Get your copy of the 7th Annual State of Smart Manufacturing and hear from 300+ manufacturers in this new survey report!
You can hardly turn on a news or media channel these days without a reference to artificial intelligence (AI) and how it affects everyone. AI, alongside other core technologies from Industry 4.0, are already transforming many industries.
For manufacturing, AI is poised to be no less impactful. AI as the driver of manufacturing operations is advancing rapidly, but despite its potential, there are questions about how AI in manufacturing came to be and how it’s used.
Most companies realize the arrival of AI in the manufacturing industry is upon us. And they understand that it will revolutionize their industry and improve their operations.
Having come a long way in the journey through automation, software platforms designed for manufacturing, and other technology tools, manufacturing leaders are no strangers to the value of real-time data. Many have already experimented with or implemented solutions such as IoT and edge computing to tap into their data's value. But there are still concerns and questions in leaders' minds about how it will affect them.
One concern is the impact on manufacturing jobs. Early reporting on AI and manufacturing automation sounded the alarm that it could result in losing as many as 30 million jobs. However, other sources state that AI will create 58 million new jobs.
The reality is that manufacturing is undergoing a technology-driven renaissance and has an acute shortage of workers that will likely extend into the future. Rather than destroying jobs, AI in manufacturing is being used to fill the deficit.
A second misconception is familiar to those who have experienced adopting new technology, like software platforms. New technology requires a lot of expertise and high-level skillsets. This perception is especially true for AI, where manufacturers may assume they need hyper-skilled staff like data scientists.
But as AI adoption increases, tailored solutions can be deployed without needing such expertise. Many are easily integrated or plug-and-play and can be brought online with in-house resources.
Because solutions like IIoT and advanced automation are maturing, manufacturing leaders have more data than they know what to do with. Adopting these technologies has opened the floodgates for data, and many feel overwhelmed.
With cloud-based data storage now a cost-effective option, the question becomes what to do with data and how to put it to work. The answer lies within the adoption of AI in factory settings. Collected data that isn't viewed because of its volume is no better than error-prone manual data.
But put that data to work with AI and it becomes usable. AI and advanced analytics contextualize and standardize the data, revealing patterns and dependencies you were unlikely to find on your own, giving you insight for better business decisions. Linked to powerful smart manufacturing platforms, the data that was too much to understand can be delivered to the user in the context and setting that makes sense for their tasks and purposes.
To further show how AI is a game-changer in a manufacturing setting, there are use cases to consider that are likely applicable to most manufacturers:
Manufacturers use advanced software or manual data collection to understand the impact of downtime and develop solutions to report and reduce it. The problem is that these systems give them tools and views that show the reason for the downtime without telling them what happened.
AI goes beyond the reason for downtime and can dig deeper and make connections to the dependencies that cause downtime so managers can address specific causes. These causes can be wide-ranging issues, such as training by specific operators or problems that occur on a particular OEM or generation of equipment.
AI is drastically impacting design in manufacturing. Instead of traditional engineering specs and topology, AI software offers design solutions to meet specific constraints. It’s highly iterative and works with a feedback loop to judge and refine designs until they’re optimal for production.
Generative design is highly valuable for constraint management. Not only does it reduce time spent in design, but the resulting parts and finished products are lighter, stronger, or more cost-effective.
AI is an excellent application for vision intelligence for quality. In one example, BMW utilized an AI-based vision system to reach 100% conformance on one of their lines. As inspection becomes more automated, vision systems can be deployed to manage throughput speed and scan for defects.
AI in manufacturing is just getting started. Understanding the misconceptions and considering the use cases will help decision-makers understand how they can put this technology to work for their enterprise.
To discover more articles from Jerry Foster and keep up with his conference appearances, follow Plex on LinkedIn.