A board meeting. A slide shows "94% operational efficiency" rolled up across the network. Someone asks how the number is calculated. Nobody in the room can answer with confidence. That's not a dashboard problem. It's what operational reporting quietly becomes once it...
A line goes slow, a batch fails QC, output drops for no obvious reason — and someone points an AI tool at the operations data and asks it why. Increasingly, that's the first move: teams are adopting or piloting AI troubleshooting tools precisely because manual...
Most manufacturers reach the same conclusion once an MES (Manufacturing Execution System) is up and running: "we have visibility now." The system tracks work orders. It logs quality checks. It shows the schedule on a screen. The visibility problem feels solved. It...
A plant manager pulls up the shift dashboard. According to the screen, six CNC machines are running, the calibration cart is parked in bay 3, and work-in-progress on line 2 is on pace. None of that matches what's actually happening on the floor right now — two of...
Search "industrial IoT sensors" and you'll find plenty of lists: temperature sensors, vibration sensors, pressure sensors, proximity sensors. Useful, but incomplete. Knowing what a sensor measures doesn't tell you whether that data will actually make it off the floor...
Most operations teams don't have a shortage of data — they have a shortage of usable data. Sensors are logging vibration, location, and temperature every second, cameras are capturing footage nobody reviews, and machines are throwing off status codes nobody's parsing....
Most AI in operations stalls for the same reason. The model is fine. The strategy is reasonable. Even the pilot even works in the demo. Then it meets the actual factory floor, hospital wing, or shipyard — and it can't see anything that matters. It reasons from a...
Every release of Sonar comes down to the same question. Can an operations team see more of what is happening on their floor, and act on it sooner? Sonar Web 6.1.0 answers yes on three fronts. It captures new kinds of signal from the physical world, makes that signal...
Every wave of enterprise technology has had a foundational architecture. For cloud, it was virtualization. For analytics, it was the data warehouse. For the current wave of AI in physical operations, the architecture has a name: Capture, Learn, Act. This is the...
Everyone says IT/OT convergence is the goal. Fewer people explain what it requires to get there — and almost no one talks about why so many attempts stall before they produce anything useful. The answer, in most cases, comes down to data. Specifically, to whether the...
