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Industrial Automation

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Industrial Automation

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Industry 4.0's Impact on Industrial Automation Branches

By Jeff Shepard for Mouser Electronics

Sponsor: Infineon, TE Connectivity

As problem-solvers, engineers combine practical knowledge of science and mathematics to drive creativity and innovation for society’s benefit. Engineering is a broad and diverse field, and the emergence of Industry 4.0—the combination of the Industrial Internet of Things (IIoT), automation, robotics, and additive manufacturing—is having a significant impact on the branches of industrial automation, including systems, process, and design engineering. That impact ranges from how products are designed for manufacturing in smart factories to redesigning the factory operations and manufacturing processes themselves.

Traditional manufacturing methods are optimized for mass production. Industry 4.0 factories are designed to support flexible manufacturing and mass customization. Mass customization and the delivery of more personalized products drive higher brand loyalties and increase business success. The various branches of industrial engineering are essential to developing the innovations needed to support Industry 4.0.

Coming out of universities, new engineering graduates all have fairly similar backgrounds with a carefully regulated set of required classes and only a few electives to explore their interests. Once they take that first step into the career of their choice, these engineers will embark on a wide variety of design roles, each requiring different elements from their education and different capabilities from the individual.

The following examines the roles of systems, process, and design engineers within the design chain of bringing new industrial automation products and projects to fruition and how those roles are expanding and evolving because of Industry 4.0.

The Development of System of Systems

The emergence of cyber-physical systems (CPS) and systems of systems (SoS) are two defining characteristics of Industry 4.0. CPS support increased human-machine collaboration with ubiquitous wireless connectivity that integrates computational and physical assets from the factory to the cloud. CPS is having a disruptive impact on the branches of industrial automation and are enabling the SoS development.

An SoS is a group of systems that work together to enable a new and more complex system that supports greater capability and performance than simplify the sum of the constituent systems. SoS is an emerging field in Industry 4.0, and systems engineers and researchers are still developing the quantitative analysis tools to optimize SoS.

Systems Engineering for Industry 4.0

The term systems engineering was first used during Industry 2.0 at Bell Telephone Laboratories in the early 1940s. Systems engineering consists of five phases, and while systems have become increasingly complex with Industry 4.0, the basic phases remain the same:

  1. Preliminary system studies and program planning.
  2. Exploratory planning, which includes selecting objectives, preliminary systems synthesis, and analysis, beginning to identify the preferred system solution.
  3. Development planning, which includes refining the objectives, refining the systems synthesis and analysis, identifying a final system solution.
  4. Development implementation, which includes the development of system elements and components and the integration and testing of these parts.
  5. Concurrent engineering, a continuous process taking place while the system is operational and being refined and updated.

Systems engineering is a multi-disciplinary field. A system typically includes hardware, software, equipment, facilities, personnel, processes, and procedures needed to accomplish a given task or set of tasks. The primary purpose of systems engineering is to organize information and knowledge to assist those who manage, direct, and control the planning, development, production, and operation of systems (Figure 1).

SoS engineering takes the systems engineering process further and focuses on planning, analyzing, organizing, and integrating the capabilities of multiple systems.

A woman engineer looking at a computer with another woman standing over her showing her something on the screen
Figure 1: Systems engineering is a multi-disciplinary field including hardware, software, equipment, facilities, personnel, processes, and procedures. (Source: Gorodenkoff/Shutterstock.com)

The use of the IIoT and cloud computing can support the integration of multiple systems into an SoS. The collaborative yet autonomous systems can deliver greater capabilities than the sum of the capabilities of the individual constituent systems. Increasing the complexities that systems engineers must deal with when considering SoS implementations, the mix of systems can evolve and include yet-to-be-designed systems or capabilities and technologies.

The emergence of Industry 4.0, CPS, and SoS has increased the complexity of the challenges faced by systems engineers. Among these challenges are:

The new generation of system engineers has developed skills for mining and analyzing data. Configuring how that information is captured is paramount. Increasingly, that data is captured by embedding an intelligent edge or gateway that seamlessly collects the correct data from the factory floor. That calls for the combined skills of process engineering to identify meaningful and valuable data and design engineers to develop equipment and devices capable of capturing and reporting data in real-time.

