Get Ready for The Industrial Intelligence Revolution9 November, 2022 / Articles
Recommended article from Forbes
Factory automation was first implemented in the Ford assembly line and has evolved with technology innovation to where robots are now the norm. The goals of augmenting human-based construction have advanced beyond strength and uptime to now include quality, team productivity and factory efficiency. A growing interest in the environment and social justice has led more companies to manage operations with sustainability and social equity as key priorities. And hyper-personalized product businesses are expanding with additive manufacturing, combined with an integrated storefront, supply chain, factory and distribution ecosystem.
Early adopters are reaping the benefits of improved throughput, high yield and reduced scrap, resulting in increased profits. Making all this possible is the Fourth Industrial Revolution, or Industry 4.0—a wave of electronics and systems engineering innovation that’s just beginning to reshape global economies. These technologies include the sensors that monitor everything, the compute data center that crunches the data lake and the controls for the sophisticated machines that bend, weld, solder and print the products of tomorrow.
Smart factories optimize manufacturing, utilizing digital capabilities that provide innumerable solutions for one engineering problem. In the semiconductor industry, data and analytics provide a high return. Semiconductor devices are made from billions of transistors, and that complexity continues to grow. Multiply that with an increasingly complex manufacturing process, and the risks of failure are remarkably high. Quality improvements produce higher yields, which leads to higher margins. Faster failure analysis and other production efficiencies can be achieved using data analytics.
Semiconductor fabs cost billions of dollars—much of that on equipment. Predictive maintenance can save unplanned maintenance time. And monitoring equipment health and automatic calibration can increase reliability and help reduce installation, configuration and maintenance costs.
The factory now extends all the way to the virtual storefront, with companies utilizing analytics to create hyper-personalized customer experiences. Hyper-personalized manufacturing can be a strong differentiator for niche or global manufacturers and can drive inclusive growth. Predictive supply chain management is even more top of mind with current disruptions and limited visibility.
Data is required to make all this work—and lots of it. A smart factory can generate more than 5 petabytes (PB) per week and all that data needs to be transmitted, stored and analyzed, resulting in the emergence of industrial edge data centers.
The original realm of analytics software was procedural and algorithmic, following strategies conceived by MBA graduates and software engineers. The number of data points has expanded exponentially to the point at which the procedural approach breaks down. The advent of machine learning (ML)—a technology in the sphere of artificial intelligence (AI)—now allows factories to analyze patterns in very large datasets, which are well suited to the kind of massive data analytics common in Industry 4.0.
The New Factory Infrastructure
Automotive factory automation boomed in the 1970s and continues to drive innovations. While Industry 4.0 is like a conductor of a factory orchestra, the factory itself must undergo changes to bring the concert to its full crescendo.
Robots and sensors will be connected to a high-speed wireless communication network. 5G cellular is the leading technology for this purpose, although there are strong Wi-Fi 7 solutions. There are also discussions that 6G will be needed to handle the data volumes and address security vulnerabilities. These technologies require a line of sight to the antennae for the highest speeds and bandwidth, placing new requirements on factory design to avoid dark zones. And there are strategies for implementing factory and global communications networks.
Each device, or manufacturing “thing,” will require standardization, such as discoverability and self-awareness, and should be capable of defending itself. Its data generation and consumption model must be matched to the system architecture. And it will need to be able to self-configure to whatever system it connects into. All this wireless communication is achieved with electromagnetic broadcasts, and sensors, robots and other manufacturing equipment must be tolerant of the noise.
Underlying all this functionality are semiconductor chips that create, transmit and crunch the data. This requires a dedicated compute facility—an edge data center—to provide real time reliability needed by the facility.
Machine vision is thought to be essential to Industry 4.0 in observing the massive details of operations of the factory. The electronics digitize the action, and vision processing creates information about what’s happening.
Then, we have high-speed radio frequency (RF) chips, which allow 5G protocols to transmit up to 20 Gbps. Finally, there’s ML algorithms, which were first implemented in data centers, requiring very large computational chips to process the large datasets. Now, we have very-low-power ML in sensors for localized data analysis to reduce the amount of data that must be transmitted.
Industry 4.0 is bringing disruptive changes, converting industrial spaces to smart, connected and intelligent environments. Manufacturing has been irreversibly changed. The intelligent factory is more than a quality initiative, 3D printer, electronic sensors or analytics—it’s all of those things, optimized together, for performance, production and community.
It’s not far off that the metaverse will enable the next realm of opportunities. Planning a transition can be a difficult decision because of the investment costs and the pervasive changes that are possible. But the old maxim still applies: eat or be eaten. Disrupt or be disrupted. Early movers have the advantage!