The U.S. manufacturing industry is expected to recover on a large scale. The collapse of the supply chain caused by the COVID-19 epidemic has fully exposed the real risks that may arise from relying on long supply chains. Therefore, the United States is working hard to bring more supply links back to the country.
In addition, growing tensions with China have also forced the United States to reexamine its basic strategy of relying on Chinese manufacturing to achieve its own economic success. Against this background, American manufacturing companies have decided to return their jobs locally.
#But the problem is that the U.S. manufacturing industry is severely lacking the labor force necessary to drive this revolution. There are not enough skilled workers, and non-skilled people have no intention of learning manufacturing skills.
But necessity is always the mother of invention. The current situation of labor shortage in the manufacturing industry has also paved the way for innovative achievements in the field of manufacturing AI. This part of the results is already quite abundant. McKinsey predicts that by 2025, the value created by it will reach about 3.7 trillion US dollars.
Before getting to the point, let’s first understand this far-reaching labor crisis.
Even if all skilled workers are mobilized, there are still 35% more vacant positions in the daily necessities manufacturing industry than there are employees overall. Deloitte predicts that by 2030, the U.S. manufacturing labor shortage will exceed 2 million, with an annual opportunity cost of US$1 trillion.
If left unchecked, the situation will only worsen. At present, the main labor force in the United States is still about 40 million baby boomers, accounting for a quarter of the entire labor market, and most of them are still engaged in "traditional" manufacturing. As the baby boom generation retires, younger workers are generally reluctant to pursue manufacturing jobs, clearly preferring technology, health care and other career directions with better working conditions and more attractive pay.
The United States can certainly quickly introduce workers from foreign groups who want to immigrate, but this will also bring a series of related challenges and even further increase political instability in the United States. Furthermore, unless there is another wave of supply chain disruptions caused by epidemic lockdowns, employers obviously do not want to go to the trouble of training these new manufacturing talents themselves.
So in order to keep equipment running, American manufacturing companies urgently need to find new artificial alternatives.
AI technology will undoubtedly become an important force in solving this crisis. Like other industries, many jobs in the manufacturing industry are bound to be replaced by AI. But in the face of the current labor shortage, what we have to worry about is not whether AI will eliminate jobs, but how AI can help companies maintain operations and fill the fatal employee gap.
Below, let’s take a look at several possibilities for AI to alleviate labor shortages in the manufacturing industry and completely change the way manufacturing is done in the United States:
For decades , robots have been used for a long time in fields such as automobile manufacturing and steel smelting, taking on high-risk repetitive tasks such as lifting and joint welding. However, these traditional robots are designed to only operate in highly predictable scenarios and perform very specific tasks.
Today, AI applications such as Siemens’ Simatic neural processing units are guiding robotic arms to grasp and manipulate objects, fully adapting to various orientations, speeds and positions. This means that robots and “cobots” (robot assistants that work specifically with humans) can also be trained to perform a variety of more complex assembly line operations like humans. In addition, AI technologies such as supporting maps, surface anomaly detection and object avoidance have allowed automated guided vehicles (AGVs) to replace loaders and forklift drivers to flexibly transport parts and finished products between warehouses and factories.
Taken together, these AI-driven robot innovation solutions can save at least 75% of labor costs, maintain 24-hour continuous production, and eliminate potential damage caused by assembly lines, heavy material handling, and repetitive operations. It’s no wonder that modern robotics has revolutionized manufacturing in places like Singapore and South Korea, and it’s time for the United States to take this important step.
Another major way for AI to alleviate labor shortages in the manufacturing industry is through 3D printing. According to traditional methods, high-level designers and engineers need to use years of experience and "gut guesswork" to come up with the best design solution. But AI is now able to quickly produce complex and highly optimized designs and quickly deliver finished products through 3D printing.
Take Autodesk's Netfabb as an example. The machine learning technology in this type of software system can receive design parameters input by manufacturers and provide the most efficient manufacturable solution. After selecting a design blueprint, AI solutions from manufacturers such as NNAISENCE will use neural networks and digital twins to predict, monitor and eliminate defects in the additive manufacturing process to avoid production delays and errors. AI software such as Intellegens Alchemite can even use imagination to provide novel main material suggestions that suit specific manufacturing and product usage needs.
If these complex functions were all performed by humans, it would undoubtedly require a large team of high-level engineers and designers, and the final results may not be satisfactory.
When it comes to manufacturing assembly lines, the first thing that comes to mind is often a product conveyor belt, with workers at different stations operating and inspecting it at any time. However, this repetitive, labor-intensive work is often error-prone, making it difficult to provide satisfactory quality assurance.
This leads to the autonomous machine vision (AMV) technology provided by AI vendors such as Inspekto and Matroid. The autonomous machine vision system can accurately identify the shape, orientation and condition of assembler products under different lighting conditions. It uses cameras and AI to count and track products as they pass by, quickly discovering defects and classifying them accordingly. This eliminates the high dependence of the QA process on manpower.
Machine vision is suitable for packaging, palletizing and cargo loading, which can greatly save labor, time and costs. Solutions from vendors such as RobitIQ and Spiroflow can determine the best palletizing method and direct robotic arms to automatically grab cartons and place them on shipping pallets.
Once production equipment fails, professional analysis and maintenance personnel from the equipment manufacturer are often required to intervene, which is time-consuming and expensive. The AI achievements of manufacturers such as Vanti and 3DS can not only monitor machine and mold wear to choose the best time to schedule preventive maintenance, but can also monitor the temperature, humidity and operating status of different products and materials, and make decisions on equipment based on actual production conditions. optimization.
Once a problem occurs, AI can analyze all potential causes and propose the best response. In the past, most plants could only rely on their most experienced maintenance engineers to get the job done.
This technology has applications far beyond maintenance and damage control. AI-driven cloud/edge systems such as General Electric’s Brilliant Manufacturing Suite and Siemens’ Mindsphere can even access and manage the entire end-to-end manufacturing process, covering demand planning, material inventory, energy consumption and even final logistics.
Imagine if there was an anthropomorphic robot that could be widely adapted to production needs and driven by AI, it could even take over almost all of the tasks that humans can currently do. All physical labor, what will be the result. When this becomes a reality, the labor cost advantage that developing countries are proud of will disappear. AI-driven manufacturers will no longer have to rely on many human employees and will no longer be subject to possible future epidemics and isolation blockades. From this perspective, the supply chain crisis currently plaguing the world will no longer exist.
As the AI system absorbs more and more data, the model itself will continue to improve, creating a flywheel effect, and any company that cannot keep up with this trend will be eliminated by history. This revolution is also expected to revitalize the U.S. manufacturing industry and even once again become one of the most market competitive countries in the world.
The AI manufacturing revolution is happening—not in the future, but now. Please note that the current workforce crisis is not a temporary emergency, but a new normal for business that will persist for years to come. Therefore, manufacturers with AI as their core driving force will gradually win out this decade and firmly maintain this emerging competitive advantage.
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