Process Engineering

Traditionally, process engineers are responsible for designing, implementing, and optimizing chemical and biochemical processes, especially continuous ones within the chemical, petrochemical, agriculture, mineral processing, food, pharmaceutical, and biotechnological industries. With the developments of Industry 4.0, that definition has expanded to include the processes needed to support mass customization of all types of products.

Almost everything is connected in Industry 4.0 factories—and sensors are everywhere (Figure 2).

Process engineers need to maximize the value of massive networks of connected sensors.

A woman engineer looking at a screen with male engineer standing next to her
Figure 2: Massive deployments of wireless sensors and the growing use of additive manufacturing (also called 3D printing) technologies are new opportunities and challenges for process engineers. (Source: Gorodenkoff/Shutterstock.com)

What data is relevant, what is not required, should the data be analyzed on the edge of the machine or robot, or in an on-site data center that has more computing power, or in the cloud with even more computing power? At all levels, process engineers use sophisticated software that collects, transfers, and processes sensor data to monitor production processes and identify inefficiencies in specific processes and machines in need of preventative maintenance. Adding AI and ML results in operations that are data-driven and self-optimizing in real-time.

Many factories include a mix of equipment, including legacy machines without intelligent controls, sensors, or communications, and new systems optimized for Industry 4.0. That can result in production islands in which parts produced by one set of machines go to another for the next step in the production process. Still, the various islands are not connected, and data from the first production island does not follow the part of the next island. This is another place where IIoT can be leveraged. Using wireless sensors and IIoT, process engineers can connect previously isolated production islands and significantly improve the factory's operation.

To achieve maximum benefits, process engineers analyze each process to determine the leanest approach to provide a flow of meaningful data between the islands and a central monitoring and control platform. Process engineers are empowered to implement continuous improvement programs throughout the factory with the proper sensors and interconnects.

Additive manufacturing (AM) is another powerful new tool that process engineers can leverage in Industry 4.0. AM can produce structures with internal lattices for lightweight parts with increased structural strength. Using AM, process engineers can design systems to customize personalized components such as medical devices and implants that are fitted to individual patients. AM is also being leveraged in various market segments, from consumer devices to defense and aerospace systems. Effectively developing AM processes requires a close partnership between process engineers and design engineering. Design-engineering tools for AM are increasingly sophisticated and enable component consolidation and product simplification and generate complex internal structures.

Design Engineering 4.0

The tools that design engineers have available to them in Industry 4.0 have advanced. New computational modeling techniques such as digital twins and virtual reality are emerging. Together with the development of AM, these new modeling techniques support the development of complex shapes and lightweight and strong internal structures (Figure 3). Conventional computer-aided design (CAD) tools do not help model highly complex surfaces, shapes, and interior structures. New design platforms appear to create and validate thousands of options in real-time, identify the design with the best cost/performance tradeoff and deliver the design files ready for use by automated AM work cells.

Digital Twin technology is the most advanced of the new computational modeling tools that design engineers can use. A Digital Twin is a digital representation of all the aspects of a physical object, including its geometry, various constraints, performance capabilities, manufacturing parameters, and so on. These Digital Twins can be individual components or complete assemblies whose design and production needs can be optimized simultaneously.

Using these virtual design tools, engineering teams can collaborate from different locations in real-time throughout the design process. Because Digital Twins is based on complete multi-physics modeling, they are robust and optimized for specific manufacturing process capabilities. The use of Digital Twins also supports rapid innovation and shorter product development timelines.

Virtual reality (VR) technology is also expanding the tools available to design engineers. VR can be used to produce a virtual prototype. Using VR to supplement or even replace traditional CAD tools can accelerate and enhance innovation, speed the identification of operational obstacles and refinement of features, and support collaboration between a range of specialists in real-time. Designing products suited to mass customization requires that product design and simulation of manufacturing processes occur interactively, and that demands the real-time interaction of teams of specialists.

