What is Industry 4.0 and How Does It Change Manufacturing?

If you’ve never heard of “Industry 4.0,” the first thing you need to know is that it’s one of the newest buzzwords in the manufacturing industry. To describe it in words that might be familiar to more people, Industry 4.0 merely refers to the Fourth Industrial Revolution.

Still, this might not adequately describe what Industry 4.0 is and what it means for manufacturing companies. How does Industry 4.0 change manufacturing processes? What are the technologies that companies need to invest in to keep up with Industry 4.0 trends?

The Fourth Industrial Revolution and its predecessors

To understand the relevance of Industry 4.0, we must first look at the previous iterations of the Industrial Revolution and how each one represented a massive jump in manufacturing technology.

The first Industrial Revolution remains the most important. Occurring right after the end of the World War, the First Industrial Revolution marked the transition from mostly human or animal-driven work to steam-powered engines and other machines. Some of the processes developed during the first Industrial Revolution are still relevant today in different forms.

The introduction of electricity was the harbinger of the Second Industrial Revolution. A far more convenient mechanism for delivering power, this paved the way for more mobile factories and machinery that can be located more strategically. Again, this efficient delivery mechanism further boosted the productivity of manufacturing.

As computers started to become introduced in the 1950s, the Third Industrial Revolution was marked by a reduced dependence on human interventions. Instead, automated software and digital technology became more prominent. This made it easier to maintain consistent quality standards, sped up processes, and made work environments a little safer.

The fourth Industrial Revolution or Industry 4.0 builds on the progress of the previous generation for automated systems. This generation will focus on creating “smart” systems using data-gathering technology, data processing, and machine learning. The goal is for automated systems to transition to autonomous systems. The key difference is that these systems can make decisions based on the data that they collect.

The essential technologies of Industry 4.0

With each Industrial Revolution, manufacturing processes are improved by technologies that did not exist in previous generations. To make the successful transition to Industry 4.0, an enterprise will need to invest and develop on these fairly new pieces of technology:

• Internet of Things (IoT) refers to a system in which physical objects can connect and communicate with each other through the Internet. This data can then be stored, reported, or processed as needed. Even in households, the IoT concept is already heavily used by smart home security systems that can be controlled and monitored via smartphones even when the residents are outside the house.

In industrial facilities, the IoT concept is implemented through sensors and actuators. Through the data collected through IoT devices, insights about the process can be made and the system can react accordingly.

• Big data is quite self-explanatory – it literally refers to large sets of data. A special term for data of this scale became necessary to emphasize the scale, as big data sets can contain several million data entries. Big data requires powerful processors, networks that support rapid data transfer rates, and large digital storage banks.

It is through the use of big data that autonomous systems can gather insights on process quality and detect anomalous behaviors. To design a truly data-driven system, companies will need to commit to the scale required by big data.

• Digitization refers to a process by which any real-world parameter is converted into a digital equivalent that can be received and processed by a computer. This is the ultimate goal of setting up sensors in an industrial setting. If an image, numbers, or text cannot be converted to digital data, then it does not help the goals of Industry 4.0.

• Machine learning, to put it more familiar terms, is the artificial intelligence that drives an autonomous system. With machine learning, a system can identify trends and behaviors and react accordingly without the need for human intervention. It is the same technology that identifies the pages you visit online and uses that data to show you targeted advertisements.

Through machine learning, an automated system quite literally “learns” from a base data set and uses this intelligence to influence future behavior. A machine learning algorithm can have several degrees of human supervision depending on the needs of the application.

• Cloud computing refers to the centralization of data collection, storage, and processing on a cloud-based server. This provides the physical infrastructure needed by demanding big data applications without having to burden the individual manufacturing facilities. Companies can also benefit from engaging third-party services that can offer cloud computing instead of having to invest in the technology themselves.

The centralization of data afforded by cloud computing makes data validation and reporting more streamlined. It also facilitates the repair and maintenance of servers when needed. Cloud-based servers are often very powerful and sensitive equipment that need to be kept in highly controlled conditions.

• 5G connectivity

With an increasing number of sensors and IoT devices, it no longer becomes practical to maintain wired connections to collect and upload data. Thus, there needs to be an increased dependence on wireless connectivity.

This could not have come at a better time as the rollout of the 5G standard has started. With data transfer speeds of up to 10 Gbps, 5G can better support the rates needed to upload and download data at the scale needed for IoT systems, big data, and machine learning. Equally important is that its low latency characteristics allow for real-time feedback and data processing.

• Enterprise resource planning is a management system that defines how information and data is shared across an organization. As corporations move into a data-driven environment, there needs to be a system for data to be managed with regards to security, privacy, storage, retention, transmission, and organization.

Examples of Industry 4.0

To fully realize the potential of Industry 4.0, we should look at use cases that are already being implemented now. These are some real-world examples of companies that have started to embrace the concepts of Industry 4.0.

• UK-based manufacturing firm TWI uses virtual models or “digital twins” to analyze data collected from wind turbines. Through digital twins, they can create mathematical models that can simulate the performance of these turbines using data collected through sensors installed in their real-world counterparts. The predictive capability of a digital twin allows operators to anticipate structural failures and plan maintenance works ahead of time.
• BJC Healthcare provides healthcare services and supplies for several hospitals in Missouri and Illinois. To help keep track of medical supplies, BJC Healthcare used Radio Frequency ID (RFID) tags. This allows for real-time tracking and reduces the need for manual labor, enhancing the efficiency of the supply chain and avoiding the expiration of medical supplies.
• In 2018, the Chicago Facility of Fast Radius was recognized as one of the top nine smart factories in the world. This is perhaps one of the most comprehensive applications of smart technologies in manufacturing facilities. This level of adoption has allowed their facility to create customized parts at high efficiency and fast turnaround times. They also maintain a “virtual inventory” that allows them to offer parts without the high costs of storage.
• Self-driving cars may be one of the most revolutionary products of Industry 4.0. These cars will use an array of different types of sensors that allow them to make autonomous and split-second decisions for navigation and safety. Through a combination of machine learning and cloud-based data processing, the systems of these cars can be incrementally improved as more data on the road is collected.

As Industry 4.0 proves to be a competitive advantage, manufacturing companies will have no choice but to adapt or be left in the dust. For this reason, careers in data science, analytics, coding, and automation have become extremely lucrative in the last couple of years.

On the road to Industry 5.0?

With Industry 4.0 on the rise, it is never too early to think about what the next stage of the Industrial Revolution will bring. It’s hard to imagine this prospect given that many of the technologies used in Industry 4.0 are very new.

Experts believe that Industry 5.0 will have humans and robots working more closely together. This offers the optimal combination of efficiency and productivity without losing the creative and collaborative touch of a human operator. This will likely require the creation of new rules on the interaction of humans and machines and the roles that they will play in the manufacturing process.

Final thoughts

Industry 4.0 is still in its early stages, but it is well on its way. The technologies involved in Industry 4.0 are truly groundbreaking – even the scale at which data is collected and processed is already beyond the capacity of the human brain. This allows companies to access levels of productivity and efficiency that would not have been possible before.

As industries move towards automation in this generation, the next one will likely involve placing rules on the roles that robots will play in industries. After all, humans can still offer value to a manufacturing process that robots cannot.