MT Magazine January/February 2025

FEATURE STORY

JANUARY/FEBRUARY 2025

17

the CAD design, space that is necessary when going from digital to physical iterations. But an alternative is to create an AI agent that can go through the CAD model and assess whether the included tooling clearances are sufficient. If they aren’t, then the agent flags them and provides recommendations to address the issue. He says that this can take months out of the development cycle. Note that the system only makes recommendations. The human user is not out of the loop but rather significantly supported in their decision-making. Fixing the Robot Another benefit Laaper cites of GenAI is in the area of production maintenance. Say there is robot, and all of the available information about the robot – from the manuals to the operational data, historic and current, to failure mode and effects analysis to failure codes to faults and resolutions – has been used to train the GenAI system. Now imagine it is third shift, when a fairly new maintenance crew is working. “One of the problems that many manufacturing companies have is transitioning the institutional knowledge that has been built up over the years to new people,” Laaper says. People like those working third shift. Something goes wrong with the robot. The maintenance people can then query the GenAI system with natural language (i.e., the way they ordinarily talk – not some sort of technical language) and get recommendations to fix the problem. An immediate effect of this is that mean-time to repair goes down. But there are other benefits as well. Consider that when someone is trying to repair something, they may change out a part, then see if it does the job. If it doesn’t, then that new part likely remains, and another is tested. Eventually, the repair is made. But at a cost in time – and parts. One of the things that Laaper emphasizes is that the GenAI systems operate as assistants. This is not a case of decision making done by a system but rather of a system providing recommendations to the human operator, who then decides a course of action. Integrating the Existing Another thing Laaper notes is that this isn’t a case where what has been operational in the past (e.g., a lean production system) is simply replaced. Rather, he says that it is important to integrate the existing systems with the digital systems in order to get a better result. While he doesn’t minimize the amount of work that must be done to train the AI system – after all, there are probably manuals and notebooks on shelves that need to be inputted and up-to-date information, like spare-parts inventory, must be accessible – he says that, in his experience, he has seen solutions achieved in as little as 10 weeks’ time facilitated by things like pre-built accelerators, technology that Deloitte has invested in.

Software Changes In the auto world, efforts are underway to develop “software defined vehicles,” where modifications can be made to the performance of a vehicle through software – such as increasing the range of an electric vehicle by adjusting parameters with an over-the-air update – like updating a smartphone. Laaper says that “software-defined manufacturing” is emerging. Analogously to the vehicle situation, improvements in throughput or enhanced functionality can be achieved digitally. None of this is merely theoretical. And those who embrace it will undoubtedly be the ones who are the most competitive in their industries, which, arguably, is the case with all of these emerging technologies. The takeaway? Don’t wait for tomorrow, because this tech is here today. And the Robots The thing about industrial robots is that they’re not exactly new. At least for some companies. The first application of an industrial robot occurred in 1961. The Unimate robot, which was produced by Unimation Inc., used an arm that had been invented and patented by George Devol in 1954. Devol and his business partner, Joe Engelberger, founded Unimation in 1956. The application in question was tending a diecasting machine in a General Motors plant. Chuck Brandt, chief technology officer at the ARM Institute, points out that while there are several companies – like General Motors – that have a long history in robotic deployment, most of the manufacturing firms in the United States are small, and consequently, for many of these smaller firms, robotic technology is emerging. According to the most recent figures from the International Federation of Robotics, there are 285 robots per 10,000 manufacturing employees in the United States; by contrast, there are 397 in Japan, 415 in Germany, and 1,012 in South Korea. With that kind of disparity, there is a lot of upside to robots in U.S. firms. Brandt says this is particularly the case, as finding employees is difficult. So, automation makes sense for simpler tasks, like machine tending. Brandt says there is notable growth in the deployment of collaborative robots, thanks to their simplicity in deployment (“These companies don’t have roboticists.”) and safety, which allows them to work in closer proximity to humans than conventional industrial robots. If you have any questions about this information, please contact Gary at vasilash@gmail.com.