Predict equipment failure before it happens. Catch defects before they ship.

A regional manufacturer. producing precision industrial components approached Vibba after a series of unplanned downtime incidents on a critical production line had disrupted delivery commitments to several key accounts, creating both direct financial cost and a growing reputational concern among the plant's largest customers.

The problem in detail. The plant's maintenance program ran on a fixed calendar schedule, servicing critical equipment at set intervals regardless of actual condition. Several recent failures had occurred between scheduled maintenance windows, catching the maintenance team by surprise and requiring emergency repairs that stopped the line for longer than a planned maintenance window would have. Separately, the plant's quality control process relied on manual visual inspection at the end of the line, a process that had recently missed a batch of defective units that reached a key customer, triggering a formal quality escalation and a customer audit.

Implementation. Vibba began with a sensor assessment across the plant's critical equipment, identifying which machines already had usable vibration and temperature sensor data and which required additional instrumentation. We deployed the predictive maintenance model on the highest-priority equipment first, the machines whose failure had caused the recent unplanned downtime incidents, training the model on historical sensor and maintenance data to establish failure-pattern baselines. In parallel, we deployed computer vision quality control cameras at the end-of-line inspection point, training the model on the plant's specific product line and the defect types identified in the recent customer escalation.

Deployment and staff training. Maintenance staff received training on interpreting predictive alerts and the automated work-order system, a shift from purely calendar-driven maintenance planning to a hybrid model incorporating real-time equipment health data. Quality control staff received training on reviewing computer vision-flagged units, transitioning their role from inspecting every unit manually to managing exceptions the AI system flagged for closer review.

Results. Within the first two quarters of deployment, the plant experienced no unplanned downtime incidents on the equipment covered by predictive maintenance, with several early-warning alerts allowing the maintenance team to schedule repairs proactively during planned downtime windows instead of reacting to failures mid-shift, consistent with the 30 to 50% unplanned downtime reduction reported broadly across the industry for comparable deployments. The computer vision quality control system caught defect patterns consistent with, and in some cases earlier than, the issue that had triggered the customer escalation, giving the quality team confidence to present the new system as part of the corrective action plan during the customer's quality audit.

Long-term improvements. Based on these results, the plant expanded predictive maintenance coverage to additional critical equipment beyond the initial deployment, and the quality team has used the defect pattern data captured by the computer vision system to identify and correct an upstream process issue that had been contributing to the defect rate, a root cause that manual inspection alone had never surfaced. The customer relationship that had triggered the original escalation has since stabilized, with the plant citing the new quality control system directly in ongoing account reviews.