologies can be. As the technology continues to advance, there is no doubt that the impact on Industry 4.0 shall be far-reaching (Otto Motors, 2014).

Real-Time Location System (RTLS) and Radio Frequency Identification (RFID)

Real-Time Location System is used for automatic identification and tracking of people or objects in real time. The tracking typically happens within the confines of a building or contained space. Wireless Real-Time Location System tags are fitted on people who then transmit data to fixed reference points (Parker, 2017). The data is used to determine location of an individual. There are countless real-time locating systems such as locating medical equipment in a health center, finding merchandise pallets in a warehouse, and tracking automobiles in an assembly line (Wikipedia).

The RTLS technology has a physical layer that is usually some kind of radio frequency communication (Centrak, 2018). However, there are other RTLS that use acoustic or optical technology rather than RF. Radio-Frequency Identification (RFID) is the most preferred tracking methodology. It harnesses electromagnetic fields for the automatic identification and tracking objects attached on tags. These tags are supplied with electronically stored information. There are two types of tags; passive tags and active tags.

  • Passive tags: Used in collecting energy from nearby radio waves
  • Active tags: Have a local source of power and may be able to cover hundreds of meters from the RFID reader

There are numerous industries which make use of RFID tags. For instance, the automobile sector may use them during production to track progress made in the assembly line. Warehouses may also use these tags in tracking pharmaceuticals, while livestock may be fitted with RFID microchips for positive identification of animals. Shops may also use tags to prevent customer and employee theft as well as speed up checkout (Wikipedia).

Despite these possibilities, it does not mean that RFID has not had its share of controversies. For example, it is possible to attach the tags on possessions, clothing, and cash as well as implant them in animals and people. This raises the ethical question of whether it is the most ideal solution as it lets the observers get a deeper look into an individual’s life. There is a high chance that personally-linked information could be read by unauthorized persons (Wikipedia).

Such fears have resulted to the development of standards that can be used to deal with privacy and security concerns. The standards are ISO/IEC 29167 and ISO/IEC 18000 which are used for cryptography and addition of authentication elements throughout the tracking system. As RTLS becomes the norm in many industries and more security features are added to RFID, Industry 4.0 continues to advance (Centrak, 2018).

Augmented and Virtual Reality

Augmented Reality refers to a real-world environment interactive experience in which case computer-yielded perceptual information is used to augment real-world objects (Wikipedia). This mostly happens via numerous sensory modalities like olfactory, somatosensory, auditory, haptic, and visual. The overlaid sensory information may be constructive as well as destructive, and is often intertwined with the real-world so that it brings out a perceptive feeling of the real-world. As a result, augmented reality changes your perception of the real-world. This is a completely different approach from what Virtual Reality does (Cramer).

Virtual Reality entails interaction of a computer-yielded experience which happens in a simulated environment. It integrates numerous visual and auditory feedback. It may also allow room for sensory feedback but this is optional rather than mandatory as is the case with augmented reality. The immersive environment may be an accurate representation of the real world or it may be fantastical.

Both Augmented Reality and Virtual Reality are keys to Industry 4.0. For a long time, the only way that manufacturers were able to get a detailed idea of how products progressed on the assembly line was to be in proximity. The same is true with regard to determining the state of industrial equipment. However, as the industrial sector becomes more connected and computing power increases, this task can be virtualized through creation and maintenance of digital representation, which is otherwise known as digital twin. A digital twin is created in such a way that it changes exactly as its physical counterpart. It continuously learns and updates itself based on information sourced from a wide range of sources   to retain real-time status (Wikipedia).

In general, the digital twin leads to a state where we have an accurate virtual environment of industrial equipment and products (Parroyy & Warshaw, 2017). Production can be done in the virtual environment repeatedly, each time fixing all errors and possible lags before transferring to the real-world. With the aid of virtual reality and augmented reality, we are able to interact with digital twin freely. Manufacturers do not have any problem making mistakes in the virtual environment since there are no real-world consequences. After the virtual production has been twisted to factor in all elements that can enhance efficient production, the ultimate process is accurately transferred to the physical production space.

