Michael Ungar presents Squint, a system that tracks what human operators actually do on factory floors—where they get stuck, how long each step takes. By analyzing this behavioral data, it spots bottlenecks like step 7 of a changeover where 90% of operators struggle, helping teams fix the biggest problems first.
And I think what went kind of unnoticed, or maybe just because it wasn't possible before, was what is the data that we can get from the humans on the floor? Like, what are the human actions that are being taken on top of just what's happening with the machine? Because what's happening with the machine tells you, you know, like the status when things go in, and then the status when things go out, and when things break along the line, but it doesn't tell you or explain The variability that you see between 2 workstations run by 2 different operators, one is high performing and one is low performing. It doesn't, the machine metrics can't tell you why. It just tells you that it is this way. So now with Squint, we're actually starting to capture like operator behavior, where they're getting stuck, which steps they're spending more time on, and starting to do anomaly detection on that. So both in terms of like which operators might need more help, And also in terms of, hey, these processes need improvement. So like, if you're going to do continuous improvement, you should probably focus your effort on step 7 of this changeover because 90% of people get stuck or get it wrong.