Design engineers have always operated as part of a team with other engineers and designers, including process engineers, test engineers, project engineers, marketing specialists, industrial designers, etc. The development of virtual environments and tools will increase opportunities for collaboration.

An engineer wearing VR goggles and holding a 3D-printed model in the air inspecting it
Figure 3: Design engineering for Industry 4.0 can combine AM and VR technologies to develop customizable products. (Source: FrameStockFootages/Shutterstock)

A trend in Industry 4.0 design engineering considers energy efficiency and energy savings issues on a more holistic basis. Expanding on the concept of energy efficiency, the new focus is developing methodologies that can quantify energy consumption throughout the entire product lifecycle chain, including obtaining the materials, manufacturing the product, and even recycling or disposing of the product when its useful life is over.


Industry 4.0 is having an enormous impact on all branches of industrial automation. It is changing the systems, processes, and products that are being designed to support mass customization. It is changing the tools available to engineers and the interactions that engineers have with other members of the design teams. It also is changing how products are defined to include comprehensive measures of energy consumption and energy efficiency for manufacturing processes and product operation and use.

Photo/imagery credits (in order of display)
Gorodenkoff/Shutterstock.com, DedMityay - stock.adobe.com, AA+W - stock.adobe.com, Me studio - stock.adobe.com

The Tech Between Us Podcast
Industrial Automation

Sponsor: Advantech, Molex

Full Podcast (38:05mins)

Introduction (00:53mins)

Condensed Podcast (09:42mins)

Join us in our technology conversation with Clara Vu of Veo Robotics as we discuss Industrial Automation and the relationship between humans and cobots.

View more from Advantech »

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Podcast Host

Raymond Yin

Director of Technical Content, Mouser Electronics

Podcast Guest

Clara Vu

Co-founder and Chief Technology Officer, Veo Robotics

Customers Driving Industrial Automation Innovation

By Darshan Pandya for Mouser Electronics

Sponsor: Analog Devices, Phoenix Contact

Customer needs and expectations are the driving force behind product development and manufacturing. Customers want product options and personalization. Customers want customized products manufactured and delivered quickly, and increasingly, customers expect manufacturers to align with their values. Although the traditional manufacturing goals of efficiency, accuracy, and safety remain relevant, manufacturing environments are trending toward new characteristics, such as being agile, accessible, data-driven, collaborative, resilient, and sustainable. The following examines these characteristics and how they help meet customers’ rapidly evolving needs and expectations.


Agile systems are rooted in batch production approaches, where the base product is mass-produced, but customization is carried out in batches—say, producing one batch with red paint, another with blue, and another with pink. Agility relates to market changes and what customers want; it changes the concept of producing one or two variations to instead offering those variations as options, taking manufacturing from customizing products to personalizing them. Whereas customization leads to customer satisfaction, personalization has an aspect of customer delight attached to it.

The most advanced systems produce each product as a separate order along the production line, using customer data to drive personalization. Imagine ordering a product during the week of your birthday, for example. The manufacturer might add a birthday note in the box or print “Happy Birthday!” on the packaging to personalize the product. Or, say you have been researching meal delivery services online: A manufacturer might put a coupon in the box for a popular food delivery service. With batch production processes combined with customer data, manufacturers can do a lot to personalize products and not just satisfy customers but delight them.


In industrial automation, accessibility refers to two different aspects. First, accessibility refers to connecting a distributed workforce through integrated, secure systems. Such systems have been evolving for decades, but only recently have the technologies necessary converged to enable seamless and secure collaboration. In earlier decades, we saw various components and pieces of technology make collaboration possible, but only now do we have what we need to fully integrate distributed workforces, resources, and services across all aspects of business.

Second, accessibility refers to integrating human factors into system design in terms of ease of use for installers, operators, technicians, floor workers, and others. Designing with humans in mind used to be an afterthought or a nicety. Now, companies realize that the installers, operators, and all the rest are the consumers in these cases. Therefore, considering human factors and applying related guidelines are important parts of design success.

