Look around your personal or your professional space and you’ll see examples of artificial intelligence (AI) everywhere. You can open your phone with a quick scan of your face and then, using natural language, ask Siri for directions to the closest lunch spot or turn down the thermostat at the house. At work, don’t worry about writing up those summary notes from the meeting, as the AI companion embedded in your Zoom app can do that for you.
These are examples of how AI makes everyday life easier. But what about in manufacturing? Specifically, within the consumer packaged goods (CPG) and food and beverage industries, where can AI make a difference?
Turns out, there’s a lot of places AI will make an impact. CPGs just have to start implementing the technology. That was the message from Andy Lomasky, senior director of IT for PMMI, the Association for Packaging and Processing Technologies, during a presentation at PACK EXPO Las Vegas in September.
According to Lomasky, CPG manufacturers have a tendency to be hesitant to try new things, and as a result they can lag behind the technology evolution curve. He’s right. According to a recent survey by CPG Next, 66% of the CPG respondents have not started on a factory floor digital transformation.
But it’s time to start adopting new technology—or get left in the dust by competitors.
“The takeaway, manufacturers should get started somewhere,” Lomasky told CPG Next. “The goal is to start small, pick a pet project, pick one machine, one line—and one use case that you can think of—and get started somewhere. And then learn from the experience. Figure out what didn’t go well or what you would do differently and use that knowledge to scale and try again. Iterate through the process to get it to a place where you can really start generating value through AI.”
But before plunging in, be aware of the risks associated with AI and the governing policies that will be required for a successful deployment.
Where to start
According to Lomasky, there are some key areas of manufacturing that can benefit from the power of AI, including: increasing productivity via robots that can work on a packaging line 24/7 to increase output and efficiency; analyzing consumer data to create personalized products, packaging, and branding; improving quality control via AI-powered vision systems to quickly detect defects and flaws; and connecting machines and systems to enable real-time analysis that optimizes production and decreases downtime.
To that end, there are specific use cases for AI in manufacturing such as generating step-by-step work instructions for technicians performing assembly and inspection processes, analyzing machine data to optimize OEE or reduce waste, or assisting design engineers to create new product concepts using 3D models and prototypes.
Applied correctly, AI has the potential to disrupt—in a good way—the current approach to product design, production, packaging, and distribution. It also has the potential to destroy an organization if not approached or managed correctly.
“There’s a lot of risk with AI, so you have to use it responsibly,” Lomasky said. “My recommendation is to start with a policy. It doesn’t have to be anything super formal or even anything that robust, a page or two will do, but write down on paper ways that are acceptable in your company to use AI, or ways your staff should never use AI.”
While there are hazards associated with unintended bias, as AI tools can make assumptions based on the information it consumes, the bigger risk is the potential for a data breach.
“There’s risk of confidential data getting into the wrong hands,” explained Lomasky. “Cybersecurity is extremely important, especially if you’re talking about putting in machinery data or recipe data or anything that is proprietary, you want to make sure any AI platforms you interact with are protected with a really strong password, have multi-factor authentication if they offer it, and make sure any access privileges are using the rule of least privilege, that is the least amount of access someone might need to get their job done.”
You also want to properly vet any AI platforms to ensure they have controls around how they use the data or the prompts put into to train the AI model, Lomasky said. This ensures none of your data ends up in someone else’s AI response. Much of this can be solved by having the right AI governing policies in place around acceptable use, data collection, ethical use, prompting, and outputs.
AI in the future factory
It’s still very early in the AI game. “If you think about a maturity curve, we are not crawling yet,” Lomasky noted. Therefore, there’s a lot of room for AI to evolve in manufacturing.
Some futuristic applications include superior data analytics that will uncover new kinds of insights previously unimaginable, as well as empowering machinery to do prescriptive maintenance in that it will self-heal, tweak a recipe, or stop a job all on its own.
Lomasky has big visions for the future of AI. “We talk about the concept of predictive maintenance to predict [machine] failure, but what if it could use AI to take corrective action to keep the line running?”
The good news: People are still important to the process. Highly-skilled individuals will still be needed to train AI, validate what it’s doing, and adjust to ensure accurate outputs.