There have been three basic industrial revolutions. The first was the steam engine, the second was the assembly line driving mass production, and the third was the speed of computers. Today there is a fourth revolution taking place called Industry 4.0. Industry 4.0 uses IoT (internet of things) and IoS (Internet of Systems) to improve manufacturing using machine learning and artificial intelligence. With this revolution comes not only new technology, but also a need to learn how to take advantage of it efficiently and responsibly. Perhaps Klaus Schwab said it best, “We must develop a comprehensive and globally shared view of how technology is affecting our lives and reshaping our economic, social, cultural, and human environments. There has never been a time of greater promise, or greater peril.”
The Internet of Things will have anywhere from a 9 trillion to 11.1 trillion economic impact by 2025. Two-thirds of the value will be generated by B2B. The fastest-growing market for the future of IoT is Industry 4.0. Here are some technologies that are going to affect this revolution…
One of the first questions people ask when being introduced to 5G is how fast it really is. The speed ranges anywhere from 10 Gbps or up to 20 Gbps. In comparison, 4G is only 1Gbps. To illustrate how fast 20 Gbps is, users can download 600 MB video in about 34 seconds, while 4G would take 2.3 minutes. 5G will have seamless open roaming capabilities between cellular and WiFi. Users will be able to stay connected outdoor and indoor without re-authentication? 5G will allow for MIMO (multiple input, multiple output). This means that one device can have multiple transmitters that will be able to receive and transfer data. Right now, 5G non-standalone is built on existing 4G infrastructure. However, 5G stand alone should become commonly available by 2022. 5G is going to transform our world. Not only will 5G increase speed and reduce latency, but it will also create innovation that would not have been possible with 4G. Faster speeds will allow business IoT devices to connect more quickly.
Manufactures are collecting 2 exabytes of operation data annually. This information comes from sensors built into motors, conveyor systems, and 5-axis machines. This is why machine learning is so important. The raw data needs to be transformed into useful information. Business intelligence IoT transforms this data using artificial intelligence - for example, real time location data analyzed to provide planning, forecasting, and actionable information. The first step in AI has been to find critical use cases such as product quality inspection and demand planning, but today that isn’t enough. With AI, smart machines will be taking care of themselves with predictive maintenance. Meaning that the machine will be able to fix their own problems before there is a problem. Right now, AI prototypes are being placed in live engineering environments. These prototypes “that collect real-time live data” are being integrated with current manufacturing systems to test out new ideas, at least until throughput can be successfully proven for stand-alone AI systems. One of the biggest challenges with AI is the solid foundations of data governance and AI/data talent. Critical use cases will continue to change rapidly, which is why it is important to set up a framework that can define and respond to critical use cases quickly.
Whether you like it or not, robotics can enable a level of quality, accuracy, and productivity beyond human ability (even in hazardous environments) In fact, dark factories (where manufacturing processes are handled entirely automatically) have already started making their way into the manufacturing industry. That does not mean the need for humans will completely disappear. Collaborative robotics (or Cobots) is starting to become an alternative in manufacturing and is expected to be a huge disruptor for SME’s. The cobot market value is expected to reach 9.7 billion by 2025. This type of robotics will enable machines and man to work together. Although cobot might sound like the opposite of IIoT, it’s actually a huge step forward. Especially stage six cobot, which not only involves close human and robot interaction, but the robot is on a mobile platform and can be moved anywhere on the manufacturing plant. With mobile assets comes the need for asset tracking.
Cloud computing has three basic service models: SaaS, PaaS, IaaS. However, its application to the future of manufacturing is real-time or close to real-time information. As mentioned earlier, manufactures are collecting 2 exabytes of data annually. And it’s going to take strong cloud computing to turn that data into information that can be used for fast evidence-based decisions. The challenge with turning data into information is processing power. This is where edge computing comes in to help. It reduces latency by avoiding the cloud and brings the edge network closer to the device via a decentralized computing infrastructure. Edge computing is how sensors can tell the local weather or where the device is located in real-time. According to Gartner, “traditional data centers will be dead by 2025.”
The Future of Industry 4.0
No journey is without hurdles, and there are several obstacles that manufacturers will need to resolve before Industry 4.0 can become mainstream. These challenges come in many shapes and forms, such as privacy and security or installing the new technology. Right now, most manufacturers are in the testing season. They are layering new sensors and AI prototypes on top of existing infrastructure. Here at Link-Labs, we specialize in cost-efficient indoor and outdoor asset tracking and RTLS that provide the reporting and analytics to use that data to efficiently manage your processes. If you need help taking a step into the future of manufacturing IoT, then please contact us for more information.