How to Measure ROI on Industrial AI
A plant-floor method for baselining, attributing, and pricing industrial AI value before the dashboards outrun the returns.
Zoniax
Field notes on industrial operations intelligence — sensors, edge telemetry, and machine learning for processing plants.
A plant-floor method for baselining, attributing, and pricing industrial AI value before the dashboards outrun the returns.
An anomaly score tells you something changed. Getting to what changed first is a separate, harder problem in correlation, direction, and disciplined alarms.
A field note on putting a language model in the rack: the hardware, the memory-bandwidth wall, the heat, and why the box never closes a control loop.
The first international standard for managing AI is a management system, not a model test — and for a plant that distinction is the whole point.
How a sensor-to-model control loop trims the biggest power load in an activated-sludge plant without risking the permit.
An AI copilot is a useful reference librarian for operators, not an engineer — here's where it earns its keep and where it has to be fenced out.
What the OPC Foundation's Field eXchange profile actually standardizes between controllers, the TSN and Ethernet-APL wires underneath, and where it isn't ready yet.
How infrared inspection turns invisible heat into lead time before a connection fails.
How years of plant tag history move out of the historian into an open, queryable lakehouse, walked layer by layer from sensor to served model.
How a bolt on a bearing housing becomes a named fault, a severity grade, and a replace-by date, and where the pipeline quietly breaks.
How to turn historian energy and throughput data into a supplier-specific Scope 3 footprint an auditor will accept.
Why cell plants still scrap 15-30% of early output, and how inline measurement and cell genealogy move defect detection from the gate back to the coater.