Factory of the future: Industry 4.0 & IIoT innovations deliver smarter, safer, more efficient manufacturing

Author : Eric Wendt | Director of Automation & Electrical Products | Digi-Key Electronics

01 December 2021

Digi-Key_Factory of the Future_Industrial Robotics
Digi-Key_Factory of the Future_Industrial Robotics

Eric Wendt, Director of Automation & Electrical Products at electronic component distributor, Digi-Key Electronics explains how innovations in industrial automation across robotics, sensors & connectivity – allied to new ways to collect & analyse data – are driving the digital transformation of manufacturing – with the end goal of improving the safety, quality & reliability of products, productivity & efficiency across industries…

This article was originally featured in EPDT's H2 2021 IoT & Industry 4.0 supplement, included in the December 2021 issue of EPDT magazine [read the digital issue]. And sign up to receive your own copy each month.

The COVID-19 pandemic has accelerated all things digital – and manufacturers are no different, quickly adopting new technologies to advance Industry 4.0. The Industrial Internet of Things (IIoT) enables smarter, predictive and more proactive processes that lead to improved efficiencies and safer working environments – and ultimately better, more reliable products.

But what are the innovations driving Industry 4.0? From robotics and artificial intelligence (AI) to edge computing, let’s take a look at the cutting-edge technologies and tools helping to make the factory of tomorrow a reality...

Industrial robotics

Robotics were first widely adopted in the automotive industry. But for the first time, non-automotive robot shipments are now exceeding automotive. This signals a broader adoption that will continue to drive development of different robotic applications.

Robotics are influencing numerous aspects of modern manufacturing, ultimately leading to a more productive and efficient factory floor. Industrial robots are becoming more flexible and adaptable. This is achieved by a combination of more powerful computing at the edge and AI. Instead of using five robots that each specialise in specific tasks, they can be replaced by one robot that can complete all the tasks, because it is more capable and agile. Also, these more capable robots can be networked together to work in concert. Networked robots can cooperate to perform tasks that a single robot cannot perform. The ability to network robots also enables fault-tolerance in design. Robots that can dynamically reconfigure themselves using the network are more tolerant to robot failures.

Digi-Key_Factory of the Future_distribution centre
Digi-Key_Factory of the Future_distribution centre

The marriage of AI and robotics is the next step. The intersection of robotics and AI is not the equivalent of designing the first production line – it is more analogous to the invention of the wheel: it will change everything. As robots learn, they become more efficient. For example, if a robot is picking a product out of a tray, and the products are never the same and they’re scattered in different places, the robot can use the intelligence it has gained to identify and pick the right product.

Co-bots

Adding new technologies into manufacturing processes creates a positive impact on efficiency, safety and new opportunities within the workforce. There will be less and less need for unskilled labour, but more need for a skilled workforce that works in tandem with robots – collaborative robots (co-bots). Robotics take on the simple, menial, tedious and repetitive tasks, as well as lifting heavy objects, keeping people focused on more creative and decision-based work, improving safety and reducing injuries.

While robots are sophisticated machines, it often does not require a degree in computer science to learn how to re-program them to do more than one task. Robotics are being designed for ease-of-use, with computer vision that can adapt to different environments, and quickly be re-programmed to serve more than one function.

The intersection of robotics technology and human workers in manufacturing creates numerous opportunities for enhanced efficiency and collaboration, as well as improved morale, reduced stress and increased job satisfaction. Providing these benefits is extremely important at a time when labour is extremely tight: job openings in the UK are at a record high, according to the Office for National Statistics (ONS).

The intersection of AI & IoT

Artificial intelligence (AI) applications can unlock the true potential of IoT and edge applications across manufacturing. Real-time analytics, connected sensors, predictive maintenance, supply chain automation and other process advancements are propelling the manufacturing industry forward into a new era. With the application of AI and IoT, factories will become more connected than ever.

Digi-Key_Factory of the Future_distribution centre
Digi-Key_Factory of the Future_distribution centre

Within the manufacturing industry, and especially across the supply chain, companies tend to spend around 6% of their revenue on efficiency improvements every year, including companies that are digital and have invested heavily in architecture and infrastructure. One way we’re seeing the intersection of AI and IoT play out to proactively improve efficiency in manufacturing is through digital twins. By bringing together different stacks of software, databases, workforce management systems and execution systems, manufacturers have access to how the data flows through and represents real materials and real processes. Machine learning models can then map those movements of processes, people and things through those systems to create a representation of how they move in the real world, based on the data. Using these models, manufacturers can see how changes will proactively play out in the real world, instead of reactively deal with bottlenecks and other issues that can slow down productivity.

Through machine learning, changes can be made through minimal programming, allowing factories to continuously make adjustments to their processes and continue to increase efficiency. Agility is the key to improving manufacturing processes. In a line of tasks, the ability to compensate for a slight mistake or a borderline output from the previous step will result in a better final product. All these advancements contribute to that perfect outcome. Machine learning allows for better intelligence and not making the same mistake twice; humans need only tweak the process with minimal coding knowledge, enabling a stronger and more engaged workforce. Cumulatively, all these technology upgrades don’t just bring incremental improvements: they produce step-function changes in performance, efficiency and safety.

Edge computing

The factory of the future will be home to a huge number of sensors, which then gather an immense amount of data. But the expense of communicating that data back to the cloud is both slower and more costly than processing it closer to the source – at the edge.

Edge computing greatly increases data efficiency. Not every bit of data that is captured needs to be stored or sent to the cloud for further analysis. Much of the analysis can be done at the point of data collection. Image detection and identification is a good example – cameras, especially high-definition ones, generate a ton of data. It’s more efficient to run the image detection and identification software on the camera, and only send up data in the case of an anomaly. Edge computing is distributed computing, and as such needs greater monitoring and maintenance. But this additional upkeep is a worthwhile trade-off for applications that generate large amounts of data, or ones that require a fast response.

New connectivity options, like Long Range Wide Area Networks (LoRaWAN), are enabling edge computing at much faster rates in the manufacturing industry, making it an effective and efficient connectivity option for data analysis in factories.

Digi-Key_Factory of the Future_pick and place
Digi-Key_Factory of the Future_pick and place

Edge solutions are best suited for applications that require high-bandwidth (large amount of data) and fast response times. Image recognition and robotics are two use cases that benefit from edge computing. The response time required by robots doesn’t allow for data to be sent to the cloud, where the analysis is done and commands sent back: compute and control must be done at the source. Use of edge computing in these high-bandwidth and fast response applications will reduce the burden on the rest of the network.

What’s ahead…

One area that has large amounts of potential is in reshoring manufacturing jobs, thus making it more local and flexible. In the coming 5-10 years, the increased number of robots and other technologies could make mass customization (lot sizes of one) and regionalised manufacturing a reality. Rather than a factory being dedicated to making a million of one item, perhaps a factory could be dedicated to making items that fit exact customer needs. This would help eliminate some of the wasteful aspects of manufacturing across the entire supply chain – wasted resources, lengthy shipping routes and returns, making the entire process more individualised and less resource-intensive.

The intersection of real-time data and AI technologies creates numerous avenues for insight into the factory floor, along with exciting evolutions in predictive maintenance and supply chain automation. The application of new sensors, transformed robotics and unique hybrids, like IoT and AI, are all contributing towards this evolution.

Robotics and AI will allow for a more holistic view and control of the production process, from raw materials to finished products. The future factory will be a hybrid environment of machines and humans, working together in a complementary way. The software and data will increase operator productivity, reduce quality defects and achieve real-time improvements. The innovations implemented in Industry 4.0 will benefit everyone – from employees to customers to the environment.


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