As you can see, Virtual Reality and Augmented Reality go beyond gaming. Large companies that are able to afford installation of expensive Cave or Power-wall systems are integrating VR in their engineering and manufacturing workflows. With digital realities, solutions have been created to be integrated into current processes and may be the visualization pipeline for supporting current processes and systems. Virtual Reality and Augmented Reality are 100% immersive, digital experience. The headsets completely cover the field of vision of a user, completing turning off their surrounding world encounter and make them feel as though they are actually in the displayed scene. The two technologies enrich Industry 4.0.

Collaborative and Autonomous Robots

Collaborative Robots are fitted with the ability to carry out material handling, machine tending, packaging, assembly tasks, and inspection. They are guided by ISO/TS 15066 standards to ensure safety of all involved persons. The design of these robots is normally in such a way that they can identify proximity and interact with their human counterpart, conducting their duties in such a way that they are not a threat to humans. Therefore, the human has a better chance for responding to their own actions as well as those brought forth by the robot (Sofge, 2014).

The most important thing about Collaborative and Autonomous Robots is that they are some of the most advanced pieces of equipment. Therefore, they have to undergo detailed risk assessment before they are implemented. The assessment can be done following various procedures. The procedure taken is guided by the complexity of assessment required. In general, every collaborative robot has to undergo assessment for the safety of an operator.

Even though Autonomous robots are slightly different from Collaborative Robots, they are also yielding a positive impact on Industry 4.0. Experts have been able to create Autonomous Robots that can walk themselves through a busy room, while at the same time interacting with humans as needed. These robots are able to do more than just walking. They can even position themselves besides Collaborative Robots from which they get load they can recognize and take to the original location. For a long time, various industries have faced the issue of operators having to undergo high-force strains. Autonomous Robots are changing the dynamics. They can effortlessly transfer products in a facility, a feature that is a key in Industry 4.0 (Sofge, 2014).

Both Autonomous and Collaborative Robots are creating a new industry where manufacturers do not have to be troubled by inaccuracies in the production processes.

Artificial Intelligence and Machine Learning

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that is focused on making predictions with the aid of Big Data. ML is built upon pattern recognition to draw knowledge from experience in an independent manner. Thanks to the advanced nature of the technology, manufacturers have found various ways that they can use it (Jabil).

Gone are the days when we used to talk about AI as a future technology. It is here with us. Large data centers have been established and enabled humanity to do things that were thought of as distant concepts just a few decades ago (Gavstech, 2018). The two branches of Artificial Intelligence, that is, Machine Learning and Deep Learning, are able to optimize processes with the aid of possibilities presented by Big Data. They are also paving ways for new possibilities.

Production processes of a smart factory are connected based on the Industrial Internet of Things. Interfaces, machines, and components are linked in such a manner that they freely communicate, exchanging vital information. The essence of Industry 4.0 is minimization of human errors and increasing productivity as production costs are optimized. These are possibilities which have been achieved as a result of Machine Learning and Artificial Intelligence.

There are numerous roles that AI and ML play in Industry 4.0 as discussed below:

  • Inventory management, supply chain management, and asset management are the main reasons why manufacturers are in a rush to adopt ML, AI, and IoT in their processes. A combination of these technologies has led to a state where there is high accuracy with regard to asset tracking, inventory optimization, and supply chain visibility.
  • Machine Learning techniques such as analytics, quality optimization, and machine intelligence driven processes aid in predictive maintenance.
  • Artificial Intelligence and Machine Learning technologies provide accurate information which can be use to reduce errors in supply chain forecasting as well as getting back lost sales. With high sales rate, manufacturers are motivated to improve on productivity.
  • A combination of Overall Equipment Effectiveness (OEE) and ML leads to a state where yield rates are improved and better preventative maintenance accuracy is achieved. OEE is a globally accepted metric in manufacturing given that it defines production effectiveness using quality, performance and availability.

