Depending on your perspective, your thoughts on artificial intelligence (AI) probably fall somewhere between the technology being an abstract threat or possibility, and a real-world solution with concrete use cases where you may not even know it is at work. This ARC Insight summarizes the case studies presented at the AI workshop at ARC’s recent virtual European Industry Forum and shows potential usage of AI in machinery applications.
One of our key findings is that a clear use case for AI is needed, and the target established for that use case must be met to determine the ultimate success of the AI project. While the use case must be defined clearly, there is almost no limitation to the types of applications AI can support. When edge and cloud are leveraged in the right way and connectivity to other systems assured, the possibilities are almost endless.
Finally Replacing Muscles and Brains
Starting around 2009, people began talking about the fourth industrial revolution, Industrial IoT, and other related concepts. However, in retrospect, the second and third industrial revolutions largely just replaced human muscle and manual labor with machines and computers that basically repeat pre-programmed behavior. While the fourth industrial revolution increased the level of digitalization, until recently, even the most educated machines and computers did not make human-like decisions. Now, with AI entering the plant floor, we’re finally starting to use digital technology to replace not only muscles but also brains. Most experts agree that while AI will become deeply embedded across industrial and other applications and initial use cases have emerged, AI in manufacturing today is still a niche technology. In addition to the numerous AI-related related sessions and ad hoc surveys at our recent Industry Forums, ARC is conducting an ongoing online survey for industry participants to identify and support the most suitable applications.
What Will AI Look Like in the Future?
When asked how they believe AI will be used in future, more than 100 industry participants shared their responses.
Most respondents agree that machinery will have AI in the future, but there is no overall agreement whether AI will be used in most machinery or just for high-end machinery. One possible explanation for this is that people have different perspectives on what constitutes “high end” machinery. Also, we intentionally did not specify a time horizon for this question. ARC’s initial conclusion from this is that AI applications will start with more high-end machinery and then gradually migrate toward simpler machinery, such as palletizers and packaging machines.
In contrast, there is almost total agreement that AI will be deeply embedded. This may be in the controller, the engineering tool, or even embedded right into the device.
Technical constraints do not seem to be a big issue among our survey participants, but cultural issues are. ARC agrees with this. AI will take decisions away from the well-understood controller and, especially when deeply embedded, the results of the AI techniques are not 100 percent transparent. This is a real drawback in a generally conservative industry such as industrial automation.
Another finding from our online survey is that unclear use cases are among the top inhibitors for AI in manufacturing. This line up with ARC’s observations from other industries: adopting new technology for technology’s sake will not succeed. Hence, our European Industry Forum has featured use cases and best practices from leading OEMs, end users, and suppliers of AI, which we will summarize and discuss below.
Where We Are with Artificial Intelligence in Manufacturing ?
Many technology suppliers now offer AI-enabled products and many machine builders have started to evaluate the technology. However, there are several roadblocks, most prominent among these are the lack of data scientists, lack of available data, legal aspects, human factors, and – finally – unclear use cases.
ARC market research on AI in machinery applications identifies the current distribution of AI. The blue line in the chart at left summarizes these. As readers can see, maintenance applications in particular are prominent in the market. The green bubbles in the chart represent the case studies presented at our recent ARC virtual European Industry Forum.
Case Studies from ARC European Forum
We segmented the following case studies from the ARC European Forum by application, rather than company. The expert presenters were Andreas Geiss from Siemens, Maarten Stol from BrainCreators, Prabhu Venkatramanan from Larsen & Toubro (L&T) Construction, and Sander Aerts from Toyota Material Handling.
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Keywords: Artificial Intelligence, AI, Industrie 4.0, Cloud, Edge, Quality Control, Maintenance, Optimization, ARC Advisory Group.