In both cases, cloud infrastructure, with system integrators such as infrastructure as a service, platform as a service, software as a service, and similar concepts are the most significant enablers of accessibility. Teams working in real-time with minimum lag time provide considerable extensibility for the types of collaboration possible. Augmented reality (AR) is a good example. Rather than engineers, technicians, manufacturers, and other stakeholders flying in to install, operate, troubleshoot, or repair a mechanical system, AR enables stakeholders to access data analytics, see the system in real-time, and use visual overlays to make repairs or alterations. The efficiencies related to cost, time, and product life cycle are already significant, and we can expect them to increase the number of AR technologies improved over time.

Data Driven

In today’s industrial automation, data drives many insights. For instance, data can evaluate how a manufacturer is doing in terms of what is most important to the company. Here, value matrices are used to identify the five or six aspects most important in measuring the company’s success, such as speed, accuracy, conformance to standards, conformance to regulations, and customer satisfaction. Using real-time data and data trends over time, stakeholders can see how the company performs overall, performs in terms of its values, and performs compared with industry averages.

Data insights have moved humans from simply responding to manufacturing issues to instead proactively preventing and addressing them. Fluctuations in data can be used to identify when something is about to go wrong and then trigger email messages to responsible technicians, support tickets filed with the help desk, or text messages to supervisors. In some cases, fluctuations can be benign; nonetheless, real-time data and trends can help put humans in a proactive rather than reactive role.

Finally, data can be used to evaluate the industrial automation characteristics discussed here. For instance, data can enable greater manufacturing agility by providing insights about when a manufacturer should start offering a sofa in additional colors or different fabrics rather than manufacturing those instances as product variations. Data can also drive process resilience by minimizing downtime for product variations and ensuring that humans are in the right places at the right times in the processes.

Collaborative (with Robots)

The nature of collaboration is also evolving with humans collaborating with other humans and robots. The goal of using machines and robotics in industrial environments has historically been to shift heavy, repetitive, and dangerous tasks away from humans to mechanical and automated systems. Collaborative robotics (cobots) expands these use cases, however, to include:

  • Skills that require years of training for humans to become proficient. Welding is one such skill where more than 100 hours of classroom time and three to four years as an apprentice are required. Even then, human welders typically cannot match the quality and consistency of welds that robots can make. By way of example, even highly skilled welders can weld only a 60cm seam in one continuous motion, and starts and stops can affect the overall quality of the weld. In contrast, robots can weld roughly a 121cm seam in one continuous motion, producing a higher-quality weld. A collaborative approach for welding might have the robot completing steps that require skills and might affect quality. The human would do the tasks that are more intuitive to humans, such as setting up the process and handling exceptions. The idea here is to use the best of both worlds to achieve maximum efficiency. (Figure 1)
  • Tasks where robots can do part of the work. Imagine receiving components that are fragile or sensitive to handling or that require a clean room. In these instances, using a cobot to open boxes of various sizes and shapes would pose several design challenges and be less efficient or effective than having humans perform such tasks because the tasks are intuitive rather than calculated. In contrast, a cobot could easily meet the sensitive component handling requirements after the box has been opened and perhaps do so faster and more consistently than humans.
Man controling robot arms as they weld in factory setting
Figure 1: Smart industry robot arms welding (Blue Planet Studio - stock.adobe.com).


Resilience is about enabling systems to adapt quickly when variations in product manufacturing are needed. For example, imagine a system that is designed to produce product A. At the start of the production run, someone sets up the system to manufacture that product. However, what happens when variations of the product are needed—a different color, size, fabric pattern, module, or packaging? In less resilient systems, someone would need to manually change the setup to accommodate these variations, which means production downtime and higher labor cost. Resilient systems can withstand such unforeseen adversities and recover quickly. The aim of resilient systems is to account for possible variations that the system might encounter to adapt with as little downtime and human intervention as possible.

From a design engineering standpoint, it’s easy to get trapped in the idea of developing super-complex systems that can do many things and account for even rare scenarios. The more complex the system, however, the more opportunities for failures. A simpler system that handles 80 percent of production scenarios might be better than a more complex system that handles 90 percent of scenarios. Designing for resilience is about finding a balance between simplicity that requires more human time in the loop and complexity that has more failure points and potentially affects other production areas.


