Industry 4.0 is here to revolutionize how new products—and the equipment that makes them—are built. Advanced technologies, such as the Internet of Things (IoT), cloud computing and analytics, and artificial intelligence are being integrated into production facilities and industrial operations. These technologies collect data throughout the production or product lifecycle, analyze it, and provide insights for better decision-making.

This fourth industrial revolution follows a rich history of innovation. It starts with the steam-powered machines of the late 18th century that took manufacturing beyond human and animal power. A second industrial revolution began in the late 19th century, introducing assembly lines and machines powered by oil, gas, and electricity. Computers, telecommunications, and nuclear energy in the mid-20th century brought about the third industrial revolution, which opened the door to space exploration, research, and biotechnology.

The concept of a fourth industrial revolution, or Industry 4.0, was popularized by Klaus Schwab of the World Economic Forum in 2015.(1) Schwab listed three reasons this is a new industrial revolution and not a continuation of the third: velocity, scope, and systems impact. He said, “When compared with previous industrial revolutions, the Fourth is evolving at an exponential rather than a linear pace. Moreover, it is disrupting almost every industry in every country.”

But this fourth industrial revolution, just like the ones before it, comes with challenges. Technical skill is a big one that has affected every industrial revolution. Each new technology requires a new skill set, and finding people with the right skills isn’t easy. A recent survey from Salesforce found that most respondents said they don’t think they have the right digital skills to prepare them for today’s jobs. (2)

Interoperability with legacy routines is another challenge. For years, organizations have built up systems and production lines, added to them, and improved upon them. This can quickly add up to a mishmash of technologies that don’t work together well. Adding in more new technologies might worsen the issue before it gets better. IT modernization can help manufacturers avoid the interoperability issues of legacy equipment and systems.

Despite the challenges, Industry 4.0 brings a wealth of new opportunities. But each new digital initiative needs to be accompanied by a solid plan aligned to business strategy to have the best chance of success. Otherwise, projects may end up in scale purgatory. According to the Manufacturing Leadership Council, 75% of manufacturers’ digital pilot projects fail to scale. (3) However, the companies that unlock transformational value with speed and scale across the production network can potentially realize a massive positive financial impact.

Overview of Technologies Driving Industry 4.0

A number of technologies are responsible for the velocity and impact of Industry 4.0, from machines to communication to data processing. These technologies are changing how decisions are made and how factories operate.

Internet of Things (IoT) & Operational Technology (OT)

While IoT and OT are technically not the same things, it’s hard to talk about one without the other from an industrial standpoint. The line between them is becoming less distinct, especially with Industrial IoT (IIoT), where interconnected devices are used to collect data in manufacturing and industrial settings.  IIoT is enabling IT/OT convergence, bridging technologies—and their networks—that were previously separate. IT systems can now analyze data from OT systems to generate insights and improve processes. Real-time asset tracking and monitoring, predictive maintenance, and equipment-as-a-service are all possible due to IT/OT convergence.

Digital Twins

IoT has enabled the creation of digital twins to simulate any physical process or object. For example, a digital twin could simulate a new product’s dimensions or create a digital replica of the equipment on the factory floor to see how machinery operates under certain conditions. It can also be used for product design, testing a digital version to iterate faster.

Cloud Computing

Cloud computing has become so ubiquitous that it doesn’t seem like one of the driving technologies of Industry 4.0, but it’s the backbone making this innovation possible. The amount of data produced by various IoT and OT devices must be processed at scale, where cloud computing shines. In addition, the typically large amount of data being stored and analyzed can be processed more efficiently and cost-effectively with the cloud. It also powers other data-intensive technologies, like AI and machine learning.

Edge Computing

Data analysis often needs to be done physically near where the data is created or at the edge of the network to support real-time operations. As IoT devices may be geographically scattered, edge computing moves analysis closer to the point of use, delivering faster insights, rapid response times, and better bandwidth availability. Edge computing is better suited to time-sensitive data than cloud computing, which would suffer from latency. Gartner estimates that by 2025, 75% of data will be processed outside the traditional data center or cloud. (4)


Just yet, carriers haven’t delivered on the promise of breakneck 5G data speeds. However, new networks such as the C-band are still coming online. 5G is expected to be a significant factor in supporting distributed computing, from remote communication with IoT and OT devices to edge computing. 5G will allow a much faster data transfer from remote devices to enable better real-time monitoring and analytics. While it might be 2027 before we see the full speeds and advancements expected with 5G, (5) this advancement will become a critical component of industry 4.0.

