01
An operating plant is not a blank canvas
The fictional manufacturer Stonebridge Works had equipment from several generations, incomplete maintenance histories, and production targets that left little room for disruption. A proposed modernization program initially focused on a new dashboard, but the operational team could not trust several of the underlying signals.
We started by asking which decisions a better system should support: responding to recurring stoppages, identifying the right maintenance window, and understanding the difference between a machine fault and a material shortage. Each decision required a different set of evidence.
02
Create a trustworthy baseline
The first phase normalized machine identifiers, defined downtime categories, and reconciled shift notes with telemetry. A small set of read-only adapters captured signals without changing machine control. Where source quality was uncertain, the interface showed that uncertainty rather than replacing it with a precise-looking number.
Maintenance and production teams reviewed the baseline together. They found that some apparent machine faults were actually waiting time recorded under a generic code. Correcting that classification changed the proposed investment priorities before any predictive model was deployed.
03
Pilot within an explicit operational boundary
The scenario pilot covered one production cell and one recurring failure pattern. Alert thresholds were tested in shadow mode. Operators could mark an alert as useful, incorrect, or too late, and those categories remained separate in reporting.