Sustainability refers to designing and operating systems in ways that do not compromise the natural environment or the ability of future generations to meet their needs. Industrial automation supports sustainability goals by improving productivity, reducing energy use, and reducing waste. More and more, customers align with companies that share their values in doing good for the environment and reducing environmental damage. In this sense, sustainability in industrial automation can help companies move from satisfying customers to delighting them. (Figure 2)

Sustainability is also a potential result of increasingly automated industrial environments:

  • Data insights make processes more efficient, leading to lower energy requirements and reduced waste in manufacturing products.
  • Data-driven insights make processes more efficient, which can lead to less manufacturing-related pollution.
  • Data-driven insights make predictive maintenance possible, leading to a reduced need for redundancy in cyber-physical systems.
  • Accessibility enables collaborative installation, operation, troubleshooting, and repair from afar, which requires fewer people on-site and reduces travel-related fuel consumption and pollution.
  • Access-enabling technologies such as AR can reduce the need for volumes of paper documentation and updates for installation, operation, troubleshooting, and repair.
  • Collaborative robotics can reduce the amount of physical space needed, reducing manufacturers’ footprint on Earth.

Finally, sustainability also results in impressive monetary benefits. Today, a range of solutions is available for capturing, mining, and monitoring energy-related data that translates into insights for cost reductions.


Customers want product options and personalization. They want customized products manufactured and delivered quickly. They expect manufacturers to align with their values. The traditional manufacturing goals of efficiency, accuracy, and safety remain relevant, but new characteristics are emerging to help meet customer needs and expectations, including agility, accessibility, data-driven decision-making, collaboration, resilience, and sustainability. Manufacturing engineers are already incorporating these characteristics into system designs, and we can expect to see them become more prevalent.  

Green nature from above. Aerial view of river landscape
Figure 2: Aerial view of lush river landscape (dzmitrock87 - stock.adobe.com).

Photo/imagery credits (in order of display)
Zapp2Photo / shutterstock.com, greenbutterfly - stock.adobe.com, I Viewfinder - stock.adobe.com, xiaoliangge - stock.adobe.com

Lack of Standards Hinders RPA Adoption

Sponsor: Maxim

A human-like robot interacting with a holographic screen

BLOG: Lack of Standards Hinders RPA Adoption

Robotic process automation (RPA) is about democratization, and that implies some level of standardization. But there are almost no standardization efforts. So far, the only standard available is a taxonomy from the IEEE, while RPA vendors continue to offer proprietary and disjointed solutions.

Read more »

View more from Maxim »

Evolution of Human Machine Interfaces

By Jeff Shepard for Mouser Electronics

Sponsor: Intel, Littelfuse

An HMI, or Human-Machine Interface, is a user interface or control panel that connects an individual to a machine, system, or device. It’s typically used in the context of an industrial process. The first rudimentary HMI was the Batch Interface that appeared around 1945, during Industry 2.0.

The Batch Interface is a non-interactive user interface, where the user specifies the details of the batch process in advance and receives the output when all the processing is done. Batch processing does not allow for additional input once the process has started.

Before the Batch interface, Industry 1.0 was ushered in with the invention of the steam engine. Operators interacted with each machine individually with simple gauges, switches, and levers. The introduction of electricity heralded Industry 2.0, which started with similarly limited operator/machine interactions. As Industry 2.0 advanced, it became possible to monitor the entire production process from isolated control rooms filled with specialized dials and panels. That led to the development of the Batch Interface. Industry 3.0 started in 1969 with the introduction of the first programmable logic controller (PLC).

This article traces the development of HMI technology, beginning with the Batch Interface. It will then follow the evolution of advancing HMI technologies from primitive Command-Line Interfaces used by the first PLCs to today’s advanced touch-screen-based Graphical User Interfaces and the use of networked handheld mobile devices to control and monitor automation systems via the internet of things (IoT) that characterizes Industry 4.0. It will conclude by briefly peering into the future of HMI advances in Industry 5.0.