Artificial Intelligence & Machine Learning

AI and machine learning allow manufacturing companies to take full advantage of the volume of information generated not just on the factory floor but across their business units and even from partners and third-party sources. Using other technologies such as IoT, OT, and edge computing, AI and machine learning can create insights providing visibility, predictability, and automation of operations and business processes.


The downside to all of these new technologies enabling Industry 4.0 is that they also pose a security risk. Because industry and manufacturing are becoming more connected, they can also be open to exploitation. Therefore, cybersecurity is a critical component to safeguard your business, from IT to manufacturing. 

Efficiency and Quality Gains of Industry 4.0

The technologies behind Industry 4.0 can open new doors for industry and manufacturing to increase efficiency, reduce costs, and improve quality. Let’s examine three possibilities: digital threads, predictive maintenance, and dark factories.

Digital Thread

A digital thread is a set of connected records capturing the data and activities that define a product or, in some cases, a process. The thread closes the loop between the digital and physical worlds and can transform how products are designed, manufactured, and serviced. This is the next level up from a digital twin—instead of just the current state, the digital thread captures the entire lifecycle.

The thread starts in the digital world during product development with data such as CAD models or a bill of materials (BOM). The digital design is then manufactured, bringing it into the physical world. Throughout the product’s lifecycle, the digital thread record is continuously updated through technologies like IoT. What the product experiences, and when and how it is serviced, continue to update the record, which can also be passed on as the product goes from the manufacturer to the first owner and any subsequent owners. The thread can also provide valuable real-world usage and performance data back to the original designers to aid in future decisions around updates or redesigns.

A digital thread can help manufacturers see a bigger picture than digital twins alone can enable. Ideally, the digital thread data is broadly available for data analysis in cloud infrastructure to provide the greatest value and insights. An effective digital thread can improve product design, manufacturing processes, service, and maintenance.

Predictive Maintenance

Machines need to be maintained, or they can fail at inconvenient times, causing delays or outages. Predictive maintenance is meant to be an early warning system of a problem, using IoT or OT sensor data and AI to detect failure patterns in machinery and components. By understanding when a machine or part is likely to fail, manufacturers can take preventive action and maintain their equipment more effectively than regular maintenance intervals.

And this isn’t just limited to new equipment. Siemens has used such sensors on older motors and transmissions – and by analyzing the data from these sensors, Siemens says it can interpret a machine’s condition, detect irregularities, and fix machines before they fail. (6) This shows how predictive maintenance processes can be applied even to legacy machinery.

Predictive maintenance can also be used in conjunction with digital thread to understand better when and how parts may fail. Sharing digital thread data with AI can improve predictions for just-in-time maintenance that is efficient and cost-effective.

Dark Factories

With technologies like AI, digital twins, and cloud computing, machines are now capable of carrying out more and more tasks that were previously reserved for humans. This has enabled fully automated production lines where work happens without direct human intervention on-site, called dark factories or lights-out manufacturing. These automated factories can run 24 hours a day for increased productivity and accuracy at lower costs.

However, humans won’t be out of a job just yet. They’re needed to monitor the machines, perform maintenance, and inspect output quality. A dark factory is best suited to simple mass production of a standard product on a fixed schedule. Complexity and customization make full automation more difficult, leading some manufacturers to implement it for specific operations or shifts. (7)

If you’re looking to embrace Industry 4.0 and transform your business, Taos can help. They offer Advisory Services, Professional Services, Managed IT, and Security Services to help industrial enterprises grow their business by capitalizing on scalability, data-enabled decision-making, enhanced security, and cloud economics.

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1 – The Fourth Industrial Revolution: what it means, how to respond, World Economic Forum, January 2016

2 – Salesforce Launches Global Digital Skills Index: In-Depth Insights from 23,000 Workers, Salesforce, January 2022

3 – A Practical Guide to Scale Industry 4.0, Manufacturing Leadership Council, August 2020

4 – What Edge Computing Means for Infrastructure and Operations Leaders, Gartner, October 2018

5 – What Is 5G?, PC Magazine, May 2022

6 – The 10 Biggest Future Trends In Manufacturing, Forbes, January 2022

7 – What is a Lights-out Factory?, Siemens, retrieved May 2022