Manufacturers are responding to ongoing supply-chain disruptions with AI investments to create “smart factories” that are more resilient and agile. 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 revolutionized manufacturing through steam and water power, machine tools, and mechanized factory. Industry 2.0 introduced electricity to the production line. The latest round of innovation in the industry, known as Industry 3.0, is characterized by the adoption of physical robots — think human-programmed robotic arms that make cars on a factory floor.
In Industry 4.0, robots are becoming smarter and more independent. The robots that make cars take data — how many cars they can make per hour, how many parts they need to build each car — and use AI to interpret that data and make sure the production line runs without a hiccup. AI can organize its own work without human involvement.
[Read also: The modern, digital factory]
According to a global survey from ServiceNow and ThoughtLab, which polled 900 executives in 13 countries on the state of industry optimization, manufacturers are investing more in optimization technologies like AI than any other vertical except for financial services.
Thorsten Wuest, an associate professor of smart manufacturing at West Virginia University, said manufacturing companies have access to more data and better analytical tools than ever before. “The factory floor 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, processing has improved. We have better algorithms, open-source programming languages like Python, and low-code tools, which make processing easier to access. ”
Since the pandemic began, a quarter of manufacturers have taken major steps to optimize production using AI and machine learning, according to survey results. That percentage is set to grow by more than half over the next few years. Executives reported that they are investing in modernizing IT platforms and technologies to better share data with organizations.
Smart factories vs. labor shortages
At the start of the pandemic, approximately 1.4 million Americans lost their manufacturing jobs. Although the industry has re-hired many workers, hundreds of thousands of positions remain unfilled. According to a report from Deloitte and the Manufacturing Institute, manufacturers are having trouble finding entry level and skilled labor for factory jobs. This skills gap, according to the report, is expected to leave more than two million jobs unfilled by 2030, costing the U.S. economy up to $ 1 trillion.
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Manufacturers who took major steps to optimize production using AI and ML during the pandemic
Smart factories can help alleviate that problem. Wuest highlights the importance of robotic process automation (RPA), which uses software bots to automate repetitive computer tasks such as changing passwords or regularly entering data. The result is faster, more accurate workflows that require fewer manpower. “Automation helps us avoid injuries and boring work on the physical part, AI helps us do that on the cognitive part,” Wuest said.
Factory equipment such as “cobots,” or collaborative robots, perform common tasks on the factory floor without human assistance. Although cobots require configuration, they are programmed using low-cost code, which divides complex programming languages into simple building blocks that can be used by non-technical people. Cobots free developers to perform more difficult work on the factory floor.
The fear that these automation tools will replace human jobs has been put elsewhere, according to Wuest. Technologies like RPA and cobots are designed to work with human workers, not to replace them. “In AI that performs predictive maintenance,” he says, “we know when a tool will break. But people keep the robots and the equipment.”
Although manufacturing was known as a conservative industry, the pandemic forced it to adopt new technologies. “Manufacturers use Zoom, train staff remotely, almost take meetings,” said Adrian Dima, a technology consultant and engineer. “Pandemic is an accelerator.”
Reaching the skeptics
Despite the potential, many manufacturers are reluctant to implement AI and ML systems because of the high costs associated with such investments, says Rui Alves, an associate professor of economics at the Polytechnic Institute of Setubal in Portugal. “When we talk about exceptional technologies like ML and AI, we’re talking about radical changes,” he said. “And for conservative sectors, this radical change can sometimes be a hindrance.”
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Number of uncompleted manufacturing jobs by 2030
Moreover, most IT tools designed for manufacturing are not user-friendly. And as many of those working in these fields have no background in IT, learning such tools requires a substantial investment of time and money. “Implementing these technologies can be costly,” Alves said. “Change has value, especially in an industry where resources are invaluable.”
To reach skeptics, Dima advises developers to educate potential manufacturing clients why, how, and when to use these tools in their native language. Operational technology developers often interact with manufacturers as if they share advanced understanding of the technology, he says, which doesn’t always happen. “We need to educate them at their level,” Dimas said. “We can’t talk to them like they’re in IT.”
Before executives invest in these technologies, they must understand the processes they want to automate and what they expect to get out of it, Wuest says. He also warns executives to allow experts who understand the technology to have the space to use it innovatively in the production line. “This process shouldn’t be top down or bottom up,” he said. “You must have support from above but the freedom to implement from below.”
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