Industry 2.0 and batch processing

At the beginning of Industry 2.0, people adjusted (trained) to work with machines. HMIs didn’t exist. All that was available were simple gauges, levers, and switches carried over from Industry 1.0. Industry 2.0 began when steam power was replaced by electricity in manufacturing processes in about 1890, leading to the division of labor, mass production, and assembly lines. It wasn’t until 1945 that batch processing was introduced and primitive HMIs emerged.

Batch processing was unlike today’s automation systems. Computing power was limited and expensive, and HMIs were crude. Users accommodated computers. HMIs were added overhead, and software was designed to maximize processor utilization and minimize overhead such as HMIs.

Punch cards and tapes were one form of HMI for batch processing systems
Figure 1: Punch cards and tapes were one form of HMI for batch processing systems: (Source: Alisa - stock.adobe.com)

Programmers didn’t interact directly with machines in real-time; they produced punch cards or paper tapes passed along to machine operators to implement (Figure 1).

Punch cards and paper tapes were the advanced versions. In earlier designs, machines were “programmed” using a plugboard similar to those found in telephone central offices at the time. In contrast, intermediate designs had the instructions entered into a matrix of switches on a control panel that were toggled on and off to produce the control code directly in the machine. Programs were necessarily straightforward and were often limited to opening and closing relays.

Submitting punch cards or tapes improved machine productivity. The programs could be more complex and produced apart from the machines, enabling machines to be operated on a more continuous basis. But there were drawbacks to the use of punch cards and tapes. Strick syntaxes were required, and the cards and tapes could be damaged. Errors were common. But they were found only after waiting hours or days for the machine operator to run the program. Once the identified errors were corrected, it took another period of hours or days to rerun the program, hopefully with success.

Hints of computer operating systems

Primitive operating systems evolved from these inefficient batch interfaces. Initially, the program card decks or tapes of batch systems needed to include instructions to communicate with input/output (I/O) devices and perform any other needed housekeeping functions: Activities handled by today’s operating systems.

So-called “load and go” systems began to appear staring about 1957. Computers had become more powerful and flexible (though still a faint shadow of today’s systems). Load and go systems included an embedded “monitor” program to provide generic I/O and other services for the batch programs. The monitor programs also implemented improved error checking of the batch programs, sometimes catching errors before the programs ran and providing better feedback to machine operators and programmers. These monitor programs are considered to be primitive computer operating systems.

Next, command-line interfaces (CLIs) evolved from batch monitor programs on system consoles. Falling costs for computing resources helped boost the use of CLIs. Latencies dropped from days or hours to seconds, and systems became more efficient. Early CLIs were primitive and were based on concepts also being developed for emerging time-sharing computer systems. CLIs employed a series of request and response interactions between the operator/programmer and the system.

As a result of the (almost) real-time interaction between operators and machines, operators could alter later stages of the program as they received near-real-time feedback on the results from previous processes. Software was more interactive but still far from user-friendly. Early implementations placed a heavy mnemonic demand on users. Still, it represented a significant improvement from simple Batch Interfaces that could not deliver immediate performance feedback. But machine users wanted more.

Industry 3.0

Industry 3.0 is traced to the beginning of the computer era, and people began taking more direct and real-time control of machines. It is highlighted by the shift from mechanical systems and analog electronics to digital electronics. The introduction of the first PLC in 1969 is generally cited as the start date for Industry 3.0.

In 1968, GM Hydramatic (the automatic transmission division of General Motors) began seeking an electronic replacement for hard-wired (and difficult to reprogram) relay-based control systems. That resulted in the first PLC being produced the following year. It was invented by Bedford Associates and was called the Modicon (modular digital controller) PLC (Figure 2).

The Modicon PLC was a significant advancement in HMI. It was more user-friendly than general-purpose computers and operated with a simple programming language focused on the logic and switching operations needed for industrial automation. PLCs include an HMI for machine configuration, alarm reporting, and general control.

