Details anonymised at client request.
Business Situation
The company faced recurring unplanned downtime across critical CNC machining lines despite increasing preventive maintenance frequency across the plant. Leadership had visibility into rising downtime costs and production disruption, but lacked a reliable system for identifying equipment failure risks before breakdowns occurred during active operations.
What the Consultant Did
An independent AI and manufacturing operations consultant implemented a predictive-maintenance system across critical machining infrastructure using sensor-based monitoring and historical maintenance analysis. The engagement included deployment of condition-monitoring sensors, maintenance-data digitisation, failure-pattern modelling, alert-system development, and redesign of maintenance response workflows across plant operations. Particular focus was placed on improving maintenance visibility, reducing avoidable downtime, and integrating predictive alerts into existing operational workflows without disrupting production continuity.
What Changed
The revised maintenance process improved early identification of equipment-risk patterns and reduced unplanned downtime across the monitored production lines. The company also established a more structured maintenance decision framework that combined predictive monitoring with plant-level operational oversight.
Evidence, not adjectives.
The measurable changes recorded during or following the engagement.
Unplanned downtime reduced from 14.2 to 5.7 hours per line per month
Estimated avoided downtime costs of approximately ₹27 Lakh over eight months
Predictive maintenance workflows implemented across critical CNC lines
Early-warning monitoring system established for equipment-risk management
