Agents track energy consumption and material waste against production output at every step to surface anomalies that manual monitoring never catches.
“If we can get good quality we're reducing waste, we improve our efficiency, we save energy, we save materials and … we save greenhouse gas emissions or CO2. We've moved from a conventional model which is a reactive model and now we're using data to drive that prediction so that we can respond before the problem even happens.”
Agents track energy per unit of good output — by line, by shift, by product type. When consumption rises without a corresponding output increase, the agent knows.
When consumption spikes, agents investigate why — tracing it back to specific equipment states, process parameters, and operating conditions to understand exactly what drove the waste.
When the agent finds the cause, it surfaces a corrective recommendation in real time — specific enough to act on immediately, before the waste compounds through the rest of the shift.