Programmable logic controller, the Modicon PLC 584, connected to an early PC as seen in the National Museum of Science and Technology of Catalonia, Spain
Figure 2: Programmable logic controller, the Modicon PLC 584, connected to an early PC as seen in the National Museum of Science and Technology of Catalonia, Spain. (Source: Belogorodov - stock.adobe.com).

Early PLC HMIs took various forms: buttons and lights were used in simple systems, text displays, basic icons, and CLIs were also common. Beginning in the 1970s, PLCs used more complex programming and HMI monitoring systems running on remote computers, with the entire system connected through a communications interface. In the 1980s, desktop computers began replacing remote computers to provide HMIs connected directly with PLCs, supporting vendor-specific hardware platforms that simplified machine programming and monitoring.

In the early 1990s, IEC 61131-3 was published. It was the first vendor-independent programming language for industrial automation and PLCs and made HMIs even easier to use. Also, in the 1990s, HMI functionality on PLCs was expanded to include machine diagnosis and troubleshooting in addition to process control. And PLCs designed in the late 1990s had graphical touch screen HMIs and brought internet connectivity to the factory floor.

Industry 4.0

Industry 4.0 began with the emergence of the industrial internet of things (IIoT). During earlier stages of HMI development, the goal was to enable increased control of machines and industrial processes by people. Industry 4.0 HMIs were developed to support growing levels of collaboration between people and machines. And HMIs evolved to support an expanded definition of a “machine” to include decision support systems, software, and big data in the cloud.

Human-machine collaboration in Industry 4.0 is supported with wireless connectivity and portable devices ranging from touch screens on handheld controllers or mobile phone handsets to augmented-reality glasses (Figure 3). The result is so-called cyber-physical systems (CPS) that integrate computational and physical assets from the cloud to the factory floor. CPSs start with embedded computers and sensor networks to monitor physical processes, often with the data sent to the cloud for analysis. The sensors and actuators of the CPS blend seamlessly with the surrounding environment, creating the IIoT and a shift from centrally controlled processes to decentralized control.

In the past, there was an HMI associated with each machine or each PLC that controlled multiple machines, either locally or in a centralized control room. Industry 4.0 HMIs are distributed and are more human-centric.

Human-machine collaboration in Industry 4.0 is supported with wireless connectivity and portable devices ranging from touch screens on handheld controllers to augmented-reality glasses
Figure 3: Human-machine collaboration in Industry 4.0 is supported with wireless connectivity and portable devices ranging from touch screens on handheld controllers to augmented-reality glasses. (Source: torwaiphoto - stock.adobe.com).

Each person in a factory will have their own HMI wirelessly connected to the various assets under their control.

Industry 4.0 HMIs are often delivered with pre-installed apps for viewing documents, watching instructional media clips, and securely accessing external Web-based systems, in addition to controlling and collaborating with robots and other machines. These HMIs are often expandable and can include a variety of apps to support specific activities such as:

  • Perform advanced production algorithms and calculations
  • Connect to data from multiple sources over multiple protocols, including direct connections with IIoT connected devices
  • Visualize historical data
  • Automate workflows
  • Manage inventory
  • Analyze machine and motor drive health for predictive maintenance
  • Create notifications and send alerts

Industry 5.0 – what’s next?

Industry 5.0 will see an expanded definition of human-machine collaboration and a corresponding change in HMIs. Industry 5.0 will be built on big data, the cloud, machine learning, and artificial intelligence. Real-time cross-reality interfaces will supplant today’s HMIs.

Industry 5.0 HMIs will support an expanded communication pattern – a two-way pathway between humans and machines, robots, and cobots. In Industry 4.0, people are in direct control of collaborations with machines. That will expand in Industry 5.0, where HMIs will enable machines to initiate collaboration with people (Figure 4). The continued evolution of HMIs will ensure that people are more and more tightly integrated into industrial processes and manufacturing environments.