The impact that Machine Learning and Artificial Intelligence are having on Industry 4.0 cannot be understated. As better algorithms are developed for ML to recognize characteristics and relationships, the opportunities in Industry 4.0 are limitless (Gavstech, 2018).

Big data, Analytics and Simulation

Big Data analytics and simulation are a crucial element to the success of Industry 4.0. A major part of the fourth industrial revolution revolves around the way different devices and machines interact, exchanging information and learning from each other. This kind of interaction happens via the Industrial Internet of Things (IIoT). At the center stage of all these interactions is Big Data. The availability of enormous amounts of data means that there is something that machines can actually exchange with each other through platforms provided by Industry 4.0 (Ratliff, 2018).

Big Data analytics is crucial in Industry 4.0 since it is the basis upon which predictive manufacturing, an important theme in the modern industrial revolution, can take place. There are numerous Big Data analytics solutions that manufacturers can make use of to gain competitive advantage by improving efficiency of their processes and higher yields. The production systems are fitted with sensors which collect data for analysis through Machine Learning algorithms (Matthews, 2017). Information gained may warn potential system fails and anomalies as well as predict product quality. Such intelligent solutions for the manufacturing sector pave the way for other machining applications, building up on the essence of Industry 4.0. That is, one technology being used to facilitate the emergence of other helpful technologies.

But even as countless advanced Big Data solutions promise to make Industry 4.0 a success, there is a need for manufacturers to also change their approach on analytics. Most organizations make the mistake of viewing analytics simply as a tool they can use to gain more productivity when they silo data in their business and various groups are used. A problem emanates when each of these groups focuses on different goals, blocking them from getting new outcomes. This leads to a state where their business intelligence is not synchronized with the business itself, making them miss tremendous opportunities. In order to overcome this barrier, it is vital for companies to make analytics a part of their whole value chain (Matthews, 2017).

Just like prototyping is a part of product development, the process of manufacturing that product ought to undergo optimization to maximize on efficiency. Simulation software can approximate the manufacturing process into various events in order to model each step of the manufacturing process for better performance.

Thanks to technological innovations in Industry 4.0, data harnessed by the digitized components allow for simulation of the whole production line using appropriate software (ITRI). It is possible to fit into the model real-time information regarding component histories, inventory levels, and transport logistics. This makes it possible to develop various schedules and plans via simulation. Potential loss is minimized as alternative supply sources and production mechanisms are compared against each other.

Whenever there is a change in the manufacturing process, simulation software automatically creates models that indicate likely impacts on production. The software is advanced enough to capture the slightest change like simple stock out or major shifts such as unexpected natural disasters. The courses of action proposed by the model are assessed before being implemented.

Visual recognition

There are numerous industrial applications that make use of computer vision-based products. Factory automation is the most prominent of such applications. Machine vision is amongst the first, and remains to date, the most mature computer vision opportunity. As Industry 4.0 continues to see an increase in automation of manufacturing processes, the opportunities for visual recognition similarly expand in strength and scope (Embedded-vision).

Visual recognition plays a significant role in automation systems for Industry 4.0. The growth of Big Data and its accessibility by vision equipment opens avenues for the identification and flagging of defective products, realization of their deficiencies, and effective invention in this robust industry.

Recent times have seen the term smart factory dominate most discussions regarding Industrial Internet of Things and Industry 4.0. Smart factories, achieved through combined efforts of IIoT technologies, can potentially enhance productivity, lower downtime rates, reduce waste, and optimize manufacturing processes. Visual recognition is central to smart factory that is currently being built and will be available for years to come. The technology can understand contents in images, analyze images, faces, objects, colors, and other objects found in the visual content (Embedded-vision).

Industry 4.0 is not all about churning out products but also ensuring these products are bought by the customer. Advertising helps companies attract the attention of their target audience. For better marketing, technologies that help with identification of content in images and videos can be leveraged. The images that individuals share on various social media platforms are a resourceful insight into the products they like. Manufacturers can use such information to customize their products to meet the needs of individual customers. Combined with Machine Learning, visual recognition algorithms helps with the identification of unique elements in images. Such information is not only useful to marketers within Industry 4.0 but also to manufacturing process itself.


