Manufacturers are responding to continued supply chain disruptions by investing in AI to create more resilient and agile “smart factories”. The result is an explosion of interest in new technologies that analysts call “Industry 4.0”.
“Industry 1.0” was the original industrial revolution of the late 18th and early 19th centuries, which transformed manufacturing via steam and water power, machine tools and mechanized factories. Industry 2.0 introduced electricity into the production chain. The most recent cycle of industrial innovation, known as Industry 3.0, has been characterized by the adoption of physical robots, such as human-programmed robotic arms, which build cars in a factory.
In Industry 4.0, robots are becoming smarter and more independent. Robots that build cars collect data – how many cars they can build per hour, how many parts they need to build each car – and use AI to interpret that data and ensure the production line runs smoothly. hitch. AI can refine its own work without human intervention.
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According to a global survey by ServiceNow and ThoughtLab, which polled 900 executives in 13 countries on the state of optimization in the industry, manufacturers are investing more in optimization technologies such as AI than in any other industry. vertical, with the exception of financial services.
Thorsten Wuest, associate professor of smart manufacturing at West Virginia University, says manufacturing companies have access to more data and better analytical tools than ever before. “The factory is now connected to the IoT [internet of things] devices and sensors, so manufacturers have access to more real-time data,” he told Workflow. “At the same time, the treatment has improved. We have better algorithms, open-source programming languages like Python, and low-code tools, which makes processing more accessible.”
Since the start of the pandemic, a quarter of manufacturers have made major progress in optimizing production with AI and machine learning, according to the survey results. This percentage should increase to more than half in the next few years. Leaders said they were investing in modernizing IT platforms and technology to better share data between organizations.
Smart factories vs labor shortage
At the start of the pandemic, about 1.4 million Americans lost their manufacturing jobs. Although the industry has rehired many workers, hundreds of thousands of positions remain vacant. According to a report from Deloitte and the Manufacturing Institute, manufacturers are struggling to find skilled, entry-level labor for factory jobs. According to the report, this skills gap is expected to leave more than two million jobs unfilled by 2030, costing the US economy up to $1 trillion.
Manufacturers who have made major strides in optimizing production with AI and ML during the pandemic
Smart factories could help alleviate this problem. Wuest stresses the importance of robotic process automation (RPA), which uses software robots to automate repetitive computing tasks such as changing passwords or entering routine data. The result is faster, more accurate workflows that require fewer human workers. “Automation helps us avoid injuries and boring jobs on a physical level, AI helps us do that on a cognitive level,” says Wuest.
Factory equipment such as “cobots” or collaborative robots perform routine tasks in the factory without human assistance. Although cobots require setup, they are programmed using low code, which breaks down complex programming languages into simple building blocks that non-technical people can use. Cobots allow developers to perform more difficult tasks in the factory.
The fear that these automation tools will replace human jobs is misplaced, according to Wuest. Technologies like RPA and cobots are designed to work with human workers, not replace them. “With AI doing predictive maintenance,” he says, “we know when a tool is going to fail. But humans maintain the robots and the equipment.”
Although manufacturing is known as a conservative industry, the pandemic has forced it to embrace new technologies. “Manufacturers are using Zoom, training staff remotely, taking meetings virtually,” says technology consultant and engineer Adrian Dima. “The pandemic has been an accelerator.”
Reaching the Doubters
Despite the potential, many manufacturers are reluctant to implement AI and ML systems due to the high costs associated with such investments, says Rui Alves, associate professor of economics at the Polytechnic Institute of Setubal in Portugal. “When we talk about breakthrough technologies like ML and AI, we’re talking about breakthrough innovations,” he says. “And for conservative sectors, this radical innovation can sometimes be a barrier.”
Number of unfilled manufacturing jobs by 2030
Additionally, most IT tools designed for manufacturing are not user-friendly. And since many of those who work in these fields do not have a computer background, learning such tools requires a significant investment of time and money. “It can be expensive to implement these technologies,” says Alves. “Change comes at a cost, especially in an industry where resources are very important.”
To reach skeptics, Dima advises developers to educate potential manufacturing customers on why, how and when to use these tools in their own language. OT developers often communicate with manufacturers as if they share an advanced understanding of the technology, he says, which isn’t always the case. “We have to educate them to their level,” says Dimas. “We can’t talk to them like they’re in IT.”
Before investing in these technologies, executives need to understand the processes they want to automate and what they hope to gain from it, says Wuest. He also warns leaders to allow space for experts who understand the technology to use it innovatively on the production line. “This process shouldn’t be top-down or bottom-up,” he says. “You should have the support from the top but the freedom to implement from the bottom.”