When you think of artificial intelligence in manufacturing, many people imagine a network of Smart robots zipping between shelves of inventory, stocking and restocking with ultra speedy precision.
And while automated robots are definitely a part of the AI surge, they’re more aspirational than practical for most manufacturers smaller than Amazon or Tesla.
AI is all the rage these days – from autonomous vehicles to neural networks to robotics – and it feels like industry is teetering on the precipice of technological advancement like never seen before.
It also feels a bit overzealous, and you’re not alone in wondering exactly how many of these AI promises will actually come to fruition for practical use in your own factory.
There’s a massive gap between an old-fashioned, human-powered assembly line and a factory that whirs to life at the touch of a single button. The frustrating thing is that most of us are in the middle of that gap.
If you’re somewhere in the middle of your digital transformation journey, it can be difficult to assess which AI applications are worth your research and money.
Here are some guiding principles for prioritizing different AI initiatives in your factory.
The most common uses of artificial intelligence in the manufacturing industry are less flashy than robots, but just as important, if not more:
Of course there are so many more applications for AI in manufacturing, and we’re learning more and more of new ways to apply this tech:
The crucial takeaway is that we’re seeing leaps and bounds in terms of progress from AI, not just from the researchers that are uncovering new uses, but also from manufacturers that solve their own logistical problems using the AI capabilities that fit within their own strategies.
There are two things of which AI is incapable: expertise and experience.
But wait! you may ask, isn’t AI good at mimicking expertise by running millions of data points representing near-infinite scenarios?
AI is great at optimizing – but sometimes the best changes in strategy don’t come from optimized data, but from seasoned workers who understand the interrelated parts of the process.
AI is also capable of great bias, which we covered in a previous article, 3 Case Studies for Proper AI Implementation.
Put simply, AI isn’t perfect, and if you’re blindly accepting that AI will always come to the best conclusion rather than the most common solution present within the data it is fed, then you’re going to hit a lot of dead ends and incorrect conclusions.
Ultimately, these dead ends will hurt your bottom line.
Don’t get us wrong, we are excited to see the various applications of artificial intelligence in factories. There are myriad reasons why AI will be a gamechanger in the manufacturing industry.
But you don’t want the hype to overextend your resources and operations, especially when there’s so much at stake these days with weaker supply chains and tighter operating margins since the start of the global pandemic.
Google released a document titled “Why We Focus On AI And To What End”, in which they write, “We understand that AI, as a still-emerging technology, poses various and evolving complexities and risks. Our development and use of AI must address these risks. That’s why we as a company consider it an imperative to pursue AI responsibly. We also believe that getting AI right — which to us involves innovating and delivering widely accessible benefits to people and society, while mitigating its risks — must be a collective effort involving us and others, including researchers, developers, users (individuals, businesses, and other organizations), governments, regulators and citizens” (emphasis added).
A couple of insights that are important to highlight in this quote:
You wouldn’t kill a fly with a flamethrower, would you? Not only would it be an unthinkable waste of specialized resources, but it would also risk the safety of anyone around, not to mention probably inefficient at actually stopping the fly. If you want to kill a fly, a fly swatter is the best option, even though it’s the simplest option.
The same principle goes for adopting artificial intelligence. Sure, you could sink money into upgrading every machine on your shop floor to create an interrelated IIoT network of Smart devices to optimize output, but that’s kind of like killing that fly with a flamethrower.
Here are the areas in which you should focus on artificial intelligence without compromising the integrity of your operations:
Unsure of which machines to automate or fit with artificial intelligence? Start with those that are the most risky, dangerous, and require extensive setup and maintenance.
This doesn’t necessarily mean you need to buy new equipment – simply adopting automated Smart forms and other AI tech like sensors and real-time monitoring can greatly increase your workers’ safety and efficiency.
Some business leaders preach that AI will help you slash your budget because you no longer need to hire human workers. We strongly disagree that this is a good strategy, as does McKinsey (from their insights “AI in Production: A Game Changer”).
Human manpower is the most crucial synthesizing force for manufacturing success.
McKinsey writes, “More and more companies will seek to develop their own systems to meet their unique needs. Successfully creating and maintaining your own AI entails assembling the right people. Designing, building, connecting, improving, and maintaining an AI solution such as the asset optimizer requires people with solid skills and experience, a big-picture perspective, and the interpersonal skills to work collaboratively toward a common goal" (emphasis added).
To sum up, only you and your human workers know what’s best for your specific goals at your specific plant. Humans are your most valuable resource.
Therefore, consider upskilling and standardizing the basic workloads you have by using work instructions software. Automation doesn’t have to be an onerous task; it’s as simple as capturing all of the tribal knowledge you currently already have and are not fully taking advantage of.
We’ve mentioned using AI for supply chain and raw materials estimates because it’s so helpful in keeping costs down for suppliers and for anticipating disruptions that could sink your company for reasons outside of your direct control.
By limiting use of fossil fuels and unsustainable inventory practices, you can be extra responsive to your customers by being quick and reliable when it comes to supply and demand.
Fostering a healthy relationship with the environment around you (including local communities, natural resources, and government or non-profit climate groups) will protect your reputation and help you become more lean in your production.
You don’t need to start replacing human workers with robots to throw yourself fully into the artificial intelligence wave as it continues to rise in manufacturing. Start small and effective, with strategies to free up your human resources for Smarter manufacturing practices in the future, as tech continues to develop.
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