Production was recorded on paper at the end of each shift. A mould running below its expected cycle rate could lose output for hours before anyone noticed — and when targets were missed, no one could say whether it was a machine, mould, material or manning problem.
Before building anything, we mapped the operation. These are the findings that shaped what we built.
Under-production was typically discovered at end-of-shift reconciliation — hours after the loss occurred and too late to correct.
There was no machine- or mould-wise view of output; targets were tracked as a single daily number.
When a target was missed, the reason was rarely captured, so the same losses recurred shift after shift.
The data foundation, and the AI agents that turn it into decisions and action.
Custom BLE devices near each mould stream every production cycle to a central platform with no change to the machines — the real-time signal the agents reason over.
An agent continuously compares each machine and mould against its configured cycle rate and, the moment output drifts below threshold, flags the shortfall while the run is still fixable — instead of it surfacing at end-of-shift.
Shortfalls are escalated to the line owner with the likely cause, and every deviation is captured as a structured, reasoned record — so recurring loss patterns are learned and the same losses stop repeating shift after shift.
Measured against the baseline the audit established — before and after, on the metrics that move the P&L.
Start with a Business Transformation Audit — a structured working session where we map where AI changes your P&L, prioritised by impact.