How manufacturers can learn to trust in AI

While the manufacturing industry may be slow to rely on AI, the benefits of adopting it are clear: AI can help reduce errors, increase efficiency, and analyze data, helping manufacturers turn that data into actionable insights.
  • As artificial intelligence (AI) becomes more prevalent, many traditional industries, such as manufacturing, are slow to trust the technology.
  • AI You can automate manufacturing processes to increase efficiency and reduce errors, increase innovation with generative design, and create safer working conditions.
  • 68% (PDF, p. 6) Most manufacturers already have at least one AI-powered use case or process, and those small steps will demonstrate the value of AI and build trust.

Artificial intelligence, or AI, is making its way into everyday life—from smart assistants like Siri and Alexa to personal robotics and automotive automation to emerging advances in healthcare. But there is still a perception problem, as people struggle to understand the technology and fear its downsides: security concerns, job displacement or even a sense of depersonalization.

As AI becomes more prevalent, so does a reluctance to hand over tasks to technology, especially in more traditional industries like design and manufacturing (D&M). However, the potential of AI has barely been tapped. According to projections of the World Economic Forum (PDF, p. 3), could generate up to $13 trillion in global economic activity and increase global GDP by 2%. For businesses, choosing to use AI-powered tools can raise concerns, particularly around data sharing and security. But as companies see real benefits from using AI without risks to their data or special expertise, trust in AI will grow.

State of AI in D&M

AI may seem like a recent phenomenon, but it has deep roots in manufacturing. "I started my AI career in 3D vision-guided robotic automation systems for the General Motors manufacturing plant 40 years ago," he says. Dr. Jay Lee, a pioneer in industrial AI and Clark Distinguished Professor and Director of the Center for Industrial Artificial Intelligence in the Department of Mechanical Engineering at the University of Maryland College Park. “If people tell you that AI is just getting started, no, we made it work 40 years ago. “The robots assembled cars using intelligent vision to automatically identify and adjust the trajectory with compensation,” adds Lee, who is also member of the World Economic Forum's Global Future Council on Advanced Manufacturing and Production.

Robotic arms working on cars on a factory assembly line.
AI has been around longer than you think. It has deep roots in manufacturing; For example, car manufacturers have been using robotic automation systems for 40 years.

Companies have long sought Dr. Lee to help them improve their operations. When the compressed air system at the Toyota plant in Georgetown, KY, continued to fail, unplanned shutdowns cost money and delayed production at that facility where a new car typically rolled off the line every 25 seconds. Lee incorporated AI into the production line using sensors and AI to detect anomalies and prevent accidents. Maintenance costs went down fifty%and this issue has not caused any downtime since the fix was implemented in 2006.

AI has become more robust since these early use cases, going beyond basic operational functions. Now you can help companies innovate with generative design, which allows the iteration and simulation of different scenarios to deliver the best possible results. sixty-six percent of business leaders believe they will need AI in the next two to three years. But a recent study by the Boston Consulting Group found that only sixteen% of manufacturing companies have achieved their AI goals. Despite its initial advancement, manufacturing has been slow to put AI to work.

To trust the process, you need the right data

The manufacturing industry produces approximately 1,812 petabytes of data annually, and turning that data into insights and actions can drive innovation if manufacturers allow it. But according to Deloitte67% of executives feel uncomfortable providing their data to other organizations.

"If data isn't created to do a particular thing, it probably can't be used for that new purpose without being reworked," says Alec Shuldiner, director of Data Acquisition and Strategy at Autodesk. "Data acquisition is the work required to reuse data so that it can be used to drive some new process, for example, for an analytics or machine learning application."

Person looking at different design iterations on a computer.
Generative design, an example of AI, can help manufacturers innovate through rapid iterations and simulations of different scenarios to deliver the best results.

AI is only as good as the data it receives. It will only produce the desired results if that data is reliable, accurate and relevant. "If you give me crap, I can't help you," Lee says. “You have to give me data that is useful and usable. You need to have the right context so the data can connect to the purpose you want. For example, I want to predict failures in a machine. Well, you have to give me data related to the state of the machine. “If you have a fish, it is useful, but if the fish comes from contaminated water, it is not edible.”

To close the gap between the persistent reluctance to adopt AI and maximize its full power, manufacturers must learn to trust what they can't see. They are comfortable letting AI handle predictive maintenance, but generative AI is the big unknown. But it's a risk worth taking. As manufacturers better understand how AI enables end-to-end visibility, it will create more possibilities for their organizations.

Build trust and unlock the value of AI

Dr. Lee defines the benefits of AI as the “three questions”: reduced work, reduced waste, and reduced worry. "We have a lot of things we don't know," he says. “For example, some people walk around the factory, they want to check everything. Because? They care, even if a machine never fails.” AI alleviates those fears by enabling greater visibility. “If everyone in a community has a surveillance camera, don't worry. You can have applications to view your house. Who's there? Oh, Amazon delivery.” As AI proves effective and people become more aware of how it works, they are beginning to incorporate it more into their operations.

As cloud-connected factories become the norm, AI can be supercharged, collecting all this data in real time and generating insights quickly. But until then, manufacturers are stuck making decisions.

robotic systems moving boxes in a warehouse with digital data overlays
Now that cloud-connected factories are becoming the norm, AI can collect tons of data in real time to generate insights, helping manufacturers make better, more informed decisions faster.

“In current design, we are often forced to make trade-offs that we would rather not have to make,” says Dr. Shuldiner. “You can design something quickly or design it to be easily manufactured or design it to achieve some sustainability goal, such as recyclability. But many times it is not possible to do all those things at the same time. So if you want to add recyclability to the design, then you have to spend a lot more time on that design and it may end up making it more expensive to manufacture. AI will take us to a point where many of those trade-offs will disappear. You will be able to design quickly and efficiently and still achieve multiple complex design objectives.”

Dr. Lee points to outliers in the industry that used advanced technologies from the beginning, such as Toyota and General Motors, companies that are still innovating, using cloud computing and artificial intelligence to build better, lighter and more efficient vehicles. But often, moving more of their operations to AI is a gradual process for manufacturers. "Our traditional industry will need continuous improvement," says Lee. “It is not an overnight success. First do something small, make it happen. Wow. I have it. Good. Let's move on to the next one.”

sixty eight percent (PDF, p. 6) of manufacturers have at least one AI-driven use case or process, and those small steps will demonstrate the value of AI and build trust. "The priority is to be aware of the benefits of AI," says Lee. “People are afraid of AI threats or negative things. But you shouldn't stop moving forward because you worry too much.”

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