Big data, artificial intelligence, and cross reality will enable new paradigms of HMI in Industry 5.0 where machines reach out to humans for help solving difficult situations
Figure 4: Big data, artificial intelligence, and cross reality will enable new paradigms of HMI in Industry 5.0 where machines reach out to humans for help solving difficult situations. (Source: Viacheslav Lakobchuk - stock.adobe.com).


The evolution of HMIs started during Industry 2.0 with people seeking greater control over machines with the Batch Interface. Even greater levels of fine-grained control were achieved during Industry 3.0 with advancements related to PLCs and real-time HMIs. Industry 4.0 experienced a paradigm change where the goal of HMIs shifted from strict control of machines to increased collaboration with machines and the development of HMIs optimized for the IIoT and big data. The level of collaboration between humans and machines will continue to expand in Industry 5.0. HMIs will evolve to include cross-reality environments that can accommodate the need for closer and real-time connections between people and machines, where machines ask for inputs from humans to help optimize various activities and processes. 

Photo/imagery credits (in order of display)
Gorodenkoff - stock.adobe.com, monsitj - stock.adobe.com, greenbutterfly - stock.adobe.com, sdecoret - stock.adobe.com

Industrial Automation Technologies

The rise of automation has been transforming the manufacturing sector, thanks to the adoption of these enabling technologies.

robot arm

The Industrial Internet of Things

The IIoT utilizes relevant information to complete essential tasks through edge devices. In real time, businesses can use this performance data to adapt and streamline their operations.

 Harley-Davidson, for instance, leveraged the IIoT to reduce the time it takes to produce a motorcycle from 21 days to 6 hours.

Artificial Intelligence

AI algorithms look for patterns linking location, optimization, worker safety, factory efficiency, and quality to optimize processes and estimate market demands.

Over 60% of manufacturing companies have already adopted AI technology to deliver high-quality products that meet unique consumer demands.

Big Data

New standards in transferring, updating, searching and protecting data are emerging. Specialized Enterprise Resource Planning (ERP) solutions give manufacturers the critical edge they need to thrive.

As the adoption of Big Data in manufacturing grows, so does its market size. It is predicted to reach $9.11B by 2026, according to Fortune Business Insights.

A graph with 4 columns and the largest displaying $9.11B in 2026

The Cloud

Companies of the future will rely on the cloud technology's intangible nature to work remotely and collaboratively in real time.

The top use cases of cloud computing in the high-tech and industrial manufacturing sectors, according to the Infosys Knowledge Institute, are as follows:

63% - Improve visibility for inspection and quality check functions61% - Use cloud-based engineering and CAD tools for product development58% - Deploy new sensor-driven capabilities such as telematics and IoT (Internet of Things)44% - Better integrate with suppliers and partners25% - Improve supply chain planning, forecasting visibility and inventory management50% - Create intelligent energy capabilities


As more manufacturers move to cloud-based solutions and rely on a robotic workforce, there are more opportunities for cyber threats than ever before.

66% of manufacturers said their company had a past cyber incident
69% said they were confident in their cybersecurity preparation.

Additive Manufacturing Tech

Modeling / Simulation / Visualization / Immersion

Innovations like virtual reality, immersion tools, and 3D are utilized across sectors from the sciences to manufacturing to finance and more.

A chart with a cube in the center with 6 hexagon shapes surrounding the cube and moving outward. Next to each of the hexagon shapes is a years sequentially from 2019, 2020, 2021, 2022, 2023.

The additive manufacturing market grew by 21% in 2020, to a total of $12.6B, and is expected to continue to grow by 17% annually over the next three years.

Phoenix Contact

This Empowering Innovation Together content is brought to you by Phoenix Contact Single Pair Ethernet (SPE) Connectors

Innovations like virtual reality, immersion tools, and 3D are utilized across sectors from the sciences to manufacturing to finance and more.

Single Pair Ethernet forms the basis for all Ethernet-based communication, utilizing new fields of application and enabling smart device communication.

SPE from Phoenix Contact is offered as an IP20 PCB connector as well as an IP67 M8 shell for use in indoor and outdoor environments

SPE has the advantage to deliver both fast and gigabit ethernet combined with power over a simple two-wire interface

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