Predictive maintenance for manufacturing: enhancing operational efficiency
A manufacturer sought to reduce unplanned downtime and maintenance costs by moving from reactive to predictive maintenance. Vimix deployed an AI-driven predictive maintenance system that used sensor data, historical failures, and operational context to forecast equipment failures and recommend optimal maintenance windows—reducing downtime by 30% and optimizing operational efficiency.
Project Overview
The Challenge
The client faced frequent unplanned stoppages and high maintenance spend. They had sensor and maintenance logs but no predictive capability. They needed a solution that could integrate with existing OT/IT systems and provide actionable alerts and work orders.
Our Solution
Vimix's predictive maintenance solution gave the manufacturer a data-driven approach to equipment health, reducing downtime by 30% and improving operational efficiency with scalable, explainable AI.
Project Details
Quick Actions
Our Approach
Data Integration and Feature Engineering
We connected sensor streams, maintenance records, and asset metadata into a unified data layer. We engineered features for failure prediction and validated signal quality and coverage.
Predictive Model Development
We developed and validated ML models (e.g. survival analysis, gradient boosting) to predict failure probability and remaining useful life. Models were tuned for precision and recall to balance false alarms and missed failures.
Deployment and Operations
We deployed the solution with scheduled and real-time scoring, integrated alerts and work-order recommendations into the client's CMMS, and established retraining and monitoring processes.
Impact & Results
30% Reduction in Downtime
Predictive alerts enabled maintenance to be scheduled before failures, reducing unplanned stoppages.
Lower Maintenance Cost
Optimized scheduling and condition-based interventions reduced unnecessary preventive work and parts usage.
Operational Efficiency
Production planning and maintenance teams could coordinate around predicted windows, improving throughput.
Technology Stack
AI/ML
Integration
Related Services & Industries
Services Used
Project Conclusion
Vimix's predictive maintenance solution gave the manufacturer a data-driven approach to equipment health, reducing downtime by 30% and improving operational efficiency with scalable, explainable AI.
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