Industry 4.0 is associated with higher connectivity and use of communications protocols. These give rise to the need to ensure that critical industrial systems are safeguarded against cybersecurity threats. There is high urgency by manufacturers to ensure that their valuable data is safeguarded against such risks while at the same time remaining compliant to regulatory controls regarding data use in the cyberspace. This is a need that has made it essential to establish communications that are reliable and secure while building sophisticate identity for users and managing access to machines (ENISA, 2018).

Cybersecurity plays the role of protecting valuable data so that third parties do not exploit it. The use of technologies such as Cloud Computing and IIoT exposes the industrial system to countless risks. As a result, these environments need heavy investment for their security to be strengthened. This is an area that many tech firms are willing to cash in, motivated by the financial forecasts. It is estimated that cybersecurity will be as high as $1 trillion worth by 2021 (ENISA, 2018).

It is a good thing that manufacturers are taking action at an early stage. This is because thinking of various ways to deal with cyber risk after implementation of a strategic process is simply an unviable move. Cybersecurity ought to be an integral part of any strategy, operation, and design adopted in Industry 4.0.

Organizations have to take action as early as possible in order to protect their information from getting to the hands of wrong people across the connected network. Furthermore, there will also be a need for high level of discipline when maintaining the different protection mechanisms for supporting processes like system access, information sharing, and vendor acceptance. These processes could be both proprietary and points of access to internal information.

While implementing Industry 4.0 technologies, it is essential that manufacturers put into consideration machinery, digital processes, and objects that are likely to be impacted. The term ‘uniting IT and OT’ is mostly used to describe this situation (Lemke, 2018).

Cyber risks in Industry 4.0 cover a scope that has gone beyond supply network and manufacturing. They are a part of the product itself. As products continue to interconnect and also connect back to the supply network and manufacturer, organizations ought to realize that cyber risk does not just come to a stop after the sale of a product.

Cloud Computing

The rapid rate at which Cloud technology has been adopted aids in the digitization of organizational processes. With Cloud Computing, businesses in different industries are empowered to exploit dynamic modern technologies. Cloud Computing refers to a mechanism in which data is stored and accessed via the Internet as opposed to that happening through computer hard drive. The ‘Cloud’ is used to imply the Internet. As Artificial Intelligence, Machine Learning, and Automation become part of the production process, businesses may base on Cloud Computing as a way to keep up with changes without having to lose their most valuable data (Paige, 2019).

Numerous Cloud-based softwares are in use today for some enterprise and analytics applications. However, as Industry 4.0 increases the scope at which machinery and devices are interconnected, company boundaries will be expanded and data sharing will be viewed. Increased data sharing makes it necessary for the presence of Cloud-based platforms given that they provide a remote location where companies can store and retrieve data without having to worry about onsite infrastructure. Similarly, there is a continued improvement in how Cloud technologies perform, making it possible for an almost immediate reaction to data change. Companies that invest in data analytics tool will have an edge as they get real-time insights that aid in the decision-making process.

Technology advances at a rapid rate. Such high-speed changes have the effect of making Cloud Computing a cheaper as well as economically viable investment. The costs that firms used to incur sometimes back when renting Cloud space have dropped. In other words, companies get thrice or double storage space for almost half the amount of money used a few years ago in Cloud storage. In addition, Internet speeds have increased as costs go down. Therefore, file transfer happens at a faster rate, saving on time which can be invested in other areas of the company.

There are numerous Cloud Computing providers including Apple iCloud, Google Cloud, and Amazon Cloud Service. Each of these providers offer services on different business models including subscription basis and one-time payment plan. In most of these services, data is handled from the real-time paradigm. That is, company’s systems could be configured to automatically send data changes to the Cloud. Technologies such as Machine Learning help the firms get more information from the data. This forms a solid background for the advancement of Industry 4.0.

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